For a company between $100M and $1B in revenue, AI transformation is a series of operational decisions - about governance, training, tooling, and measurement - made in a specific order, repeated in cycles, and owned by leadership rather than IT. It looks less like a software rollout and more like building a new organizational muscle.

An AI audit is a structured assessment of how your organization is currently using AI: what tools are deployed, how employees are actually using them, where the gaps are, and what's working versus what's theatre. It's less of a compliance exercise and more of a diagnostic. And most companies that think they don't need one are the ones who need it most.

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.

There's a moment I keep witnessing and I need to talk about it.

A founder - usually smart, usually busy, usually someone who built their business on the quality of their thinking - tells me they've been using AI for a year. Maybe two. They're producing more content. Sending more outreach. Drafting proposals in half the time.

And then comes the pause.

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.

Should you invest time and resources in building custom AI agents, GPTs, or Gems, or is the key simply to master the art of prompting? The answer, as it often is with powerful technology, is nuanced: it depends entirely on the problem you are trying to solve.

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?”

When leaders tell me “AI feels cool, but nothing sticks”, my first question is never about the model. It’s about the knowledge base.

AI can’t use what you haven’t organized.