Enterprises should be able to switch AI models without having to rebuild their workflows, lose their institutional knowledge, or surrender the unique intelligence that sets them apart. While discussions about AI strategies often focus on identifying the "best" model, this focus can be misleading. New models are frequently released, each offering new capabilities and shifting the competitive landscape. This constant change can lead to confusion and distract organizations from a more critical concern: Can their AI operations continue to function if the model they rely on suddenly becomes unavailable or changes significantly? Every enterprise should have an exit strategy for any single AI model it uses. This doesn’t mean moving away from cutting-edge models, which will continue to be essential. Instead, the goal is to ensure that workflows, intellectual property, and institutional knowledge are not tied to a single model or provider. As AI models become more like infrastructure—similar to cloud computing—organizations need to focus on building applications, processes, and proprietary knowledge on top of these models rather than relying on the model itself for business logic or decision-making. In healthcare, for example, a model might be able to summarize a medical record or interpret a policy, but it doesn’t inherently understand how specific health plans apply those policies or when a case should be escalated. This domain-specific knowledge is the organization's own intelligence, not the model’s. While general-purpose models can handle about 70% of a task—such as extracting information, classifying documents, or answering questions—the remaining 30% is crucial for trust and reliability in real-world use. This final part requires domain-specific terminology, internal policies, and traceable evidence to ensure decisions are accurate and can be audited. Organizations must also consider the risks of depending too heavily on one model, especially as AI moves from experimental use to real-world production. A provider might update a model, changing how it structures information or responds to instructions, which could affect outcomes. Additionally, changes in pricing, availability, or the discontinuation of a model can disrupt operations. By separating model-specific tools from the enterprise's own knowledge and governance systems, organizations can maintain control over their intelligence and avoid the need to rebuild workflows from scratch when switching models. An effective AI exit strategy ensures that critical assets—such as proprietary data, policies, workflows, and evaluation benchmarks—are owned, governed, and portable. These assets form the organization’s intelligence layer, which defines how it works and makes decisions. When these elements are embedded in a specific model or platform, the organization risks losing control over its own intelligence. Maintaining a clear separation between models and enterprise systems also helps protect human expertise, as interactions between experts and AI systems generate valuable insights that should strengthen the organization, not disappear into a provider’s system. Leaders should ask a simple but critical question: What would we lose if this model became unavailable tomorrow? Addressing these risks doesn’t require a complete rebuild. Enterprises can begin by separating business logic from model calls, creating standardized interfaces, and maintaining model-independent evaluation data. Testing model interchangeability before a change is needed can also reveal hidden dependencies and provide valuable insights into performance and cost. Ultimately, the purpose of an AI exit strategy is not to avoid innovation, but to ensure flexibility, resilience, and long-term ownership of enterprise intelligence.