The new AI lock-in

"Models are getting easier to swap. The operating, integration, and governance machinery that surrounds them is much harder to replace. Even as models get easier to swap, the work that surrounds them is not. Developers already move among Claude Code, Codex, Gemini, and local models with less ceremony than vendors would like. At the API layer, substitution is getting easier, too. Not effortless or free, but easier than replacing the workflow machinery around the model."

"That is the part enterprise buyers may be underestimating. Yes, open standards, better APIs, and improving model parity are weakening one form of lock-in, but they are strengthening another. The model call is getting easier to replace; the surrounding workflow, governance, and operating model are not. Lock-in didn't disappear. It moved"

"Greyhound Research's Sanchit Vir Gogia puts it this way, "Lock-in is not going away. It is relocating. At the model level, substitution is becoming easier." He continues, "At the orchestration level, however, substitution remains difficult. Once your workflows, controls, identity layers, and governance structures are built around a particular system, changing that system is not a small task.""

"Remember MIT's NANDA initiative report, which suggested that 95% of enterprise generative AI pilots fail to deliver measurable business impact? The number has been contested on methodology, but even the more optimistic counter-readings put the gap between AI investment and AI value capture in painful territory. Most failures aren't about model capability, but rather operational fit. The tools don't learn the workflow, don't sit inside the approval path, and don't carry the right permissions."

Model calls are increasingly easy to swap across providers and local systems, with developers already moving among multiple offerings. API-level substitution is also improving, though it still requires effort. Lock-in is shifting from the model itself to the surrounding orchestration layer. Workflows, controls, identity layers, and governance structures built around a specific system are hard to change once established. Enterprise generative AI pilots often fail to deliver measurable business impact due to operational fit rather than model capability. Tools may not integrate into approval paths, may not learn existing workflows, and may not carry the required permissions, preventing successful deployment in real organizations.

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