The problem success creates
Most AI failure stories are about models that didn't work. This one is the opposite. At Google, I watched a large grocery chain build a powerful customer purchase prediction model — and it worked well enough that it became the most valuable asset in the building. Merchandising wanted it. Marketing wanted it. Regional teams wanted it. Success had turned into a distribution problem nobody had planned for.
Why "just give everyone access" doesn't work
Once a model proves valuable, the organization faces a binary choice it usually isn't prepared for: hand over the model weights and training data to every team that wants it, or build bespoke API access for each one individually. Both paths expose proprietary IP. The first gives away the asset outright; the second is slow, expensive, and still leaks architecture details team by team. Innovation had effectively stalled at the exact moment the model proved itself.
The fix: move the answer, not the model
This exact problem — seen across multiple engagements, not just this one — is what led to a different architectural pattern: the model never moves. Business teams access predictions through a governed inference layer instead of direct access to weights or training data. The model owner retains full control and can retrain freely; the consuming teams see no disruption and never touch the underlying IP.
Distributing a model doesn't have to mean distributing the model. It means distributing the answer it produces.
How to tell if you're facing this
The signal is usually a good one to have: a model works well enough that demand for it outpaces your team's ability to safely provision access. If your data science team is fielding requests for "can we just get access to that model" from multiple business units, that's the moment to solve the distribution architecture — before ad hoc access requests turn into ungoverned exposure.