Last month’s “partner chat” format was our best performing post in the history of this blog! In the spirit of “give the people what they want,” we’re back with another chat. This time, a remix of our post from last November about Using Powerful AI.

📰 What’s Happening in GenAI

Working with Cowork

Lately, we’ve been describing Claude’s Cowork as “the AI you didn’t know you wanted.” For so many of our clients, using this new harness has been a huge unlock. But it is very different from just chatting with GenAI. If you’re new to Cowork, we recommend this guide on how to get set-up.

In the News

Our friend Stu @ Just Curious interviewed us, you can catch a clip on LinkedIn here or listen to the whole thing on Spotify. Justin also co-authored a piece for Inc. with his old IDEO colleagues David Schonthal (Northwestern U entrepreneurship professor) and Katherine Otway (CMO at MIT’s The Engine) about the stages of AI adoption for SMBs. Enjoy!

Agent Racers

Remix is closely following the folks who are trying to build “dark software factories” in which no humans ever look at a line of code. It’s a fascinating community that reminds us of those wild experimenters in the early days of automobiles who tried to set the land speed record. Their innovations are in all of the cars that we drive today. Enjoy.

Remix: Using Powerful AI

TL;DR

AI got more powerful. The question is no longer which plan to buy, it’s how you actually work with these tools. Stop thinking of AI as a smart intern you manage; start thinking of it like a product manager you give a goal to and trust to figure out the rest.

In this post, Jason and Justin break down what that looks like in practice, why Cowork and Claude are dominating client conversations right now, and what separates true agentic AI from the workflow automation most businesses are actually running. Picking tools? Don’t pick one; run a bake-off. Directors and above should have access to everything (~$200/month, obvious ROI). And if you’re not on paid plans, you’re missing the biggest capability jumps in AI history — and you don’t even know it.

Justin: Last November we wrote about using powerful AI. That post was really about access. Which plans to buy, what context windows are, how to pick the right tier. Useful stuff, but the landscape has changed a lot since then. Today I want to talk about what it actually means to use powerful AI. Not the plans. The experience. So maybe we start there. What makes AI powerful to you right now?

Jason: When I can one-shot something. Whether it’s an image, a video, a workflow, a research query. And if it’s not a one-shot, how few iterations does it take to get where I need to go? That’s how I measure it. How quickly is the AI accomplishing the task I threw at it?

Justin: I’m with you on that. There are definitely moments where I want to offload work to a really smart intern that needs almost no management. Just get the thing done. But there’s another mode I’ve been living in more and more. Sometimes I want the AI to go back and forth with me. To elicit things out of me that I wouldn’t have been able to articulate on my own. Those feel like very different ways of working, and they’re both getting better fast.

Jason: The other thing that’s changed is when the AI asks me something that shows it knows me better than I expected. Like talking to someone with a thousand years of memory (as opposed to the 2,000 year-old man?). It’ll say, “Because you asked for that data point, I found the latest version and here’s what I think about it.” It saved me two or three prompts because it somehow knew what I was after. Those moments are happening more and more since the new year.

Justin: The formal AI term for what we’re talking about is alignment. The model doing what the human actually wants. And one of my favorite new benchmarks is BS Bench. What I love about it is that it measures the inverse of what most benchmarks test. It asks: when I’m trying to do something the AI shouldn’t help me with, will it challenge me? An AI model that just agrees with everything I say is not aligned to what I want. I want something that pushes back.

Jason: Doesn’t that humanize AI in a strange way? If you’re working with a teammate or a peer, hopefully you want to be challenged. With AI, you get to be challenged with the privacy of sitting in front of your computer. You get to save face and produce better work.

Justin: Exactly. The Socratic method works. Debating and going back and forth is a great way to get good outcomes. And AI doesn’t care about the power dynamic.

How We Each Use It

Jason: The tool that’s changed everything for me is Cowork. It does more than a chat session would. You can pull in projects, bring context over. I’m not rushing to get back in front of a terminal window, and Cowork is powerful enough that I don’t have to…yet.

Justin: I use Cowork a lot too. I describe it as the AI you’ve been waiting for. Capable over long time horizons. Persistent. Relentless. I’ve yet to meet a project it couldn’t handle. That was not the case with Chat, where I’d regularly hit walls. But I also spend a lot of time in the command line, because I like the constraint. You and I reach similar outcomes through different paths, and that says something about where these tools are headed. It’s not just about capability. It’s about the fit between the tool and how you think.

What We’re Seeing With Clients

Jason: We’re hearing more about Cowork than any other tool right now. And more about Claude than any other model.

Justin: My favorite pattern is when a client gets excited, has a couple of breakthrough moments, and then three days later I get the late-night text: “I hit my rate limits. What should I do?” The fact that people are burning through their monthly limits in days tells you how powerful this is. We should note that Microsoft just announced their own Cowork variant with Anthropic. We haven’t tried it yet, but we’re intrigued to see how this category develops.

What’s Next

Jason: I’m thinking about collaboration. These tools were built for individuals. Claude has been deliberate about the enterprise and small business market. They’ve recently introduced ways for people to collaborate that weren’t there before. And I think Anthropic could bring something game-changing here. Microsoft and Google already have collaboration baked into their core tech stacks. Small businesses should lean into the Team collaboration plans and see what works for them.

Justin: Agreed. And we’ve been experimenting with giving tasks to an AI agent the same way you’d give a task to a teammate. Figuring out how AI becomes a collaboration tool across teams, rather than just within a single person’s workflow, is the next frontier. We’re in the early days.

Agentic-ish vs. Actually Agentic

Justin: Outside of coding, if I reflect on 2025 the primary way people started pushing the edges was building workflows with AI slotted into specific spots. Tools like n8n, Make, Clay, Re:Live. For the right business process, those can be very valuable. But I’ve started calling all of them “agentic-ish.”

The agents we’ve been building at Remix are different. There’s no linear workflow. It’s an AI model in a loop with a set of instructions and some context. I don’t tell an agent,”step one, two, three.” I give it the instruction manual and let it loose. Cowork supports the same approach. It’s powerful not because you give it a task list, but because it generates its own.

Jason: Six months ago you couldn’t open LinkedIn without someone posting a prompting guide. Now, to your point, it’s treating Claude like a product manager, not an intern. You give it the goal and trust it will figure out how to get you there. That’s a significant shift in just a few months.

Don’t Pick One. Run a Bake-Off.

Justin: We’ve been talking a lot about Claude in this conversation. We’re fans of the current tools. But give us a few months and we might be on to something else. The leading position keeps changing. So for the businesses who bought an annual subscription or invested heavily in training their teams on one tool, is our recommendation to rip everything out and switch? No.

Jason: Assuming you can set aside some budget, some of the most well-spent R&D dollars right now are arbitraging at least two if not three big tools against each other. For example if you’re working on a contract, have one model proofread and even argue against another. Massively effective. The more little tests like that you can run, the better POV you’ll have on where each model excels versus what you need. And it’s too early to declare a winner. The Law of Duality is alive and well (Trout/Ries) – there’s always going to be a Coke and a Pepsi and a third challenger for years to come.

Justin: Agreed. This isn’t going to consolidate. So here’s how I think about it: directors and above should get access to everything. It’s so cheap. You’re not going to spend more than about two hundred bucks a month. The benefit relative to the expense is a no-brainer. Upper middle management, people leading teams, should probably have two or three subscriptions. If you’re a Microsoft shop, maybe Gemini matters less. If you do a lot of graphics and you’re a Microsoft shop, maybe Gemini matters more.

Individual contributors are a different story. Not every person needs every model. But everyone on a team using the same tools so you can manage and train them together? That makes a lot of sense.

Jason: So it’s tiered access based on role and need.

Justin: Yes. And the key point is this: if you’re not on the paid plans, you are kidding yourself that you’re getting the best version. Based on publicly available data, the capability jump from GPT-3.5 to GPT-4, the one that took the world by storm, barely registers compared to the jump from Opus 4.5 to 4.6. That happened in about four months. Most people haven’t experienced that difference because they’re not using those models. So they can’t see the trajectory.

Using powerful AI means you actually have to use it to have an opinion. Get the paid plans. Use multiple tools. Run a bake-off. The pace of improvement is accelerating, and the only way to stay ahead of it is to keep your options open.

✨ ✌🏻 ✨

Questions? Email us at info@remixpartners.ai - we read every message.

Early on in AI for SMBs, we included massive sample prompts. Long-time readers will have noticed there are far fewer lately. Generative AI models no longer need special incantations to unlock great outcomes; just be verbose, specific, and clear.

If we wrote a step-by-step guide today, it would be wildly out of date within a couple of weeks. The interfaces and capabilities of the LLMs are shifting that fast.

We’re trying to be an example of what we think will be true for all businesses moving forward: by the end of 2026, every business will need to remix itself if it wants to thrive in the new paradigm.

So it’s time to remix this blog yet again; just a few months after the last change. Something tells me this won’t be our last remix this year.

As AI for SMBs evolves, we’re going to focus on mindsets, approaches, and attitudes. Especially for leaders and executives in small and medium-sized businesses. You’ll see us publishing a bit less frequently.

This week’s post is an entirely new format for us: a lightly edited transcript of a conversation Jason and I had about how small and medium-sized businesses should think about building vs buying technology right now. It’s in a new-ish format we both love in which two commentators at a large publication share their own internal chats. Enjoy!

It's the start of a new calendar year. Many teams' budgets have been refreshed. Given recent developments in generative AI, we're seeing a lot of organizations increase their technology budgets.

Exciting! But also precarious. If you’re procuring technology today the way you bought it two years ago, you’re setting yourself up for problems later this year+.

We hope this week’s conversation gets you thinking about how to bring technology into your business in a way that meets your needs today without limiting your ability to take advantage of what’s coming this year and beyond.
💬
Justin Massa: You’ve bought a lot of technology in your life. How much, roughly?

Jason Rubinstein: I’d guess I’ve directly or indirectly influenced tens of millions of dollars worth of software and services purchased over my career. Which is both exciting and frightening at the same time.

Justin: For most of our careers, the model was: commit a bunch of money upfront to buy the right thing, implement it correctly, recoup that investment over multiple years. What was the shortest ROI time horizon you’d normally think about?

Jason: It’s not simple. Typically, you joined a company where there was already a foundation: your email, your CRM, your messaging apps. But based on what your business does, you’d have specialty applications you either built yourself, or bought. That build-versus-buy decision becomes critical in this new AI era.

For that base layer of productivity, I didn’t really see CFOs or CIOs heavily measuring ROI (beyond cost) on core applications. What I always saw, both as a buyer and a builder/seller of software, was having to justify why you wanted something new or different above the baseline. What value would it add? That’s what made the business case. And those ROIs were typically measured in years.

Justin: I remember when I was at IDEO and we changed our CRM. We agonized over it. Found the right partner, the right implementer, finally pulled the trigger. We thought we’d be ahead on the investment in less than two years. Too much money upfront, too much training, too much transition complexity. And just figuring out what we needed and who would implement it sometimes took six months. So what’s different now?

Jason: Two pieces. First, how GenAI software is actually being implemented today. Second, the software we can build using GenAI ourselves.

On the first – for those of us in Microsoft or Google shops, it was an easy yes for Copilot or Gemini as an add-on. That was really the beginning of everyone going, “Oh, I got this thing, for free or at a discount, that just showed up in my core office suite. Maybe I got an email about it.” But nobody really knew how to use it. We were reading more headlines about kids cheating in school than AI business transformation case studies.

Let’s peg that moment at 2-3 years ago. This massively powerful capability just sort of turned up one day on the desktop, but we had no idea about it. It’s just there. It’s “free.” It plugs into my company’s tech stack. We became desensitized.

Justin: And now?

Jason: Now we’re at a point where, if we have the guts, we can start to replace the foundational layer software ourselves. The way we want it to look and be. No extra features!

A few weeks ago, you texted me on a Sunday night saying you’d canceled our CRM subscription because you started building a new one. I replied, “We should have talked about that first!” A couple of weeks later, I’m all in. I’ve built out a bunch of additional capabilities on my own. Between the two of us, we’re saving over a hundred hours, and $170, a month.

Justin: What I didn’t think about until this conversation: while the cost and complexity of buying technology is coming down, the training isn’t. I don’t even mean training on what buttons to click. The mindsets, attitudes, and growth orientation to leverage generative AI effectively matter even more than with prior waves of technology. The complexity of how it integrates into day-to-day work is racing ahead.

Jason: Right. And this is where all companies are not created equal. Who’s best positioned to roll up their sleeves and rebuild their friendly neighborhood CRM application? Small businesses. They don’t have a CIO who would say no way. Small businesses say yes to themselves, run experiments in parallel, and see how it works. Fortune 500s won’t be ripping out major SaaS apps like Salesforce anytime soon. But small companies can punch way above their weight class by building what they need instead of being forced to accept the small business package with fewer features.

Justin: And here’s what’s wild: the very process of building software for your business teaches you how to use the technology. Double payoff. We spent quite a bit of time and money on Claude in January. If the only benefit were replacing some systems, it would have been worth it. But we also got deeply comfortable using these tools. That’s the double bottom line.

Even if we toss out what we built a month from now and go back to some SaaS application, none of this would be wasted. We’d make a far better buying decision because we’d deeply understand how hard and how valuable every single feature is. We built them ourselves.

Jason: Picking up on that: the big enterprise apps are fighting back (“supporting end user needs”) by putting in prompts. I was messing around with Figma’s then-beta AI features last fall, looking for buttons or menus to execute some basic operations…then I realized… there are no buttons or menus. Just a prompt - like the beginning of Zork. I told it what I wanted and sat amazed as a coding window immediately opened and started writing software in real-time.

That’s how the incumbents are responding. Hundreds of millions of people a day are getting used to a prompt - let’s make that the UI and shortcut building out new features and extend our SaaS application. And pray, because we’re stuck between two or three business models!

We’ve seen the same thing with a recent client. In their industry, there’s a major AI-native software application that’s in headlines. We asked them how they handle custom feature requests. They spun up a demo and said, “We put a custom workflow builder in. As the end user, you can build your own workflow in real time.” The workflow builder is a prompt. And the software company doesn’t charge extra for it.

Justin: Which brings up something everyone procuring technology needs to investigate: that unbelievably cool feature you see in a new enterprise SaaS product, how hard is it actually to pull off yourself? Some of these magic tricks are truly magic because they’re powered by generative AI. With an hour of work, you could do the exact same thing in your own environment.

I think a lot of buyers get excited about magical features without realizing that unless the vendor is making the model itself, they’re just leveraging off-the-shelf capabilities and baking them into their product. Try replicating it in ChatGPT or Claude before you pay someone else. Sometimes you’ll realize it’s genuinely hard. Sometimes you’ll be dumbfounded that you did it yourself in fifteen minutes.

Jason: On the flip side, it’s important to recognize where we are. Building software this way is still a solitary experience and generally for the benefit of one user. None of the major LLMs have talked about or rolled out what a “vibe-coded enterprise software deployment” looks like for teams. But that’s where small companies have an advantage. They can mess around, configure things in different ways, and figure it out. That’s the next adventure. And it’s what the incumbent SaaS providers need to be very wary of.

Justin: Okay, some rules of the road for people procuring technology right now. I’ll start. One: you don’t want a long-term contract. If it’s two dollars more per user for month-to-month, do month-to-month. You will want to switch.

Jason: Two: if you’re willing to try recreating something, don’t turn off your existing system first! Run the new thing as a parallel experiment until you’re sure it works and can be sustained. Check all of your key boxes before you do something dramatic like shut down working software (Justin!). ;-)

Justin: Three: data portability matters. These enterprise apps will get jealous about guarding your data. Ask direct questions (prompts!) about export, what you can save, what you can move. The capability jumps we’re seeing mean making a change could be an exponential improvement. You don’t want to be locked out of that.

Jason: Four: understand the real roadmap of what you want your new or replacement app to do versus what the LLM or coding tool you’re using is capable of. For example, you couldn’t have an agent log into paywalled apps as “you” a few months ago. Now Claude has a beta version with Chrome that is enabling that. Knowing what’s actually available today versus hoping for what will be possible soon is critical so you don’t have higher expectations than reality.

Justin: Five: if you rely on proprietary data feeds, start having conversations with those vendors about their AI strategy. You don’t want to buy something that locks you into their bolt-on AI tool. You want the option to pull that data into your own environment and use it across multiple AI tools.

Jason: Six: if you’re building a one-for-one swap of something you currently pay for, your ROI window should be three months or less. If you can only build part of what you need, six months. But even then, you’re getting a head start. You’ll be ready before anyone else to make the leap when full replacement becomes possible.

Justin: Last thought. When you buy big enterprise software and everything lives in the cloud, the only mechanic you get to call is the dealer. If something goes wrong in your OpenAI web account, you deal with OpenAI. No choice.

But if you’re just buying tokens of intelligence, which are really a commodity, you’re not calling Shell Oil when you need your car fixed. You can take it to any local mechanic. If you’re building your own apps, you’re in control. Your friendly neighborhood software engineer becomes your mechanic. You don’t need big implementers. That radically expands, and lowers the cost of, who will help you.

Jason: And your implementer (we call them AI Business Architects) doesn’t have to be an engineer. It can be someone who’s a non-engineer, but technical enough to understand how these things work together. Like you or me.

Justin: For the first time in a long time, technology has become an advantage for SMBs over big enterprises. I’m trying to think of other examples in my career and I struggle. Large technology expenditures have almost always started in government, then gone to corporate, then eventually made their way to SMBs and non-profits. Most of the small businesses we work with are now using generative AI in significantly more sophisticated ways than big enterprises.

Jason: It’s the unfair advantage for small, growing businesses. Revenge of the (SMB) Nerds. Return of the Jedi. You get it.

Questions? Email us at info@remixpartners.ai - we read every message.

The topic of measuring the ROI of generative AI comes up constantly. Everybody wants to know about hours or dollars saved, throughput percentage increases, and improvements to quality. These are all good and important ways to measure the return on investment in GenAI, but they are far from the only way.

Today’s post is a deep dive into how we think about measuring ROI here at Remix.

📰 What’s Happening in GenAI

Claude Cowork

You may have caught wind of the explosion of excitement around Claude Code while we were on break; watch this space for more on our own experiments with it. 🤯 But the learning curve for Claude Code is as steep as El Capitan. Fortunately, the good folks at Anthropic were paying attention and, using Claude Code, created a far more accessible and intuitive version called Claude Cowork. It’s currently still a “research preview” and only available to Max subscribers. Think of what happens when you run a Deep Research report in a chat model, and now imagine that it can not only look on the internet and generate documents, but can also browse your local computer (with permission) and create all kinds of files beyond research documents. It’s a glimmer of the future that we’re quite excited about and think you should experience for yourself.

A GenAI Price War? Winner: SMBs

Microsoft just introduced “Copilot Business” at $21/user/month, but only for organizations with 300 seats or less. It’s a solid price cut at ~35%, but we still can’t recommend Copilot as your primary or only frontier model given what we hear from our clients 🙁. As of January, Gemini is now included in all Google Workspace plans rather than an add-on. This represents a 56% functional price cut - just weeks after they launched one of the most powerful frontier models in Gemini 3. When they say, “intelligence too cheap to be metered,” maybe we should start to believe them.

“96% of SMBs Plan to Use GenAI”

This is a truly excellent round-up of recent research about SMB adoption of GenAI from the Small Business Administration, the US Chamber of Commerce, Salesforce, and Thryv Survey. Most interesting? 96% of SMBs report an intent to adopt GenAI this year, but only 58% report that they are currently using GenAI (up from 40% the year before). In 2026, GenAI is going to truly become what strategy guru Roger Martin refers to as a “flattening technology.” It will move from being a competitive advantage for early movers to table stakes for everyone. Are you ready?

Reframing the ROI of Generative AI

One of the most common challenges we encounter at Remix Partners is confusion about the ROI of generative AI. When we’re working with SMB leadership teams on their AI strategies, there’s rarely confusion about whether GenAI creates value. Most leaders believe it does. The confusion is about how to measure it, how to justify it internally, and how to think about the investment in the first place.

The pattern usually looks like this: a leader is excited about GenAI’s potential but stuck in a conversation with their CFO or board. The CFO wants payback periods. They’re asking for an “hours saved” analysis. They’re pattern-matching to every other enterprise technology purchase they’ve evaluated.

Here’s the problem: that template was designed for six-figure implementations, eighteen-month deployments, and irreversible commitments. When you’re spending $500K on a system you’ll live with for a decade, rigorous ROI analysis makes sense.

GenAI doesn’t work like that.

A ChatGPT Business subscription costs $30/user/month. Claude Pro is $20. You can cancel them tomorrow. There’s no multi-quarter implementation cycle. Your team can begin testing frontier capabilities immediately..

When the “I” in ROI drops by an order of magnitude, the decision framework should change with it.

Leaders trained on big-bet thinking are asking questions like, “What’s the three-year NPV?” for investments that cost less than the monthly coffee budget. They’re treating reversible experiments like irreversible commitments.

The better frame: treat a 90-day GenAI program as a learning investment with option value. The subscription cost is the premium. The deliverables are information and reduced uncertainty. The goal isn’t to perfectly forecast a three-year NPV. It’s to buy high-quality evidence that improves your next set of larger, less reversible decisions.

Here’s how to think about it.

Three Returns Worth Measuring

Most ROI conversations focus exclusively on operational metrics: hours saved, errors reduced, tasks automated. These matter, but they’re the third most important return. The first two are where the real leverage lives.

Return #1: Transformational Intelligence

Early GenAI initiatives function as paid reconnaissance for your broader transformation roadmap.

Here’s what you should actually have after 90 days of structured experimentation. These are concrete artifacts you can act on:

• A Prioritized Use Case Map. Not a generic list from a consulting deck, but 10-15 specific use cases ranked by feasibility and impact in your specific context. You’ll know which ones work because you tried them. You’ll know which ones flopped and why.

• Tested Assumptions. Every organization has theories about where AI will help. After 90 days, you’ll have evidence. “We thought AI could handle customer inquiry triage, but our edge cases are too complex,” is valuable intelligence. So is “AI-assisted proposal writing cut our time by 60%, but only after we built a custom jig with our past winning proposals.”

• Identified Blockers. The obstacles that would torpedo a bigger investment: data quality issues you didn’t know existed, skill gaps on your team, process dependencies that require upstream changes, compliance or security constraints that limit certain applications. Better to discover these with a $5K experiment than a $500K implementation.

• A Change Roadmap. Clarity on which activities should be stopped entirely, which should be redesigned before AI is applied, which are ready for immediate automation, and which require infrastructure you don’t yet have.

This is intelligence you can act on. It’s the difference between making your next major technology investment based on vendor promises versus making it based on evidence from your own operations.

What to measure: Number of use cases tested. Percentage of tested use cases that clear a defined value threshold. Time-to-first-usable-artifact. Blockers identified early (before they derail larger investments). Readiness score by function.

Return #2: Leadership Judgment

The most valuable return from early GenAI investment isn’t operational efficiency. It’s what happens to your judgment as a leader, in two distinct ways.

First, you develop informed judgment about GenAI itself.

Every significant technology decision you make over the next five years will be shaped by whether you understand what GenAI can and cannot do. Vendor selection. Build-versus-buy choices. Hiring priorities. Process redesign. Competitive response. Capital allocation across your entire technology portfolio.

Leaders with hands-on GenAI experience make fundamentally different decisions than those evaluating it from a distance. They can distinguish real capability from vendor noise. They know which employee experiments to fund and which to kill. They spot automation opportunities that GenAI-illiterate leaders miss. They understand realistic timelines and can call out inflated promises.

Second, GenAI amplifies your judgment across everything you do.

GenAI functions as an augmentation of human intellect. It’s not just a tool for automating tasks. It’s a thinking partner that can sharpen your judgment on every aspect of your business.

You now have access to an on-demand advisor, available 24/7, that can challenge assumptions, generate alternatives, and stress-test plans without internal politics. It will tell you what the people on your team may never say because of power dynamics.

Used well, GenAI helps you make better decisions about hiring, pricing, market positioning, competitive response, organizational design, and every domain where clear thinking matters. There’s a double bottom line: judgment about GenAI because you understand the technology, and judgment with GenAI because you now have an intellectual sparring partner in your pocket.

This is something we’re deeply passionate about at Remix Partners: helping leaders understand the unique mindsets and approaches required to use generative AI effectively as a strategic tool. The technology is powerful, but unlocking its value for leadership work requires new mental models that most executives haven’t yet developed.

The math is asymmetric. A few thousand dollars in subscriptions and experimentation buys you informed judgment that will shape decisions worth hundreds of thousands…or even millions. Even if year-one operational savings are modest, GenAI fluency pays back quickly by improving the quality and timing of decisions you’ll be forced to make anyway over the next 24 to 36 months.

What to measure: Reduced vendor evaluation cycle time. Fewer “replatforming” regrets. Higher quality investment memos and requirements documents. Faster pattern recognition on strategic questions.

Return #3: Economics That Finally Pencil

Here’s what the “hours saved” frame misses: it only captures value from tasks you were already doing.

GenAI’s true impact is transforming the cost-prohibitive into the cost-effective.

Let’s be honest: SMBs could do sophisticated competitive intelligence before GenAI. They could hire someone, or pay an agency, or re-assign internal resources. Spending $50K/year on competitive monitoring that might yield $30K in value doesn’t pencil. For far too long, so many things that could drive an SMB forward were simply out of reach as they were too expensive and/or required highly specialized talent.

