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.

When people talk about AI, the focus often lands on speed, productivity, and cost savings. But here’s the uncomfortable truth - AI won’t be inclusive by default.

Left unchecked, AI systems replicate the same biases and inequities already baked into our data and our workplaces. That’s not just a technical problem - it’s a leadership gap.

We unpack how AI and DEI (diversity, equity and inclusion) intersect inside organizations. From the reality of companies quietly pulling back on DEI to the urgent need to make AI adoption equitable, Inclusion is not a trend - it’s a value.

When people think of getting found on AI (the new world search/SEO) they picture a magic formula - some secret prompt or hack that pushes their brand to the top of ChatGPT, Gemini, or Perplexity results. 

In reality, AI discovery works the same way as Google did in its early days: reputation + content + consistency.

This is the Findability Flywheel. 

You don’t need another tool.
You need a system that makes AI send you customers.

When people talk about AI, they often focus on outputs - faster graphics, smarter copy, automated workflows. That’s real ROI, of course. But it’s not the whole story.

The deeper return on AI literacy isn’t about content at all. 
It’s about confidence.

This August, I lived a dream I’ve been chasing for years - working and playing (mostly) while traveling.

I’ve always managed day-to-day flexibility. Choosing my hours, building around my family, even leaning into the 4-day-week fantasy (though never quite nailing it). But this… being able to step away for weeks at a time, while the business keeps humming? That’s new.

And it simply wouldn’t have been possible without AI.

We tell ourselves stories.

“I’m late.”
“Everyone else is miles ahead.”
“I’ll start when I understand it better.”

That’s not truth. 
That’s procrastination, dressed up as prudence.

In this episode, I sat down with Deborah Carraro, a long-time educator, founder of ideborah, and AI Lead at Coralus. She’s helping early-stage entrepreneurs and changemakers develop creative confidence with AI - not just competence.

You don’t need another AI tool.
You need to get out of your own way.

When it comes to adopting AI, most are asking: “Which tool should I use?”

Clarity first. 
Tools later. 
Always.

You pulled a latte, blocked off an evening, and gleefully clicked Create a GPT. 

A few hours later you had a shiny private model with a clever name and high hopes. 

Fast-forward to today and… meh. 

Generic answers, awkward tone, zero time saved.

Ready to stop cursing at that under-performing GPT (that you lovingly built - only to watch it spit out meh)?