I’ve observed more AI pilots than I can count. The patterns of failure are remarkably consistent. Here are the five mistakes I see most often, and how to avoid them.

Mistake #1: Choosing the Wrong Use Case

The most common mistake happens before any technology is selected: picking a use case that’s either too easy or too hard. Gartner research consistently shows that use case selection is one of the primary predictors of AI project success.

Too easy: Simple automation that could be done with traditional software. Yes, you’ll have a successful pilot, but the business case for AI specifically is weak. “We automated this with AI” sounds impressive until someone asks why you needed AI.

Too hard: Transformational use cases requiring perfect data, complete process redesign, and organisation-wide adoption. These might be your ultimate goal, but they’re terrible pilots.

The sweet spot: A use case that genuinely benefits from AI capabilities (pattern recognition, natural language, prediction), has reasonably good data available, affects a contained user group, and can show results in 8-12 weeks.

One framework I use: the pilot should be valuable enough that the business would want it even if it only achieves 70% of projected benefits, but contained enough that failure wouldn’t be catastrophic.

Mistake #2: Treating the Pilot as a Demo

Pilots designed to impress stakeholders are different from pilots designed to learn.

Demo-oriented pilots:

• Use handpicked data
• Involve enthusiastic volunteers
• Operate alongside rather than within existing processes
• Focus on showing what the technology can do
• Declare success when a presentation goes well

Learning-oriented pilots:

• Use representative data (including the messy parts)
• Include sceptical users alongside believers
• Integrate into actual workflows where possible
• Focus on understanding what’s required for scale
• Declare success when you’ve answered critical questions

The demo approach gives you a story to tell. The learning approach gives you information to act on. They’re not the same thing.

If your pilot succeeds but you can’t answer basic questions about scaling requirements, adoption challenges, and total cost of ownership, you haven’t actually succeeded.

Mistake #3: Underinvesting in Data

Every AI vendor promises their technology works with your existing data. Every client discovers their existing data isn’t quite ready.

Common data problems that derail pilots:

• Missing fields that turn out to be critical for the model
• Inconsistent formats across different source systems
• Historical data that doesn’t reflect current processes
• Quality issues that were acceptable for reporting but aren’t for AI
• Access constraints that delay getting the data you need

The solution: budget at least 40% of your pilot effort for data preparation. Yes, that feels like a lot. It’s usually not enough.

Also, involve data owners early. The people who maintain your source systems know where the problems are. Ask them before you discover issues the hard way.

Mistake #4: Ignoring Change Management

“We’ll figure out adoption during rollout” is a phrase I’ve heard countless times. It’s almost always followed by disappointing adoption numbers.

Even for a pilot with a small user group, you need:

• Clear communication about what the pilot is and isn’t
• Training that goes beyond tool mechanics to new ways of working
• Support for users when they get stuck
• Feedback mechanisms so you learn what’s not working
• Time for users to learn (not just “go live and hope”)

Change management for a pilot might be modest – maybe a few hours of effort per user. But it shouldn’t be zero.

Here’s a test: can every pilot user explain, in their own words, why this change is happening and what success looks like? If not, your change management needs work.

Mistake #5: Unclear Success Criteria

“We want to see if AI can help with this process” is not a success criterion. Neither is “improve efficiency” or “better outcomes.”

Before starting your pilot, define:

• Specific metrics you’ll measure (processing time, error rate, user satisfaction, etc.)
• Baseline measurements of current state
• Target thresholds that constitute success
• How you’ll measure (automated tracking, surveys, observation)
• When you’ll measure (not just at the end)

Write this down. Get stakeholder agreement. Refer back to it throughout the pilot.

I’ve seen pilots declared successful or failed based on vibes rather than data. That’s a waste of the opportunity to learn.

Good criteria look like: “Reduce average invoice processing time from 12 minutes to under 5 minutes for 80% of standard invoices, while maintaining current error rates.”

Bad criteria look like: “Show that AI can help with invoice processing.”

Bonus: The Questions to Ask Before Starting

Before kicking off any AI pilot, make sure you can answer:

1. What specific business problem are we solving?
2. How do we handle this problem today, and what does it cost?
3. What data do we need, and do we have access to it?
4. Who will use this, and have we talked to them?
5. What does success look like, quantitatively?
6. What happens after the pilot if it succeeds?
7. What happens if it fails – what do we do with what we learned?

If you can’t answer these clearly, you’re not ready to start.

The Meta-Mistake

All five mistakes above share a common cause: treating the pilot as an end in itself rather than a learning vehicle.

A pilot that “succeeds” but doesn’t give you the information to scale successfully isn’t actually a success. A pilot that “fails” but teaches you crucial lessons about data requirements, user needs, or integration challenges might be more valuable than a superficial win.

Frame your pilot as an investment in learning. Design it to generate useful information regardless of whether the technology works perfectly. Ask questions, not just “does this work?” but “what would it take to make this work at scale?”

That shift in framing changes everything about how you approach the pilot – and dramatically improves your odds of eventual success.