GenAI changed the math:

• Personalized customer communication at scale. Before: hired writers, built sophisticated CRM workflows, spent weeks on setup. Cost: tens of thousands. After: one person, one afternoon, dozens of tailored sequences. The capability was always theoretically possible. It just didn’t pencil for a business your size.

• Continuous competitive monitoring. Before: dedicated analyst or expensive agency retainer. Cost: $50K+/year. After: structured prompts, weekly synthesis, fraction of an FTE. Same capability, radically different economics.

• Custom internal documentation and training. Before: hired a technical writer or pulled senior people off revenue-generating work. Cost: the opportunity cost plus hardline expense made it easy to deprioritize. After: generate first drafts from existing content, refine with subject matter experts. The ROI calculation completely changed.

The pattern isn’t “things you can do faster.” It’s “things that now make economic sense for the first time.” When the cost of an activity drops by 90%, the entire portfolio of what’s worth doing shifts. And these capabilities compound. Each one you develop makes the next one easier.

What to measure: New activities unlocked that weren’t viable before. Incremental revenue protected or created. Pipeline velocity improved. Churn reduced. Risk reduced through better information.

Asking the Right Questions

Wrong question: “Exactly how many hours will this save, and by when?”

This question assumes you already know which tasks to automate, that those tasks won’t change, and that time savings is the primary value. All three assumptions are usually wrong.

Better questions:

For leadership judgment:

• What decisions are we making poorly right now because we don’t understand GenAI capabilities?

• How would our technology investment strategy change if our leadership team had hands-on experience?

• Where in my role would an on-demand thinking partner create value?

For organizational intelligence:

• After 90 days, what specific artifacts will we have to guide our next investments?

• What do we need to learn about our own workflows and constraints?

• What blockers might exist that we can’t see from the outside?

For changed economics:

• What capabilities have we decided weren’t worth the investment at previous cost structures?

• What do larger competitors do that’s always been economically out of reach for us?

• What becomes rational when the cost of certain activities drops by 90%?

How To Make It Stick

Here’s the objection I hear most often: “We already bought subscriptions to a couple of AI apps and ChatGPT. People used them for a month. The novelty faded. Nothing stuck. Why would this time be different?”

Fair question. And honestly, if you just hand people subscriptions and say, “experiment,” you’ll get the same result.

The difference is structured experimentation versus open-ended exploration.

Open-ended exploration looks like: “Here’s a ChatGPT subscription, go find ways to use it.” This reliably produces a few weeks of enthusiasm followed by abandonment. Without specific goals, accountability, or end points, the urgent always crowds out the experimental.

Structured experimentation looks like:

• Identify problems and “if I could only do x” scenarios

• Specific use cases to test, not open-ended “find something”

• A simple log: what we tried, what happened, what we learned

• Someone accountable for driving the experiments forward

• A 90-day end point with defined deliverables

• Regular check-ins to share learnings across the team

The subscription isn’t the investment. The structure is.

When people say, “we tried AI and it didn’t stick,” what they usually mean is, “we provided tools without structure and hoped something would emerge.” That’s not experimentation. That’s wishful thinking.

The Cost of Waiting

The most expensive decision isn’t picking the wrong tool or investing in a use case that doesn’t pan out. Those are recoverable errors with limited downside.

The expensive decision is waiting.

Every month you delay, you make decisions without understanding what’s possible. You’re evaluating vendors, prioritizing initiatives, and allocating capital based on secondhand information rather than direct experience. Your competitors build compounding advantages. Organizations that started 12 months ago aren’t just 12 months ahead; they’re multiple learning cycles ahead. Your team either experiments without guidance or falls behind industry norms. You miss the stacking effect that early movers are already benefiting from.

The investment is a few thousand dollars and some structured attention. The return is clarity on decisions worth orders of magnitude more.

That’s the ROI that matters. ✨ ✌🏻 ✨

Questions? Send us email - we read every message.

Back in Issue #8, we introduced the concept of jigs: low-investment, custom-built AI tools that solve specific problems. At the time, that meant custom GPTs in ChatGPT, Claude Projects, and Gemini Gems. Chat interfaces with specialized knowledge. Useful, but fundamentally limited to conversations inside the AI platform itself.

It’s wild how much has changed in the months since.

What you can accomplish when “just building a jig” has expanded so dramatically that it barely resembles where we started. Today, you can build a jig that is a fully functional, deployed web application that can be shared with anyone via a link.

After the news round-up, we’ll dig into just how far that line has moved, how you can experience it yourself, and how to enter our inaugural ❄️Winter Jig Competition ❄️.

📰 What’s Happening in GenAI

ChatGPT 5.2

Sam Altman declared a “code red” after the release of Gemini 3.0, and less than two weeks later they released ChatGPT 5.2 alongside rumors of another new model coming in early January. We’re still getting to know this model, but in early testing we enjoy working with this one much more than ChatGPT 5 or 5.1. They’ve clearly worked on its personality and its ability to use tools; it feels much more natural to talk to than its predecessors.

New Standards

Late last year, the team at Anthropic launched “Model Context Protocol,” a structured way for GenAI models to talk to other applications. It exploded in usage, with both Gemini and ChatGPT adopting it even though it was technically owned by Anthropic. In what is a positive sign for interoperability across GenAI models, Anthropic has gifted MCP to the Linux Foundation, which has created a new subsidiary: the Agentic AI Foundation. Avoiding GenAI vendor lock-in is a huge consideration for SMBs; open standards go a long way towards mitigating that concern. Here’s hoping that the next thing Anthropic gives to this foundation are “Skills” 🤞🏼.

The Word of the Year Is…

Slop! Continuing their annual tradition, the good folks at Merriam-Webster have picked this AI term as 2025’s Word of the Year, defining it as, “digital content of low quality that is produced usually in quantity by means of artificial intelligence.” We think this video captures the essence of slop perfectly; enjoy.

“What’s Possible” Keeps Evolving

In January, the frontier of what a non-technical person could build was a custom GPT, Claude Artifact, or Gemini Gem. Helpful, but limited.

By spring, tools like Lovable.dev and Clay.com expanded the boundary. You could create working prototypes and marketing automations, though deployment still required some technical navigation.

By fall, Replit Agent 3 made it possible to go from idea to fully functional, deployed web app just by chatting (and forking over maybe $100). The agent handles everything: writing code, testing, setting up databases, checking security settings, configuring hosting, deploying to a live URL, and even hosting.

Today, you can do all of this basically for free using Google AI Studio’s Build mode.

This signifies a pivotal shift in how users leverage technology to drive outcomes. We’re not quite at the point where you can get any software on demand, but that day is coming soon. We’ve crossed a threshold where certain categories of applications are genuinely accessible to anyone willing to learn a simple process and put in a bit of time.

The “jagged frontier” of GenAI capabilities is going to keep advancing. Exploring its edges today and learning how to build and deploy applications is the best way for you to deeply understand just what’s possible today and get ready for what’s going to be possible tomorrow.

Same Philosophy, Bigger Canvas

The jig philosophy hasn’t changed: build something low-investment knowing it might end up back in the scrap pile. Solve an immediate problem, learn something, and turn yourself into a “savvy buyer” of the right solution. What has changed is the canvas you’re working on.

Today, “just a jig” can mean a deployed application with a database, user authentication, and integrations with other platforms. The disposable mindset stays the same. The capabilities are radically different.

This matters because it should change how you think about what to build. You’re still not creating enterprise software meant to run for years. You’re building functional prototypes that unlock business value right now, and you’re developing new skills so that you can build new jigs whenever you need them.

A Real Example

Last month, I needed a tool to conduct voice interviews about attitudes toward generative AI. I wanted something that could ask questions verbally, capture responses, and store the data for analysis.

Within about an hour, I had a fully working application that used the Gemini Live API for real-time voice conversation, asked interview questions in a natural conversational flow, captured and stored responses, and was deployed to a URL I could share with anyone.

No code written by me. No deployment configuration. Just conversation and iteration with GenAI until the app worked.

Will I still be using this exact tool in six months? Nope. Heck, I probably won’t ever use this same app again. The capabilities will have evolved, my needs will have shifted, and I’ll build something better. But that’s fine. The tool served its purpose, I learned the process, and I can rebuild something faster and better the next time.

The Three-Step Process

Here’s the approach that works:

Step 1: Design Through Conversation

Start with your preferred frontier model (Claude Opus 4.5, ChatGPT 5.2, or Gemini 3.0) and have a conversation about what you want to build. Use extended thinking or reasoning mode for this phase.

Don’t just describe the end product. Work through it collaboratively:

• What problem are you solving?

• Who will use this and how?

• What’s the user flow from start to finish?

• What are the core features vs. nice-to-haves?

Ask the AI to help you create a looks-like/feels-like prototype. In Claude, this means building an Artifact. In ChatGPT, use the Canvas feature. The goal is a non-functional mockup that captures what you’re trying to build.

This conversational design phase is critical. You’re not just planning; you’re pressure-testing your assumptions. The AI will ask clarifying questions that reveal gaps in your thinking.

Prefer something more structured or aren’t quite sure where to start? Check out this very cool PRD-builder that my friend Burton Rast made. If you work better by going through a questionnaire, start here instead.

Step 2: Generate a Detailed PRD

Once you’ve refined your concept through conversation, ask your AI to write a comprehensive Product Requirements Document. Use this exact prompt:

“Write an incredibly detailed PRD (product requirements document) as though you are an employee at Google. Include product summary and goals, user personas and use cases, detailed functional requirements, technical specifications, data models, error handling, and acceptance criteria for each feature.”

The PRD isn’t bureaucratic overhead. It’s the blueprint that will guide the vibe coding tool in the next step. The more detailed and precise your PRD, the better your results. Think of it as explaining your project to a very literal-minded junior developer who will do exactly what you say, nothing more, nothing less.

Review the PRD carefully. Edit anything that doesn’t match your vision. This document becomes the source of truth.

Step 3: Build and Deploy

Now for the magic. You have two paths:

Free Path: Google AI Studio “Build”

Navigate to aistudio.google.com and select the Build tab. Paste your entire PRD into the input box and hit enter.

Watch as Gemini generates your code in real-time. You’ll see files populate in the explorer, a live preview appear, and the AI’s reasoning process unfold. It’s transparent about what it’s doing: analyzing concepts, outlining API integrations, mapping project components, developing app logic.

Once it’s working in Preview (more on how to get there below), click the <deploy> button. Google will deploy your app to Cloud Run, giving you a public URL. The entire process can take under 2 minutes for simple applications.

Key advantages: no cost for experimentation, one-click deployment, and usage by people who view your shared app is attributed to their own free quota, not yours. Note that Gemini will train on this data, so don’t put anything sensitive or personally identifiable. Remember, this is a free tool.

Paid Path: Replit

If you want more sophisticated features (databases, authentication, integrations with Slack or email) as well as more privacy (your data is excluded from training), Replit’s Agent 3 is a better option. It costs $25/month for the Core plan, but you’ll likely need to load up around $100-150 in credits in order to create, test, and deploy a sophisticated app.

Paste your PRD, turn on “Autonomous - Max” and “App Testing” in the settings menu inside of your prompt box, and let Agent work. It will ask if you want to see a prototype or have it just start building. I usually pick the latter. It will test its own code, fix issues it finds, and iterate autonomously for about 20 minutes at a time. I find working with Replit is a few minutes of work a few times an hour; it’s become something I love to do on the side while watching a football game.

When you’re satisfied, hit Publish. It really is that easy.

Replit’s advantages include built-in databases and object storage, the ability to build agents that connect to other platforms, more robust production features, and privacy.

Expect to Iterate

These tools will rarely nail your entire vision on the first pass.

More often, Google AI Studio or Replit Agent will build a substantial chunk of your app, then pause and ask for feedback. Or it will complete something that mostly works but misses details. This is normal. This is the process.

Your job at this point is to click through the app in the preview window. Test every button. Try every flow. Identify what’s working and what isn’t. Then give the model specific feedback: “The submit button doesn’t save to the database,” or “The confirmation message appears before the form validates,” or “Add a loading spinner while the API call runs.”

The detailed PRD ensures the AI is building the right thing in the right direction. It has the holistic picture. But from our experience, even a comprehensive PRD won’t capture every nuanced detail of how you want the app to behave. That’s what the iteration loop is for.

This human-in-the-loop feedback cycle isn’t a failure of the tools. It’s how vibe coding actually works. You’re collaborating with AI to refine something into existence, not ordering a finished product from a menu.

What Makes This Work

Three things separate success from frustration:

Clarity of intent matters more than technical knowledge. The AI can handle the technical implementation. What it can’t do is read your mind about what you actually want. Invest time in the design conversation and PRD.

Start simple. The best vibe coding advice I’ve encountered: “Solve small problems. Don’t try to build it all with one prompt.” Even with a detailed PRD, complex applications benefit from iterative building. Get the core working first, then add features.

Give specific feedback. When something doesn’t work, describe the problem precisely. “It’s broken” helps no one. “The date picker allows past dates but should only allow future dates” gives the AI exactly what it needs to fix the issue.

❄️ Winter Jig Competition ❄️

Pick something to build that meets three criteria:

1. Creates immediate value. Don’t build something speculative. Build something you’ll actually use in the next month. A client intake form. A simple dashboard. A tool that automates one tedious thing.

2. Keeps a human meaningfully in the loop. This isn’t about building autonomous systems. Build something where you’re still making decisions, but the tool handles the tedious parts.

3. Doesn’t need to last forever. This is key. You’re not building enterprise software. You’re building a jig—something that solves a problem right now and might be obsolete or rebuilt in three months. That’s fine. That’s the point.

The real value of this exercise isn’t the app you build. It’s learning the process. Once you’ve successfully gone from idea to deployed application once, you can do it again. And again. The capability compounds in ways that a single tool never could.

Go through this process. Design it through conversation. Generate the PRD. Build it in Google AI Studio or Replit (or whatever tool you’re most comfortable using!). Iterate until it works. Deploy it.

🏆 How to Enter 🏆

Send us an email at winterjigs@remixpartners.ai by midnight on December 31st with “Winter Jig” in the subject line. Include a link to a 2-3 minute video of your app in action (Loom works great for this), tell us how you built it, and a bit about what you learned along the way.

We’ll pick the most interesting jig, with a panel of ChatGPT 5.2 + Gemini 3.0 + Claude Opus 4.5 serving as our judges. We’ll give them each the full text of this post + your submissions and ask them to rank everything. The winner gets a limited edition Remix Partners hat and will be announced and featured in a January edition of AI for SMBs.

We want to see what’s possible when people who understand their business problems can directly build solutions. Show us what you’ve got 🙂.

The Bigger Picture

One of the things we most love about generative AI is that it democratizes who can control technology to accomplish their goals. It reduces the amount of technical complexity you need to understand in order to command machines.

We’re still far from a world where anyone can get any software on demand. But 2025 has been the year where certain categories of software became genuinely accessible to non-technical builders.

And this line keeps moving. Early this year, that line was “helpful chat assistant with custom knowledge.” Today, it’s “deployed web application with real functionality.”

By this time next year? I genuinely don’t know. But if you want to stay ahead of where that line is going, the best thing you can do is push against it right now.

Happy building. Happy holidays.

Questions? Email us at info@remixpartners.ai - we read every message.

The free tiers are useful. The paid tiers are transformational. If you haven’t made the jump yet, you’re leaving an enormous amount of capability on the table, often for less than the cost of a streaming subscription. If you were to max out the rate limits on a $20/month Gemini plan, your cost would be $0.00000033 per page at a maximum capacity of 60M pages of text. If you did the same with the free plan of ChatGPT, you’d only produce 7,500 pages in a month and the level of intelligence in those pages would be meaningfully lower.

As you wrap up the year and work to lock down your budgets for 2026, don’t be penny wise but pound foolish when it comes to your investments in GenAI.

📰 What’s Happening in GenAI

Four+ New Frontier Models

ChatGPT, Gemini, Grok, and Claude all released new, more powerful models. OpenAI released ChatGPT 5.1 (as well as 5.1 Pro and a specialized coding model). Anthropic released Claude Opus 4.5 just last week; in early testing it performs incredibly well. Google released Gemini 3, which has quickly become our new favorite collaborator. X.ai released Grok 4.1, which we’re still testing.

Maturing GenAI Tools

Along with a slew of new models, most of the major frontier labs made big updates to their core products. Claude brought Claude Code to the browser plus released Skills (fingers crossed this becomes an open standard like MCP). ChatGPT and Gemini both released multiple new shopping tools integrated into their consumer apps. Gemini released Antigravity, their own coding tool. They also made truly massive improvements to NotebookLM - too many to list here.

(Almost) Pixel Perfect

Gemini 3’s launch was followed-up a day later by their release of Nano Banana Pro, which is the current state of the art (SOTA) image generation model. It’s hard to describe how powerful this model is; go try it right now. Folks are already using it to design packaging and then in one step get full die cuts. To give you a sense of what it can do, we gave it the final draft of this post and the prompt “make this post into an infographic” - one shot. 🤯

How to Use the Most Powerful GenAI

Generative AI is simultaneously the most powerful technology humankind has ever created and the most equitably distributed technology humankind has ever created.

For as little as $20/month, pretty much anybody on earth can access incredibly capable generative AI. The same GenAI capabilities available to Fortune 500 companies are available to a small consulting shop in the midwest or a mid-sized manufacturer in Florida.

But only if you know where to look, how to pick, and what settings to turn on.

The landscape has become genuinely confusing. Five major platforms. Multiple model generations. New models launched monthly. Free tiers, paid tiers, business tiers. “Thinking” modes that may (or may not) be enabled. Rate limits that quietly shift to weaker models mid-conversation.

Here’s what nobody’s saying clearly: the version of AI you’re using right now might be dramatically less capable than what’s available—sometimes on the same platform you’re already paying for.

This post is all about helping you fix that. This isn’t a comprehensive specs comparison. A practical guide to understanding what you’re actually getting and what you need to do to access each platform’s most capable models.

Four Factors

Every AI lab faces the same constraint: compute is finite. The most capable responses—especially those using extended “thinking” or “reasoning” modes—require 10-100x the processing power of a quick answer. Labs can’t give everyone unlimited access to their best models all the time. The GPUs simply don’t exist (yet).

Instead, they tier access based on what you’re paying and how much you’re using. Understanding these four factors tells you whether you’re getting a platform’s best or a watered-down version.

1. Context Window

The context window is how much text the model can “hold in mind” during a conversation, inclusive of your prompt, any uploaded documents, and the conversation history combined. Generative AI is metered, and context windows are measured, in tokens. A token is basically a portion of a word.

Why it matters: If you’re analyzing long documents, working across multiple files, or having extended conversations, a small context window means the AI “forgets” earlier material. Free tiers typically offer smaller windows; the free version of ChatGPT runs out of its context window after only ~18 pages of text.

2. Model Capability

The frontier labs building generative AI models are on a mission to build AGI, artificial general intelligence. Until they achieve this goal, each successive model they release is primarily a stepping stone to the next iteration. As such, they are relentlessly releasing new models (four in the past month!). As new models are released they are typically offered first to paying customers while older models are then offered to free plans before they’re completely retired.

On top of this endless flow of models, most platforms now offer thinking or reasoning modes that apply extended compute to hard problems. Remember when we used to start prompts with, “take a deep breath and think about this step by step”? The reasoning models have a bit of that approach baked in; rather than immediately generate a response, they have an internal monologue that “thinks” before it responds. This uses dramatically more compute but also produces substantially better results for coding, analysis, math, and multi-step reasoning

The capability gap between a quick response and a “thinking” response on the same model can be larger than the gap between different models entirely. Unless you’re building a product that needs sub-second responses, waiting for the smarter answer is almost always the right tradeoff for knowledge work.

Personally, I almost always use the thinking models. When they launched ChatGPT 5, OpenAI noted that only ~8% of all ChatGPT users had ever tried a thinking model.

3. Rate Limits

Every platform caps usage. There are limits on the number of messages you can send per hour, weekly caps across all usage, and “dynamic throttling” which shifts you to lighter models when you hit limits. Sometimes without notifying you 🙁.

Rate limits are where free vs. paid diverges most dramatically. A free tier might give you 10 substantive messages per day whereas a paid tier might give you more than 160 every 3 hours.

4. Auto-Routing

This is the sneakiest issue.

Some platforms default to “auto” or “router” modes that choose the model for you based on your query. The lab decides whether your question deserves the flagship model or can be handled by something lighter and faster.

Unfortunately, GenAI often underestimates complexity. A question you know requires deep reasoning might get routed to a fast, shallow model because it doesn’t look complex on the surface.

The fix is simple; manually select the model you want. Every platform allows this. Don’t let the system decide for you on important work.

The Most Powerful GenAI, Platform-by-Platform

ChatGPT (OpenAI)

GPT-5.1 runs in “Instant” (fast) and “Thinking” (extended reasoning) modes. Paid users get access to “Pro” mode, even more compute for the hardest problems.

How to access the best version:

1. Click the model selector dropdown—don’t accept “Auto”

2. Choose GPT-5.1 explicitly

3. For complex work, toggle to Thinking mode (if available on your tier)

4. Watch for degraded responses that signal you’ve hit a limit

Is $20+/month worth it? Yes. The jump from 10 messages/5 hours to 160 messages/3 hours is the difference between “occasional helper” and “viable work tool.”

Claude (Anthropic)

Opus 4.5 is their most capable model and works well with large chunks of text or code. Sonnet 4.5 performs similarly to Opus 4.5, although . Haiku 4.5 is their fast and light model; honestly, I almost never use it. Extended thinking available across models.

How to access the best version:

1. Click the model name at the top of any chat

2. Select Opus 4.5 for complex reasoning work

3. Enable extended thinking (click the clock/timer icon in the chat controls)

4. Use Projects to maintain context across conversations

Is $20+/month worth it? Absolutely. Free Claude doesn’t give you Opus 4.5 at all; you’re on the previous generation. Claude is my primary GenAI model; I’m on the $100/month Max plan but regularly go over. Claude is unique in that, in the Max plans, you can add a credit card and pay per-token for any overages rather than being locked out until your rate limits reset.

Gemini (Google)

With the launch of Gemini 3, Google’s taken a different approach. There are just two models offered, Gemini 3 “Fast” and “Thinking 3 with Pro”; free users can only access Fast.

How to access the best version:

1. Check your model selection before important work

2. Choose “Thinking”

Is $20/month worth it? If you run on Google Workspace and want GenAI integrated across Docs/Sheets/Gmail/Meet, this is a no-brainer. The $249.99/month Ultra tier is harder to justify unless you specifically need Deep Think or experimental Agent features.

Copilot (Microsoft & OpenAI)

While Copilot is not a frontier model itself, it is the primary way that many people access GenAI at work. As of this writing, you can access both Claude and ChatGPT via Copilot, depending on your plan and what your admin has enabled.

How to access the best version:

1. Understand that “standard access” means possible degradation during busy periods

2. For business accounts: ask your IT admin if Claude models are enabled (they’re optional add-ons)

3. Don’t confuse consumer Copilot with M365 Copilot—different products, different capabilities

Is $18+/user/month worth it? Now that ChatGPT, Gemini, and Claude can all deeply connect into the Microsoft ecosystem, it’s getting harder to justify spending much on Copilot. We consistently hear frustrating stories about Copilot, especially their “agent,” Copilot Studio. If you’ve only used Copilot so far, we recommend you experiment with Gemini, ChatGPT, and/or Claude with their M365 connectors turned on.

Grok (xAI)

Currently, you can access Grok 4 Fast, Expert, and Heavy as well as Grok 4.1 and “SuperGrok”, which requires an upgraded plan.

How to access the best version:

1. Pick “Expert” or Grok 4.1 rather than Fast or defaulting to “Auto”.

2. If you want Grok without X, SuperGrok works standalone

Is $30-40/month worth it? Grok’s real-time X data integration is unique. If you need current information (market sentiment, breaking news, trend analysis), it offers something others don’t. For pure reasoning, ChatGPT or Claude may be better value and are more full-featured products.

The Bottom Line

If you’re using free tiers, you are almost certainly using significantly less capable AI than what’s available. Every platform throttles free users through message limits, model downgrades, or reduced reasoning modes.

The gap matters. In my experience, the difference between free and paid tiers is roughly equivalent to asking a smart undergraduate versus a PhD researcher for help with a complex problem. Both can help. One helps much more effectively.

$20-30/month is less than most streaming subscriptions. If AI is genuinely part of how you work, the upgrade from free to paid is one of the highest-ROI investments available right now.

Check what you’re actually running. Select models manually instead of accepting auto-routing. Enable thinking modes for complex work. Consider whether the paid tier delivers enough additional value to justify the cost.

For most knowledge workers, it does.

Questions? Email us at info@remixpartners.ai - we read every message.

This week, we’re opening our tool box and showing you exactly what we use at Remix Partners, what we love (and hate), and how they work together.

