There's a line between measuring work and monitoring people. Most teams cross it by accident.

A pattern I’ve seen play out more than once: a team rolls out a helpful internal tool. Nothing dramatic — a simple way to track tasks inside a platform they already use. A manager sends a quick request, the recipient taps back a status — done, blocked, in progress. Clean, fast, genuinely useful. Everyone likes it.

Then a later update adds one small field. When you mark a task complete, a new box appears: how long did this take you? No explanation, no note about why. It just shows up one morning, quietly asking everyone to put a number on their minutes. And the whole feel of the tool changes.

The analyst's job didn't shrink. It moved.

It’s tempting to think that tools which answer instantly reduce the need for human judgment. The opposite is true. When answering becomes cheap, the value shifts entirely upstream — to framing the question, defining the terms, and deciding what would actually count as evidence. The model handles the retrieval. The human owns the inquiry.

So when someone tells me the AI “gave the wrong answer,” my first question is rarely about the model. It’s: what exactly did we ask it? More often than not, the data was fine. The question was the thing that needed the work — and that, not the answer, is where real analytical expertise lives.

A friend sent me Pam Didner’s recent piece in Inc., “Forget Prompting. The Most Important AI Skill of the Next 5 Years Is Something Else.” Her argument stopped me because it lands exactly where I’ve spent my career living: the gap between what your best people know and what your organization can actually reproduce.

Her thesis is simple. The next real AI skill isn’t better prompting or vibe coding. It’s writing a skill file — a one-page document that tells an AI tool how to do a specific task your way, every time. Not a style guide. Not a prompt library. One task, your standards, your format, written down so the work runs the same whether or not the person who mastered it is in the room.

I’d put it more bluntly, from an operator’s chair: a skill file is institutional knowledge that finally stops walking out the door.

There's a moment in every growing company where the founder realizes the operating system they've been running on — the spreadsheets, the founder-in-the-loop approvals, the tribal knowledge living in three people's heads — has quietly stopped scaling.

The team is bigger. The compliance surface area is bigger. The customer base is bigger. And the founder is spending more time on operational maintenance than on the strategic moves that actually grow the business.

This is the moment when most founders start thinking about hiring an operator. It's also the moment when a lot of them get distracted by a different question: “Can AI just do this instead?”

The honest answer is more interesting than either yes or no.

AI cannot replace operations. But AI can — and should — fundamentally reshape what operations looks like inside a company scaling past early stage. The companies that figure this out get a multiplier on every dollar they spend on operations leadership. The companies that don't usually end up paying for the lesson twice.

This guide is for founders trying to figure out the right way to integrate AI into their operations — without making the mistakes the early movers just made at scale.

Every week I hear some version of the same conversation.

A CEO has decided the company is going “AI-first.” They’ve purchased a suite of tools. They’ve announced it in an all-hands. They’re expecting efficiency gains within the quarter.

Six months later, they’re frustrated. Adoption is patchy. Half the team is using the tools, half isn’t. The efficiency gains haven’t materialized. And somewhere in a Slack channel nobody checks, there are three different AI workflows doing three versions of the same thing, none of them talking to each other.

This is not an AI problem. It’s an operational design problem. And it stems from a handful of mistakes I see CEOs make almost every time.

Most executives spend their first 90 days listening. I spend mine fixing.

That’s not a brag. It’s a structural reality. When a founder brings in a fractional COO or Chief of Staff, they’re not hiring someone to observe. They’re hiring someone because something — often several things — has quietly broken, and the company is scaling faster than its operating system can handle.

The “listen first” advice exists for a reason: premature action based on incomplete information is a real failure mode. But there’s another failure mode nobody talks about. It’s the executive who spends 90 days in discovery while the team keeps bleeding, the process debt keeps compounding, and the founder keeps wondering when they’re going to see a return on the hire.

Here’s what I actually do.

Say the word "process" in the wrong room and watch what happens.

Screens don't go down. Eyes don't light up. What you get instead is a specific kind of stillness — the kind that means everyone in the room just added something to their mental list of things they're going to have to tolerate.

That reaction is data. Not about process. About what people believe process is.

They believe it's a rulebook. A stack of requirements. A manager's way of adding friction to work that was flowing just fine. They think of process as the ten-page document that nobody reads, the approval chain that slows everything down, the form that has to be filled out before anything can move.

There’s a moment in almost every AI rollout that nobody talks about.

It’s not the launch meeting. It’s not the training session. It’s the moment — usually in the first few weeks — when a leader gets a suggestion from a team member, pauses, and says: “let me just check what the AI says about that.”

What happens next determines everything.

If the AI confirms the employee’s suggestion, the leader nods and moves on. If the AI says something different, the leader goes with the AI.

The employee notices. They always notice.

And what they’ve just learned isn’t anything about the AI tool. What they’ve learned is that their judgment — their experience, their contextual knowledge, their read of the situation — is now subject to machine review. That the AI is the authority. That they are not.

