Insights
Why AI pilots often stall, and how organisations can avoid it
Many organisations begin with AI through a pilot: a small group gets access and starts experimenting. It is a sensible first step, yet the impact sometimes stays within that one group. This article explores what helps a pilot grow beyond it.
When experiments stay personal
In a pilot, each participant often works out on their own what works. That knowledge lives in people's heads and personal chat histories, not in the organisation. Once the pilot ends, it is hard to say what was actually learned.
Four building blocks for lasting AI implementation
There is no fixed formula, but these elements make the move from experiment to working practice more likely:
Structured experimentation
Agree up front which tasks you will test and how you will judge whether the result is usable.
Knowledge sharing
Schedule moments for participants to show what worked and what didn't. Failed attempts are useful information too.
Reusable use cases
Write up good applications so a colleague can repeat them without prior knowledge.
Shared ways of working
Agree how the team phrases requests and checks results, so AI does not remain an individual side project.
From pilot to team
A pilot only truly succeeds when people outside the original group can use what it produced. AI training for teams can help, provided it starts from the work the team actually does.
Looking to build a shared way of working with Copilot? Discover our Copilot training for businesses.