📰 What’s Happening in GenAI

Everything Works with Everything

The three primary frontier model families now connect to the two major email / shared drive systems. ChatGPT, Claude and Gemini can now connect to Microsoft 365 as well as Google Workspace. ChatGPT, Claude, and Gemini can all access your files whether they’re in SharePoint, OneDrive, or Google Drive. This means your choice of AI no longer depends on your office suite; you’re free to pick based on capability, not compatibility.
Connect Gemini to M365 >
Connect Claude to M365 >

Google Released Gemini 3

Google launched Gemini 3 on November 18th. Based both on its scores on a slew of benchmarks, as well as the vibes, this model seems quite impressive! It is still too early for Remix to have a strong POV, but we’re planning to test this model on a couple of projects over the next few weeks.
Read more >

Google Flows: Promising but Proceed with Caution

Google Workspace now has a (limited) agent builder baked in called “Flows”. Create an email draft when a document is shared, auto-summarize meeting notes to Chat, or generate weekly reports from your spreadsheet data. Flows can connect to pretty much every Google Workspace service, and a few external tools as well, like Asana,Salesforce, and Quickbooks. Unfortunately, they’re failing about 25% of the time in our testing with no clear error messages. Experiment, but don’t use them for anything mission-critical yet. Note that all of our testing was before the launch of Gemini 3, which 🤞improves things.
Read more >

Agent Skills Give Claude Superpowers

Claude has launched “Skills,” which is - in a way - their version of a Custom GPT. Dig into your settings and enable them, especially the Skill that creates Skills! We’ve transformed a number of our projects into skills, which empowers Claude to leverage them no matter which project you’re in, and only when that skill is needed. For example, we’ve turned our Claude “Proposal Writer” project into a skill, which makes keeping cleanly organized client data massively easier in Claude.
Read more >

Remix’s GenAI Stack

Frontier Model Promiscuous

Model monogamy might be holding your business back.

We maintain subscriptions to Claude, ChatGPT, Gemini, Copilot, and Grok. Per person, this costs us about ~$250/month (note: this includes the $100/person/month Claude Max plan). We know our time is valuable, and the return on investment is incredibly obvious for us.

Each model has distinct strengths:

• Claude: Writes like a human, reasons through complex problems, longer context window

• ChatGPT: Excellent researcher, top-notch data analyst, shortest context window

• Gemini: Deep integration with Google Workspace, massive context window

• Grok: Real-time information from X

Despite repeated attempts, we’ve been unable to find a practical business application of Meta AI. Maybe this will change in the future; it would be great to have yet another tool in the toolbox. Microsoft Copilot provides access to both ChatGPT and Claude, but is not otherwise a frontier model.

The learning curve is real. But once you develop model fluency, you can match the right tool to each task and save hours each week. On any given day, we’re likely to use at least three of the frontier models, and there are frequently days we’re in all four.

Below are all of the primary GenAI tools that we use, what we love about them, what drives us crazy, and a quick example of how they show up in our work. If you’re inspired to try one of these platforms, note that some of these links are to referral programs that give you a discount, and a small fee to Remix..

Claude

What we love: Projects keep context organized across client projects. Claude has the second-biggest context window among the models we use and is great at working on longer documents (especially with Opus 4.1). Skills are incredibly easy to create and Claude knows when to use them without loading the whole skill, preserving space in the context window. I find its integration with Google Workspace better than Gemini (caveat: I’ve not re-tested this with Gemini 3). Its writing quality is unmatched; it sounds like a thoughtful colleague, not a robot. MCP on the desktop app opens up a world of possibiltiies.

I’m on the Max plan ($100/month) which means no more hitting rate limit walls at 2 PM and having to stop work. Worth every penny if you’re using Claude for a few hours per day.

What drives us crazy: No scheduled actions, and it can’t generate images or videos (although it can understand images quite well). The rate limits on the base plans can be extremely frustrating.

An example of our Claude workflow: We use Asana for tasks, Attio for our CRM, and Google for calendars and emails. I’ve got a Claude project that’s my “Chief of Staff” which, when prompted to “give me the rundown,” will look across my calendar, emails, tasks, and pipeline to help me prioritize the most important actions I should take - adjusted to the amount of heads-down time I have that day.

ChatGPT

What we love: ChatGPT 5.1 is an incredibly capable model; most notably, it is a better, more human-sounding writer than ChatGPT 5. Its deep research capability, while not as great at uncovering sources as Gemini, seems to be better at analyzing research and finding unique insights. Several of our client projects from 6+ months ago live here, and ChatGPT works well enough that migration isn’t worth the effort; for much of 2024, this was our primary tool.

What drives us crazy: For 52 days, we attempted to get a human at OpenAI to resolve an issue when we migrated the domain on our account. Their bot-based support highlighted that it could not fix my issue and promised to tag in a human. For 52 days, no human replied, until literally yesterday. Our issue isn’t resolved, but at least we’re one step closer. It’s the single worst customer service experience I have ever had in a professional setting and reinforces the fact that the frontier labs just don’t care about small businesses.

Our ChatGPT workflow: We have a number of custom GPTs that are quite helpful, including a “write in my voice” one. I love to be working with all of the contextual documents in a Project, get to a document that is close, then “tag in” my custom GPT (by using the “@” symbol) and adjust the text to be in my voice without having to copy/paste my work across multiple projects and GPTs.

Gemini

What we love: Inside Google Docs, Sheets, and Slides, it’s unbeatable. Gemini’s notes and transcripts in Chat are top notch. The 1-million token context window means we never worry about running out of space. NotebookLM is our favorite research assistant; upload 20+ documents and it finds connections you’d never spot. Deep Research consistently surfaces better sources than other models. Gemini 3 is brand new, so we’ve not yet had a chance to really push it… stay tuned.

What drives us crazy: The writing quality (at least in Gemini 2.5 Pro) was noticeably more robotic than Claude or ChatGPT. It tended toward stale, corporate buzzword bingo (at least in our opinion). But we’re eager to try Gemini 3! Stay tuned. Gems feel rudimentary and have not seen an update in quite some time. Its ability to find things in Gmail and Calendar pales in comparison to Claude.

Our Gemini workflow: When analyzing a large set of documents, we dump everything into NotebookLM. Ask it to find patterns across all materials and it surfaces insights like, “your client mentioned budget concerns in 7 different contexts but never directly.” The automated FAQ generation has saved hours of work. This tool seems to get better on an almost weekly basis. .

Grok

What we love: Real-time access to the X/Twitter firehose. “I remember seeing a tweet about [some half-remembered link I clicked on], can you help me find it?” works shockingly well. It understands context and culture in a way other models don’t. Like it or not, the primary public GenAI conversation happens on X.

What drives us crazy: The different personalities that Grok has baked in are…interesting. I’ve described some as “a harassment lawsuit waiting to happen.” Be exceedingly judicious with how you use this model.

Our Grok workflow: It’s great at generating weekly trend reports on AI discourse. “What are people saying about [new development in GenAI land] and what are the main criticisms / points of discussion?” It synthesizes hundreds of posts into coherent themes, catching early signals we might otherwise miss.

Supporting Cast

Images: ChatGPT + Gemini Nano Banana

The images for this newsletter are made with ChatGPT’s image generation capabilities; it’s a workflow we’ve used for a while. But lately, we’ve been using Gemini’s “Nano Banana” image model (look for the 🍌 icon) and are quickly falling for it. The new version powered by Gemini 3 is best in class.

Transcriptions: Granola + Wispr Flow

Granola has quickly become one of my favorite GenAI tools. It doesn’t take up a tile in an online meeting, can record offline meetings, and the integration of LLMs into the interface is great. I regularly use it as a form of augmented memory; it stays open all day on my desktop. The only downside: backing up to Google Drive requires a separate Zapier automation for each folder (annoying).

Wispr Flow is a dictation app, although describing it that way hides just how useful it is. I am someone who talks much faster than I can type, so this empowers me to engage with technology at the speed of my brain. I especially love how it automatically switches from “spoken text” to “written text,” cleaning up stray words as well as adding proper punctuation and formatting.

CRM: Attio

I’ve held a number of business development roles and have been the decision maker who’s bought Salesforce, HubSpot, and Copper. Attio is the first CRM I don’t hate. Setup took less than an hour. The onboarding call was 30 minutes. It just works.

Vibe Coding: Replit

Every few months, I have an experience with GenAI that keeps me up at night thinking about the implications of what I just experienced. My most recent night like this came after a day of using Replit’s new Agent 3 “vibe coder”.

Turn on full autonomy, load up your credits (I’d start with ~$50), and it’s wild what you can build without writing a line of code. Its “secrets” feature ensures you don’t accidentally expose API keys, and the fact that you can design -> code -> security check -> deploy -> host all in one platform is freaking amazing.

Workflow Automation: Zapier + Google Flows

Zapier is fantastic in that it connects to pretty much everything and has an increasingly powerful automated workflow builder. Zapier is wildly frustrating in that you need to be somewhat technical in order to get a Zap to work perfectly, consistently. But we love it because it talks to just about everything.

Google Flows are the newest “agent” builder inside of Google Workspace. Our favorite one so far reviews all incoming emails, decides if that email is a receipt, forwards it to Quickbooks, and archives it. Super simple yet powerful. Unfortunately, we’ve had some of our more complex, multi-step Flows fail ~25% of the time (although this was pre-Gemini 3). These are certainly worth experimenting with, but still feels like prototype software.

We’re also playing around with the new AI features that have been added to enterprise apps like Notion, Asana, and Quickbooks. More on those in the future, but it’s so clear to us that the disruption of big enterprise apps by native AI apps and DIY vibe coding workflows is upon us. Sam Altman recently commented that we’re entering, “the fast fashion era of SaaS very soon.”

Networking: Happenstance.ai

While we won’t delete our LinkedIn account anytime soon, we increasingly first turn to Happenstance.ai to search our network. Imagine the powerful search tools that you find in a team LinkedIn Sales Navigator dashboard, but with a plain language chat-like starting point. It pulls data from LinkedIn, Gmail, your address book, and Twitter / X. We’re excited to see what they do next.

A Living Toolbox

In June, we wrote about how to think about picking a model family. Just six months later, things have radically changed. “Connectors” among the frontier models have exploded, and we’re rapidly approaching a world where everything can work with everything.

That’s equal parts exciting and overwhelming.

My grandfather was a carpenter, and I vividly remember going into his workshop and marveling at the array of hammers, screwdrivers, planes, and saws hanging on the wall. If asked, he would explain how each tool was there for a reason, with each bringing some speciality that the other tools didn’t provide.

Think about your own GenAI toolbox in this same way, with each tool that you bring into your work serving a specific purpose. Some will be more generalized, others highly specialized. Don’t hoard them, but know that a master craftsperson’s workshop usually has many highly specialized tools in it.

Over time, expect your tool set to evolve, driven by what you’re working on and the ever-evolving capabilities of the tools.

Questions? Email us at info@remixpartners.ai — we read every message.

I have a hunch this isn’t the last time we’ll revisit this topic.

📰 What’s Happening in GenAI

Project Pomelli Brings AI to Creative Process

Google Labs just launched Pomelli, an experimental AI tool designed to enhance creative workflows by helping users organize ideas, generate content, and collaborate with AI in real-time. Think of it as a creative partner that actually understands context and can help you move from scattered thoughts to structured outputs. For SMBs doing any content creation, this represents the next evolution of AI assistance — less about one-shot generation, more about ongoing creative partnership.
Read more →

An Actually Useful Guide to Using AI

Ethan Mollick’s latest “Opinionated Guide to Using AI” cuts through the noise with practical, tested approaches for getting real value from AI tools. His key insight: stop treating AI as a search engine and start treating it as a junior colleague you’re training. The guide includes specific prompting strategies and workflow integrations that SMBs can implement immediately.
Read more →

Google’s New Public Policy Stories Platform

Google launched a public policy platform sharing stories about AI’s real-world impact across industries and communities. While it’s partly PR, there are genuine case studies here showing how smaller organizations are using AI for everything from accessibility improvements to operational efficiency. Worth mining for ideas relevant to your industry.
Read more →

🔀 Capturing Transcripts Remixed

Eight months ago, we showed you how to capture meeting transcripts to supercharge your AI workflows. Set up transcription, organize your files, feed them to AI for proposals, summaries, and insights.

If you built that workflow and it’s working, congrats; you’re ahead of 90% of businesses.

But the transcription landscape has evolved significantly. It’s time for a remix.

The Platform Problem

The native transcription in Zoom, Meet, and Teams has gotten remarkably good. Gemini in Google Meet provides seamless integration with automatic transcripts saved to Drive. Teams finally got its act together. Zoom... well, Zoom is still Zoom (requiring manual starts unless you record everything), but the quality of the transcription and notes have improved.

Unfortunately, your customers don’t care about your preferred platform. You use Teams, they use Zoom, and your vendor insists on Meet. Suddenly you need four different transcription systems, four different file locations, four different workflows. Oof.

And those meetings where six transcription bots join before any humans? Your clients hate those as much as you do. Some CEO’s are booting them out of meetings altogether.

Granola solves this elegantly. It captures audio directly from your device — no bots joining calls, no platform limitations. It works for Zoom, Meet, Teams, or that random videoconferencing tool your European client insists on using. It even works for in-person meetings. Lately, I’ve been noticing people using it at conferences to capture transcripts of public sessions.

The live transcript works regardless of the platform, and before a call, you can jot down rough notes or questions that get automatically enhanced with context from the transcript. No more choosing between taking notes and being present.

They’ve got some nascent organizational tools as well, and Granola integrates with Zapier; you can build a simple automation to keep everything nicely organized on Google Drive, Box, or Dropbox.

We’re updating our prior recommendation; we still love Gemini in Google Meet, but Granola has replaced it as our tool of choice because it works everywhere, has killer sharing features baked-in, and is AI workflow automation friendly.

Keep It Legal

In a number of states, recording conversations without all-party consent is illegal, including California, Connecticut, Florida, Illinois, Maryland, Massachusetts, Michigan, Montana, New Hampshire, Pennsylvania, and Washington.

If you’re calling across states, the strictest law applies — even if you’re in a one-party state, you need permission from all parties if anyone is in a two-party state.

Because Granola captures audio directly from your device without announcing itself, YOU must inform participants. Every. Single. Time. Forget once in California? That’s potentially a felony.

The safe play: Start every meeting with “I’m using an AI assistant to take notes — is that okay with everyone?” If they continue talking, you’ve got implied consent. Document this in your meeting practices.

There’s an experimental feature inside of Granola to automate posting this message to meeting chats; I’ve found it to work most of the time. If you’re using Granola, turn it on now.

Drowning in Documents

Success creates new problems. Six months of transcripts later, you’re drowning in data.

How do you:

• Find that pricing discussion from three months ago?

• Share relevant context without oversharing sensitive details?

• Turn meeting insights into actual business processes?

• Know what’s safe to feed to AI and what needs redaction?

If you’re a one-person shop or very small team, then Granola could be your all-in-one solution. But for larger businesses, you’re going to need to get creative with a goal of getting everything into one, unified place where it’s searchable and shareable.

Tools like Zapier (and many other workflow builders) can automatically take transcripts and route them to your systems — creating tasks in Asana, updating CRM records, or building searchable knowledge bases. Automation without organization is just faster chaos.

The winning approach:

1. Get it all in one place: If everyone in your org is using Granola, you can use their integrated tools - but that can also get expensive. It’s easy enough to use Zapier and get them all into a shared drive.

2. Establish naming conventions: [Date][Client][Topic] works better than “Meeting with John”

3. Separate by sensitivity: Keep compliance discussions separate from general meetings, and have clear rules of the road for which meetings are always transcribed and which meetings should never be transcribed → shared.

Before feeding transcripts to AI or sharing with teams:

• Remove client-specific pricing that shouldn’t be generalized

• Redact personal information (health issues, family matters mentioned in passing)

• Strip out legally privileged communications

• Delete competitive intelligence that shouldn’t leave your inner circle

Once organized and sanitized, your transcript corpus becomes a goldmine. What’s actually working for SMBs:

Sales Teams… Connect Granola or similar tools to your CRM to automatically log call summaries, update deal stages, and flag at-risk accounts based on conversation sentiment.

Product Development… Query across all customer calls to identify feature requests, pain points, and usage patterns. “Show me every time someone mentioned our checkout process” becomes actionable intelligence.

Operations… Use AI to generate formatted daily summaries of all previous day’s activities, including escalated issues and resolutions, improving team communication.

Transcription isn’t the differentiator anymore — everyone can do it. The winners will be those who turn transcripts into intelligence, automate what matters, and protect what’s sensitive.

Your transcripts are either an asset or a liability. There’s no middle ground.

The tools have evolved. The legal requirements haven’t gotten easier. But the opportunity to build a true meeting intelligence system — one that learns from every conversation and improves your business systematically — has never been more accessible.

Just remember: With great transcription comes great responsibility. Use it wisely.

Questions? Email us at info@remixpartners.ai — we read every message.

We’re in the process of rethinking our AI Models for SMBs Comparison Chart…like many other evaluations of frontier AI models, what was once a helpful measurement of the frontier has turned into a sea of green checkboxes. Watch this space for more in the coming weeks.

📰 What’s Happening in GenAI

Evaluations of “All Economically Valuable Work”

There have been a couple of quite interesting evaluations of the business capabilities of frontier models. OpenAI released GDPVal and Mercor released APEX, the AI Productivity Index, with some interesting (and conflicting) assessments of models on a wide array of tasks across a large number of industries. We recommend reading up on your industry for inspiration of new capabilities to try in your business.

So. Many. New. Features.

We could write an entire post about all of the new things launched in the past few weeks and still miss something. If you haven’t done this in the past 2 weeks, poke around all of the settings, features. and buttons in your frontier model of choice. In every frontier model, you’ll find a slew of new and powerful additions. Oh, and OpenAI released their own web browser yesterday (Mac only).

AI Bubble?!?!?

There is a fierce debate among AI insiders about whether the eye-popping numbers in recent data center deals constitute a bubble. Cory Doctorow makes a compelling case that we’re in a bubble, Azeem Azhar writes extensively that we’re not. 🍿

🔥 Remix News

• We’ve relaunched our website, with more to come soon! Hope you like it; we’re just getting started.

• Our friends at MATTER interviewed Justin about how healthcare leaders can build confidence in generative AI.

• IDEO U’s Creative Confidence podcast had Justin as a guest to discuss how to invest in AI for your business (Spotify).

🔀 Faster + Better Proposals Remixed

This post is an update to the original from March 6, 2025

Six months ago, we showed you how to use AI to write customer proposals in a fraction of the time. Capture meeting transcripts, load up a custom GPT with past proposals, let AI synthesize everything into a draft, iterate, and ship same-day proposals.

If you built that workflow and it’s working, high five. You’re already ahead of most businesses.

Capabilities are evolving so quickly that workflows which felt cutting-edge six months ago can be significantly upgraded with what’s available today. The fundamentals haven’t changed: transcripts, context, iteration, human review. But the infrastructure around those fundamentals has gotten substantially better.

This isn’t about starting over. It’s about taking something that works and making it work even better.

The workflow was straightforward: transcripts → custom AI loaded with winning proposals → first draft → iteration → human review → ship. Proposals went out faster, and you focused on customers instead of documents.

What’s Different Now

Four things have evolved that open up new possibilities:

1. AI Can Actually Reason Through Complex Problems

Six months ago, reasoning models were quite new. These models don’t just respond instantly. They take time to think through problems before answering, leading to substantially better outputs for complex work.

The practical impact for proposals:

• AI spots gaps in your discovery notes and asks for missing information before drafting

• It analyzes your pricing patterns and suggests strategies based on similar successful deals

• It catches inconsistencies between customer needs and your proposed solution

• It self-corrects when an approach isn’t working, rather than plowing ahead

You don’t need your proposal back in seconds. It’s worth waiting a couple of minutes for a dramatically stronger response. Here’s how to access reasoning models:

• Claude: Turn on “Extended Thinking“ in your chat box controls (the little clock)

• ChatGPT: Use “GPT-5 Thinking“ (or GPT-5 Pro for even more capability)

• Gemini: Use Gemini 2.5 Pro

• Microsoft Copilot: Pick ChatGPT 5 and turn on “Thinking” (note that in workspaces in larger companies, your admin will need to enable this model)

The difference between reasoning and non-reasoning models isn’t subtle. For complex proposals with multiple stakeholders, custom pricing, or technical specifications, reasoning models produce noticeably better first drafts with fewer revisions needed.

2. Context Windows Have Grown Dramatically

Six months ago, most models offered around 200K tokens. Today, the landscape looks different:

Note: “Pages” assumes ~650 tokens per page of text. Actual capacity varies by content type.

Paid plans now offer dramatically more working memory. With hundreds to thousands of pages available, you can provide much richer context than ever before: past proposals, customer histories, product documentation, and pricing guides all in one conversation.

However, it’s important to be realistic: even the largest context windows have limits. A comprehensive 2-hour client meeting transcript with detailed technical discussions can easily consume 50,000+ tokens. You won’t be loading your “entire institutional knowledge” into a single conversation.

3. Transcript Capture Is Much Better

Getting good meeting transcripts used to require setup and fiddling. Now it (mostly) just works.

Gemini in Google Meet is perhaps the best integrated solution. If you’re a Google Workspace user, turn on Gemini in Meet and it will automatically capture transcripts of every meeting, save them to your Drive, and email you when they’re ready. The integration is seamless and the transcripts are excellent.

For those who want more flexibility, Granola.ai captures audio directly from your computer (works with any video conferencing tool), combines it with your own notes, and creates collaborative meeting memories. No bots joining calls, no disruption to the meeting flow.

Other solid options include Otter.ai, Fireflies, and Fathom. All now deliver 95%+ accuracy, automatic speaker identification, and action item extraction. They join meetings automatically and integrate directly with your CRM.

Customer context now flows from meetings into your proposal workflow without manual copying. We’ll be revisiting our “Capturing Transcripts” post soon with updated recommendations and workflows for 2025.

4. AI Connectors Are Expanding Rapidly

Through Connectors (standardized connections between AI and your business systems), AI can now access your data where it lives. The connector ecosystem is expanding fast.

Through Model Context Protocol (MCP) and native platform integrations, AI can now connect to:

• Your CRM (customer history, deal stages, past conversations)

• Shared drives (proposal templates, product docs)

• Communication platforms (email threads, Slack channels)

• Project management tools (timelines, resource availability)

• Version control systems (code repositories, documentation)

• Databases and business intelligence tools

No more manual copying between systems. When you ask AI to draft a proposal, it can pull relevant context directly from your CRM, grab the latest pricing from your shared drive, and reference similar proposals from your document library. All automatically.

New connectors are being released weekly, making it easier to connect AI to the specific tools your business uses. As of this writing, ChatGPT and Claude are in fierce competition for the widest network of connectors, although Claude can’t yet connect to Microsoft. Gemini has a much smaller set, but can connect to the Microsoft ecosystem through Gemini Enterprise.

Once connected, AI can reference “the transcript from yesterday’s call with Acme Corp” and find it automatically. No manual work required.

Your Remixed Proposal Workflow

For high-stakes proposals (multiple stakeholders, custom pricing, technical specifications), use a reasoning model and give it time to think:

“Use extended thinking to: (1) identify gaps in the customer information I’ve provided, (2) analyze which past proposals are most relevant, (3) recommend pricing based on similar deals, (4) flag potential risks. Then draft the proposal.”

This approach (reasoning models taking a couple minutes to think before drafting) consistently produces better output for complex deals. The wait time is negligible compared to the time you’ll save on revisions.

With the larger context windows available in paid plans, you can now provide significantly more background than before. Instead of selecting just a handful of past proposals, upload multiple examples. Include your complete pricing guidelines, multiple customer histories, and relevant product documentation.

Let AI find patterns:

• Which approaches win in different industries

• How your pricing strategies have evolved

• What language resonates with different customer types

• Which terms get negotiated most often

The combination of expanded context windows and system connectors means AI can work with much richer information about your business. Both what you provide directly and what it retrieves from your connected systems.

If you’re using Claude, create a “Proposal Writing” Skill that encodes your methodology, pricing guidelines, and quality standards. Skills are reusable packages of expertise that Claude automatically loads when relevant, making your approach consistent across all proposals.

For other platforms, update your custom instructions with specifics:

• Your unique value propositions

• Decision criteria (margins, resource availability, strategic fit)

• Common objections and how to address them

• Required compliance or legal language

Set up your transcription tool to save transcripts automatically to a shared drive folder. Connect your AI tool to that folder through extensions or connectors. Now transcripts flow into proposals without you touching them.

Better yet, connect your CRM. When AI drafts a proposal, it pulls the customer’s complete history and tailors the proposal accordingly. Automatically. This is where connectors really shine: they ensure you’re working with current data without manual copying or context window limits.

Rapid Evolution

Six months ago, AI proposal writing was about speed. Today, you can build something more sophisticated: an intelligent system that learns from every customer interaction and improves over time.

The infrastructure is here:

• Reasoning models that think through complex problems

• Context windows that hold substantial amounts of your business context

• Automatic transcription that captures every customer conversation

• Direct connections between AI and your business systems

If your current workflow is saving you time, that’s real value. But the tools have evolved enough that you can now build workflows where AI doesn’t just write faster. It writes better by knowing more about your business than was possible six months ago.

Questions? Email us at info@remixpartners.ai — we read every message.

When I hit publish on the first issue of AI for SMBs Weekly back in March, I had no idea what kind of response to expect. Twenty-three issues later, more than 2,000 of you subscribed and more than 50% of you open this newsletter every week. 🤯

You’ve stuck with me through proposals and cash flow forecasting, inventory optimization and meeting intelligence, tariff mitigation and competitive analysis.

But the world of GenAI has changed dramatically since Issue #1.