That’s not an AI problem. That’s a trust problem. And it didn’t start with the rollout.

The first piece made the operational case for structure inside a flat organization. The second piece argued that structure only works when leadership has the trust to let go. The third piece showed the ROI — that healthy culture, built on clear structure and real trust, outperforms the S&P 500 by roughly 2,000% in cumulative returns over time.

This last piece is about the question every leader I work with eventually asks: where does AI fit into all of this?

The short answer is that AI belongs in service of culture, not in place of it. It’s a supporting role, not a replacement. And the reason most companies get the AI rollout wrong has very little to do with the technology — and almost everything to do with how leadership communicates it.

Culture Is a Financial Moat: The ROI of Healthy Work Culture

This is the third piece in a series. The first made the operational case for structure inside a flat organization — that good fences make good neighbors and that explicit scaffolding is what enables culture, not what undermines it. The second made the leadership case — that structure only works when leaders, especially founders, are willing to make the leap of faith that lets trust become a system of evidence rather than an act of faith.

This third piece is the one founders ask for first and then quietly never come back to: the business case. The ROI. The numbers that prove healthy culture isn't a soft investment — it's one of the most durable financial assets a company can build.

Healthy culture is a moat. Most companies don't see it that way until they look at the numbers.

In the last piece, I made the operational case for structure inside a flat organization. Clear roles. Mapped decision rights. Visible operating cadences. The argument was that good fences make good neighbors — that structure isn't the enemy of culture, it's the precondition for it.

But there's a part of that argument I deliberately left for a second piece, because it doesn't fit cleanly inside an operational frame.

Structure only works if leadership actually trusts the people inside it.

Without trust, even the cleanest org design becomes a more elegant form of micromanagement. You can map every role, document every decision right, run flawless operating cadences — and still squeeze the life out of the team if the founder or CEO can't bring themselves to let go.

For founders especially, that letting-go is the hardest part of the job. It's also the most strategic one.

There's a misconception that's been quietly costing growing companies for years. It usually surfaces around the time a startup hits 30–50 people. Leadership has worked hard to build a flat, collaborative culture, and they get nervous about anything that smells like “process” or “structure.” They worry that defining roles, building operating cadences, or writing things down will somehow undermine the culture they've built.

So they hold off. They keep doing what worked at 10 people. And then engagement starts slipping, execution gets sloppier, and the team that used to feel agile starts feeling chaotic. Leadership diagnoses it as culture drift and tries to solve it with more all-hands meetings, more values posters, or louder “we're a flat org” messaging.

The actual problem is upstream. The team doesn't need more flatness. They need clarity.

There’s a moment every growing company hits. Revenue is climbing, the team is expanding, and the CEO is busier than ever — yet somehow everything feels harder. Decisions stall. Hiring is reactive. Compliance is a someday problem. The founder is in every meeting because nothing moves without them.

Kindness isn't soft. It's a system. And the leaders who build the system show up on the P&L in ways nobody is tracking carefully enough.

The most common mistake I see people make with AI is starting with the tool.

They sign up for ChatGPT or Claude or Copilot, stare at the blinking cursor, and try to think of something to do. A few half-useful prompts later, they decide AI is overhyped and go back to their day.

That's a tooling mindset. And it's the wrong way in.

If you actually want AI to make a dent in your work, you have to start with your role, not with the model. Here's the framework I use, and how it plays out for some of the most common operational roles I work with.

Operations is the goalkeeper position — nobody notices the saves until one gets missed. How AI sharpened my goalkeeper instincts, and why sharing those wins with your team is the real MVP move.

A few different countries have started saying the quiet part out loud: AI is going to replace large portions of the workforce, and they're not going to pretend otherwise.

The rest of the business world is paying attention. Boardrooms are running the numbers. Consultants are pitching "AI workforce optimization" decks that are really just headcount-reduction plans with a new label. And the conversation has clearly moved from "if" to "when."

There's an assumption baked into a lot of early-stage thinking right now: that the operations function — the people who keep the company running — is the next obvious target for AI replacement. Founders are looking at their org chart, seeing the cost of an experienced operator, and quietly wondering if they can skip the hire and let AI cover it.

I understand the temptation. The tooling is impressive. The economics look good on paper. And there's no shortage of voices on LinkedIn telling founders that "ops is the first to go."

It isn't. And the companies finding that out the hard way are already starting to reverse course.

This is the piece I want every founder scaling past early stage to read before they make a workforce decision they'll spend a year unwinding.

The companies that cut hardest in the name of AI are quietly rehiring. Here's what the data says — and how operations leaders can make the case for building over cutting.

There's a version of the AI conversation I'm tired of.

The one where AI is either coming for everyone's job, or it's a magic button that does the work for you. Both takes miss what's actually happening — at least in mine.

AI isn't doing my job. It's making me more efficient at the parts of my job that were never the point in the first place. And the time it's giving back is showing me something uncomfortable: I had quietly turned strategic thinking into repetitive duty. AI is the thing that pulled me out of it.