CEOs and CMOs lead AI change management by making four decisions: what the organization is allowed to do with AI, who gets trained first, how success gets measured, and what happens when it goes wrong. Everything else - the tools, the pilots, the vendors - is downstream of those four calls.

Most organizations now have two AI realities running at the same time.

There's the official universe: approved enterprise tools, visible to leadership, documented, sanctioned. And there's the unofficial universe: personal subscriptions, private devices, capable tools, where the real problems get solved quickly - and completely outside your guardrails.

Leadership sees one. Work happens in both.

Let me tell you the language that makes me nervous.

Pilot. Initiative. Six-week sprint. Phase one. Test project. AI budget line.

All perfectly reasonable words. All describing things that are built to end.

I want you to think about the most important process in your business. The one that generates revenue, or onboards clients, or turns a messy idea into a deliverable someone pays for.

Now ask yourself: is the process written down anywhere?

That proposal that used to take an hour? The client recap that ate your entire Friday afternoon? The campaign brief, the research synthesis, the follow-up email you used to agonize over at 10pm? All faster. Measurably, undeniably faster.

Congratulations. You've saved time.

Now: what happened to it?

Your organization did an AI 101 training. Maybe it was an hour. Someone showed a few impressive demos - a website generated in thirty seconds, a marketing email drafted in ten. Everyone clapped. Someone in the back said "game changer". The facilitator left. People went back to their desks.

And then absolutely nothing changed.

Marketing Power Circle (MPC) is now AI Power Circle (AIPC). 
Same program. 
Same audience. 
Same Wednesday afternoon energy. 
New name.

AI makes most people faster. It makes very few people better. The difference comes down to how you use the output. Speed is what happens when you let AI do the work. Quality improvement is what happens when you use AI to do the work, then apply your own judgment to make it sharper than you could have produced alone.

Most organizations are getting the first thing. Almost none are systematically getting the second.

Here's why that happens, and how to fix it.

A few years ago, I walked into a boardroom to lead a strategy session and was asked to fix the projector.

Brown woman.

Tech thing broken.

Obviously, I was IT support.

You know AI could help. You've seen the LinkedIn humblebrag posts - "Just used ChatGPT to land three discovery calls!" You've downloaded the prompt packs. Maybe you've even fed it a request for outreach messages.

And what came back sounded like... a robot cosplaying as you.

While everyone's busy panicking about job losses, a quiet hiring boom is revealing an uncomfortable truth about how unprepared we really are.

Here's a number that has my attention - 35,000.
That's roughly how many AI policy and governance jobs are currently posted on Indeed alone.

For many mid-market organizations, scale is the dream! 

You get traction, then the work multiplies. More leads. More follow-ups. More handoffs. More “can you just…” requests. And suddenly your sales process becomes less like a pipeline and more like a game of whack-a-mole.

For most business tasks, a well-prompted ChatGPT conversation outperforms a poorly designed Custom GPT. For recurring, role-specific workflows, a well-designed Custom GPT outperforms even the best ad-hoc prompting. The question isn't which is better — it's which is right for the task.

I was not in the famed first 6 million people who tried ChatGPT that first weekend.

I’d seen too many clunky “AI-ish” tools before that overpromised and underwhelmed.

I have a confession.
I’ve been “writing a book about AI” for over a year… mostly in my head and in a slightly tragic Google Doc.

There are outlines.
There are pillars and frameworks.
There are half-finished drafts and messy notes from conversations with founders.

Founders don’t need more hustle slogans. We need a repeatable way to reach the right people - without sounding like a bot or burning a day on each message.

Last week I shared how to build an AI-ready knowledge base so your custom GPTs, agents, and AI tools actually work. 

The #1 reply I received?

 “Loved the organization - thank you! How do I create an Offer One-Pager?”

AI tools underperform in most organizations for one reason: the information they need to do useful work is scattered, inconsistent, or inaccessible. The tool isn't the problem. The knowledge structure is.

Most teams treat AI governance like a brake pedal. 
Legalese. 
Red tape. 
A reason to move slower.

That mindset kills momentum.

Most organizations move through four stages on the way from AI dabbling to enterprise-wide adoption: uncoordinated experimentation, governance foundation, structured rollout, and compounding operations. The companies that stall almost always stall at stage two — not because they lack tools or ambition, but because they try to skip governance and go straight to scale.