Back then, o3-mini had just launched. DeepSeek R1 was making waves as a disruptive force that challenged everything we thought we knew about AI training costs. The idea that AI agents could autonomously handle complex business workflows was just beginning to move from theory to reality.

Today, we’re living in a completely different landscape – one where the frontier models release new capabilities almost weekly, where AI can actually do things rather than just write about them, and where the gap between “AI experiment” and “AI-powered business transformation” has collapsed to almost nothing.

Which brings me to today’s announcement.

Midwest Quality Consulting is becoming Remix Partners.

This isn’t just a rebrand, it’s a recognition that the work has fundamentally changed. When I started MQC, I was helping businesses understand and experiment with generative AI. Today, my co-founder Jason Rubinstein and I are working hands-on with leaders to actually build AI-powered capabilities that drive growth.

The lines between strategy, design, and implementation have dissolved. Companies don’t need another consultant explaining what’s possible – they need partners who can roll up their sleeves and build alongside them.

We wrote an open letter explaining our thinking, which you can read here on LinkedIn. I won’t repeat it all, but the core idea is simple: every business needs to remix itself with AI – holding dear what makes them unique while embracing new possibilities.

What’s Changing with AI for SMBs Weekly?

The format is evolving.

Each issue will start with a few highlights from the world of generative AI that we think are particularly relevant to SMBs – the signal in the noise. Then we’ll dive into one of three types of content:

1. Remixes: Taking our older posts and updating them for 2025’s AI landscape (because a post about “Picking a Model Family” from May 2025 is already outdated)

2. Points of View: Sharing Remix Partners’ perspectives on what’s happening in the GenAI world

3. Case Studies: Conversations with our clients about their successes and challenges in AI transformation

We’re shifting from weekly to “every-other-week-ish.”

Rather than forcing out content every Thursday just to hit a schedule, we’re moving to roughly twice a month. This gives us room to go deeper, be more thoughtful, and honestly, only publish when we have something genuinely useful to say.

This is the last issue of AI for SMBs Weekly you’ll receive through Kajabi.

Starting with our next issue, we’re moving to Substack. Don’t worry – the newsletter remains completely free, and you’re already subscribed. You’ll just see emails coming from our new Substack address instead of this one. You might need to update your filters (and double check your spam folder).

The next issue will go out on October 23rd.

Why We’re Remixing

The advice from five months ago isn’t wrong, but it’s definitely stale.

Which is why remixing makes sense. Rather than constantly chasing the next new function to cover, let’s revisit the fundamentals with fresh eyes and current capabilities. Let’s take what worked and make it better. Let’s acknowledge what’s changed and update accordingly.

What’s NOT Changing

The newsletter remains free. The focus remains ridiculously practical. The commitment to helping SMBs cut through the hype and actually use this technology remains absolute.

If anything, we’re doubling down on that promise. Moving to twice a month means more depth, more testing, more real-world validation before we hit publish.

What’s Next

Our first Substack issue drops in two weeks. It’ll be a remix of one of our most popular posts, completely updated for where the technology is today.

After that? We’ve got some case studies lined up that I think you’ll find fascinating – real businesses, real challenges, real results. And a few points of view on where this whole AI transformation thing is actually heading (spoiler: it’s not where most people think).

Thank You!

Building something from scratch is humbling. Watching 2,000+ people choose to spend their Thursday mornings with what I’ve written? That’s been one of the most rewarding experiences of my career.

Thank you for every read, every reply, every forwarded email to a colleague who needed to hear it. Thank you for the questions that pushed my thinking, the success stories that made my day, and the honest feedback that made this newsletter better.

You’ve helped make AI for SMBs what it’s become. I can’t wait to see where we take it next.

cheers,

-justin

✨ ✌🏻 ✨

P.S. – We’ve got some super exciting things we’re working on at Remix Partners. Stay tuned.

You know AI is working: proposals ship faster, customer service runs smoother, and your team seems less overwhelmed. But when pressed for numbers, you realize you’ve been flying on vibes rather than data.

This is the ROI reckoning every business faces: the moment between enthusiastic experimentation and strategic commitment. The businesses that answer this question with clarity will double down and pull ahead. Those that can’t will quietly abandon their AI initiatives and fall behind.

The challenge isn’t just measuring what AI does today. It’s quantifying both the immediate bottom-line impact AND the strategic readiness you’re building for what’s coming next. Because in 2026 and beyond, the question won’t be “Can we afford to invest in AI?” but rather “Can we afford not to?”

Here’s how to measure what actually matters.

Step 1: Audit Your Current AI Usage

Before you can measure ROI, you need to understand exactly how your business is using AI today. Most businesses discover their team members are using AI far more than leadership ever anticipated, and some efforts are delivering far better returns than others.

Start with Your AI Show-and-Tell Forum

If you’ve been doing AI right, you should have an internal forum where team members share their AI wins: a Slack channel, Teams chat, or regular meeting where people show what they’ve built and learned. This is where your inventory should start.

Don’t have one yet? Create it right now. Before you do anything else in this exercise, set up a #ai-show-and-tell channel (or whatever you want to call it) and send a message:

“We’re measuring the impact of our AI investments. Please share in this channel: (1) How you’re using AI in your work, (2) What tasks it helps with, (3) Roughly how much time it saves you each week. Bonus points for sharing your favorite prompts or jigs!”

Use this ROI measurement exercise as the catalyst to start this ongoing knowledge-sharing practice. You’ll need it not just for measurement, but for spreading best practices across your organization.

Focus on Prevalent Patterns, Not Every Use Case

Don’t try to capture every single instance of AI usage. You’ll drive yourself crazy and miss the forest for the trees. Instead, look for the most prevalent use cases that are shared across multiple team members:

• Which tasks have 3+ people using AI in similar ways?

• What use cases generate the most excitement in your show-and-tell channel?

• Which AI applications have spread organically through your team?

• What jigs or prompts are being shared and reused?

This pattern-focused approach helps you identify the high-impact applications that actually move the needle, rather than getting lost in one-off experiments.

Quick Discovery Process

Tool-Level Inventory:

• Which AI tools/subscriptions are you paying for? (ChatGPT, Claude, Gemini, specialized tools)

• What are the monthly costs for each?

• Who on your team is using each tool regularly?

• Are there shadow AI tools being used that you’re not aware of?

Prevalent Use Case Inventory:

• What are the 5-10 most common ways your team uses AI?

• Which use cases have multiple team members doing similar work?

• Which use cases generate the most conversation in your show-and-tell forum?

• Which jigs, custom GPTs, Projects, or Gems have been shared across the team?

Quick Team Survey:

Rather than asking people to track every AI interaction for a week (exhausting and often ignored), send a quick survey:

“Think about the last two weeks. What are the 2-3 most valuable ways you’ve used AI? For each:

• What task did AI help with?

• Roughly how much time did it save you?

• How often do you do this task?”This lightweight approach surfaces the high-impact patterns without creating survey fatigue. You’re looking for signal, not comprehensive documentation of every ChatGPT query.

Step 2: Build Your “AI ROI Analyzer” Jig

Now let’s create a specialized AI assistant that helps you measure and communicate ROI effectively. This jig will become your strategic partner for quantifying AI’s impact across your business.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my AI ROI Analyzer. Your purpose is to help me measure, quantify, and communicate the return on investment from our generative AI initiatives across both immediate financial impact and strategic organizational readiness.

BUSINESS CONTEXT:

[Brief description of your business, team size, key functions, and how you’re currently using AI]

MEASUREMENT FRAMEWORK:

When analyzing AI ROI, consider both:

IMMEDIATE FINANCIAL IMPACT:

- Time savings and labor cost reduction

- Revenue acceleration (faster proposals, improved conversion)

- Cost avoidance (hiring delays, efficiency improvements)

- Quality improvements that reduce rework or customer issues

- Cash flow improvements from faster processes

STRATEGIC READINESS:

- Workforce capability development (AI fluency, new skills)

- Competitive positioning and market differentiation

- Organizational agility and adaptation speed

- Innovation capacity and experimentation culture

- Future-proofing against AI disruption in our industry

ANALYSIS APPROACH:

1. Calculate tangible dollar impacts with conservative assumptions

2. Quantify intangible benefits using proxy metrics

3. Compare AI investment against alternative approaches (hiring, outsourcing, status quo)

4. Project forward to show compounding benefits over time

5. Identify hidden costs or risks in AI adoption

6. Benchmark against industry standards where possible

OUTPUT STRUCTURE:

- Executive Summary: Bottom-line ROI in dollars and percentage

- Immediate Impact: What AI is delivering to the bottom line today

- Strategic Value: Long-term competitive advantages being built

- Detailed Breakdown: Category-by-category analysis

- Recommendations: Where to double down vs. scale back

- Communication Framework: How to present these findings to different stakeholders

TONE:

Data-driven but accessible. Focus on business outcomes, not technical capabilities.

Acknowledge both wins and areas for improvement. Emphasize practical insights over

theoretical benefits.

Always ask clarifying questions about:

- Specific business goals and success metrics

- Current pain points or constraints

- Stakeholder concerns about AI investment

- Industry-specific benchmarks or competitive dynamics

Upload your Step 1 inventory data to give your jig context about your current state. Also include:

• Recent financial statements (to calculate labor cost savings)

• Any productivity metrics you track

• Customer satisfaction or quality data

• Sales pipeline or conversion metrics

The more context you provide, the more sophisticated your ROI analysis will be.

Step 3: Measure Immediate Financial Impact

Let’s start with the hard numbers: the bottom-line impact that’s happening right now. Even if you haven’t been tracking AI usage carefully, you can retroactively calculate meaningful ROI.

Time Savings Analysis

Start a conversation with your AI ROI Analyzer and try:

Based on our AI usage inventory, calculate the time savings we’re achieving. For each use case:

1. Estimate weekly hours saved per person

2. Calculate monthly time savings across the team

3. Convert to dollar value using our labor rates

4. Compare to the cost of our AI subscriptions

5. Calculate ROI percentage and payback period

Use conservative estimates—assume AI only saves 30-40% of time on tasks, not 100%.

Present findings in a table with columns for:

- Use Case

- People Involved

- Hours Saved/Week

- Monthly Dollar Value

- Annual Projection

Your jig will generate something like:

“Your proposal writing process has saved approximately 12 hours per week across 3 team members (4 hours each). At a blended labor rate of $75/hour, this equals $3,600/month or $43,200/year. Your ChatGPT Team subscription costs $600/year (2 users × $25/month × 12 months), delivering a 7,100% ROI on this single use case alone.”

Revenue Acceleration Analysis

AI doesn’t just save time. It can accelerate revenue by enabling faster responses, better proposals, or improved conversion rates. Try this prompt:

Analyze how our AI usage impacts revenue generation:

1. Has our sales cycle shortened? (Compare pre-AI vs. post-AI proposal turnaround times)

2. Has proposal conversion improved? (Compare win rates)

3. Can we now pursue opportunities we previously couldn’t? (New market segments, deal sizes)

4. Has customer satisfaction improved? (Leading indicator of retention and referrals)

For each area with positive impact, estimate the dollar value:

- Shorter sales cycles = revenue pulled forward + capacity for more deals

- Better conversion = additional deals won

- New opportunities = incremental revenue

- Improved satisfaction = reduced churn and increased referrals

Use our historical data to calculate conservative estimates.

Cost Avoidance Analysis

One of AI’s most significant impacts is preventing costs you would have otherwise incurred. Try:

Calculate the cost avoidance benefits of our AI implementation:

1. HIRING DELAYS: What roles would we have needed to hire without AI? When would we have hired them? What’s the fully-loaded cost of those positions?

2. OUTSOURCING REDUCTION: What work would we have outsourced to agencies or contractors? What would that have cost?

3. TOOL CONSOLIDATION: What expensive software subscriptions are we avoiding because AI handles the functionality?

4. ERROR REDUCTION: Has AI reduced costly mistakes, rework, or customer service issues?

5. TRAINING EFFICIENCY: Has AI reduced the time/cost to onboard new team members?

Present as: “Without AI, we would have spent $X on [solution]. Instead, we invested $Y in AI subscriptions and saved $Z.”

Step 4: Measure Strategic Readiness

The immediate financial impact tells only half the story. While harder to quantify, strategic readiness determines whether your business will thrive or merely survive as AI reshapes your industry. Let’s focus on two critical areas where the impact is most measurable.

Workforce Capability Development

Your team’s growing AI fluency isn’t just a nice-to-have—it’s a quantifiable strategic asset that impacts hiring costs, retention rates, and productivity multipliers. Try this analysis:

Assess the financial value of our team’s AI capability development:

1. RETENTION IMPACT:

- What’s our current turnover rate vs. industry average?

- How many team members cite AI skills/tools as improving job satisfaction?

- What’s the replacement cost for team members who might leave without AI development opportunities?

- Calculate: Avoided turnover costs = (# retained employees × replacement cost)

2. HIRING ADVANTAGES:

- Can we now hire more junior talent and upskill them with AI? (Cost difference?)

- Are we attracting better candidates because of our AI-forward approach?

- Has our time-to-productivity for new hires decreased?

- Calculate: Hiring cost savings = (senior vs. junior salary difference) + (reduced training time × hourly rate)

3. PRODUCTIVITY MULTIPLIERS:

- Which roles can now handle work previously requiring more senior/expensive talent?

- What tasks previously requiring specialists can generalists now handle with AI?

- Calculate: Capability uplift value = (work now handled internally vs. previously outsourced)

For each area, use real data from your HR records and industry benchmarks.

Your jig might respond:

“Based on your data, two team members who considered leaving stayed because of AI development opportunities. At a replacement cost of $15,000 each, that’s $30,000 in avoided turnover. Additionally, you’ve successfully hired 3 junior developers at $65,000 instead of senior developers at $95,000, saving $90,000 annually while achieving similar output through AI assistance. Your new hire productivity time has dropped from 90 to 45 days, saving approximately $11,250 per new hire in onboarding costs.”

Competitive Positioning

Your AI capabilities directly impact win rates, pricing power, and market share. These aren’t abstract benefits; they translate to measurable revenue and margin improvements. Ask your jig:

Quantify how our AI capabilities create competitive advantages with real dollar impact:

1. WIN RATE IMPROVEMENTS:

- Track our proposal/pitch win rate pre-AI vs. post-AI

- Compare our close rate to industry averages

- What’s the value of additional deals won due to faster/better proposals?

- Calculate: Additional revenue from improved win rate = (win rate increase × average deal value × number of opportunities)

2. SPEED-TO-MARKET ADVANTAGES:

- How much faster do we deliver vs. competitors? (proposals, projects, customer service)

- How many deals have we won specifically because of our speed?

- What opportunities can we now pursue that we previously couldn’t?

- Calculate: Speed premium = deals won due to speed × average margin

3. PRICING POWER:

- Can we charge more because of superior service/quality enabled by AI?

- Have we been able to maintain prices while competitors discount?

- Are customers choosing us despite being more expensive?

- Calculate: Margin improvement = (price differential × volume)

4. MARKET SHARE CAPTURE:

- Which competitors’ customers have switched to us?

- What new market segments can we now serve profitably?

- Calculate: New market revenue = revenue from segments previously unprofitable to serve

Focus on deals and customers where you have specific data about competitive dynamics.

The key is connecting these capabilities to actual business results. Instead of saying “we’re more innovative,” you’re saying “we won 3 additional deals worth $150,000 because we could deliver proposals in 24 hours while competitors took a week.”

Step 5: Create Your ROI Dashboard

Numbers in spreadsheets don’t drive action. Clear visualizations do. Let’s build a dashboard that brings your ROI story to life for different stakeholders.

Ask your AI ROI Analyzer:

Create a comprehensive ROI dashboard that shows both immediate impact and strategic value. Include:

EXECUTIVE SUMMARY SECTION:

- Total AI investment (subscriptions + implementation time)

- Total quantified return (immediate + strategic value)

- ROI percentage and payback period

- Top 3 value drivers

IMMEDIATE IMPACT VISUALIZATION:

- Time savings by function (bar chart)

- Revenue acceleration metrics (before/after comparison)

- Cost avoidance breakdown (waterfall chart)

- Monthly ROI trend line

STRATEGIC VALUE METRICS:

- Workforce development: Retention savings, hiring advantages, capability uplift

- Competitive wins: Win rate improvement, speed advantages, pricing power

- Simple gauges showing progress toward strategic goals

VALUE DRIVER ANALYSIS:

- Rank all use cases by ROI (highest to lowest)

- Highlight top performers and underperformers

- Show adoption rates across the team

RECOMMENDATIONS:

- 3 areas to double down on (highest ROI + strategic value)

- 2 areas to improve or sunset (lowest returns)

- Next quarter priorities based on data

Format this as an interactive HTML artifact that I can share with stakeholders. Use clear visualizations that tell the story at a glance.

Your jig will create a visual dashboard that executives can understand in seconds and teams can explore for details. Claude’s artifacts feature is particularly powerful here—you’ll get an interactive dashboard you can screenshot for presentations or share via link.

Make sure your dashboard balances precision with clarity. You want numbers that are defensible but not so hedged with caveats that they lose impact. Include both the “wow” numbers (7,100% ROI on proposal automation!) and the realistic aggregate (overall 400% ROI across all initiatives).

The Compounding Advantage

The proposal jig you built saves 12 hours this week. But it also trains your team to think differently about customer communication. Six months later, they’re using similar approaches for case studies, presentations, and customer success documentation—multiplying the initial value by 10x.

Your cash flow forecasting AI prevents one cash crisis this quarter. But it also builds your confidence in data-driven decisions. A year later, you’re using AI for inventory optimization, demand forecasting, and pricing strategy—turning a single use case into a comprehensive competitive advantage.

Each AI capability you implement creates:

• Data assets that make future AI implementations more powerful

• Team fluency that accelerates adoption of new tools

• Cultural momentum that attracts innovative talent and forward-thinking customers

• Strategic optionality that enables pivots and opportunities you can’t yet imagine

The businesses that will dominate the next decade aren’t those with the highest immediate AI ROI—though that helps fund the journey. They’re the ones building compound advantages: workforce capabilities that adapt to any new tool, competitive positions that widen with each AI advance, and organizational DNA that treats AI as a core capability rather than a cost-cutting tool.

Your CFO asked what you’re getting for your $500/month AI investment.

The answer isn’t just the $50,000 in time savings or the $200,000 in accelerated revenue. It’s the ability to compete in a world where AI capability determines who thrives and who gets left behind.

The immediate ROI pays for your AI investment. The strategic ROI determines your company’s future.

That’s the difference between measuring what’s easy and measuring what matters.

✨ ✌🏻 ✨

You ask them to document their approach. They send you a half-page email about “building relationships” and “understanding customer needs.”

What they don’t capture: the story about why Acme Corp’s procurement team hates phone calls on Fridays. The lesson learned from losing the Johnson account because nobody understood their budget cycle timing. The informal network of industry contacts who tip them off to opportunities. The subtle signs that indicate when a “maybe” is really a “no.”

This isn’t just about losing a good employee. It’s about watching years of strategic intelligence, relationship capital, and hard-earned wisdom disappear forever.

Most businesses focus on documenting processes; the “how to do things” knowledge we covered in Issue #21. But the knowledge that truly drives competitive advantage isn’t found in procedure manuals. It’s the accumulated wisdom, relationship insights, and strategic context that exists entirely in people’s heads.

The businesses that capture institutional memory effectively don’t just survive key departures—they accelerate growth by making strategic wisdom accessible to everyone, not just the person who learned it the hard way.

Here’s how to extract and preserve the knowledge that usually walks out the door.

Step 1: Build Your “Knowledge Archaeologist” Jig

Unlike process documentation, which focuses on workflows, institutional memory capture is about extracting strategic context, relationship intelligence, and lessons learned over years of experience. This requires a different approach entirely.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my Knowledge Archaeologist. Your purpose is to help me systematically extract and preserve institutional memory—the strategic wisdom, relationship intelligence, and hard-earned lessons that typically exist only in people’s heads.

BUSINESS CONTEXT:

[Brief description of your business, key relationships, and strategic challenges]

KNOWLEDGE EXTRACTION FOCUS:

When conducting institutional memory interviews:

1. Strategic Context: Why decisions were made, what alternatives were considered

2. Relationship Intelligence: Client preferences, partner dynamics, vendor nuances

3. Lessons Learned: What didn’t work, expensive mistakes, successful pivots

4. Market Intelligence: Industry insights, competitive dynamics, timing patterns

5. Cultural Wisdom: “How things really work here” vs. official procedures

6. Network Effects: Who knows whom, informal influence patterns, key connections

INTERVIEW APPROACH:

- Ask story-based questions rather than abstract inquiries

- Dig into specific examples and real situations

- Uncover the “why” behind successful approaches

- Extract warning signs and red flags from past failures

- Capture nuanced relationship dynamics and preferences

- Document informal networks and key connections

DOCUMENTATION STRUCTURE:

- Strategic Intelligence: Key insights about markets, customers, competition

- Relationship Maps: Detailed context about important business relationships

- Lessons Database: What worked, what didn’t, and why

- Cultural Knowledge: Unwritten rules and informal dynamics

- Network Intelligence: Who to know and how relationships really work

- Decision Context: Historical background on major choices and their outcomes

Focus on preserving wisdom that would be impossible to recreate through training or hiring.

This jig becomes your strategic interviewer, designed to extract knowledge that goes far deeper than job descriptions or procedure manuals.

Step 2: Conduct Strategic Knowledge Extraction

Start with your most critical knowledge holders—people whose departure would significantly impact your business. This isn’t just senior executives; it’s anyone with unique relationship capital, market intelligence, or strategic insights.

Use your Knowledge Archaeologist to guide structured extraction sessions:

Help me design a knowledge extraction interview for [Employee Name] who is [role/situation].

Their unique knowledge likely includes:

- [Key client relationships, strategic insights, etc.]

- [Market intelligence, competitive knowledge]

- [Lessons learned from major projects/failures]

Create an interview structure that captures:

1. Strategic insights they’ve developed about our industry/market

2. Relationship intelligence about key clients, partners, vendors

3. Lessons from major successes and failures during their tenure

4. Informal networks and influence patterns they’ve observed

5. Cultural wisdom about how things really get done here

6. Predictions or concerns about future challenges

Design questions that elicit stories and specific examples rather than abstract advice.

The key is moving beyond surface-level knowledge transfer to deep strategic extraction. Instead of asking “How do you manage client relationships?” ask “Tell me about a time when you saved a major client relationship that was going south. What weren’t we seeing that you caught early?”

Example questions that extract institutional memory:

Strategic Context: “Walk me through the decision to stop pursuing the healthcare vertical. What factors weren’t obvious to everyone else?”

Relationship Intelligence: “Describe the politics at our biggest client. Who really makes decisions, and how do they prefer to work?”

Lessons Learned: “What’s the most expensive mistake we’ve made that others don’t know about? How would someone spot the warning signs?”

Market Intelligence: “Which competitors do people underestimate? What do you see coming that others miss?”

Cultural Wisdom: “When you need something done quickly here, what’s the real process versus the official process?”

Step 3: Create Living Institutional Memory Systems

Raw knowledge extraction is useless unless it’s organized and accessible. Transform your captured insights into searchable, interactive systems that guide future decision-making.

Use your Knowledge Archaeologist to structure the information:

Help me organize the institutional memory we’ve captured into an accessible knowledge system.

Raw knowledge includes:

- [Upload transcripts from extraction interviews]

- [Client relationship histories and preferences]

- [Strategic decision histories and outcomes]

- [Market intelligence and competitive insights]

Structure this into:

STRATEGIC INTELLIGENCE DATABASE:

- Key insights about our industry, market, and competitive position

- Historical context behind major business decisions

- Lessons learned from successes and failures

RELATIONSHIP INTELLIGENCE SYSTEM:

- Client profiles with preferences, politics, and relationship history

- Partner/vendor dynamics and optimal management approaches

- Key contact networks and influence patterns

CULTURAL WISDOM GUIDE:

- “How things really work” vs. official procedures

- Informal decision-making patterns and influence networks

- Unwritten rules that drive success here

EARLY WARNING SYSTEMS:

- Red flags from past failures that indicate potential problems

- Success patterns that predict positive outcomes

- Decision frameworks based on accumulated experience

Make each section actionable—focus on insights that inform real business decisions.

Step 4: Build Interactive Knowledge Assistants

Static documentation doesn’t get used. Transform your institutional memory into conversational AI assistants that team members can actually consult when making decisions.

For critical knowledge areas, create dedicated assistants. For example, a “Client Intelligence Assistant” with custom instructions like:

You are our Client Intelligence Assistant. Your knowledge contains years of relationship history, preferences, and strategic context about our key clients.

When team members ask about client relationships:

1. Provide specific context about client preferences, politics, and history

2. Flag potential issues based on past experiences with similar situations

3. Suggest approaches that have worked with this specific client

4. Warn about approaches that have failed with similar clients

5. Connect current situations to historical patterns and outcomes

Always ground your advice in specific examples and learned experiences from our relationship history.

For new team members: Provide background context that would take years to learn through direct experience.

For experienced team members: Surface relevant historical patterns they might not remember or connect to current situations.

Never make generic relationship advice—always base recommendations on our specific experience with each client.

Upload your structured institutional memory to create assistants that preserve strategic wisdom across your organization.

Step 5: Implement Continuous Memory Capture

Don’t wait for departures to capture institutional memory. Build systems that continuously preserve strategic insights, relationship intelligence, and lessons learned as they develop.

