The Pilot Trap
Across industries, pilot projects have become the easiest way to signal progress with AI adoption. A low-risk executive message to investors, boards, and employees that AI transformation is underway. Yet 88% of pilots never reach production1. At that rate, another AI pilot invites skepticism, not curiosity.
The Illusion of Progress: Why Pilots Fall Short
Pilot projects traditionally form the bridge between experimental proof of concepts, and business wide implementation, by validating and refining a solution's ability to operate, deliver value, and be owned within the real conditions of the business.
Somewhere along the way though, pilot projects have stopped being the real world validation. What should naturally build toward a decision to scale, has become an extension of the proof of concept rather than a next step, and teams stay busy proving value instead of creating it. The pilot has become the experiment itself.
When AI Pilots Become Experiments
Our experience shows that SMEs often follow the right sequence: Proof of concept, then pilot, then scale. Yet many still watch their pilots stall before they scale. The flaw isn't in the framework. It's often in the execution.
In many cases we've seen, proving feasibility in this early stage of AI adoption has become a purely technical exercise under synthetic circumstances. Under pressure to show results, we've witnessed teams rush to just make something – anything - work. Understandably, the AI tech side is all but mainstream. However, the instinct to prove success first pulls focus to the technology, not the business problem it's meant to solve or whether the solution will thrive in real-world conditions2.
This is where the gap starts to open. We consistently observe with our clients that proof of concepts focus on technical feasibility instead of business fit, the critical questions get left for later:
How will it integrate with our systems?
Where does it fit in our workflows?
Who owns it when it does fit?
At first it seems harmless, but delaying these questions in early proof of concept stage means the solution risks never finding a home and adoption never taking off3.
The next phase, the pilot, should be an adoption exercise that validates performance in production and readiness to scale. Instead, it inherits the fundamental questions left unresolved in the earlier proof of concept phase. By the time they surface, it's too late: What looked like progress is now proof the experiment wasn't built for what's next.
The pilot trap starts in the proof of concept phase. The escape lies there too. Fail faster, and learn faster, because the golden AI use case isn't found, it's created through experimentation discipline.
Where Experiments Earn the Right to Scale
To experiment with AI starts by not asking 'can we build it', but 'can it live here'? The only way to find out is to build it where it's meant to live: With your real data, your production workflows, your governance frameworks, in a partnership with real users.
Building early experiments in production-like circumstances can feel like slowing down innovation, yet it does the opposite. When ideas are tested under the same conditions they must eventually operate in, success and failure reveal themselves faster and with greater clarity. With less at stake, it keeps early decisions cleaner, less influenced by optimism or internal politics, more grounded in evidence.
Fail-fast thinking comes alive when a small, cross-functional team from business, data, and IT owns the experimentation from start to finish. It's lean enough to move fast, but broad enough to see the full picture, typically 4-8 people. Most importantly, they work with real users in real conditions, where assumptions meet reality.
Traditional project teams are built to deliver; fail-fast teams are built to discover. They cut across silos, plug into operations, and hold the authority to start, stop, or pivot on their own terms. Their goal isn't to finish a plan, it's to generate proof: Does this idea work here, in this environment, for these users? Leadership's role shifts from steering to shielding, making sure learning can happen without penalty.
Moving Forward
The path to successful AI adoption doesn't start with the perfect pilot. It starts with asking the right questions early and building the circumstances and discipline to learn fast. For leaders, the opportunity is clear: Shift the conversation from "Can we build it?" to "Can it thrive here?"