Your AI Rollout Is a Change Management Project. That Makes It the COO's Job.
Most teams with AI licenses split three ways: a few power users, a lot of email drafters, and everyone who quietly stopped. Here's how a COO closes that gap.
Don't Automate Out the Governance: What a COO and Chief of Staff Keep Human When AI Takes Over a Process
In an AI rollout, the Chief of Staff and COO decide which steps stay human. Here's how to automate a process and keep the approvals that make it trustworthy.
Automate the Workflow, Not the Role: Why AI Rollouts Need a Chief of Staff and a COO
Every founder wants AI to make the company faster. The people best placed to make it work are the ones who already know how the work runs.
Authenticity Isn't a "No AI Used" Label. It's Using Your Own Product.
An AI company labeling its own content "no AI used" won't prove it's human. Showing how its team uses the product will. Here's the trust signal most skip.
AI Didn’t Make Me Dumber. It Made Me the Strategist.
A colleague told me AI is making us all dumber. Here is a workflow I built last month, the six decisions the AI could not make, and a demo you can click.
You Don't Need an AI Policy. You Need Decision Rights.
When the AI rollout gets messy, the reflex is to write a policy. But a policy nobody acts on governs nothing. What works is decision rights — and that's org design, not paperwork.
The Most Dangerous Agent Is the One Failing Quietly
The AI failure that hurts isn't the dramatic one. It's the agent that's been quietly, confidently wrong for three weeks in a workflow nobody's watching. Here's where to look.
Give Every AI Agent a Name
The fastest way to lose control of your AI isn't a rogue model — it's a dozen anonymous agents nobody owns. Naming them is where governance actually starts.
The Three Questions That Are Your Whole AI Governance
The governance-gap piece struck a nerve, and the follow-up question was always the same: okay, so what do I actually do? Here's the answer — and it's smaller than the enterprise crowd wants you to think.
Please Stop Putting AI in Everything (From a Pro-AI Operator)
I'm a giant believer in AI for operations. That's precisely why forced, bolted-on AI drives me up the wall. If a task takes thirty seconds and works, making it twenty-nine isn't efficiency — it's theater.
AI didn't shrink operations — it made operations the surface where growth actually happens.
The takes said AI would gut operations first. The data says the opposite. Ops isn't the cut line — it's the surface where everything you build actually scales.
Nobody Decides to Build a Replacement. They Default Into One.
Nobody sits down and decides to replace a person with AI. They default into it — the moment they build for a job they've never done and a user they never talked to. And AI agents punish that mistake harder than anything before them.
The COO Is Quietly Becoming the Company's AI Owner. Good.
Ask who owns AI and most people say the CTO. It's landing somewhere else: the COO. Because the hard part of AI was never the model — it's everything operations already owns.
You Don't Have an AI Problem. You Have a Governance Gap.
Everyone deployed AI agents. Most companies can't govern them. Governance isn't a model setting — it's decision rights, escalation paths, and accountability. In other words, operations.
I Watched AI Come for My Job — So I Learned to Wield It
A field report from the messy, exhilarating middle of reinventing an operations career around AI.
There's a specific kind of quiet that settles over you when you realize the tools being built across your industry could do your job. Not help you do it better. Do it instead of you.
I felt that quiet a while back. I work in tech, in operations — the connective tissue that keeps a company actually running. And I watched the market lean toward tools that weren't really designed to make operational people faster or sharper, but to make them optional. When you spend your days close to how these tools get built, you start to notice the assumption baked into a lot of them: that the work of operations is mostly automatable. Sitting with that trajectory is a strange thing.
I could have spent that season being anxious about it, and some days I was. But somewhere in there I made a different decision, and it's the decision this whole article is really about: I decided I would rather out-learn the shift than wait to see where it left me.
AI Isn’t Coming for the Cheapest Jobs First. It’s Coming for the Most Structured Ones.
The first wave didn’t go the way the headlines predicted
If AI simply displaced the lowest-cost roles first, we’d expect the economics to be obvious. Instead, an MIT analysis put the share of roles where automation is actually economically viable at roughly 23% — meaning humans remain the cheaper option in more than three-quarters of them. The capability exists in far more places than the cost case does.
The reversals tell the same story. Klarna cut roughly 700 customer-service roles on the expectation that AI would absorb the work, then moved to rehire when service quality slipped and customers noticed. Amazon’s “Just Walk Out” shopping technology, widely described as AI doing the work, leaned heavily on remote human reviewers behind the scenes. And Forrester now projects that about half of AI-attributed layoffs will be quietly reversed — often refilled at lower cost rather than admitted as a miss.
It's Not the Data. It's the Question.
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.
The Most Valuable Thing AI Can Do for Your Operation Isn’t Replace People. It’s Remember Them.
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.
AI-Augmented Operations: A Field Guide for Founders Scaling Past Early Stage
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.
What CEOs Get Wrong About "AI-Augmented" Teams
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.