The 6 Biggest AI Implementation Mistakes (And How to Avoid Them)

I've seen AI implementations fail more times than I can count.

And honestly? Most failures follow the same patterns.

After 2 decades in tech and countless AI projects, I've identified the 6 biggest mistakes organisations make—and more importantly, how to avoid them.

---

How This Guide Works

For each mistake, you'll see:

1. The Mistake - What organizations get wrong
2. Why It Fails - The underlying reasons
3. Do This Instead - The right approach with practical frameworks

This structure helps you understand what to avoid and what to do instead.

---

Mistake #1: Starting with Technology Instead of the Problem
Mistake #2: Expecting AI to Work Perfectly Out of the Box
Mistake #3: Ignoring Data Quality (or Lack of Data)
Mistake #4: Not Involving the People Who'll Actually Use It
Mistake #5: Skipping the Governance and Ethics Conversation
Mistake #6: Trying to Do Everything at Once

Let's dive in.

Read more here!