Vendor Lock-In & Platform Risk

AI vendor lock-in can be more severe than traditional software lock-in because the dependencies run deeper. Your data formats, model integrations, prompt engineering, workflow automation, and team expertise all become tied to a specific platform. Switching costs aren't just financial - they include the time to rebuild integrations, retrain models on new platforms, and reskill your team. Platform risk is particularly acute with AI because the market is consolidating rapidly, pricing models are still evolving, and vendors regularly change terms, deprecate features, or get acquired. Mitigating these risks doesn't mean avoiding vendors entirely - that's impractical for most organisations. It means being deliberate about where you accept lock-in and where you insist on portability. Use open standards and formats where they exist. Maintain ownership of your data and any custom models trained on it. Build abstraction layers where practical so you can swap underlying providers. And diversify your vendor dependencies for critical capabilities, even if it adds some complexity, so that no single vendor decision can hold your business hostage.