The advice from every major consultancy right now is the same: stop experimenting with AI, start scaling. Move out of pilots, get into production, show the board a return.
It sounds decisive. It is also dangerously incomplete.
That advice assumes the technology you are scaling into is stable. It is not. The model you put into production this quarter will be superseded next quarter. Locking into delivery mode without a parallel experimentation capability means you are always optimising for yesterday.
Admiral Grace Hopper kept a clock on her wall that ran backwards.[1] Her point was simple: solving new problems requires actively resisting the pull of what worked before. AI demands exactly that posture.
Experimentation is not the same as piloting
There is an important distinction to make here. Permanent piloting is indecision dressed as prudence. Permanent experimentation is something different. It is a structured, funded, governed capability that continuously asks: given what models can do now, what should we build next, rebuild, or retire? It runs alongside production. It does not replace it.
This is precisely what Tanhill’s Innovation Labs work is built on. Not a one-off deployment. A permanent internal engine that keeps organisations ahead of the capability curve rather than catching up to it.
One more thing worth noting. The firm telling you to stop experimenting and start scaling has a financial interest in large, long-running implementation programmes. Permanent experimentation makes you more self-sufficient.
Draw your own conclusions.
So ask yourself honestly: is your experimentation capability funded, governed, and permanent? Or is it a pilot that someone is quietly waiting to kill?
If you would like to build a permanent experimentation capability alongside your production AI, our Innovation Labs work may help. You can read more on our services page or get in touch.
This article was first shared as a post on LinkedIn.
Sources
- Vassar College, Grace Hopper: Life.