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AI vendor exit planning starts before the contract is signed

If you're accountable for enterprise AI, vendor exit planning should start before the contract is signed. This isn't a procurement afterthought; it's the same discipline you apply to system design. Good architectures already plan for failure. Vendor failure deserves the same rigor.

Push data export, model weights, and inference rights into the contract—not the renewal. At renewal time, the vendor has all the leverage. If they're struggling, they may restrict exports to force a renewal or make migration painful. If they're gone, there's no one to negotiate with. The contract must specify that you own your fine-tuned weights, that you can export your data in a usable format, and that you retain the right to run inference on your own infrastructure if the relationship ends. Waiting for the renewal means you've already lost the negotiating position.

Wrap every proprietary model call behind an interface. This is plain software engineering: depend on abstractions, not implementations. If your application directly calls a vendor's SDK or API in dozens of places, swapping to another model means rewriting those integrations and often rethinking the surrounding logic. But if you define your own internal interface—say, a ModelClient with a predict method—then changing vendors becomes a matter of writing a new adapter. That's code, not architecture. It's the difference between a week of work and a quarter-long migration.

Track funding runway, leadership churn, and support SLA misses as early exit signals. Public information about a vendor's funding often lags reality, but you can watch for signs: delayed product releases, shrinking support responsiveness, key engineers leaving. These are the equivalent of disk SMART errors. They don't guarantee failure, but they tell you when to start exercising your exit plan before it's an emergency. Support SLA misses are especially telling—if a vendor can't meet its contractual support obligations, that's a breach, but it's also a sign of internal strain.

Run a forced failover drill for each AI vendor. The drill is the only test that matters. You can have a documented plan, but until you actually cut over to a fallback model or a self-hosted alternative, it's theory. A failover drill forces you to find the gaps: the missing data export, the hardcoded endpoint, the team that doesn't know the runbook. If it's not tested, it's not an exit plan.

Good architectures already plan for failure. Vendor failure deserves the same rigor.

The CIO piece "When your AI vendor goes belly up" is a good read on the failure modes. But the takeaway for anyone accountable for enterprise AI is simple: do the exit planning before you sign, not after. The procurement cycle is your best chance to set terms, build abstractions, and define what a clean break looks like. Once the contract is inked, that leverage disappears. And by the time the vendor starts missing SLAs, it's already too late to start planning.

Originally covered at cio.com ↗