Our AI pilot is stuck. Why don't experiments scale?

Common questions from Omaha leaders

Why do most AI pilots fail to scale?

Three patterns dominate. (1) The pilot was built in a sandbox, not in real systems — when scaling means rebuilding for production, momentum dies. (2) The pilot didn't redesign workflow; it just made existing steps faster. McKinsey's 2025 data shows AI high performers are nearly 3x as likely to have fundamentally redesigned workflows. (3) Governance wasn't started in parallel. Deloitte found 69% of organizations expect implementing a governance strategy will take more than a year — if you wait until pilot ends, you've already lost a year.

How common is this?

Very. Deloitte's Q4 2024 GenAI survey found over two-thirds of organizations reported only 30% or fewer of their experiments would fully scale within 3–6 months. McKinsey's 2025 State of AI found nearly two-thirds of organizations have not begun scaling AI across the enterprise. Only ~6% qualify as 'AI high performers' attributing 5%+ EBIT impact.

We have ROI on the pilot. Isn't that enough?

It's necessary but not sufficient. ROI on a pilot tells you the use case works. Scaling requires a different set of decisions: real-system integration, workflow redesign, governance, training, manager enablement, and outcome metrics. Each of those is a separate work stream. ROI gets you permission to start them; it doesn't replace doing them.

Should we just start over with a different pilot?

Sometimes — but only if the pilot was truly off-target. Usually the better move is to pick the actual scaling work as the next phase: workflow redesign for the use case the pilot already validated, real-system integration, governance, and trained users. The pilot's ROI is the case for funding the scaling work; restarting throws that away.

How do we know when the pilot is ready to scale?

Three signals: (1) the pilot ran on real data and real users, not synthetic. (2) The use case has clear stopping points where humans approve final output — not just 'we'll add review later.' (3) You have a workflow redesign sketch — what changes for the people doing the work. Without all three, scaling means building the foundations during the rollout, which is where most stalls happen.

What about pilots that worked but only saved 10–15% — is that worth scaling?

Honestly, sometimes no. McKinsey's data shows 39% of organizations report any EBIT impact at the enterprise level from AI, but only ~6% are high performers with 5%+ EBIT. Saving 10% on one workflow is fine; expecting that to compound to enterprise-level transformation usually disappoints. The high performers got there by redesigning workflows, not by adding small efficiencies to many.