Why it matters now
Pilots are easy to start and hard to finish. I have seen many that worked in a demo and then quietly faded, because nobody had planned how they would fit into real work, who would own them, or what would count as success. The financial value comes from the step after the pilot, when the new way of working becomes the normal way.
- Only 16% of companies have scaled AI across the enterprise.IBM Institute for Business Value, 2025 CEO Study ↗
- Companies lagging on AI deploy 12% of their initiatives, compared with 62% at the most advanced, “future-built” firms.BCG, The Widening AI Value Gap, 2025 ↗
- The biggest financial gain comes when companies move from piloting AI to scaled, AI-enabled ways of working.MIT CISR, Enterprise AI Maturity Update, 2025 ↗
Common mistakes
- Running experiments that were never designed to scale.
- Adding a tool to an unchanged process and expecting different results.
- Choosing tools that do not fit the workflow or learn from it.
- Letting pilots run on with no decision to scale or stop.
What to do instead
- Agree the owner and scale criteria up front. Before a pilot starts, agree who owns the result in the business and what evidence would justify scaling it.
- Redesign the workflow. Map the work before and after. Decide which steps the system does, where a person decides, and what happens when it fails.
- Test on real work. Use representative cases, including the difficult ones, and measure quality, reliability, usage and cost.
- Build once, reuse often. Invest in shared platforms, data access and standards, so the second and third use cases cost less than the first.
- Decide on evidence. Set a date for the decision. Scale what earns its place, change what nearly works, and stop the rest.
Your first step this week
Pick your most promising pilot and write one page: who owns it, the baseline, what success looks like and what scaling would require. If you cannot write it, that is probably why it has not scaled.
Related guides
- Why aren’t we seeing a return on AI, and how do we measure it?
- Is our data ready for AI, and what foundations do we need?
- What should our AI strategy be, and where do we focus?
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