Set up monthly “wisdom capture” sessions where team members document:

Recent Strategic Insights: “What did we learn about our market/customers/competition this month that we didn’t know before?”

Relationship Intelligence Updates: “What did we discover about key relationships that changes how we should approach them?”

Lessons from Successes and Failures: “What worked really well this month that we should replicate? What failed and why?”

Cultural Evolution: “How is the way we really get things done changing? What informal patterns are emerging?”

Use your Knowledge Archaeologist to process these regular updates:

Help me process this month’s institutional memory updates:

[Paste team submissions about insights, relationships, lessons learned]

Structure these updates to:

1. Identify patterns or themes across different team members’ observations

2. Connect new insights to existing knowledge in our system

3. Flag strategic implications that require leadership attention

4. Recommend actions based on accumulated intelligence

5. Update our early warning systems based on new experience

Focus on insights that will inform future business decisions.

Step 6: Prepare for Strategic Transitions

The most valuable institutional memory often exists at leadership levels—strategic context about major decisions, industry relationships, and long-term competitive dynamics. This knowledge is especially critical to preserve.

For senior leadership transitions, create comprehensive strategic context documentation:

Help me conduct a strategic knowledge transfer session with [departing leader].

Focus areas:

- Strategic decisions: Major choices made during their tenure and why

- Competitive intelligence: What they know about rivals that isn’t documented

- Industry relationships: Key contacts and relationship dynamics

- Market insights: Patterns they’ve observed that guide strategic thinking

- Cultural evolution: How they’ve shaped organizational direction

- Future concerns: What keeps them up at night about our strategic position

Create questions that capture not just what they know, but how they think about strategic problems and what frameworks guide their decision-making.

Structure the output as strategic intelligence that informs leadership succession and long-term planning.

This level of knowledge transfer ensures continuity of strategic thinking rather than just operational capability.

Beyond Survival to Acceleration

Institutional memory capture isn’t just about preventing knowledge loss—it’s about making accumulated wisdom accessible to accelerate growth. When strategic insights that took years to develop can be accessed by anyone making relevant decisions, your entire organization operates with the benefit of collective experience.

The businesses that master institutional memory capture gain compound advantages:

Faster Decision-Making: New team members make decisions informed by years of experience rather than learning through trial and error.

Relationship Continuity: Client relationships survive personnel changes because relationship intelligence is preserved and transferable.

Pattern Recognition: Strategic patterns and early warning signs identified over years become part of organizational intelligence.

Cultural Preservation: The informal knowledge that drives success gets preserved rather than eroded by turnover.

Competitive Intelligence: Market insights and competitive intelligence accumulate rather than reset with each departure.

Your competitors lose strategic intelligence every time someone leaves. You’re building systems that preserve and compound wisdom across your entire organizational history.

That’s the difference between businesses that struggle with each transition and those that use personnel changes as opportunities to strengthen their strategic capabilities.

The knowledge that drives your success shouldn’t be locked in individual heads. It should be part of your competitive infrastructure—accessible, searchable, and continuously growing.

✨ ✌🏻 ✨

But you’re missing the most valuable part of the entire engagement: understanding what actually happened and why.

Most SMBs treat project completion like crossing a finish line - celebrate briefly, then sprint toward the next deadline. But you’re missing the most valuable part of the entire engagement: understanding what actually happened and why.

Traditional project postmortems fail because they’re either skipped entirely (”we’re too busy”) or devolve into polite corporate theater. You know the script: “Communication could be better,” “Let’s align expectations earlier next time,” and everyone nods sagely before making identical mistakes on the next engagement. Meanwhile, the specific decisions, timing, and trade-offs that actually determined success or failure get forgotten within weeks.

AI can now analyze project patterns that used to require expensive consultants and weeks of interviews. The same technology that’s transforming customer service and marketing can extract insights from your project data in minutes, not months. But while everyone’s focused on AI chatbots and content generation, you can use it to build something your competitors don’t have: institutional memory that compounds into competitive advantage.

Intelligent project postmortems use AI to systematically extract insights from your project data, identify patterns across engagements, and transform lessons learned into templates and processes that make every future project more profitable and less stressful.

Here’s how.

Step 1: Create Your “Project Intelligence” Jig

Before diving into analysis, build a specialized AI system designed to help you learn from completed projects. This becomes your strategic partner for extracting insights and improving future performance.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my Project Intelligence Analyst. Your purpose is to help me conduct systematic postmortems that extract actionable insights from completed projects and transform them into improved processes for future engagements.

BUSINESS CONTEXT:

[Brief description of your business, typical project types, and team structure]

ANALYSIS FRAMEWORK:

When analyzing completed projects, focus on:

1. What Actually Happened vs. What Was Planned: Identify significant variances and their root causes

2. Decision Quality: Evaluate key choices made during the project and their outcomes

3. Team Performance: Assess individual contributions, collaboration effectiveness, and skill gaps

4. Client Relationship: Analyze communication patterns, satisfaction drivers, and friction points

5. Process Effectiveness: Identify workflow bottlenecks, handoff problems, and efficiency opportunities

6. Financial Performance: Compare actual vs. projected profitability and resource utilization

OUTPUT STRUCTURE:

- Executive Summary: Top 3 insights that could improve future projects

- Performance Analysis: Detailed breakdown of what worked and what didn’t

- Pattern Recognition: Connections to previous projects and recurring themes

- Process Improvements: Specific recommendations for workflows, templates, and procedures

- Team Development: Individual and collective learning opportunities identified

- Client Success Factors: What drove satisfaction and how to replicate it

COMMUNICATION STYLE:

- Objective and data-driven without being harsh or blame-focused

- Specific about problems but constructive about solutions

- Focused on systemic improvements rather than individual criticism

- Clear about trade-offs and opportunity costs in decision-making

Always ask clarifying questions about project context, stakeholder feedback, and specific outcomes before providing analysis.

Upload relevant materials to your jig’s knowledge:

• Original project proposal and timeline

• Final deliverables and client feedback

• Team communications (Slack, email threads, meeting notes)

• Time tracking and budget data

• Any client testimonials or complaints

This jig becomes your objective analyst, helping you see patterns and lessons that might be invisible when you’re close to the work.

Before diving into analysis, create a shared document called “Q3 2025 Postmortems” (or whatever quarter/period you’re analyzing) in Google Drive, SharePoint, or your preferred platform. This becomes your central repository for all project insights, patterns, and lessons learned.

Most AI models now integrate directly with Google Drive and Microsoft platforms, so your jig can reference this growing document throughout your analysis. As you complete each project postmortem, you’ll add the insights to this master document, creating a searchable knowledge base that gets more valuable with every project you analyze.

Step 2: Gather Project Data Systematically

Most businesses have project information scattered across multiple systems: proposals in Google Drive, communications in Slack, time tracking in separate software, and client feedback in email. Before AI can help extract insights, consolidate the story of what actually happened.

Start by assembling your project materials:

Financial Reality

• Original budget vs. actual costs

• Time estimates vs. actual hours by team member

• Scope changes and their impact on profitability

• Payment timing and any collection issues

Communication

• Client emails highlighting concerns, praise, or confusion

• Internal team discussions about challenges or breakthroughs

• Meeting notes from key decision points

• Any escalations or conflict resolution

Deliverable Evolution

• How the final product differed from initial concepts

• Major revisions and what drove them

• Client reactions to drafts and iterations

• Quality issues or last-minute changes

Team Performance Data

• Who contributed what and when

• Collaboration patterns and handoff points

• Skills gaps that became apparent during execution

• Individual feedback about the project experience

Once you’ve consolidated this information, prompt your Project Intelligence jig (making sure it can access your “Q3 2025 Postmortems” document):

Conduct a comprehensive analysis of our recently completed [PROJECT NAME] project. Please reference our Q3 2025 Postmortems document for any previous insights and add this analysis to that central repository.

PROJECT OVERVIEW:

- Original timeline: [dates]

- Final timeline: [actual dates]

- Budget: $[original] vs $[actual]

- Team: [list key contributors]

- Client: [company/contact info]

KEY MATERIALS ATTACHED:

- Original proposal and statement of work

- Final deliverables and client feedback

- Financial summary showing budget vs. actual

- Team communication highlights

- Timeline of major decisions and changes

Please analyze:

1. Where our planning was accurate vs. where we missed the mark

2. Which decisions had the biggest positive/negative impact on outcomes

3. What this project reveals about our team’s strengths and weaknesses

4. How client satisfaction correlated with our internal performance metrics

5. Which aspects of our process worked well vs. created friction

Focus on insights that could improve similar future projects, and add this complete analysis to our Q3 2025 Postmortems document for future reference.

This systematic approach often reveals surprising insights that would make expensive consultants blush. Maybe scope creep wasn’t the real problem - it was unclear initial requirements that you could have prevented with better discovery questions. Perhaps the project’s profitability didn’t suffer from client demands but from internal inefficiencies nobody wanted to acknowledge. Or your team’s technical skills were strong, but project communication patterns were accidentally training clients to micromanage every decision.

Step 3: Identify Patterns Across Projects

Individual project analysis is valuable, but the real insights emerge when you compare multiple projects. AI can identify patterns across engagements that reveal systemic strengths, recurring problems, and predictable success factors.

Once you’ve added several project analyses to your “Q3 2025 Postmortems” document, your jig can analyze patterns across all of them:

Review our Q3 2025 Postmortems document and analyze patterns across all completed project analyses to identify systemic insights.

Based on all projects documented in our central repository, please identify:

RECURRING SUCCESS PATTERNS:

- What consistently drives client satisfaction across different project types?

- Which team members or combinations consistently deliver exceptional results?

- What project characteristics predict profitability and smooth execution?

- Which communication or process elements correlate with positive outcomes?

SYSTEMIC CHALLENGES:

- What problems appear across multiple projects regardless of client or team?

- Where do we consistently underestimate time, cost, or complexity?

- What types of scope changes or client requests regularly derail projects?

- Which handoffs or workflow stages consistently create bottlenecks?

CLIENT RELATIONSHIP INSIGHTS:

- What early warning signs predict difficult client relationships?

- Which client characteristics correlate with project success vs. problems?

- How does our sales process set appropriate vs. unrealistic expectations?

TEAM OPTIMIZATION OPPORTUNITIES:

- Which roles or skills are consistently stretched too thin?

- Where do we see the most growth in team member capabilities?

- What training or process improvements would have the biggest impact?

Recommend 3-5 specific changes to our project approach based on this pattern analysis.

This cross-project analysis often reveals insights that single project reviews miss. Maybe your most profitable projects share specific client characteristics you could target more systematically - insights your competitors are clueless about because they don’t do systematic analysis. Perhaps certain team combinations consistently outperform others. Or you might discover that specific types of scope changes are entirely preventable with better upfront planning.

More importantly, you’re building market intelligence that compounds over time. While your competitors wonder why some projects go smoothly and others implode, you’ll know exactly which client red flags predict disaster, which services have the highest margins, and which positioning messages resonate most strongly.

Step 4: Build Improved Processes and Templates

The goal of intelligent postmortems isn’t just understanding what happened, but creating systems that prevent future problems and amplify successes. Use your insights to build better templates, processes, and decision frameworks.

Ask your Project Intelligence jig:

Based on the pattern analysis in our Q3 2025 Postmortems document, help me create improved templates and processes for future projects.

SPECIFIC IMPROVEMENTS NEEDED:

[Based on your analysis, list 3-5 key areas for improvement]

Please create:

PROJECT KICKOFF IMPROVEMENTS:

- Updated proposal template that addresses common scope confusion

- Client expectation-setting checklist based on successful project patterns

- Team role clarity document that prevents handoff problems

- Early warning system for identifying potential client relationship issues

PROCESS OPTIMIZATION:

- Workflow improvements for our most common bottleneck areas

- Communication templates for managing scope changes professionally

- Quality checkpoints that catch issues before they become client problems

- Timeline estimation guidelines based on our actual performance data

DECISION FRAMEWORKS:

- Criteria for when to push back on client requests vs. accommodate them

- Resource allocation guidelines for different project types

- Escalation procedures for common project challenges

- Profitability protection strategies when scope expands

TEAM DEVELOPMENT:

- Training priorities based on skills gaps identified across projects

- Collaboration guidelines for our most effective team combinations

- Performance feedback templates that connect to project outcomes

Make these practical tools we can implement immediately, not theoretical frameworks.

Your jig will help create concrete improvements: better contracts that prevent common scope creep, communication templates that set clearer expectations, and decision trees that help you handle difficult situations consistently.

Step 5: Create a Continuous Learning System

The final step is establishing a rhythm for project postmortems that becomes part of your operational culture rather than an occasional exercise. This ensures you’re constantly learning and improving rather than repeating the same mistakes.

Set up a simple but consistent process:

Within 48 Hours of Project Completion:

• Conduct brief team retrospective while memories are fresh

• Collect client feedback via standardized survey or interview

• Document any immediate insights or concerns

Within One Week:

• Complete comprehensive data gathering (financials, communications, deliverables)

• Run full analysis through your Project Intelligence jig

• Add the complete analysis to your quarterly Postmortems document

• Identify top 3 lessons learned and any immediate process changes needed

Monthly Pattern Analysis:

• Review your central Postmortems document for insights from all completed projects in the previous month

• Update templates and processes based on recurring themes

• Share learnings with team and integrate into training materials

Ask your jig to create a monitoring system:

Design a project learning system that ensures we consistently extract and apply insights from every completed engagement, feeding all analysis into our central Postmortems repository.

Create:

1. Post-project checklist that captures essential data within 48 hours

2. Standardized client feedback survey that gathers insights systematically

3. Team retrospective template that focuses on actionable improvements

4. Monthly review process that analyzes patterns in the central Postmortems document

5. Template update schedule that incorporates learnings from the repository into future proposals

6. Performance tracking system that measures our improvement over time

Include specific questions, timelines, and responsible parties for each element.

The system should be thorough enough to capture real insights but simple enough that we actually use it consistently.

This systematic approach transforms one-off postmortems into continuous organizational learning. Your central Postmortems document becomes increasingly valuable with each project analysis, creating a searchable knowledge base of what works, what doesn’t, and why. Instead of hoping each project goes better than the last, you’re systematically identifying what works and scaling those successes across all future engagements.

Transform Mistakes into Competitive Advantage

Most of your competitors finish projects and immediately chase the next shiny opportunity. They’re stuck in a cycle of hopeful repetition: hoping the next project goes better, hoping they’ve learned from past mistakes, hoping their team naturally improves over time.

Meanwhile, SMBs that think strategically about AI are building unfair advantages through exactly this kind of institutional learning. They’re not just using AI for the obvious stuff like writing emails. They’re using it to extract competitive intelligence from their own operations.

Intelligent postmortems give you systematic learning advantages your competitors can’t easily replicate. When you can predict client relationship challenges, identify your most profitable service configurations, and continuously refine your processes based on real outcomes, you don’t just deliver better projects—you become the vendor that clients rave about to their colleagues.

The businesses that master this approach don’t just avoid repeating mistakes. They discover winning patterns they can replicate systematically. They identify client types that consistently generate profit versus those that drain resources. They build processes that turn good team members into great ones.

Your next project already contains the seeds of your next breakthrough, but only if you’re systematic about extracting them. While your competitors hope for the best, you’ll know exactly what works and why.

✨ ✌🏻 ✨

Sound familiar?

For many businesses, Q4 is make-or-break. Whether you’re a B2C company counting on holiday sales to hit annual numbers or a B2B operation racing against procurement deadlines and year-end budgets, the final quarter often determines whether you celebrate or scramble.

The challenge? November and December are notoriously difficult months. Decision-makers disappear for vacations. Buyers get distracted by holiday preparations. Even more challenging for B2B businesses, clients start shifting mental focus to their next fiscal year’s priorities and budget planning, making it critical to stay top-of-mind with your most important accounts now, before they get consumed by 2026 planning. For retail businesses, you’re competing for wallet share during the most expensive time of year for consumers.

But here’s what separates businesses that crush Q4 from those that limp to the finish line: proactive planning executed with surgical precision. While your competitors are hoping for last-minute miracles, you can use AI to create account-specific strategies, design compelling year-end campaigns, and build systematic approaches that turn Q4 pressure into Q4 performance.

This isn’t about working harder in the final months. It’s about working smarter, using AI to accelerate deals that matter and create urgency that feels authentic rather than desperate.

Here’s how.

Step 1:
Build Your “Q4 Revenue Accelerator” Jig

Note: This guide is written primarily from a B2B perspective, but B2C businesses can easily adapt these strategies. If you’re primarily B2C, consider pasting this entire post into your favorite frontier model and asking it to update all the jig instructions and prompts for B2C contexts. We do include some B2C-specific guidance in Step 3.

Before diving into tactics, create an AI system specifically designed to help you analyze opportunities, craft strategies, and execute campaigns that drive Q4 results. This jig will serve as your strategic partner for maximizing revenue in the year’s most challenging quarter.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my Q4 Revenue Accelerator. Your purpose is to help me maximize revenue in the final quarter through strategic account planning, compelling campaigns, and systematic execution that creates genuine urgency without desperation.

BUSINESS CONTEXT:

[Brief description of your business, whether B2B/B2C, typical sales cycles, and Q4 challenges]

Q4 REVENUE FRAMEWORK:

When developing Q4 strategies, always consider:

1. Time constraints: Limited decision-making windows before holidays/year-end

2. Budget realities: Customers’ Q4 budget availability and approval processes

3. Competitive landscape: What others are doing and how to differentiate

4. Seasonal psychology: How holidays and year-end affect buying behavior

5. Value proposition: Why customers should act now vs. waiting until Q1

ANALYSIS AREAS:

- Account Prioritization: Focus efforts on highest-probability, highest-value opportunities

- Timing Strategy: Optimal windows for different types of outreach and campaigns

- Value Communication: Messages that resonate during budget-conscious periods

- Urgency Creation: Authentic reasons for customers to act before year-end

- Objection Handling: Responses to common Q4 delays and hesitations

OUTPUT STRUCTURE:

- Strategic Assessment: Current pipeline analysis and opportunity prioritization

- Campaign Framework: Messaging, timing, and channel strategies

- Account Plans: Specific approaches for key prospects and customers

- Execution Timeline: Week-by-week action plans through year-end

- Success Metrics: KPIs and tracking methods for Q4 initiatives

COMMUNICATION STYLE:

- Results-focused and action-oriented

- Specific rather than generic recommendations

- Acknowledges Q4 realities without being defeatist

- Creates authentic urgency based on genuine value

- Balances aggressive pursuit with relationship preservation

Always ask clarifying questions about current pipeline, Q4 goals, and specific challenges before providing recommendations.

Upload relevant materials to your jig’s knowledge:

• Current pipeline and opportunity data

• Historical Q4 performance patterns

• Customer feedback about decision-making processes

• Competitive intelligence and market positioning

• Year-end budget and target information

This jig becomes your Q4 command center, ensuring every strategy aligns with the unique pressures and opportunities of the final quarter.

Step 2:
Analyze Your Revenue Reality

Before creating campaigns or account strategies, conduct a brutally honest assessment of your Q4 position. Most businesses either panic about targets or assume everything will work out. Neither approach maximizes revenue.

Start with your Q4 Revenue Accelerator:

Conduct a comprehensive Q4 revenue assessment based on our current situation:

PIPELINE ANALYSIS:

- Which deals are genuinely likely to close before year-end?

- What’s the realistic timeline for each major opportunity?

- Which prospects are early-stage vs. ready to decide?

- Where do we have strong relationships vs. need to build trust quickly?

REVENUE GAP ANALYSIS:

- How much revenue do we need to hit our annual targets?

- What’s our best-case scenario if everything goes perfectly?

- What’s our realistic forecast based on current momentum?

- Which specific deals or campaigns could bridge the gap?

CONSTRAINT ASSESSMENT:

- What internal capacity limitations might slow us down?

- Which customer decision-making processes could create delays?

- What external factors (holidays, budgets, competition) affect timing?

Q4 OPPORTUNITY MAPPING:

- Which existing customers might expand or renew early?

- What new prospects could be fast-tracked with the right approach?

- Where can we create authentic urgency based on real value?

Provide specific recommendations for where to focus our Q4 efforts.

This analysis often reveals surprising insights. Maybe your pipeline is stronger than you thought but concentrated in prospects who won’t decide until Q1. Perhaps you have expansion opportunities with existing customers that could bridge revenue gaps faster than new deals. Or you might discover that your best Q4 opportunities require different approaches than your typical sales process.

Follow up with targeted questions:

Based on this revenue analysis, help me prioritize our Q4 efforts:

1. Which 5-10 specific opportunities deserve our highest attention and why?

2. What combination of new sales, renewals, and upsells gives us the best shot at our targets?

3. Which deals need immediate attention vs. can be nurtured over the quarter?

4. Where should we invest our limited time and resources for maximum revenue impact?

5. What early warning signs should we watch for that might derail key opportunities?

Create a Q4 opportunity prioritization framework with specific next steps for each category.

Step 3:
Design Account-Specific Acceleration Strategies

Q4 revenue acceleration isn’t about mass email campaigns or generic promotions. It’s about surgical precision: the right message, to the right person, at the right time, with the right value proposition. This is where the scenario-based planning approach we’ve used throughout this series really shines.

For most B2B businesses, this step involves activating your entire sales team. Rather than one person trying to plan for all accounts, have each account owner use your Q4 Revenue Accelerator jig to develop specific strategies for their own key opportunities, then share findings and insights across the team. This distributed approach ensures deeper account knowledge while building collective intelligence about what works.

For B2B businesses, use your jig to create detailed account plans:

Create comprehensive Q4 acceleration strategies for our top 5 priority accounts:

For each account, analyze:

- Current relationship status and decision-making process

- Specific business challenges we could solve before year-end

- Budget situation and approval requirements

- Key stakeholders and their individual motivations

- Competitive situation and our differentiation

- Potential objections to moving forward quickly

Then develop:

- Account-specific value propositions that justify Q4 action

- Multi-touch outreach sequences with timing and messaging

- Stakeholder engagement strategies for complex decisions

- Creative approaches to overcome timing objections

- Mutual success criteria that benefit both parties

Focus on strategies that accelerate natural buying processes rather than forcing artificial urgency.

For B2C businesses, create targeted campaign strategies based on the latest consumer insights. With consumer spending expected to decline 5% from 2024 and 84% of shoppers planning to cut back due to rising prices and tariffs, your Q4 approach needs to emphasize genuine value:

Design Q4 customer campaigns that address current economic pressures:

CUSTOMER SEGMENTATION:

- High-value customers who can still spend but want maximum value

- Budget-conscious customers looking for deals and practicality

- Early shoppers who start holiday planning before November

- Last-minute shoppers who wait until December

CAMPAIGN STRATEGIES:

- Value propositions that justify purchases during tight budget periods

- Early-bird campaigns that capture the 80% of shoppers planning to buy before Cyber Monday

- Flexible payment options for budget-conscious customers

- Experience-focused offerings since consumers want “memories, not just transactions”

TIMING OPTIMIZATION:

- October campaigns targeting early holiday shoppers

- Thanksgiving gratitude campaigns that build relationships

- Strategic Black Friday/Cyber Monday positioning that differentiates from generic discounts

- December strategies for last-minute shoppers who represent 29% of consumers

Create specific campaign concepts with messaging, timing, and success metrics.

Step 4:
Execute Systematic Outreach

With your prioritized opportunities and account strategies defined, it’s time for systematic execution. The key is maintaining consistent momentum while adapting to real-time feedback and changing circumstances.

For your highest-priority B2B opportunities, ask your jig:

Create a week-by-week execution plan for our priority accounts through year-end:

OUTREACH SEQUENCING:

- Week 1-2: Relationship building and value discovery

- Week 3-4: Proposal development and stakeholder alignment

- Week 5-6: Negotiation and objection handling

- Week 7-8: Contract finalization and implementation planning

MULTI-CHANNEL APPROACH:

- Email sequences with account-specific messaging

- Phone/video call strategies for key stakeholders

- LinkedIn engagement and social selling tactics

- In-person meeting opportunities where possible

MOMENTUM MAINTENANCE:

- Follow-up protocols that keep deals moving

- Internal stakeholder update templates

- Proposal and contract templates optimized for quick turnaround

- Implementation timeline options that work for Q4 decisions

Include specific communication templates and timing recommendations.

If you built a Brand Voice Architect jig from Issue #7, use it to ensure all your Q4 communications sound authentically like your business while addressing Q4-specific concerns.

For B2C campaigns, leverage your content creation capabilities:

Step 5: Monitor and Adapt in Real-Time

Q4 moves fast. Customer priorities shift. Competitors launch surprise campaigns. Economic conditions evolve. Your Q4 success depends on monitoring performance closely and adapting quickly based on what you learn.

Set up a weekly review rhythm with your Q4 Revenue Accelerator:

Analyze our Q4 performance and recommend tactical adjustments:

PERFORMANCE TRACKING:

- Pipeline velocity: Are deals moving as expected or stalling?

- Campaign effectiveness: Which messages and channels drive best response?

- Competitive responses: How are competitors adjusting their Q4 strategies?

- Customer feedback: What concerns or objections are we hearing repeatedly?

ADAPTATION OPPORTUNITIES:

- Which successful tactics should we amplify?

- What messaging needs adjustment based on market feedback?

