AI Pitfalls to Avoid

There’s no shortage of AI hype.

Productivity gains. Automation wins. Case studies with soaring ROI.

But here’s the part no one shares:

Most internal AI projects fail quietly.
They get stuck in planning. Or overpromise and underdeliver.
Or worse — they launch and no one uses them.

I’ve been working on 50+ AI projects (mostly brought in when things didn’t work).
Almost every time, it wasn’t the tech that broke the project.
It was the thinking behind it.

So in this workshop I’ll share what thinking traps actually make an AI project go wrong (incl. real-world case studies) — and how to avoid them even before the first prompt is written.

What We’ll Cover

Intro (10 Min.)

• Why it’s rarely the tech that kills an AI project

• What I’ve seen across 50+ projects and how to spot critical problems early

The 4 Thinking Traps (40 Min.)

1. The Automation Trap
Why “AI = automation” is a limiting mindset and what to look for instead.

2. The ROI Trap
Why traditional ROI logic breaks down in early AI projects and how to reframe it.

3. The AI Paradox
Why higher productivity promises don’t automatically lead to higher AI adoption.

4. The Carpenter Trap
Why teams waste months overplanning — and missing the real opportunities.

We Work on Your Idea (30 Min.)

Bring your own AI idea, or use case.
We’ll use the 4-trap lens to uncover weak spots in your approach and spot early signs of failure before they happen.

This is for you if:

• You’re in charge of internal AI initiatives

• You want to build an AI-powered software or service

• You advise clients in AI adoption

You’ll leave with:

1. A thinking framework you can apply to any AI project

2. Clarity on what separates successful AI rollouts from the ones that fail

3. A more robust strategy you can actually explain to stakeholders, teams, and partners