AI procurement often begins as a feature decision and ends as an infrastructure decision. A team chooses a model, copilot, scoring engine or automation vendor to solve a narrow problem. Within months, that service is connected to customer data, internal workflows, approvals, reporting and revenue operations. The vendor is no longer a tool. It is a dependency.
That is why an exit test should happen before adoption, not after a contract problem, outage or strategic change. The question is not simply whether the vendor can perform today. It is whether the organisation can leave tomorrow without losing control of data, process continuity or decision quality.
The exit test is a design exercise
An exit test is a structured rehearsal of how the company would reduce, replace or remove an AI service. It does not assume the vendor will fail. It assumes that circumstances change: pricing moves, model quality drifts, regulation tightens, security requirements evolve, a product is discontinued, or a better option appears.
The test should produce evidence, not reassurance. A contract clause that says data can be exported is not enough. The team should know what the export contains, which formats are available, how long delivery takes, whether model-specific context is included and what remains behind.
1. Map the dependency before approving the purchase
Start with a simple inventory. Which business processes rely on the service? Which systems send it data? Which teams consume its outputs? Which automated actions can it trigger? A vendor used only for drafting internal text has a different exit risk from one that ranks applicants, routes customer cases or approves marketing offers.
The map should include hidden dependencies such as proprietary prompts, workflow rules, embeddings, fine-tuning assets, evaluation sets and human review procedures. These often contain more operational value than the underlying model.
2. Define what must be portable
Data portability is broader than downloading a CSV. The organisation may need configuration, audit logs, user roles, policy settings, model versions, evaluation results and a record of human overrides. If these cannot move, the replacement system may start without the context needed to reproduce safe behaviour.
Identify the minimum portable package and put it into the procurement specification. Test the export in a non-production environment. If the exported files cannot be understood without the vendor's software, portability is only nominal.
See also: company profile
3. Set a replacement time objective
Every critical dependency needs a practical time limit for substitution. Could the service be replaced in a day, a month or two quarters? The answer depends on integrations, retraining, validation and change management. A realistic replacement objective helps determine whether redundancy, escrow, internal capability or a secondary provider is necessary.
The objective should cover the period from decision to operational stability, not merely contract termination. A new model that has not passed quality, safety and bias checks is not a completed migration.
4. Separate the business process from the model
Teams become trapped when workflow logic is embedded inside a vendor interface. Approval rules, escalation thresholds and evidence requirements should live in documentation or company-controlled systems wherever possible. The AI service can supply an output, but the organisation should retain the process definition.
This separation also improves governance. If the model changes, the business can compare outputs under the same policy instead of redesigning the entire workflow.
5. Preserve an independent evaluation set
A company cannot judge a replacement if it has no stable benchmark. Keep a representative, legally usable evaluation set that measures quality, reliability, safety and business impact. Record the current vendor's performance and the acceptable threshold for a successor.
The evaluation set should include difficult cases, not only average ones. Exit decisions are often driven by the edge cases that create reputational or regulatory risk.
6. Rehearse a degraded mode
An immediate exit may not be possible, but a controlled reduction usually is. Define which automated actions can be paused, which processes can return to human review and which services must remain available. A degraded mode limits damage while the migration is underway.
This rehearsal should involve operations, security, legal, procurement and the business owner. An exit plan written by one team will miss dependencies owned by another.
7. Make decommissioning part of the contract
Termination support should specify data return, deletion evidence, access revocation, transition assistance, documentation and service levels. It should also cover subcontractors and any retained copies used for monitoring or improvement.
The contract is strongest when the technical rehearsal has already exposed what the team needs. Procurement language should follow operational reality, not guess at it.
A five-question executive test
- Can we name every critical process that depends on this vendor?
- Can we export the data, configuration and evidence needed to continue?
- Can a replacement be validated within an agreed time?
- Can we operate safely in a reduced or manual mode during transition?
- Can we prove access was revoked and data was deleted at the end?
If any answer is unclear, the dependency is not ready to become infrastructure. The right response is not necessarily to reject the vendor. It is to narrow the scope, improve portability, add controls or renegotiate transition terms before adoption grows.
AI strategy is often framed as a race to deploy. Durable strategy also protects the freedom to change direction. An exit test creates that freedom while the organisation still has leverage, time and architectural choices.
