Why unmaintained AI degrades

There's an uncomfortable truth almost no one tells you when selling AI projects: an AI system without maintenance doesn't stay the same — it gets worse. And it gets worse silently, without throwing errors, while everyone assumes it's still working as it did on launch day.

The three ways it degrades

• Data drift: the world changes, and so do the data. Prospects ask about new products, documents change format, prices are updated. The system responds with a view of the world from six months ago.
• Process drift: the team changes how it works — new CRM, new policy, new structure — and the automation is left hanging on a process that no longer exists.
• Model drift: providers update their models. What worked with one version might behave differently with the next. Without continuous evaluations, no one notices until a customer does.

"Set and forget" is the number two cause of death for AI projects — right after choosing the wrong thing to build first.

What well-executed maintenance looks like

It's not a giant team: it's a observability system + a review cycle. Quality metrics monitored week by week, automated evaluations against real test cases, and a clear owner who reviews cases where the system hesitated or failed.

In operations with hundreds of active projects, this cycle is what keeps automated document analysis reliable month after month: every document the system couldn't confidently classify feeds into the next iteration.

The question for your board

Before approving the next AI project, ask: who will maintain it, with what metrics, and with what budget? If the answer is "we'll see," the ROI you approved has an expiration date. The cost of well-planned maintenance is a fraction of the cost of discovering in six months that the system has been responding incorrectly for three.