- Which opportunities require different approaches or timing?

- Where should we reallocate effort based on early results?

OPTIMIZATION PRIORITIES:

- Account strategies that need immediate attention

- Campaign elements to test or improve

- Resource allocation adjustments for maximum impact

- Early warning signs that require proactive responses

Provide specific weekly action items to optimize our Q4 trajectory.

This real-time optimization approach can dramatically improve results. Maybe you discover that customers respond better to implementation timelines that start in Q1, giving you a new angle for closing Q4 deals. Perhaps certain messaging resonates much better than expected, letting you double down on successful approaches. Or you might identify early warning signs that let you save deals that would otherwise slip to next year.

Use insights from Issue #9 on Understanding Customers to ensure you’re tracking the right success metrics and gathering meaningful feedback throughout Q4.

The Q4 Advantage

Q4 revenue acceleration isn’t about desperation tactics or artificial urgency. It’s about systematic preparation that creates genuine value for customers while maximizing your revenue potential.

The businesses that consistently crush Q4 understand that the quarter requires different strategies than the rest of the year. Customer psychology shifts. Decision-making timelines compress. Competitive pressure intensifies. Budget realities change.

But these same challenges create opportunities for businesses prepared to navigate them strategically. While competitors resort to discounting or generic “year-end” campaigns, you’re delivering personalized value propositions that address specific customer needs. While others send the same message to everyone, you’re crafting account-specific strategies that accelerate natural buying processes.

The scenario-based planning approach we’ve used throughout this series—from cash flow forecasting to pricing optimization—reaches its full potential in Q4. When every opportunity matters and timing is critical, the businesses with systematic approaches and AI-powered execution consistently outperform those relying on hope and hustle.

Your Q4 isn’t about surviving until the holidays. It’s about executing with precision to finish the year strong and set yourself up for momentum heading into the new year.

The tools are here. The strategies are proven. The question is whether you’ll execute with precision while your competitors wing it.

✨ ✌🏻 ✨

Meanwhile, your biggest competitor seems to have a sixth sense about exactly when to follow up, what content to send, and how to guide prospects seamlessly from interest to purchase. They’re not just converting more leads, they’re creating customer experiences so smooth that people actively recommend them.

The difference isn’t luck or intuition. It’s that they’ve mapped and optimized every touchpoint in their customer journey using AI to identify friction points, personalize interactions, and proactively address customer needs before problems arise.

Most SMBs think customer journey mapping is either too complex for their resources or too theoretical to drive real results. But AI has transformed journey mapping from an expensive consulting project into a practical system that continuously optimizes how customers experience your business.

Here’s how to build customer journey intelligence that turns more prospects into customers and more customers into advocates.

Step 1:
Build Your “Customer Journey” Jig

Before diving into analysis, create an AI system specifically designed to understand and optimize customer experiences across every touchpoint with your business. This builds on the customer insight techniques we covered in issue #9 on Understanding Customers, but extends beyond analyzing feedback to optimizing the entire customer experience.

Gather your customer interaction data:

• Website analytics and user behavior patterns

• Email engagement metrics (opens, clicks, conversions)

• Sales pipeline data and conversion rates at each stage

• Customer service tickets and resolution patterns

• Social media interactions and feedback

• Customer survey responses and testimonials

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my Customer Journey specialist. Your purpose is to analyze customer interactions across all touchpoints and identify opportunities to improve the customer experience from first contact to long-term advocacy.

BUSINESS CONTEXT:
[Brief description of your business, customer base, and typical purchase process]

JOURNEY ANALYSIS FRAMEWORK:
When analyzing customer journeys, focus on:
1. Touchpoint mapping: Every interaction customers have with our business
2. Friction identification: Where customers get stuck, confused, or frustrated
3. Moment analysis: Critical decision points that make or break conversions
4. Personalization opportunities: Where tailored experiences could improve outcomes
5. Proactive intervention points: When to reach out before customers need help

CUSTOMER JOURNEY STAGES:
- Awareness: How prospects first discover us
- Consideration: Research and evaluation process
- Purchase: Decision-making and buying experience
- Onboarding: Initial experience as a new customer
- Growth: Expansion and deeper engagement
- Advocacy: Referrals and testimonials

ANALYSIS AREAS:
- Conversion rates between each stage and drop-off patterns
- Time-to-conversion and factors that accelerate progress
- Communication preferences and channel effectiveness
- Common objections and concerns at each stage
- Successful customer patterns vs. churned customer patterns

OUTPUT FORMAT:
1. Current State Analysis: Honest assessment of journey performance
2. Friction Points: Specific areas where customers struggle or drop off
3. Optimization Opportunities: Targeted improvements with expected impact
4. Personalization Strategy: How to tailor experiences for different segments
5. Implementation Plan: Prioritized actions with success metrics

Focus on actionable insights that improve customer experience while driving business results.

Upload relevant materials to your jig’s knowledge:

• Customer interaction data from all channels

• Survey responses and feedback

• Sales team insights about common objections

• Customer service patterns and frequent issues

• Successful customer case studies

This jig becomes your customer experience intelligence system, helping you understand not just what customers do, but why they do it and how to improve their experience.

Voice Journey Mapping Exercise

Before uploading any data to your jig, take 10-15 minutes to verbally walk through your entire customer journey from your customers’ perspective. Use your phone’s voice memo app or any recording tool and just talk through the experience:

“A potential customer first hears about us when... then they probably go to our website and... if they’re interested, they might call us or... after that initial conversation, we usually... then there’s that thing where they have to wait for... and somewhere in there, Sarah from accounting calls to verify...”

The most critical touchpoints often happen outside your formal business systems. The informal phone call that builds trust. The delayed email response that creates anxiety. The moment when customers can’t figure out your pricing and give up. The conversation with their colleague who had a bad experience three years ago.

Your formal analytics capture clicks and conversions, but they miss the emotional journey, the external influences, and the human moments that actually drive decisions. This voice mapping exercise ensures you capture the full reality of how customers experience your business, not just the parts your systems track.

After recording, upload the transcript to your Customer Journey jig along with your formal data. This combination of systems data plus human insight creates a complete picture of your customer experience.

Step 2:
Map Your Current Reality

Most businesses have a fantasy version of their customer journey that doesn’t match how customers actually experience their company. Use your Customer Journey jig to uncover the truth about your current customer experience.

Start with this comprehensive analysis:

Map our actual customer journey based on the data I’ve provided. I want brutal honesty about how customers really experience our business.

JOURNEY STAGE ANALYSIS:
For each stage (Awareness → Consideration → Purchase → Onboarding → Growth → Advocacy):

1. What percentage of people move from this stage to the next?
2. How long do customers typically spend in this stage?
3. What are the main touchpoints and interactions?
4. Where do we see the biggest drop-offs and why?
5. What friction points consistently frustrate customers?

CHANNEL EFFECTIVENESS:
- Which marketing channels bring in customers who convert vs. those who don’t?
- What’s the actual customer acquisition cost by channel?
- Which touchpoints have the highest engagement vs. lowest?

CUSTOMER SEGMENT DIFFERENCES:
- Do different types of customers follow different journey paths?
- Which segments convert fastest/slowest and what drives the difference?
- Are we treating all customers the same when we shouldn’t be?

RED FLAG ANALYSIS:
- Where do promising prospects consistently disappear?
- What patterns indicate a customer is about to churn?
- Which touchpoints create confusion rather than clarity?

Provide specific insights about our journey performance, not generic advice.

This analysis often reveals uncomfortable truths. Maybe your “streamlined” checkout process has five unnecessary steps. Perhaps your email nurture sequence talks about features when customers want to understand outcomes. Or your customer service creates more problems than it solves.

The key is accepting current reality before trying to optimize it. Your jig will likely identify patterns you hadn’t noticed, like certain customer segments that consistently get stuck at specific points, or touchpoints that feel important to you but barely register with customers.

Follow up with:

Based on this journey analysis, what are the 3-5 highest-impact changes we could make to improve customer experience and conversion rates?

For each recommendation:
1. What specific problem does this solve?
2. Which customer segments would benefit most?
3. What’s the expected impact on conversion rates or customer satisfaction?
4. How difficult would this be to implement?
5. How would we measure success?

Focus on changes that address real customer friction points rather than internal preferences.

Step 3:
Design Proactive Interventions

Instead of waiting for customers to reach out when they’re confused or frustrated, use AI to identify intervention points where proactive outreach can prevent problems and accelerate progress. This approach transforms the reactive customer service strategies we covered in issue #16 into proactive journey optimization.

Your Customer Journey jig can help design systems that anticipate customer needs:

Design proactive intervention strategies for our customer journey based on the patterns we’ve identified.

EARLY WARNING SYSTEMS:
- What customer behaviors indicate they’re losing interest or getting stuck?
- Which engagement patterns predict successful vs. unsuccessful outcomes?
- When should we reach out proactively vs. wait for customers to contact us?

INTERVENTION TRIGGERS:
For each journey stage, identify:
1. Specific behaviors that trigger outreach (time spent, pages visited, emails not opened)
2. What type of intervention would be most helpful (educational content, personal call, demo)
3. Timing that feels helpful rather than pushy
4. Messaging that addresses likely concerns or questions

PERSONALIZATION OPPORTUNITIES:
- How should our outreach differ for various customer segments?
- What information should we collect to enable better personalization?
- Which communication channels work best for different types of interventions?

AUTOMATION VS. HUMAN TOUCH:
- Which interventions can be automated effectively?
- When do customers need human interaction?
- How do we scale personalized outreach without losing authenticity?

Create specific intervention workflows with templates and success metrics.

This approach transforms your customer journey from a passive experience into an intelligently guided process. Instead of hoping prospects figure things out on their own, you’re proactively addressing their likely concerns and questions.

For example, your jig might recommend:

• Automated emails that address common concerns when prospects visit your pricing page but don’t purchase

• Personal outreach when high-value prospects engage with content but haven’t responded to sales emails

• Educational content delivered when customers hit typical confusion points during onboarding

• Check-in calls scheduled automatically based on usage patterns that predict churn risk

For B2B businesses, this often connects directly to your proposal process. The techniques from issue #1 on Faster + Better Proposals become even more effective when you understand exactly where prospects are in their decision-making journey and what concerns need addressing.

Step 4:
Implement AI-Powered Personalization

Generic customer journeys assume all customers are the same. AI enables you to create tailored experiences that feel personally relevant while scaling across your entire customer base.

Use your jig to design personalization strategies:

Create a personalization framework that tailors customer experiences based on their behavior, preferences, and journey stage.

CUSTOMER SEGMENTATION:
Based on our data, identify distinct customer segments that should receive different journey experiences:
1. What differentiates these segments (industry, company size, use case, behavior patterns)?
2. How do their needs and concerns vary throughout the journey?
3. What messaging and content resonates with each segment?
4. Which channels and timing work best for each group?

DYNAMIC CONTENT STRATEGY:
- What information should we collect to enable better personalization?
- How can we customize email content, website experiences, and sales conversations?
- Which touchpoints have the highest impact when personalized?
- How do we balance personalization with operational simplicity?

BEHAVIORAL TRIGGERS:
Design personalized responses to customer actions:
- What happens when someone downloads multiple lead magnets vs. just one?
- How should we respond to repeat website visitors vs. first-time visitors?
- What personalized follow-up should occur after sales calls or demos?
- Which customer service interactions indicate expansion opportunities?

IMPLEMENTATION APPROACH:
- Which personalization elements can we implement immediately?
- What tools or systems do we need for advanced personalization?
- How do we maintain personalization as we scale?

Create specific personalization workflows that improve customer experience while driving business results.

The goal isn’t to create dozens of different customer journeys, but to identify key decision points where personalized experiences significantly improve outcomes. Maybe B2B customers need different social proof than B2C customers. Perhaps price-sensitive prospects need cost justification content while premium prospects want implementation details.

Your AI can help you identify these patterns and design appropriate responses without requiring complex marketing automation systems or extensive manual work. For maintaining consistent messaging across all these personalized touchpoints, leverage your Brand Voice Architect jig from issue #7 on On-Brand Content to ensure every interaction feels authentically like your business.

Step 5:
Build Continuous Optimization Systems

Customer journeys aren’t static. Customer expectations evolve, new touchpoints emerge, and competitive dynamics shift. Build systems that continuously monitor and improve your customer experience.

Set up ongoing journey optimization with your jig:

Create a continuous improvement system for monitoring and optimizing our customer journey performance.

MONTHLY MONITORING:
What key metrics should we track to understand journey health:
1. Conversion rates between each journey stage
2. Time-to-conversion and factors that accelerate progress
3. Customer satisfaction scores at key touchpoints
4. Channel effectiveness and customer acquisition costs
5. Intervention success rates and customer response patterns

QUARTERLY DEEP DIVES:
- Which journey stages need attention based on performance trends?
- Are our personalization efforts improving outcomes?
- What new friction points have emerged?
- How are customer expectations changing?
- Which touchpoints could be eliminated or combined?

IMPROVEMENT IDENTIFICATION:
- Where should we focus optimization efforts for maximum impact?
- What A/B tests should we run on critical journey elements?
- Which successful customer patterns should we try to replicate?
- How can we address common drop-off points?

COMPETITIVE INTELLIGENCE:
- How are competitors evolving their customer experiences?
- What new touchpoints or channels should we consider?
- Which industry best practices could we adapt?

Create a sustainable process for continuous journey optimization that doesn’t require constant manual analysis.

This systematic approach ensures your customer journey improvements compound over time rather than becoming one-time projects that get forgotten.

Consider setting up automated reports that flag when conversion rates drop, customer satisfaction scores change, or new patterns emerge in customer behavior. Your Customer Journey jig can help interpret these signals and recommend specific improvements.

Step 6:
Automate Your Optimized Journey

Once you’ve mapped and optimized your customer journey, the next step is automating the critical touchpoints that can run without human intervention. This is where your journey mapping transforms from analysis into a system that works 24/7 to guide customers toward success.

Modern AI automation platforms like Lindy.ai, Clay.com, and n8n can handle complex, multi-step customer journey workflows that previously required dedicated staff. The key is identifying which parts of your journey are predictable enough to automate and which require human judgment.

Ask your Customer Journey jig to create a Product Requirements Document (PRD) for automation:

Based on our optimized customer journey, create a Product Requirements Document (PRD) for automating key touchpoints using an AI automation platform.

AUTOMATION ASSESSMENT:
Identify which journey elements are good candidates for automation:
1. Triggered emails based on specific customer behaviors
2. Lead scoring and routing based on engagement patterns
3. Proactive outreach when customers hit friction points
4. Data enrichment and customer research workflows
5. Follow-up sequences after key interactions

For each automation opportunity:
- What specific trigger should start the automation?
- What data inputs are needed to make smart decisions?
- What actions should the automation take?
- When should it escalate to a human?
- How do we measure success?

TECHNICAL REQUIREMENTS:
- Which customer data sources need to be connected?
- What external tools or APIs are required?
- How complex are the decision trees and branching logic?
- What personalization variables should be included?
- Which touchpoints need A/B testing capabilities?

IMPLEMENTATION PHASES:
Create a 3-phase rollout plan:
Phase 1: Simple automations (welcome sequences, basic scoring)
Phase 2: Behavioral triggers (engagement-based outreach, friction detection)
Phase 3: Advanced personalization (dynamic content, predictive interventions)

Format this as a comprehensive PRD that could guide implementation on platforms like Lindy, Clay, or n8n.

Most AI automation platforms have sophisticated research capabilities built in. Enable deep research in your Customer Journey jig and ask it to research whichever platform interests you most:

Research [Lindy.ai/Clay.com/n8n] capabilities and create specific implementation guidance for our customer journey automation.

Focus on:
- Which of our automation requirements are native features vs. need custom development
- Specific workflow templates or starting points they provide
- Integration capabilities with our existing tools
- Pricing implications for our expected volume
- Implementation complexity and timeline

Provide platform-specific recommendations for building our automated customer journey.

Finally, ask your jig to create the technical specifications:

Export our customer journey automation requirements as structured data that could be imported into an AI automation platform.

Create JSON format specifications that include:
- Trigger conditions and data sources
- Decision logic and branching rules
- Action steps and integrations required
- Personalization variables and content templates
- Success metrics and optimization parameters

Format this so it could be uploaded to an automation platform or shared with a technical team for implementation.

This approach transforms your customer journey insights into working automation systems that continuously optimize customer experiences without requiring constant manual oversight.

Turn Every Interaction into Intelligence

Customer journey mapping with AI isn’t just about fixing broken processes. It’s about creating a competitive advantage that gets stronger over time. Every customer interaction becomes data that improves future customer experiences.

When you understand not just what customers do, but why they do it and how to influence positive outcomes, you shift from reactive customer service to proactive customer success. You stop losing prospects to preventable friction and start converting more opportunities because you’re addressing their actual needs and concerns.

The lead generation techniques from Issue #19 become significantly more effective when integrated with comprehensive journey mapping. You’re not just capturing leads, you’re guiding them through an optimized experience from first contact to long-term advocacy.

Your competitors are probably still using static customer journey maps created six months ago that bear little resemblance to how customers actually experience their business. While they’re debating whether personalization is worth the investment, you’re using AI to deliver tailored experiences that feel individually crafted at scale.

The businesses that dominate their markets aren’t those with the best products or the biggest marketing budgets. They’re the ones that make it easy, pleasant, and valuable for customers to do business with them at every single touchpoint.

Your customer journey is your competitive battleground. Time to start winning it.

✨ ✌🏻 ✨

Traditional performance reviews would miss all of this.

Most managers are still evaluating employees based on pre-AI metrics: time spent in meetings, adherence to established processes, collaboration on lengthy projects. But as we explored in our post about AI-native employees, the most valuable contributors today think and work differently. Just as we discussed the need to rethink job descriptions for human-AI collaboration (#14: Intelligent Hiring), performance reviews need similar transformation. They build solutions instead of attending planning sessions, automate workflows instead of perfecting manual processes, and create value through human-AI collaboration that traditional frameworks can’t capture.

If you’re still measuring what mattered in 2022, you’re incentivizing the wrong behaviors and missing your best performers.

Based on recent AI adoption data, it’s likely that about a third of your employees are using generative AI regularly, and within that group, a third are integrating it into nearly everything they do. The remaining two-thirds range from AI-curious to completely disengaged.

The trick is evolving performance evaluation so you can identify and reward the people who are heavily leaning in, shining a spotlight on them so others can follow their example. You need structures that amplify AI curiosity among the interested while providing pathways for critical employees to become more AI-native.

Most importantly, if you don’t start incentivizing these behaviors now, you’re going to lose the AI-native employees you already have. And that’s the last thing you want; there aren’t enough AI-native workers in the world as it is. Your performance review system might be the difference between retaining your most forward-thinking talent and watching them leave for organizations that recognize what they contribute.

Here’s how to transform performance reviews for the AI era.

Step 1:
Identify What You Should Actually Be Measuring

Before building new evaluation systems, you need clarity about what drives success when AI amplifies human capabilities. The metrics that mattered when humans did everything manually often work against optimal performance in an AI-augmented workplace.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my AI-Era Performance Analyst. Your purpose is to help me identify and measure the behaviors that drive success when employees work alongside AI systems.

BUSINESS CONTEXT:

[Brief description of your business, team roles, and current AI adoption level]

When analyzing performance in an AI-augmented workplace, focus on:

1. Solution creation speed vs. process adherence

2. Cross-functional impact vs. narrow specialization

3. Learning velocity with new tools vs. mastery of established systems

4. Value creation through automation vs. manual task completion

5. Judgment quality when directing AI vs. individual task execution

TRADITIONAL VS. AI-ERA METRICS:

- Traditional: Hours worked, meetings attended, process compliance

- AI-Era: Problems solved, workflows improved, customer impact delivered

- Traditional: Deep specialization within role boundaries

- AI-Era: Ability to leverage AI across multiple functional areas

- Traditional: Collaboration through extensive coordination

- AI-Era: Building solutions that reduce coordination overhead

Create evaluation frameworks that incentivize the behaviors we actually want in an AI-powered workplace.

Upload all of your current performance evaluation process documents and start by analyzing your current review process:

Review our current performance evaluation criteria and identify:

1. Which metrics incentivize behaviors that made sense before AI but now create inefficiency

2. What high-value activities our best AI-adopting employees do that we’re not measuring

3. Which evaluation categories need complete rethinking for an AI era

4. What new competencies we should assess that didn’t matter 2 years ago

Help me design performance criteria that reward results and innovation rather than process adherence and time allocation.

This analysis often reveals how misaligned traditional reviews have become. Maybe you’re evaluating marketing team members on campaign planning meetings when the real value comes from their ability to generate and test multiple campaign variations using AI. Perhaps you’re measuring customer service reps on call time when the best performers are using AI to solve problems faster.

Step 2:
Design AI-Era Competency Frameworks

With clarity about what matters, build specific evaluation criteria that capture how employees create value through human-AI collaboration.

Prompt your AI-Era Performance Analyst:

Design competency frameworks for evaluating [specific role] in an AI-augmented workplace.

For each competency, provide:

1. Observable behaviors that demonstrate proficiency

2. Specific examples of what “exceeds expectations” looks like

3. How to distinguish between AI-assisted work and AI-dependent work

4. Questions managers can ask to assess capability authentically

KEY COMPETENCIES TO EVALUATE:

- AI Collaboration: How effectively do they leverage AI tools to amplify their capabilities?

- Solution Architecture: Can they design workflows that optimize human-AI handoffs?

- Quality Judgment: Do they effectively evaluate and refine AI output?

- Learning Agility: How quickly do they adapt to new AI tools and capabilities?

- Cross-functional Impact: What value do they create outside their traditional role boundaries?

- Innovation Mindset: Do they proactively identify automation and improvement opportunities?

Focus on behaviors that create measurable business value, not just AI tool usage.

The goal is creating frameworks that recognize employees who think like the AI-native workers we discussed earlier: those who default to building solutions, think in systems rather than tasks, and create value through intelligent human-AI collaboration. This builds on the competency frameworks we explored in our guide to [writing job descriptions for the AI era](link to #14: Intelligent Hiring), but extends them to ongoing performance evaluation.

For example, instead of evaluating a customer service representative on “responded to 50 tickets per day,” you might assess:

• Problem Resolution Innovation: Did they identify patterns in customer issues and create AI-assisted solutions that prevent future tickets?

• Customer Experience Enhancement: How did they use AI to personalize responses while maintaining authentic human connection?

• Workflow Optimization: What manual processes did they streamline or eliminate through intelligent automation?

Step 3:
Conduct Forward-Looking Review Conversations

With four months remaining in most companies’ performance review cycles, now is the perfect time to reset expectations with your team. The best leaders operate under a “no surprises” performance review philosophy; that starts with giving employees clear direction about what success looks like in an AI-powered workplace.

Use this timing advantage to have conversations that transform performance reviews from backward-looking report cards into strategic development conversations about future capabilities and contributions. Your team members have four months to lean into these shifting expectations rather than discovering them during year-end reviews.

Use your AI-Era Performance Analyst to structure these conversations:

Help me structure a performance review conversation for [employee name] that focuses on growth and capability development in an AI-powered workplace.

EMPLOYEE CONTEXT:

[Brief description of their role, AI adoption level, and key contributions]

Create a conversation framework that covers:

1. Recognition of AI-era contributions they’ve made

2. Assessment of current human-AI collaboration effectiveness

3. Identification of capability gaps for our evolving business needs

4. Development planning for emerging AI tools and techniques

5. Goal setting that incentivizes continued innovation and learning

Generate specific questions that encourage honest self-reflection and productive dialogue about their professional development in an AI-enhanced role.

These conversations should feel fundamentally different from traditional reviews. Instead of judging past performance against fixed criteria, you’re collaboratively planning how each person’s unique strengths can create more value as AI capabilities expand.

Sample questions might include:

• “What’s one process you wish you could automate that would free you up for higher-value work?”

• “When you’re working with AI tools, what decisions do you find yourself making that the AI can’t?”

• “What would you build if you had the authority to implement any solution you wanted?”

• “Which AI capabilities emerging in our industry excite you most for your role?”

Step 4:
Set Goals That Drive AI-Era Success

Use performance review insights to establish goals that incentivize the behaviors your business needs in an AI-powered competitive landscape.

Prompt your Performance Analyst:

Based on our performance review conversations, help me set goals for [employee/team] that drive success in an AI-augmented workplace.

GOAL CATEGORIES:

- Innovation Goals: Specific improvements or automations they’ll implement

- Learning Goals: New AI capabilities they’ll develop and apply

- Impact Goals: Measurable business outcomes from human-AI collaboration

- Collaboration Goals: How they’ll help others leverage AI more effectively

For each goal, specify:

1. Measurable success criteria that focus on outcomes, not activities

2. Timeline and milestone checkpoints

3. Resources or support they’ll need to succeed

4. How this goal creates value for customers and the business

Ensure goals encourage experimentation and intelligent risk-taking rather than safe incremental improvements.

The best AI-era goals give employees permission to work differently while holding them accountable for results. Instead of “Attend training on new marketing automation platform,” try “Increase qualified leads by 30% using AI-enhanced campaigns and document what approaches work for our customer segments.”

Set Your Team Up for Success

This process isn’t about setting people up to fail. If your assessment reveals that team members lack the AI fluency or systems thinking needed for these new expectations, that’s a signal to invest in training and development, not to mark them down in reviews. The goal is helping everyone succeed in an AI-augmented workplace, not identifying who can’t adapt.

