Why every enterprise needs an AI model exit strategy

Enterprises must develop an exit strategy for AI models to ensure continued operation in the event of model changes or unavailability. This strategy should not lead to abandoning AI technology, but rather protect the organization's workflows, intellectual property, and institutional intelligence from becoming overly reliant on a single model or provider.

As leading foundation models become increasingly capable, their capabilities are converging, meaning that features once unique to one provider are now available from multiple providers within weeks or even days. Just as cloud computing's infrastructure became essential but not a lasting competitive advantage, AI is following a similar trajectory.

Enterprise AI should view models as components of a larger architecture, rather than the repository for business logic, operational knowledge, or proprietary processes. In healthcare, for example, a model may be able to summarize clinical records or interpret policy documents, but it does not inherently understand how a specific health plan applies that policy, when escalation is necessary, what evidence a clinician needs to review, or how decisions must be documented for audits.

The "70/30" reality for enterprise AI refers to the fact that general-purpose models can often handle the first 70% of a task, such as extracting information, classifying documents, producing summaries, answering questions, and performing broad reasoning. However, the remaining 30% is critical for determining whether an AI system is trustworthy in production.

This includes domain terminology, enterprise policies, specialized logic, consistent outputs, traceable evidence, evaluation against known standards, and clear escalation to human experts. Healthcare organizations should combine specialized models for clinical and administrative tasks with frontier models to leverage their broader capabilities while maintaining control over the final 30% of intelligence.

When models are integrated into production, dependence on a single model can become apparent when a new version changes information structure, response style, or uncertainty expression. Additionally, commercial or operational changes, such as pricing increases or model discontinuation, can further impact production workflows. By separating models from enterprises and maintaining ownership of critical assets like proprietary data, prompts, policies, decision logic, workflow definitions, evaluation datasets, and human feedback, organizations can ensure that changing models does not disrupt their existing intelligence or require extensive reconstruction of business logic, workflows, and operational knowledge.