The graveyard of enterprise AI is full of pilots that were set up to fail: too broad, too vague, and measured against a baseline nobody bothered to record. A good pilot isn't a smaller version of the whole vision — it's a sharp, contained test of one thing that matters.
Key takeaways
- Pick one painful, high-frequency process — not a showcase of everything the technology can do.
- Record the 'before' baseline first, or you'll have no honest way to judge the 'after'.
- Define what success looks like up front, in numbers, before anyone builds anything.
01Narrow beats impressive
The instinct is to make the pilot showcase everything, to justify the effort. This is the mistake. A broad pilot spreads attention thin, multiplies the ways it can go wrong, and produces a muddy result nobody can act on.
A sharp pilot picks one process that hurts and runs often — the daily report, the recurring inspection, the same handoff every week. Frequency means you get signal fast; pain means people care whether it works.
A pilot that tries to prove everything proves nothing. Pick one painful process and measure it honestly.
02Measure the 'before'
You cannot prove improvement without a baseline, and memory is not a baseline. Before anything is built, capture how long the process takes today, how often it's redone, and where it stalls. This unglamorous step is what makes the result defensible later.
It also protects everyone: it turns 'it feels faster' into 'it's this much faster', which is the difference between a pilot that gets rolled out and one that gets quietly shelved.
03Agree on success in advance
Define the finish line before the race. What specific, numeric outcomes would make this a yes? Same-day quotes? Half the clarifying calls? Agreeing this up front stops the goalposts moving and keeps the pilot honest.
The best pilots have a clear, small scope, a recorded baseline, and a pre-agreed definition of success. Get those three right and the technology is almost the easy part.