AI Implementation Requires an Exit Strategy

The pilot project is a success, the AI is running smoothly in day-to-day operations, and everything seems under control. Until the provider raises prices by half. Or shuts down a model. Or an auditor wants to know how a recommendation came about that was issued months ago by a model that has long since been replaced. In his article in ERP Information, Heiko Beier explains why many companies have little room to maneuver in such situations. Lock-in rarely stems from the language model itself. It creeps in quietly through proprietary interfaces, vendor-specific agents, and process logic that eventually falls outside the company’s control.

Eight questions reveal just how dependent your own architecture actually is—from costs to model changes and data processing to access to the source code. Some answers may be uncomfortable. For example, the fact that a replaced model may still sound plausible yet perform worse from a technical standpoint. Or that many tasks don’t require a large language model at all, because smaller models and analytical AI handle them more cost-effectively and reliably. Those who take these questions seriously will end up with an architecture in which models remain interchangeable and rules, data, and domain expertise belong to the company. This is not a task that IT can solve on its own. And it is not a recipe for failure, but rather the prerequisite for AI to become part of the infrastructure.