The Blind Spot
AI platforms can become embedded in a business much faster than traditional software. What begins as a useful tool can soon sit inside workflows, customer experiences, internal knowledge systems, agents, APIs, and automated decisions. By the time leadership realizes how dependent the business has become, switching may no longer be simple.
That risk is already showing up. IBM's 2026 research found that 71% of surveyed executives said changing their primary AI vendor or model would be difficult, while 91% said they did not fully understand their dependencies across vendors, models, and infrastructure.
The problem is not vendor dependence. It is discovering the dependence only after you need to leave.
The Mechanics
Before expanding an AI platform, test it across three areas: Value, Control, and Exit.
Value: Is the platform producing a measurable business outcome that justifies its cost?
Control: Can you manage your data, permissions, usage, integrations, and spending without relying entirely on the vendor?
Exit: Could you move critical data, workflows, or applications elsewhere if pricing changed, a model was discontinued, service deteriorated, or the vendor no longer fit the business?
The exit question matters because AI dependencies can change quickly. Pricing, APIs, model availability, licensing, privacy terms, and geographic access can all shift after a system has already become operationally important.
The fix: know what would break if the vendor changed tomorrow—and decide whether the value today justifies that dependency.
The Executive Takeaway
Vendor lock-in is not automatically a reason to reject a platform. Businesses already depend on cloud providers, payment systems, databases, and other critical vendors. A strong AI platform may be worth significant dependency if it creates enough value.
The executive issue is whether that dependency is deliberate. A company should know which AI systems are difficult to replace, which workflows depend on them, what data would need to move, and what the fallback plan would be if conditions changed.
The best AI platform is not necessarily the one you can replace tomorrow. It is the one whose value is worth the dependency—and whose dependency you actually understand.
Before the next renewal, expansion, or enterprise rollout, ask one question: If we had to leave this platform, what would it cost us in money, time, and disruption?
If nobody knows the answer, the vendor may already have more leverage than you realize.
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