🤝 Join our live course: How to Avoid AI Project Failures, For Org Leaders

Most AI pilots don’t fail because of the tech.
They fail because leaders fund the wrong things, in the wrong order.

In this 30-minute Lightning Lesson on Maven, I break down:

• why AI pilots stall before ROI
• the decision mistakes that quietly kill momentum
• a simple way to prioritize AI bets that actually stick

Built for Org Leaders who have to sponsor AI, not babysit it.

→ Reserve your Free seat (limited)

P.S. Can’t attend live? Get the recording right after.

Weekly finds

📰 AI Insight

1. Claude Code’s “ChatGPT Moment” Anthropic’s new tool is scaring software stocks as CEOs swap engineering hires for “selfware.” It’s not just coding; it’s building custom MRI viewers and tomato-growth trackers. Automation just got a lot more personal.

2. Ads Invade ChatGPT Sam Altman’s “last resort” is here. Free and “Go” tier users will soon see sponsored recommendations. It’s the end of the ad-free honeymoon, hope you like your AI with a side of commercials.

3. Claude’s New Constitution Anthropic published the rules governing Claude’s soul. It prioritizes safety over helpfulness and, get this, tells Claude to disobey Anthropic if they ask for something shady. A rare win for AI ethics.

4. Apple’s AI Pin Ambitions
   Apple is reportedly prepping a camera-equipped AI wearable for 2027. While others have flopped, Apple is betting 20 million units that they can make “wearing your assistant” actually cool.

5. Independent AI Auditors Wanted
   Former OpenAI policy lead Miles Brundage launched AVERI to stop labs from grading their own homework. Because “trust us, it’s safe” isn’t exactly a robust security strategy for world-changing tech.

THE ISSUE

1. The Real Pain: Everyone’s Talking About AI, Nobody’s Doing Anything Useful With It

You’ve probably seen this movie:
The CEO declares, “We need an AI strategy.”
Someone spins up a few ChatGPT prompts.
Ops managers nod, smile, and go back to wrestling spreadsheets and Slack pings.

Three months later, there’s a dusty “AI enablement” deck in Notion and zero measurable change.

Sound familiar?

Here’s the uncomfortable truth:
Most AI workshops for operations teams don’t fail because the tech isn’t good enough.
They fail because they never reach the point where behaviour, the way people actually work, changes.

And if you’re running a $10M–$100M company, you don’t have time for theoretical AI discussions. You care about speed, leverage, and execution, not slides about “transformative potential.”

2. What Most People Get Wrong About AI Workshops

Let’s be blunt.

Most “AI training for teams” sessions fall into one of two traps:

1. The Inspiration Trap:
   Endless hype about how AI will “redefine the future of work.”
   Your team walks out inspired… and then does nothing differently.

2. The Tool Trap:
   A quick demo of ChatGPT, Notion AI, or some workflow bot.
   People ooh and aah, then forget how to use it two days later.

Mid-market companies get stuck right in the middle: too complex for a cookie-cutter solution, too lean to hire a full AI department.

That’s why most mid market AI strategy sessions fail, they copy enterprise playbooks or startup experiments instead of building something grounded in execution.

3. So What Is an AI Workshop That Works?

Forget buzzwords.

A real AI workshop for operations isn’t about showing off prompts, it’s about rewiring the way teams think about leverage.

Here’s what it looks like in practice:

• Ops leaders learn how to identify automatable friction. Not “AI ideas,” but workflow pain points that cost hours each week.

• Teams get hands-on, they bring their actual data, dashboards, and SOPs, not generic examples.

• You leave with workflows changed, not just notes.

For a $10M–$100M company, this means practical, measurable improvement: cleaner handoffs, faster reporting, fewer missed follow-ups.

In short, it’s AI transformation for SMB without the nonsense.

4. Why This Matters Now, Not Eventually

Every week your ops team wastes hours on manual coordination, data wrangling, and decision lag.

• A deal slows because someone’s waiting on a report.

• A customer email sits unread because triage isn’t automated.

• A decision gets delayed because no one trusts the data source.

This isn’t dramatic, it’s quiet, expensive erosion.

The gap between “teams that use AI well” and “teams that just talk about it” is widening fast.

And in the mid-market?
That gap compounds. Faster response times → quicker revenue cycles → higher customer retention → more leverage per person.

That’s why AI enablement isn’t a someday project.
It’s an operations multiplier, and every month you delay, the compounding advantage goes to someone else.