AI Pilot Purgatory

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If your AI pilot has been running for over six months, it's not a pilot anymore—it's a pet project eating your budget.

Recent research shows that 88% of AI pilots fail to reach production, and for every 33 AI prototypes a company builds, only 4 make it into production. The pattern is predictable: promising demos, initial excitement, then months of "refinement" that never end.

Most executives assume the problem is technical. It's not. The problem is treating AI pilots like science experiments instead of business tests.

The Safety of Endless Testing

AI pilots feel safe.

They have small budgets, limited scope, and minimal risk.

But that safety becomes a prison. The share of companies abandoning most AI initiatives jumped to 42% this year, up from 17% last year. These aren't quick failures…they're slow deaths by a thousand refinements.

Month one: "The model shows promise."

Month three: "We need better accuracy."

Month six: "We're handling edge cases."

Month twelve: "We're preparing for the next phase."

There is no next phase. Just an expensive demo that's become comfortable.

Teams keep pilots alive because killing them feels like failure. The real failure is not deciding. A pilot must deliver on the measure of success, which should be set before you start, not after months of trying to make it work.

Testing the Wrong Thing

Most AI pilots test whether the technology works. This is the wrong question. The right question is: Will the business model work with integrated AI?

A real pilot doesn't just prove the AI can do the ‘thing’ you want it to do. It proves people will change their behavior based on AI recommendations, existing processes can absorb AI outputs, and the organization can operate differently.

Deploying an AI solution often means changing how employees work, from adopting a new tool to trusting an algorithm's recommendations or altering business workflows. If your pilot doesn't test these human and process changes, you're testing nothing that matters for scaling.

The best pilots integrate AI into real workflows with real consequences. They measure adoption rates, not accuracy rates, and track process changes, not model performance.

Why Scaling Fails

Scaling isn't about making the technology bigger. It's about making organizational change stick.

In a study by Logic 20/20, 56 percent of respondents noted that the project cost of their initial AI implementation was higher than expected, and 41 percent affirmed that the project was delivered late. But, cost overruns and delays aren't the real barrier. The real barrier is that pilots often succeed in ways that can't be repeated.

A pilot might work because one manager champions it, a small team manually handles edge cases, or data scientists constantly tune the model. None of these conditions exist at scale.

Scaling requires systematic processes, not individual effort. It requires AI systems that work without constant human tweaking. It requires change management that works across departments, not just with early adopters.

Kill or Commit

The decision is simpler than most executives make it: either commit fully or kill completely.

Double down if your pilot delivers business value that can be measured and has a clear path to scale. Double down if you have defined metrics, executive support, and systematic processes. Set scaling timelines and allocate real resources. Treat it like the business initiative it needs to become.

If your pilot lacks these elements, kill it.

Don't refine it, don't extend it, and don't wait for "just a few more months" of improvement.

Companies that succeed with AI are ruthless about this decision point. They know that endless pilots are distractions from real transformation.

Design for Scale from Day One

The solution for the AI pilot purgatory is to design pilots that force scaling decisions. Set hard timelines. Define clear success criteria. Build scaling costs into initial budgets. Make the pilot team responsible for the scaling plan.

Start with business problems that matter enough to warrant real change. Successful projects are laser-focused on the problem to be solved, not the technology used to solve it. If the problem isn't big enough to justify organizational change, it's not worth an AI pilot.

The goal with any AI pilot isn't proving that AI works. The goal is to prove your organization can work differently with AI. That's a much harder test…and the only one that matters.

Stop treating AI pilots as experiments.

Start treating them as business decisions with deadlines.

This is the kind of challenge I help CEOs work through every day. If your organization is stuck in pilot purgatory, let's talk: ericbrown.com

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