There are numerous resources available to help teams develop these capabilities, from online courses to hands-on consulting support. If you need guidance on building AI capabilities across your team, reach out through the contact page at Midwest Quality Consulting; we specialize in helping SMB teams develop practical AI skills that drive real business results.

The Performance Revolution

Performance reviews in the AI era aren’t just administrative updates to existing processes. They’re strategic conversations about how humans and AI can create value together, recognition systems that incentivize innovation over compliance, and development planning that prepares your team for accelerating change.

The businesses that figure this out first will attract and retain the AI-native talent that can operate at speeds their competitors can’t match. Those still evaluating employees based on pre-AI assumptions will lose their best people to organizations that recognize and reward what actually drives success today.

Your next performance review cycle is an opportunity to signal what your business values: process adherence or problem-solving, individual task completion or system-wide improvement, time spent or value created.

Choose wisely. The AI-native employees you want to keep are watching how you respond.

✨ ✌🏻 ✨

You know the drill: Last year’s budget gets updated with hopeful percentage increases. Department heads defend their territory. Everyone pretends the economy is predictable. The final plan gets filed away until Q1 when reality hits and you’re back to making decisions by gut feel.

But what if this year could be different? What if you could use AI to force the honest conversations your business needs, model realistic scenarios including AI’s impact on your industry, and create a plan that actually guides decisions instead of gathering dust?

The businesses that will thrive in 2026 aren’t those with the most optimistic projections. They’re the ones willing to confront brutal facts about 2025, make hard choices about resource allocation, and prepare for multiple scenarios including the continued acceleration of AI transformation.

Here’s how to build an annual planning process that delivers clarity instead of wishful thinking.

Step 1: Build a “Strategic Planning Architect” Jig

Before diving into analysis, create a jig specifically designed to guide you through rigorous strategic planning. This will serve as your objective advisor throughout the entire process.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my Strategic Planning Architect. Your purpose is to guide me through a comprehensive, brutally honest annual planning process that results in actionable strategies rather than wishful thinking.

BUSINESS CONTEXT:

[Brief description of your business, team size, key revenue drivers, and major challenges]

PLANNING PHILOSOPHY:

- Radical honesty over optimistic projections

- Scenario-based thinking rather than single-point forecasts

- Resource allocation based on evidence, not politics

- Explicit consideration of AI impact on our industry and operations

- Decision frameworks that work under uncertainty

ANALYSIS FRAMEWORK:

When conducting planning analysis:

1. Start with unvarnished assessment of current performance

2. Identify fundamental business model strengths and vulnerabilities

3. Explicitly model how AI might disrupt or enhance our operations

4. Create multiple scenarios rather than single projections

5. Force difficult trade-off decisions between competing priorities

6. Test assumptions against external market realities

OUTPUT REQUIREMENTS:

- Executive Summary: Top 3 strategic imperatives with clear rationale

- Performance Analysis: Honest assessment of what worked/didn’t work this year

- Scenario Planning: Base, optimistic, and challenging cases for 2026

- Resource Allocation: Specific recommendations for budget, headcount, and priorities

- Decision Framework: How to choose between competing strategic options

- Communication Plan: How to inspire team around chosen direction

COMMUNICATION STYLE:

- Data driven but accessible to non-financial team members

- Honest about risks and challenges without being defeatist

- Clear about trade-offs and opportunity costs

- Focused on actionable decisions rather than theoretical analysis

Always ask clarifying questions about business context, current performance, and strategic objectives before providing recommendations.

Upload relevant materials to your jig’s knowledge:

• Current year financial performance vs. budget

• Customer data and market intelligence

• Team feedback on what’s working/not working

• Competitive analysis and industry trends

• Previous strategic plans and their outcomes

This jig becomes your strategic thinking partner, ensuring you maintain objectivity even when discussing difficult topics.

Step 2: Radical Candor

Most annual planning starts with polite fiction about current performance. Instead, use your Strategic Planning Architect to conduct a no-holds-barred assessment of how this year actually unfolded.

Start with this prompt:

Conduct a brutally honest assessment of our 2025 performance. I want radical candor about what’s working and what isn’t.

FINANCIAL REALITY CHECK:

- Are we ahead/behind our revenue projections and why?

- Which revenue streams performed vs. expectations?

- Where did we overspend and what drove those overruns?

- What was our actual return on major investments this year?

OPERATIONAL EFFECTIVENESS:

- Which processes or systems consistently cause problems?

- Where do we regularly disappoint customers or miss deadlines?

- What work takes much longer than it should?

- Which team members are thriving vs. struggling?

STRATEGIC EXECUTION:

- Which 2025 initiatives delivered meaningful results?

- What did we say we’d do but didn’t follow through on?

- Where did competitors outmaneuver us?

- What opportunities did we miss due to capacity or capability gaps?

AI ADOPTION REALITY:

- How effectively have we integrated AI into our operations?

- Where are we still doing manual work that competitors might be automating?

- What AI capabilities do we need but haven’t developed?

For each area, identify root causes rather than surface symptoms. No sugar-coating.

This analysis often reveals uncomfortable truths: maybe your “growth” came from one lucky customer, perhaps your team is burning out on processes that should be automated, or your competitive advantage is eroding faster than you admitted.

Follow up with:

Based on this honest assessment, what are the 3-5 fundamental decisions we must make for 2026? Consider:

- Resource allocation: Where should we double down vs. cut back?

- Team structure: Do we need different skills, fewer people, or organizational changes?

- Market positioning: Are we competing in the right spaces with the right value proposition?

- Technology investments: Which AI capabilities are now table stakes vs. competitive advantages?

- Customer focus: Should we serve current customers better or chase new segments?

For each decision, explain why it’s critical and what happens if we avoid making it.

Step 3: Multiple Scenarios

Instead of creating one optimistic forecast, use AI to model realistic scenarios that account for economic uncertainty, competitive dynamics, and the accelerating pace of AI adoption in your industry. This builds on the scenario planning approach we covered in Issue #15 on mid-year reforecasting, but extends it to comprehensive annual planning.

Prompt your Strategic Planning Architect:

Create three distinct scenarios for our business in 2026, each incorporating different assumptions about AI impact, economic conditions, and competitive dynamics.

SCENARIO 1: “STEADY ACCELERATION”

- Economic conditions remain relatively stable

- AI adoption in our industry continues at current pace with gradual workflow integration

- Our main competitors maintain current approaches but slowly add AI tools

- Customer behavior evolves gradually toward expecting AI-enhanced service levels

SCENARIO 2: “AI DISRUPTION”

- AI agents begin handling customer service, sales outreach, and basic content creation

- Competitors successfully automate entire workflows we’re still doing manually

- Customer expectations shift toward AI-enhanced experiences and instant responses

- Traditional service delivery models become uncompetitive against AI-powered alternatives

- Labor costs advantage shifts to companies with sophisticated AI operations

SCENARIO 3: “CONSTRAINED GROWTH”

- Economic headwinds reduce customer spending in our category

- Increased competition pressures margins as AI lowers barriers to entry

- AI tools become commoditized capabilities that don’t provide differentiation

- Success requires optimizing human-AI collaboration for profitability over pure growth

For each scenario, project:

- Revenue implications by customer segment

- Required cost structure and team composition

- Technology investments needed to remain competitive

- Cash flow and funding requirements

- Biggest risks and mitigation strategies

Make these scenarios realistic, not best/worst case extremes.

This scenario planning reveals strategic choices that single-point forecasts miss. Maybe aggressive AI investment makes sense in the disruption scenario but threatens cash flow in the constrained growth scenario. Perhaps your current team structure works fine under steady acceleration but becomes unsustainable if AI reshapes your industry.

If you built a Cash Flow Advisor jig from Issue #2, you can use its forecasting techniques as the foundation for this financial scenario modeling.

Step 4: Test Options Across Scenarios

With clear scenarios defined, use your jig to evaluate how different strategic choices would perform under each set of conditions. This reveals robust strategies that work across multiple futures rather than optimized plans that only work under ideal conditions.

Try this approach:

Evaluate these strategic options across our three 2026 scenarios:

STRATEGIC OPTIONS TO TEST:

- [Option 1: e.g., “Invest heavily in AI automation and reduce staff by 30%”]

- [Option 2: e.g., “Focus on premium services and maintain current team size”]

- [Option 3: e.g., “Expand into adjacent markets with existing capabilities”]

- [Option 4: e.g., “Partner with AI-native companies rather than build internally”]

For each strategic option, analyze:

1. Financial performance under each scenario (revenue, costs, profitability)

2. Competitive position and differentiation sustainability

3. Team impact and organizational change requirements

4. Customer experience implications and retention risks

5. Implementation timeline and resource requirements

6. Downside protection if assumptions prove wrong

Identify which strategies are robust (work well across scenarios) vs. fragile (only work under specific conditions).

Recommend our optimal strategic direction based on this analysis.

This cross-scenario analysis often reveals surprising insights. The “safe” strategy might actually be riskiest if it leaves you vulnerable to AI disruption. The aggressive AI investment might provide downside protection by reducing fixed costs even if it doesn’t deliver the upside you hope for.

Step 5: Create Your Decision Framework

Transform your scenario analysis into a practical decision-making framework that will guide you throughout 2026 as conditions change and new information emerges.

Prompt your jig:

Based on our scenario analysis and strategic option evaluation, create a decision framework for 2026 that includes:

QUARTERLY DECISION CHECKPOINTS:

- Key metrics to monitor that indicate which scenario we’re tracking toward

- Trigger points that would prompt strategic adjustments

- Decision criteria for resource allocation throughout the year

INVESTMENT PRIORITIES:

- Must-have investments that are essential under all scenarios

- Conditional investments that depend on scenario development

- Optional investments we’d pursue only with excess resources

TEAM STRATEGY:

- Core team capabilities we need to maintain regardless of scenario

- Roles that might be eliminated or transformed based on AI adoption

- New skills we need to develop or hire for

CUSTOMER FOCUS:

- Which customer segments to prioritize under different scenarios

- How our value proposition might need to evolve

- Service delivery changes required for competitiveness

AI ADOPTION ROADMAP:

- Which AI capabilities to implement immediately

- Tools and processes to test based on scenario development

- Partnership vs. build-internally decisions

Create specific monthly milestones for Q1 2026 that start implementing our chosen strategy while maintaining flexibility to adjust based on scenario evolution.

Ch-Ch-Ch-Ch-Changes

By this time next year, the business landscape will look fundamentally different. Not because of economic cycles or regulatory changes, but because AI capabilities will have crossed critical thresholds that transform entire industries practically overnight.

The businesses succeeding in 2026 won’t be those that planned for gradual AI adoption. They’ll be the ones that anticipated AI agents handling customer service, sales outreach, and content creation as standard operating procedure. They’ll have planned for competitors who can launch new products in weeks rather than months, who respond to market changes in real time, and who operate with margin structures that seemed impossible just 12 months earlier.

Your 2026 planning isn’t just about setting revenue targets and budget allocations. It’s about preparing for a world where AI transforms the fundamental economics of how business gets done. Where customer expectations shift from “fast and reliable” to “instant and personalized.” Where your competitive moat might evaporate not because rivals copied your strategy, but because AI democratized capabilities you thought were proprietary.

The scenario planning you’ve just completed through this process isn’t academic exercise. It’s survival preparation. When AI agents start handling 60% of customer inquiries across your industry, when competitors automate processes you’re still doing manually, when customer expectations shift overnight, you’ll need frameworks for rapid decision making rather than quarterly planning cycles.

Most importantly, the honest assessment of your current AI capabilities isn’t just about optimizing 2025 performance. It’s about identifying exactly how far behind or ahead you are in the race that will define your next decade. Because in 2026, every business will be an AI business. The question is whether you’ll be leading that transformation or scrambling to catch up.

Your competitors are updating last year’s spreadsheet and hoping for the best. You’re building strategic frameworks that anticipate the AI revolution rather than react to it.

That advantage compounds quickly in a world where change accelerates exponentially.

✨ ✌🏻 ✨

But “vibes” is not the only way to evaluate a new AI model.

The pace of releases isn’t slowing down. If anything, it’s accelerating. In the first half of the year, we’ve seen ~three new frontier models released each mont from the leading US-based labs. You need a systematic approach to evaluate whether each new release deserves your attention, or if you should stick with your current choice.

Most businesses make one of two mistakes: either they ignore new models entirely and miss genuine improvements, or they chase every shiny release and waste time on incremental updates. Both approaches cost you money and momentum.

Here’s how to evaluate new AI models efficiently and make smart decisions about when to switch.

Step 1:
The Screener Test

Every time a new frontier model launches from OpenAI, Anthropic, or Google, spend 30 minutes with it. Not reading about it, not watching demo videos of other people using it, but actually using it yourself for tasks you’d normally handle with AI.

Use it for 2-3 routine tasks: drafting an email, analyzing some data, or creating content. This shows you how it handles your everyday work.

Once you’re gotten a feel for this new model, it’s time for you to ask what I think of as your “personal evaluation” question. I encourage everyone to develop your own signature question that you ask every new model. This gives you a consistent baseline for comparison in your area of deepest expertise.

I’m a bit of a frameworks dork, so my signature question combines three complex methodologies and asks the model to synthesize them into a consulting framework. Specifically, I ask every new model to, “Combine the the methodologies of Playing to Win as taught by Roger Martin, design thinking as practiced by IDEO, and cybernetics as expressed by Stafford Beer in The Brain of the Firm. Tell me how this combined methodology could be applied in a generative AI era.”

Before MQC, I was a partner at IDEO. I’ve been formally trained in the PTW strategy methodology, and I’d welcome you to join me in going down the rabbit hole of Stafford Beer, a pre-Internet management theorist whose ideas hit particularly hard these days (the excellent Santiago Boys podcast is how I first learned about him; highly recommended).

I’ve discussed these methodologies and their intersection points with countless humans over beers, read the books, and have asked this question so many times of generative AI that I can quickly tell when it’s re-treading a well-explored area or truly breaking new ground.

Your signature question should test something you know exceptionally well. Maybe it’s:

• A technical problem in your industry that requires nuanced understanding

• A complex customer scenario with multiple variables

• A strategic framework that combines several business concepts

• A creative challenge that requires both analysis and innovation

Ethan Mollick asks models to show him an otter on a plane using wifi. Simon Willison asks every model to make him an SVG of a pelican on a bicycle - he recently delivered this great talk that highlights just how rapidly these models are advancing over the first 6 months of 2025.

The question should be difficult enough that you can immediately spot shallow or incorrect answers, but not so specialized that you’re testing obscure knowledge rather than reasoning capability. (Yes, Stafford Beer is obscure but not too obscure; I’ve found nearly every model to be able to reasonably explain his theory of cybernetics.)

This combination of routine work and expert evaluation gives you a complete picture. The everyday tasks show you how the model handles typical business needs. Your signature question reveals whether it truly understands complex concepts in your domain of expertise. Together, these two approaches tell you whether the model deserves deeper evaluation in just a few hours - and, along the way, you got some work done and hopefully had a super interesting chat about a topic you love.

It’s hard to predict what you’ll find. Most of the time, you’ll discover the new model is a lateral move or only a slight improvement. But sometimes, maybe once every few months, you get a response so good that you need to get up and walk away from your computer for a minute.

These are the moments you’re looking for. When a whole new pathway of capabilities opens up. When you realize the model understood something about your question that previous models missed entirely. When it connects dots you didn’t even know existed.

These breakthrough moments are why the screening process matters. You’re not just comparing features, you’re hunting for step-function improvements that could transform how you work.

Step 2: The Interview Process

If a model passes your screener, it’s time for a more substantial evaluation. This is where you test whether the model can handle real work that matters to your business.

Choose a multi-step project that typically takes you 2-3 hours with your current AI setup. Something complex enough to reveal the model’s capabilities but contained enough that you’re not risking mission-critical work.

Use your existing jig instructions as a prompt library. Don’t rebuild your jigs in the new model yet, just copy your custom instructions and knowledge base content as context for individual conversations.

For example, if you’ve built a Pricing Analyst jig, copy those instructions into a new conversation with the model you’re testing, upload relevant customer data, and see how it performs compared to your established jig.

Test across different types of work:

• Analysis: Can it identify patterns in your data that your current model misses?

• Content Creation: Does it maintain your brand voice as effectively as your current setup?

• Problem Solving: How does it handle complex, multi-variable business scenarios?

• Integration: How well does it work with your existing tools and workflows?

Pay attention to subtle differences. Maybe the new model generates more creative solutions but makes more factual errors. Perhaps it’s faster but less thorough. Or it might excel at analysis but struggle with content creation.

Document these observations. You’re building a personal database of model capabilities that will inform future decisions.

Step 3: The Stress Test

If the model passes your interview, now it’s time for a test. If it’s able to handle your core use cases, how will it handle the edge cases?

Choose a high-stakes but recoverable project. For me, this means writing a client proposal from a transcript or creating an AI playbook, work where I have clear standards for quality and can immediately assess whether the output meets my requirements.

For your business, this might be:

• Generating quotes for a real customer inquiry

• Creating marketing content for an active campaign

• Analyzing financial data for strategic planning

• Developing training materials for your team

• Building a client presentation for an upcoming meeting

The key is choosing something where you can clearly judge quality and where success or failure has real business impact. This isn’t about perfection, it’s about whether the model’s capabilities justify switching from your current approach.

Set clear success criteria before starting:

• Quality: Does the output meet your professional standards?

• Efficiency: Does it save time compared to your current approach?

• Accuracy: Are the results reliable enough to use without extensive fact-checking?

• Integration: Does it work smoothly with your existing workflows and tools?

If the model fails this stress test, stick with your current setup. Better to use a proven tool than chase marginal improvements.

If it passes, you’ve found a genuine upgrade worth implementing.

Note: For many businesses, designing and deploying these “tests” is a great task for an intern. If you’re building an AI-powered product or service, I’d recommend you investigate programmatic evaluation tools such as Galileo.

Step 4: See Other Models

Don’t fall in love with any model. I see businesses (and individuals) get emotionally attached to specific AI tools, defending their choices like sports team loyalties. My perspective is that this dynamic is driving much of the frustration with ChatGPT 5; folks fell in love with the patterns and style of 4o and now are struggling with a new collaborator’s style and approach.

However, the technology is evolving too rapidly for emotional attachments. Your favorite model today might be obsolete in six months. The model you dismissed last quarter might have addressed its weaknesses in the latest update.

Force yourself to regularly test alternatives. Set a quarterly reminder to evaluate your primary model against new releases. Ask your current AI to help identify its own limitations, then test whether newer models address those gaps.

Switch models deliberately when you have evidence of meaningful improvement, not just because something new launches. But also don’t stick with familiar tools when better options exist. I compel myself to use all new frontier models for at least one project as they come out, and resist the (extreme) temptation to fall in love with a model. While I still miss that October ‘24 Claude Sonnet 3.5 update (still the best gen AI writer ever, IMHO), I’ve found things to love in (almost) every model I meet.

The goal isn’t to find the perfect AI model, it’s to continuously optimize your AI toolkit based on current capabilities and your evolving needs.

Beyond the Hype Cycle

Every new model release triggers a wave of breathless coverage about “revolutionary capabilities” and “game-changing improvements.” Most of it is noise. What matters for your business isn’t theoretical benchmarks or demo videos, it’s whether the model helps you generate more revenue, reduce costs, or serve customers better in the specific context of how your business works.

The systematic evaluation approach outlined here cuts through marketing hype to focus on practical business impact. You’re not trying to stay current with every AI development; you’re identifying genuine improvements that justify the switching costs.

Always evaluate models in areas where you have deep expertise. This lets you immediately spot errors, identify sophisticated insights, and assess whether the model truly understands the domain or is just generating plausible-sounding responses. You can’t evaluate a model’s financial analysis if you’re not strong with financial analysis yourself.

Your current AI setup probably works pretty well for most tasks. The question isn’t whether new models are objectively better, it’s whether they’re better enough to warrant changing your established workflows.

In a world where AI capabilities are advancing weekly, the competitive advantage goes to businesses that can quickly separate meaningful improvements from incremental updates. The approach here gives you a framework for making those decisions systematically rather than reactively.

Your time is finite. New models are infinite. Choose wisely.

✨ ✌🏻 ✨

This isn’t about hiring someone who “uses AI tools.” Every knowledge worker does that now. This is about hiring someone who thinks AI-first, defaults to building solutions directly, and expects the authority to execute without running everything through three approval layers.

Elena Verna just joined Lovable after watching them hit $80M ARR in seven months with 35 people. Her insight? The company isn’t just AI-powered; their employees are AI-native. They don’t submit requests to design teams or wait for developer sprints. They see a problem, prompt an AI system, and ship a solution. That day.

(If you haven’t read the post from Elena, stop right now and read it in full. Seriously.)

The more I talk to and read about this new type of person, the more convinced I am that most of these folks would never take a job in a large enterprise. SMBs have a massive competitive advantage: you can actually let these people work the way they think.

When AI-native talent joins a big company, they quickly discover that their superpower, moving from idea to execution in hours rather than quarters, gets smothered by process.

“Did you get approval for that?”
“Let’s run this through legal.”
“We need to involve the design team.”
“This requires a project brief.”

Every “no, but...” kills momentum. Every approval layer adds friction. Every handoff creates delay.

SMBs operate in “yes, and...” mode by necessity. You don’t have the luxury of bureaucracy. When someone has a good idea that could drive revenue or cut costs, you want them to pursue it immediately, not six months from now after it’s been workshopped to death.

This cultural difference isn’t just about speed; it’s about attracting talent that thrives on agency and autonomy. The most capable AI-native workers will increasingly choose environments where they can actually use their capabilities.

While your enterprise competitors are losing these people to process fatigue, you’re offering something they can’t: the freedom to build.

What Makes Someone AI-Native

Wade Foster at Zapier calls them “background-agnostic but builder-focused,“ and that captures something important. An AI-native employee doesn’t just use ChatGPT for writing emails. They default to AI for everything: research, analysis, content creation, basic coding, process optimization, even strategic thinking.

But the real differentiator isn’t technical; it’s psychological. These people have developed what Elena calls “cheap failures,” the ability to test ideas rapidly because the cost of building and iterating has dropped to nearly zero. They prototype solutions in afternoons rather than proposing them in meetings.

Wade shared impact stories from Zapier: one person auto-resolved 27.5% of IT tickets using AI, saving the team 2,200+ days and $500K in hiring costs. Another automated sales workflows that reclaimed $1M in revenue while saving 282+ workdays annually. A third cut integration build time by 99%, from months to under an hour.

These aren’t isolated success stories. They’re examples of people who see manual processes and instinctively think “this could be automated” rather than “this is how we’ve always done it.”

These folks think in systems, not tasks. Instead of asking “how do we hire someone to manage our social media?” they ask “how do we build a system that generates, schedules, and optimizes our social media with minimal human oversight?”

They’re comfortable with ambiguity because AI enables rapid iteration. Traditional employees want detailed specifications before starting work. AI-native employees start building immediately, then refine based on results.

Most are younger; not because age matters, but because they haven’t been trained out of this approach by years of corporate process. They haven’t learned that “things just take time” because they’ve watched AI collapse traditional timelines.

How to Know You’re Ready

Not every SMB is ready for an AI-native hire. These employees need real authority to be effective, and they’ll quit if you micromanage their methods while demanding their results.

You’re ready when:

Simple projects require multiple people.
If launching a basic landing page involves a marketer, a designer, a developer, and two weeks of coordination, an AI-native employee could handle it in an afternoon.

You spend more time coordinating than creating.
When your weekly schedule is dominated by status meetings, handoff discussions, and approval processes, you need someone who can skip straight to outcomes.

Your AI adoption has plateaued.
You’ve implemented the obvious use cases (ChatGPT for writing, AI for customer service) but you’re not seeing transformational impact. AI-native employees find applications you’d never consider.

You catch yourself saying “we need someone to just make this happen.”
This is the clearest signal. You know what needs to be done, you just don’t have anyone who can execute without extensive oversight.

You’re losing speed to competitors.
If nimbler competitors are launching features, campaigns, or initiatives faster than you can plan them, you need someone who operates at their velocity.

What to Look For

Forget traditional job descriptions. AI-native employees are defined by mindset, not resume lines. Wade Foster’s framework for screening these candidates focuses on practical demonstration over credentials, and that’s exactly right.

Bias toward action over analysis.

They’d rather build a working prototype than create a comprehensive project plan. When faced with uncertainty, they experiment rather than research.

Cross-functional fluency.

Unlike specialists who optimize within their domain, AI-native employees understand how different teams work and where automation can eliminate handoffs. They think about the sales team’s lead nurturing process, the support team’s ticket triage system, and the marketing team’s content workflows, then build solutions that connect them.

Portfolio of random projects.

Look for people with GitHub repos full of weekend experiments, personal websites with unusual features, or side projects that solve problems you didn’t know existed. They build things because they can, not because they have to.

Advanced prompt engineering capabilities.

They don’t just use ChatGPT—they orchestrate multiple AI systems, understand when Claude works better than Gemini for specific tasks, and can debug AI failures by refining prompts and logic flows.

Comfort with iteration and failure.

Traditional employees want to “get it right the first time.” AI-native employees expect to iterate rapidly, improving solutions based on real feedback rather than theoretical planning. When their automation breaks, they stay calm and systematically debug rather than panic.

Systems thinking.

