Examiners ask about exit plans for a reason
I have had to explain exit plans for core platforms, data centers and digital banking vendors. The useful plans identified what we could retain, what had to be rebuilt and how we would know the replacement worked. I ask the same questions about AI providers. A common API can reduce integration work, but the prompts, evaluation cases and operating process still need to survive the move.
Portability means different things: replacing a provider during an outage, moving deployment into a controlled environment, changing models for cost or avoiding long-term dependency. Each implies different engineering, and an interface built for occasional migration does not support real-time failover. Decide which scenario matters, then write a concrete exit exercise, such as evaluating an alternative configuration for your policy assistant within a defined period using retained cases and source documents.
Shared syntax is not shared behavior
A common request format reduces integration effort but does not make models equivalent. They differ in following instructions, selecting tools, handling ambiguity and expressing uncertainty. Consider an analytics workflow that should ask for clarification before comparing figures from different reporting periods. One model asks; another quietly infers. Both return valid text through the same API shape.
That is why compatibility has to be defined as tested application requirements, not a successful response from a familiar endpoint. Otherwise a technically clean migration can silently change the decisions the application makes on behalf of users. In credit and lending or in clinical operations, that is a material change, and it should be governed as one.
Write those behavioral requirements down as test cases with expected outcomes, including the cases where the correct response is to ask, decline or escalate. They become the portable specification of what your application must do, and they outlive any single provider relationship.
Own the parts that make a switch reviewable
Keep approved business definitions, access decisions and evidence requirements outside model-specific phrasing. Represent tool contracts explicitly; MCP helps by giving tools and resources a standard interface that is not tied to one model. Maintain representative evaluation cases with expected outcomes. These assets let you ask whether a new configuration meets the same business contract, rather than comparing two sets of fluent answers by feel.
This mirrors a lesson from data warehouse modernization. When we moved enterprise data to a cloud data platform, the parts that migrated cleanly were the ones we had defined independently of the old engine: business definitions, data contracts and reconciliation tests. Everything buried in vendor-specific logic had to be rediscovered. A governed semantic layer and certified metrics play the same role for AI. They are what you carry with you.
Use provider features deliberately
Some provider-specific capabilities are worth using even though they create migration work. Record the dependency and its benefit. Portability should not force every implementation to the lowest common denominator without examining the cost. The goal is to manage dependencies knowingly, not to pretend that useful differences between platforms do not exist.
Then test the path while the primary service is healthy. Run a small alternative-provider evaluation with difficult cases and tool failures, not greeting prompts. Review credentials, network paths and contract terms, because an available endpoint may not be approved for the same data or users. Document which workloads may move, who authorizes it and what will be unavailable. Include a return path for in-flight sessions and cached results when the primary comes back.
Match the rigor to the institution and keep score honestly
Ask the provider and integrator to demonstrate the parts of the exit path they own. Keep the evaluation cases and business definitions available to your team, with the permissions needed to reuse them. For a critical workflow, test an alternative on a planned cadence and after material changes. The result should show what remains compatible and which assumptions need to be revisited.
Keep a scorecard that separates tested from aspirational: supported task contracts, evaluated providers, untested features and expected effort. Repeat checks when models, tools or requirements change. Start with one workflow and a specific reason to preserve choice, run the smallest realistic transition and record what transferred unchanged and what had to be rebuilt. That gives the sponsor a tested path with known costs, which is what a risk committee actually needs.