Why AI Pilots Are Doomed From Day One (And What to Do Instead)

You’ve been asked to approve an AI pilot. The proposal looks thorough. Someone’s done their homework - vendor demos, ROI calculations, maybe even a proper business case. They want four months and sixty thousand dollars to “prove the concept” before rolling it out company-wide. It sounds like good governance. Prudent leadership. The responsible way to test new technology.

Here’s what actually happens: the pilot succeeds perfectly, and six months later nobody’s using the system. Not the pilot team. Not the department that requested it. Nobody. I’ve watched this exact scenario play out in dozens of organisations. The pilot worked. The business got zero value.

Most AI pilots are designed to prove the wrong thing to the wrong people at the wrong time. By the end of this post, you’ll have a framework for deciding whether a pilot makes sense, what it should actually prove, and what to do instead when the answer is no.

The Problem

Here’s the pattern I see in most organisations. Someone from IT or operations comes to leadership with an AI tool. They’ve identified a use case - usually document processing, customer service, or data analysis. They want to run a three to six month pilot with a small team to validate the technology works before broader deployment.

Leadership approves it because it feels like due diligence. Test before you commit. Reduce risk. Prove value. All the right management instincts.

The pilot team gets set up. They’re usually pulled from their day jobs part-time, reporting to someone who’s never run a technology implementation. The success metrics are typically technology-focused: accuracy rates, processing speeds, cost per transaction. The team works hard. They hit their targets. They present their findings to leadership.

And then nothing happens. Or worse, something does happen - leadership approves a broader rollout that fails spectacularly because the pilot tested none of the things that actually matter for adoption.

I had a client run an AI pilot for contract analysis. Twelve weeks, hand-picked documents, dedicated team. The system achieved 94% accuracy on document classification. The pilot was declared a success. When they rolled it out to the legal team six months later, usage dropped to zero within a month. The lawyers didn’t trust the classifications, the integration with their existing workflow was clunky, and nobody had authority to act on the AI’s recommendations anyway.

The pilot proved the technology worked. It proved nothing about whether the business would actually use it.

The Reality

Most AI pilots fail because they’re designed to prove technical capability rather than business adoption. They answer “can the AI do the task?” instead of “will people actually change how they work?”

The fundamental problem is that AI adoption is not a technology problem. It’s a change management problem. The technology usually works fine in controlled conditions. What breaks is everything around it - workflows, decision rights, trust, incentives, and politics.

Here’s what typically gets missed in pilot design. First, pilots use clean data and simplified workflows. Real business operations are messy. Documents are incomplete. Data is inconsistent. Edge cases are everywhere. The pilot environment bears little resemblance to actual working conditions.

Second, pilot teams are volunteers or hand-picked believers. They’re motivated to make it work. They’ll work around problems that would stop regular users cold. They’ll spend extra time training the system or cleaning up its output. When you roll out to the broader organisation, you’re deploying to people who didn’t ask for this tool and don’t have time to babysit it.

Third, pilots usually bypass the political and procedural complexity of real operations. If the AI recommends rejecting a customer application, who has authority to act on that recommendation? If it flags a contract clause as problematic, what’s the escalation process? These questions don’t get answered in pilots. They surface during rollout when it’s too late to design around them.

The result is that most pilots prove the wrong thing. They prove the AI can perform a task under ideal conditions with motivated users. They don’t prove that the organisation will actually adopt it under real conditions with regular users.

This is why I’ve started advising clients to skip pilots entirely in many cases. Not because pilots can’t provide value, but because most organisations don’t know how to design them properly. A bad pilot is worse than no pilot because it creates false confidence about deployment.

The Practical Bit

Before approving any AI pilot, ask three questions that most proposals can’t answer well.

First, what specifically will this pilot prove that a vendor demonstration hasn’t already shown? If the answer is “whether the AI can handle our data” or “whether it meets our accuracy requirements,” you probably don’t need a pilot. The technology works. The question is whether your organisation will adopt it.

Second, who is the pilot team and how representative are they of eventual users? If your pilot team consists of people who volunteered, who have been given dedicated time for this project, or who report to the person championing the AI initiative, your pilot results won’t predict real-world adoption. You need skeptics and time-pressed regular users, or the pilot proves nothing about scalability.

Third, what happens the day after the pilot ends? Most pilots have no clear path from “successful test” to “working system.” If you can’t describe the specific decisions, approvals, and process changes required to move from pilot to production, you’re not ready for either.

Here’s what to do instead of a traditional pilot. For simple, well-defined use cases where the AI replaces an existing manual process, skip the pilot and start with a limited production deployment. Choose one team, one process, real users, real data, real deadlines. Give it eight weeks. Either it works in real conditions or it doesn’t.

For complex use cases where the AI changes how people make decisions, don’t pilot the technology. Pilot the new workflow. Use humans to simulate what the AI would recommend. Test whether people actually follow the recommendations, whether they trust the output, whether they have authority to act on it. Only after you’ve proven the workflow adoption should you add AI to execute it.

If you do run a traditional pilot, make adoption metrics at least as important as technical metrics. Track daily active users, task completion rates, and user satisfaction scores. Interview pilot users weekly about what’s broken, what’s confusing, and what they’d change. Measure how much manual work is required to make the AI output useful. These metrics predict rollout success better than accuracy percentages.

The most important action: define what success looks like before the pilot starts. Not just “the AI achieves 90% accuracy” but “users choose the AI over their current process 80% of the time without being told to.” If you can’t define adoption success upfront, you’re not ready for deployment regardless of what the pilot shows.

The Close

Most AI pilots are expensive ways to prove things that don’t matter. They test technology capability when the real question is business adoption. They create confidence about deployment when they should create clarity about readiness.

The goal isn’t to prove AI works. The goal is to prove your organisation will actually use it. Sometimes that requires a pilot. More often, it requires honest assessment of workflows, incentives, and change management capability. Get that right first, and the technology decision becomes straightforward.

This episode of the Getting AI Done podcast goes deeper on pilot design and includes specific examples of what works when traditional pilots don’t.