They naturally think about workflows, automation, and scalability. Instead of just solving today’s problem, they build solutions that handle tomorrow’s similar problems automatically.

Low ego about methods.

They don’t care whether they solve a problem with custom code, no-code tools, AI systems, or manual processes. They care about outcomes, not craftsmanship.

Structuring Authority and Autonomy

The scariest part of hiring AI-native talent is giving them enough authority to be effective. These people need to make decisions, spend small amounts of money on tools, and implement solutions without checking in at every step.

Start with clear outcome-based goals rather than process requirements. Instead of “follow our established marketing workflow,” try “increase qualified leads by 25% using whatever methods you think will work.”

Set spending thresholds they can operate within independently, maybe $500/month for tools and services without approval. AI-native employees are comfortable with subscription-based tools and will quickly assemble a toolkit that dramatically amplifies their capabilities.

Define the boundaries, not the methods. They need to know what they can’t touch (financial systems, customer data, legal commitments) but should have freedom within those constraints.

Establish regular check-ins focused on results rather than activities. Weekly “what did you ship and what did you learn?” conversations work better than daily status updates.

Most importantly, resist the urge to “process” their successes. When they solve a problem efficiently using methods you don’t understand, learn from them rather than requiring them to document everything for future replication.

Test for real capability. Don’t rely on resume screening; give candidates practical challenges: “Design an AI workflow to streamline our content production process” or “Map and automate a typical sales workflow.” The best candidates will show you working demos, explain their prompt design, and address edge cases without being prompted.

Become an AI Native Company

Elena’s prediction about organizational changes (smaller teams, flatter structures, eliminated middle management) isn’t happening in isolation. It’s happening because AI-native employees make traditional coordination layers obsolete.

When one person can handle the work that previously required a designer, developer, and project coordinator, you don’t need the infrastructure to manage those handoffs. When solutions can be prototyped and tested in hours rather than months, you don’t need extensive planning processes.

SMBs that hire AI-native talent early will discover they can compete with much larger companies on capabilities while maintaining their speed advantage. A 15-person company with three AI-native employees might out execute a 150-person company constrained by traditional workflows.

The talent shortage everyone predicted? It’s not coming. Instead, you’ll see a productivity explosion among businesses that successfully integrate AI-native workers, while companies clinging to traditional org structures find themselves competitively disadvantaged.

Your enterprise competitors are debating AI strategy in committees. Your venture-backed competitors are hiring expensive specialists to implement AI initiatives.

You can hire one AI-native employee who builds solutions faster than their entire AI transformation team.

By this time next year, nearly every SMB should have at least one person who defaults to building rather than planning, who thinks in systems rather than tasks, and who treats AI as naturally as they treat email.

The question isn’t whether this shift is coming. The question is whether you’ll be early enough to attract the best AI-native talent before everyone else figures out what you already know: small companies that move fast will eat the world.

✨ ✌🏻 ✨

Sound familiar?

Most SMBs treat vendor management like a necessary evil: signing contracts, paying invoices, and hoping for the best. But your vendor relationships represent 40-60% of your total business expenses, directly impact your customer experience, and can make or break your ability to scale.

While you’re accepting mediocre performance and inflated prices, your smartest competitors are using AI to systematically evaluate vendor performance, negotiate better terms, and identify cost-saving opportunities that drop straight to their bottom line.

Here’s how to transform vendor management from administrative headache into competitive advantage.

Step 1:
Build a “Vendor Wrangler” Jig

Start by gathering your vendor data:

• All vendor contracts and monthly/annual spend amounts

• Payment terms, auto-renewal clauses, and cancellation requirements

• Recent invoices showing actual vs. contracted pricing

• Any performance issues, delays, or quality complaints from the past year

• Internal feedback from team members who work with each vendor

Now let’s create an AI system specifically designed to help you evaluate vendors systematically, negotiate better terms, and make data-driven decisions about relationships.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my *Vendor Wrangler*. Your purpose is to help me systematically evaluate vendor relationships, negotiate better terms, and optimize our supplier ecosystem for cost, quality, and strategic value.

BUSINESS CONTEXT:

[Detailed description of your business, key operational needs, and current vendor challenges]

EVALUATION FRAMEWORK:

When analyzing vendors, always consider:

1. Cost Effectiveness: Price vs. value delivered, hidden costs, payment terms

2. Service Quality: Delivery reliability, responsiveness, consistency, innovation

3. Strategic Fit: Alignment with our growth plans, scalability, partnership potential

4. Risk Management: Dependency levels, backup options, contract flexibility

5. Relationship Health: Communication quality, problem resolution, cultural fit

VENDOR CATEGORIES TO ASSESS:

- Critical Operations: Vendors essential to daily business functions

- Growth Enablers: Services that directly support expansion or improvement

- Commodity Providers: Standardized services where cost optimization matters most

- Strategic Partners: Vendors who contribute to competitive advantage

- Legacy Relationships: Long-term vendors who may need fresh evaluation

OUTPUT STRUCTURE:

1. Performance Assessment: Data-driven analysis of current vendor performance

2. Benchmarking: How vendors compare to market standards and alternatives

3. Optimization Opportunities: Specific areas for improvement or renegotiation

4. Risk Evaluation: Vulnerabilities and mitigation strategies

5. Action Plan: Prioritized recommendations with implementation steps

NEGOTIATION SUPPORT:

- Identify leverage points based on market conditions and vendor alternatives

- Draft professional communication templates for different scenarios

- Suggest win-win improvements that benefit both parties

- Create decision frameworks for retention vs. replacement scenarios

Always focus on improving total cost of ownership, not just upfront pricing.

Upload relevant materials to your jig’s knowledge:

• Current vendor contracts and pricing schedules

• Performance feedback from your team

• Industry benchmarking data (if available)

• Past vendor evaluation notes or reviews

This jig becomes your strategic partner for all vendor-related decisions, ensuring you approach relationships with data rather than gut feeling.

Step 2:
Systematically Evaluate Performance

With your Vendor Wrangler ready, it’s time to assess each critical vendor relationship. This isn’t about finding fault; it’s about understanding true performance so you can optimize relationships or make informed changes.

Upload your vendor’s contract, recent invoices, and any internal feedback to your Vendor Wrangler’s knowledge, then start with this prompt:

Evaluate our relationship with [Vendor Name] across all performance dimensions.

Please analyze:

1. Cost-effectiveness compared to market rates and alternatives

2. Service quality trends and reliability patterns

3. Strategic value they provide beyond basic service delivery

4. Risk factors if this relationship ended unexpectedly

5. Contract terms that favor them vs. us

Recommend specific improvements we should negotiate or changes we should consider.

Your jig will provide objective analysis that cuts through relationship history and personal preferences. Maybe your longest vendor relationship is also your most expensive per unit of value. Perhaps that “difficult” vendor actually delivers exceptional results despite communication challenges.

Ask follow-up questions like:

Based on this vendor analysis, help me prepare for a relationship optimization conversation:

1. What specific data should I present to support requests for better terms?

2. Which improvements would be reasonable to ask for vs. aggressive demands?

3. What alternatives should I research to strengthen my negotiating position?

4. How should I structure the conversation to maintain the relationship while achieving better outcomes?

5. What would be fair win-win proposals that address both parties’ interests?

Create talking points that are professional, data-driven, and collaborative rather than confrontational.

This approach transforms vendor conversations from complaints into strategic discussions about mutual success.

Step 3:
Research Alternatives and Market Rates

Knowledge is negotiating power. Before engaging with existing vendors, use AI’s research capabilities to understand your options and current market conditions.

Enable deep research in ChatGPT or Gemini, or turn on “research” and “web search” in Claude, and try:

Research current market alternatives for [vendor category/service type].

I need comprehensive intelligence on:

COMPETITIVE LANDSCAPE:

- Top 5-7 providers in this category with their positioning

- Pricing models and typical rate ranges for our business size

- Service level standards and performance benchmarks

- Recent industry trends affecting pricing or service delivery

ALTERNATIVE EVALUATION:

- Which alternatives might offer better value for our specific needs

- What switching costs or implementation challenges to expect

- Which providers are gaining/losing market share and why

- Any new market entrants with innovative approaches

NEGOTIATION INTELLIGENCE:

- What leverage points exist in the current market

- Which contract terms are standard vs. vendor-favorable

- What add-on services or improvements we should expect

- How vendors typically respond to competitive pressure

Present findings in a format I can use for vendor negotiations and strategic planning.

This research arms you with market intelligence that transforms negotiations. Instead of accepting vendor claims about “industry standard” pricing, you’ll know actual market rates. Rather than feeling stuck with current terms, you’ll understand viable alternatives.

After gathering intelligence, ask your Vendor Wrangler:

Based on this market research, reassess our [vendor category] strategy:

1. Which current vendors are above/below market rates for comparable service?

2. What service improvements should we expect based on industry standards?

3. Which alternatives represent genuine upgrade opportunities vs. lateral moves?

4. How should this market intelligence change our negotiation approach?

5. What timeline makes sense for vendor optimization in this category?

Help me prioritize vendor relationship changes based on potential impact and implementation complexity.

Step 4:
Execute Strategic Improvements

Now comes the action phase: using your analysis to improve vendor relationships through negotiation, optimization, and/or strategic changes. The key is approaching each situation with clear objectives and multiple options.

For vendors worth keeping but needing improvement, try this approach:

Help me structure a vendor relationship optimization meeting with [Vendor Name].

Based on our analysis, I want to achieve:

- [Specific improvement 1, e.g., 15% cost reduction]

- [Specific improvement 2, e.g., faster response times]

- [Specific improvement 3, e.g., better contract terms]

Given their current performance and our market research, help me:

1. Structure the meeting agenda to focus on mutual success

2. Present data in ways that invite collaboration rather than defensiveness

3. Propose specific improvements with clear success metrics

4. Offer incentives or changes that benefit both parties

5. Establish implementation timeline and review checkpoints

Create conversation templates that maintain positive relationships while driving meaningful change.

For vendors that need replacement, use a more strategic approach:

Plan our transition away from [Vendor Name] to [Alternative/New Approach].

Transition considerations:

- Services that must continue without interruption: [List critical functions]

- Timeline constraints: [Any seasonal or project-based requirements]

- Internal team impact: [Who needs training or process changes]

- Customer impact: [Any client-facing implications]

Help me create:

1. Detailed transition timeline with key milestones

2. Risk mitigation plan for potential service disruptions

3. Communication plan for internal team and external stakeholders

4. Success metrics to ensure the change delivers expected benefits

5. Contingency plan if the new vendor doesn’t perform as expected

Focus on maintaining service quality while optimizing cost and performance.

Step 5:
Relax 🙂

By the time you’re ready to repeat this vendor optimization process in Q4, the landscape will look dramatically different. The recent release of ChatGPT Agent represents a fundamental shift toward AI systems that can autonomously gather information, make connections, and execute complex workflows.

What does this mean for vendor management? Instead of manually collecting contracts, invoices, and performance data to feed your Vendor Wrangler, future AI agents will likely handle much of this legwork automatically. They’ll monitor contract renewal dates, track vendor performance metrics in real-time, and proactively alert you to optimization opportunities.

The tedious work of assembling contextual information (the part that currently requires human coordination across multiple systems and stakeholders) is exactly what these emerging agentic capabilities are designed to eliminate. Your role will shift from data collector to strategic decision-maker.

Set up your vendor management systems now using the approach outlined here. When you revisit this process in six months, you’ll have the foundation in place to leverage whatever agentic capabilities have emerged. The businesses that establish systematic vendor management practices today will be best positioned to benefit from the AI automation that’s coming tomorrow.

The time you invest in building your Vendor Wrangler and establishing these processes won’t be wasted. It’s preparation for a world where AI handles the routine analysis while you focus on the strategic relationships and decisions that truly matter.

Compounding Effects

Effective vendor management doesn’t just reduce costs. It transforms your operational capabilities. When vendors become true partners rather than necessary evils, they contribute to innovation, help you scale efficiently, and provide competitive advantages your rivals can’t easily replicate.

Most SMBs accept mediocre vendor performance because optimization feels overwhelming. But the businesses that systematically improve their vendor ecosystem gain sustainable cost advantages, service reliability, and strategic capabilities that compound over time.

A 10% improvement across your vendor ecosystem might save $50,000 annually while improving service quality. A strategic vendor partnership might enable market expansion that generates $500,000 in new revenue. An optimized supply chain might provide service levels that differentiate you from competitors.

Your vendor relationships are strategic assets, not just operational expenses. Time to manage them like it.

✨ ✌🏻 ✨

Meanwhile, your most experienced employee just announced they’re leaving next month, taking with them the intricate knowledge of how your most important client relationship actually works. You know there are dozens of unwritten processes living entirely in people’s heads, but documenting everything feels overwhelming.

Here’s the thing: if you can’t effectively onboard a human, you’re going to fail miserably when AI agents arrive.

And they’re coming faster than you think.

Process documentation isn’t just about training new employees anymore. It’s about preparing your business for a world where AI agents will handle routine tasks, make decisions based on your established procedures, and collaborate with your human team. Those agents will need the same clarity about your processes that your human employees do.

The businesses that document their processes well today will seamlessly integrate AI agents tomorrow. Those that don’t will spend months struggling to explain their chaotic workflows to systems that require precision and clarity.

Fortunately, generative AI can transform process documentation from a dreaded administrative burden into a strategic capability that makes your entire organization more efficient, consistent, and scalable.

Here’s how.

Step 1: Create Documentation (if it’s missing)

If you already have comprehensive, up-to-date process documentation, skip ahead to Step 2. But if you’re like most SMBs (with scattered notes, outdated procedures, or processes that exist entirely in people’s heads), this step will help you rapidly create the foundation you need.

You have two powerful approaches for tackling undocumented processes:

Approach 1: “Your AI COO”

Create a Process Discovery jig that acts like a newly hired COO who needs to understand how your business actually operates. This approach systematically uncovers processes through structured questioning.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are a newly hired COO conducting a comprehensive business process audit. Your goal is to understand how this company actually operates by asking detailed, probing questions about every aspect of the business.

BUSINESS CONTEXT:

[Brief description of your business, team size, and main operational functions]

1. Your questioning approach:

2. Start with high-level business functions, then drill down into specifics

3. Ask about both routine operations and exception handling

4. Identify who does what, when, and with which tools/systems

5. Uncover decision-making criteria and approval processes

6. Explore what happens when things go wrong or people are unavailable

For each process area, ask:

- What triggers this process to start?

- Who is involved and what are their specific responsibilities?

- What tools, systems, or resources are required?

- How long does each step typically take?

- What could go wrong and how is it handled?

- How do you know when it’s completed successfully?

- What happens next in the workflow?

Document everything as clear, step-by-step processes that could be followed by someone new to the role.

Start a conversation with your Process Discovery jig:

I need to document our business processes comprehensively. Please interview me as though you’re a new COO who needs to understand how our company operates. Start with our core business functions and help me identify and document every critical process.

Begin by asking me about our primary revenue-generating activities and work systematically through all aspects of our operations.

The AI will guide you through a structured discovery process, asking follow-up questions that reveal processes you might not have considered documenting. This approach often uncovers critical workflows that exist entirely in institutional knowledge.

Approach 2: “Watch Over My Shoulder”

For processes that happen primarily on computer screens (like using software systems, processing online orders, or managing digital workflows), document them while you actually perform them. This “director’s commentary” approach captures nuanced details that might be missed in abstract descriptions.

(Note: This approach works best for screen-based processes. For highly manual or offline processes like warehouse operations, hands-on manufacturing, or in-person customer service, Approach 1’s structured questioning will be more effective. Many businesses will need both approaches depending on their mix of digital and physical operations.)

Option A: Gemini AI Studio Stream

The most powerful approach uses Gemini’s AI Studio “stream” feature, which lets you have a voice conversation with AI while simultaneously sharing your screen:

1. Go to ai.google.dev and create a new “stream” conversation

2. Enable screen sharing and voice chat

3. Start performing your process while explaining what you’re doing out loud

4. Ask Gemini to document the process in real-time, noting specific steps, decision points, and system interactions

Try saying something like: “I’m going to walk through our client onboarding process. Please document each step I take, the decisions I make, and create a comprehensive procedure that someone else could follow. Ask me questions as we go if things are unclear or confusing.”

Option B: Screenshot Documentation If you prefer using ChatGPT or Claude, take screenshots at each major step and upload them with explanatory text:

I’m documenting our [process name]. I’ll share screenshots of each step along with my explanation. Please help me create comprehensive step-by-step documentation that captures:

1. Exact steps with specific system interactions

2. Decision points and the criteria for each choice

3. Common variations or exceptions

4. Integration points with other systems or processes

5. Quality checks and verification steps
   

Here’s step 1: [screenshot + explanation]

Continue this process through the entire workflow, building comprehensive documentation that includes visual references and detailed explanations.

Both live documentation approaches capture context and nuance that’s often lost in traditional documentation methods. You’ll end up with procedures that feel natural to follow because they’re based on how the work actually gets done.

Step 2: Turn Rough Notes into Professional Documentation

Whether you’ve used the COO interview approach or live documentation, you now have rough process notes that need to be transformed into clear, usable documentation. Let’s create a Process Documentation Helper jig to polish your raw materials into professional procedures.

Create a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these custom instructions:

You are my Process Documentation Helper. Your purpose is to take rough process notes and transform them into clear, comprehensive documentation that teams can actually use.

When creating documentation from rough notes:

1. Structure for scanability—busy people need to find answers fast

2. Include decision trees for complex scenarios

3. Specify exact tools, systems, and access requirements

4. Note common edge cases and how to handle them

5. Design for both human and future AI agent consumption

DOCUMENTATION STRUCTURE:

- Overview: Purpose, scope, roles, and frequency

- Step-by-step instructions with numbered actions

- Decision trees for complex scenarios

- Examples and templates

- Integration points with other workflows

FORMAT PREFERENCES:

-Clear step-by-step instructions

- Visual flowcharts where helpful- Specific examples and templates

- Links to relevant tools and resources

- Quality checkpoints and approval gates

Focus on documenting the process as it exists today, not how it might be improved. Capture the current reality accurately before optimization.

Always ask clarifying questions if the rough notes are unclear or seem to have gaps.

Now feed your rough process notes to the Process Documentation Helper:

Help me create comprehensive documentation from these rough process notes: [paste your notes from Step 1]

Structure this for maximum usability with:- Clear overview section explaining purpose and scope

- Detailed step-by-step instructions

- Decision points and criteria for different scenarios

- Integration points with other systems or workflows

- Common edge cases and how to handle them

Make this detailed enough that someone could follow it successfully on their first try, but organized so experienced team members can quickly find specific information.

Your jig will help create documentation that serves multiple purposes: onboarding new team members, training existing staff on updates, and eventually enabling AI agents to understand your workflows.

For complex processes, ask for additional support:

This process has several decision points that depend on judgment calls. Help me document the decision-making criteria:

1. What factors should someone consider at each decision point?

2. What are examples of edge cases and how we typically handle them?

3. Which decisions require managerial approval vs. individual judgment?

4. How do we balance competing priorities (speed vs. accuracy, cost vs. quality)?

Create decision frameworks that capture our institutional knowledge and business judgment.

The goal isn’t just to document what you do, but to capture why you do it that way and how to make good decisions when situations vary from the standard script.

Step 3: Use AI to Improve Your Processes

Once you’ve documented your current processes, AI becomes a powerful tool for identifying improvement opportunities. Unlike humans, who often become attached to “how we’ve always done things,” AI can objectively analyze your workflows for inefficiencies, redundancies, and optimization opportunities.

Use your Process Documentation Helper for process analysis:

Based on our documented [process name], analyze for improvement opportunities:

EFFICIENCY ANALYSIS:

- Which steps seem redundant or unnecessarily complex?

- Where do we see potential bottlenecks or delays?

- Which manual tasks could be automated or streamlined?

- What information gets collected multiple times in the same workflow?

RISK ASSESSMENT:

- Where are the single points of failure in this process?

- Which steps are most prone to human error?

- What happens if key systems or people are unavailable?

- Where could miscommunication or misunderstanding occur?

CONSISTENCY OPPORTUNITIES:

- Which decision points would benefit from clearer criteria?

- Where might different team members handle things differently?

- What templates or checklists could improve standardization?

- Which approvals or checkpoints add value vs. create delays?

CUSTOMER IMPACT:

- How does this process affect customer experience?

- Where might customers get frustrated with timing or communication?

- Which steps could be made more transparent to customers?

- What would happen if we eliminated or combined certain steps?

Provide specific recommendations with estimated impact and implementation difficulty.

This analysis often reveals improvement opportunities that weren’t obvious when you were focused on documentation. Maybe your client onboarding involves three separate data entry steps that could be combined. Perhaps your vendor approval process has built-in delays that made sense five years ago but not today.

For cross-functional processes, try this approach:

Analyze our [process name] from the perspective of each role involved:

For [Role A]: What are their pain points and inefficiencies?

For [Role B]: Where do they wait for handoffs or approvals?

For [Role C]: What information do they need that’s hard to access?

Then identify:

1. Where roles overlap unnecessarily

2. Which handoffs could be smoother

3. What information could be shared automatically vs. manually communicated

4. How we could reduce coordination overhead while maintaining quality

Focus on changes that would improve the experience for everyone involved.

Step 4: Bring Documentation to Life

Static documentation has a fundamental problem: people don’t read it until they need it, and when they need it, they’re usually in a hurry. AI can transform static documentation into interactive knowledge assistants that team members can actually have conversations with.

For each major process area, you have two powerful options:

Option 1: NotebookLM

Create a Notebook and upload all your process documentation, templates, examples, and reference guides. NotebookLM excels at research-style interactions where team members can ask specific questions about procedures and get answers grounded in your actual documentation.

I know of multiple businesses that have nearly eliminated human-to-human onboarding using NotebookLM. More on its unique advantages below.

Option 2: Custom GPT or Claude Project

For more sophisticated interactions, create a dedicated GPT (ChatGPT) or Project (Claude) with custom instructions like:

You are the [Process Name] Assistant for our team. Your knowledge contains our complete process documentation, templates, and examples.

When team members ask questions:

1. Provide specific, actionable answers based on our documented procedures

2. Reference exact sections of our documentation when helpful

3. Highlight decision points where human judgment is required

4. Suggest relevant templates or examples from the uploaded materials

5. Flag situations that might require escalation or managerial approval

For new team members learning this process:

- Start with overview concepts and gradually work through details

- Offer to generate practice scenarios or quiz questions

- Suggest which sections to focus on based on their specific role

For experienced team members troubleshooting:

- Quickly pinpoint relevant procedures for unusual situations

- Help interpret edge cases based on our documented decision frameworks

- Connect related processes when workflows intersect

Always acknowledge when questions fall outside our documented procedures and suggest who to contact for clarification.

Upload your process documentation to the GPT or Project’s knowledge base, then share it with your team.

Each approach offer unique advantages:

NotebookLM’s killer features for process documentation:

• Interactive FAQ Generation: Automatically creates frequently asked questions based on your documentation, which often reveals gaps you hadn’t considered.

• Timeline Creation: For complex, multi-step processes, NotebookLM can generate timelines that help team members understand sequencing and dependencies.

• Podcast Generation: Turn your documentation into an AI-generated discussion between two hosts. This is surprisingly effective for auditory learners and can make complex processes more digestible.

• Conversational Interface: Team members can ask specific questions like “What do I do if the client hasn’t responded after 5 business days?” and get answers grounded in your actual documentation.

Custom GPT/Claude Project advantages:

• Tailored Personality: Custom instructions ensure the assistant responds in your company’s voice and follows your specific guidance protocols.

• Team Sharing: Easy to share with your entire team and maintain consistent interactions across users.

• Extended Capabilities: Can generate templates, create practice scenarios, or even help update documentation based on new learnings.

Share your chosen tool with your team and watch usage patterns. Both NotebookLM and custom GPTs/Projects will show you which sections get the most questions, helping you identify documentation that needs improvement or processes that need simplification.

Preparing for the AI Agent Future

While this guide focuses on documentation and improvement rather than automation, it’s worth noting that every process you document well today becomes a candidate for AI agent assistance tomorrow. The businesses that can clearly explain their workflows to humans will find it much easier to delegate those workflows to AI systems.

Your documentation should capture not just what to do, but the business logic behind decisions. When AI agents can handle routine tasks while escalating complex situations to humans, your well-documented processes become the foundation for seamless human-AI collaboration.

Think of process documentation as building the instruction manual for your future AI workforce. The clearer and more comprehensive that manual, the more effectively AI agents will integrate into your operations.

If you’re ready to explore “hiring” an AI agent onto your team, reach out to me using this form. I’m working with a number of amazing AI implementation firms and would love to help you find a partner.

From Chaos to Clarity

Process documentation transforms organizational chaos into competitive advantage. When everyone knows how things get done (and why they get done that way), your business becomes more efficient, consistent, and scalable.

But in the AI era, documentation serves an additional purpose: it prepares your business for a world where AI agents handle routine work while humans focus on strategy, creativity, and complex problem-solving.

The companies that document their processes well today will seamlessly integrate AI capabilities tomorrow.

Start with one high-impact process. Document it thoroughly. Use NotebookLM to make it interactive. Analyze it for improvements. Build the habit of continuous refinement.

Your future AI-augmented team will thank you for the clarity. Your current human team will thank you even sooner.

✨ ✌🏻 ✨