Zapier, the most connected AI orchestration platform, released findings from its latest enterprise survey on AI vendor lock-in. The results paint a clear picture: AI has become so deeply embedded in how companies operate that most can’t walk away from their primary vendor without serious consequences. Among 542 U.S. enterprise executives surveyed, nearly three-quarters (74%) say losing their AI vendor would disrupt day-to-day operations or leave them unable to function.

The survey also uncovered a significant confidence gap around migration. While 89% of leaders believe they could switch AI vendors within a month, the majority of those who have attempted it (58%) say the process either failed or required far more effort than expected.

“Companies adopted AI to move faster, and it worked. But that speed created a new kind of dependency that most teams didn’t plan for,” said Emily Mabie, Senior AI Automation Engineer at Zapier. “The organizations in the best position right now aren’t the ones avoiding vendor commitments. They’re the ones that built flexibility into their stack from the start, so switching a model or a provider doesn’t mean rebuilding everything around it.”

Key Findings:

• Most enterprises can’t cleanly walk away from their AI vendor: Only 6% of leaders say they could stop using their primary AI vendor without any disruption. For 47%, at least one key business function would break. Another 27% say they rely on AI for most or all of their operations.
• Migration confidence doesn’t match migration reality: 89% of executives believe they could switch AI vendors within four weeks. But among the 66% who have attempted a migration, 58% say it failed outright or took significantly more effort than anticipated.
• Lock-in concerns are widespread: 81% of enterprise leaders express concern about dependency on specific AI vendors, with 29% saying they’re very concerned. Data migration challenges (46%) and overdependence on a single vendor (46%) rank as the top risks.
• Enterprises are actively building safeguards: 47% now have dedicated internal teams to evaluate and manage AI vendors. 44% use multiple AI vendors simultaneously, 42% maintain contingency plans, and more than a third (35%) incorporate open-source alternatives to reduce dependency.

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The Migration Confidence Gap

Leaders are overwhelmingly confident they could move to a new AI provider quickly. 41% estimate the switch would take just 2–5 business days, and 13% say they could do it in a single day. That optimism starts to crack once teams go through the process. Two-thirds of respondents have attempted a migration between AI platforms, and among that group, only 42% describe the transition as smooth.

The disconnect comes down to what migration involves. By the time a company considers switching vendors, AI has already been woven into internal processes, connected to other systems, and tuned to specific workflows. Swapping the vendor means untangling those dependencies, not just changing a billing plan.

What Enterprises Want from Their AI Vendors

When asked what would ease lock-in concerns, transparency came out on top. 30% of leaders say clearer pricing, features, and contract terms would make the biggest difference. Another 26% want easier data transfers between vendors, and 24% want more flexible pricing models. The theme across all these responses is the same: Enterprises want AI deeply embedded in their operations, but they also want the freedom to change direction when they need to.

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How Organizations Are Responding

Rather than waiting for lock-in to become a crisis, many enterprises are taking proactive steps. Beyond dedicating internal teams and diversifying vendors, 34% are designing around data portability and standard APIs, while 33% use third-party integration or orchestration tools to coordinate workflows across systems. Some are going further still, with 31% building proprietary AI tools and 29% negotiating shorter, more flexible contracts to maintain leverage.

“The best time to plan for flexibility is before you go all-in on a single vendor. The second-best time is now,” said Mabie. “The companies seeing the strongest results are the ones that separate their workflow logic from any one AI model. That way, when something changes, they can adapt without starting from scratch.”