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

Common questions from Lincoln leaders

Why do most AI pilots fail to scale?

Three patterns dominate. (1) The pilot was built in a sandbox, not in real systems. (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.

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 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.

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

It's necessary but not sufficient. Scaling requires real-system integration, workflow redesign, governance, training, manager enablement, and outcome metrics. 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.

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

Three signals: real data and real users, clear stopping points for human approval, and a workflow redesign sketch.

What about pilots that worked but only saved 10–15%?

Honestly, sometimes not worth scaling. McKinsey's data shows only ~6% of organizations are high performers with 5%+ EBIT impact. The high performers got there by redesigning workflows, not by adding small efficiencies to many.