Why the useful response to OpenAI’s board crisis was a days-to-switch number
No third-party risk questionnaire could have predicted OpenAI’s November 2023 board crisis. What regulated buyers controlled was the cost of moving away from any one model provider, set by three things: their contracts, their architecture and their fallbacks.
Looking Back revisits one week at a time, from January 2020 to today, and asks what it meant for the banks, health systems and public agencies we serve. The series was written in 2026, with the benefit of hindsight, and each article says which week it looks back on.
The week
On November 17, 2023, OpenAI’s board announced that it had removed Sam Altman as chief executive, saying he had not been consistently candid in his communications with the board. Over the weekend and the following days, most of the company’s employees signed a letter threatening to leave unless the board resigned. Microsoft, OpenAI’s largest investor and cloud provider, said it would hire Altman and colleagues to lead a new AI research group. Late on November 21, OpenAI announced an agreement in principle for Altman to return as chief executive with a reconstituted initial board. The episode played out in public over about five days, in the week of the U.S. Thanksgiving holiday, while thousands of businesses were building products on OpenAI’s models.
By late 2023, banks, health systems and agencies were piloting generative AI, many through OpenAI models reached directly or through Microsoft’s Azure OpenAI Service. For a few days that November it was reasonable to ask whether the company behind a fast-growing set of enterprise deployments would hold together. Services ran normally throughout. The episode still made a point that third-party risk programs had not absorbed: the governance of a model developer was unusual, opaque from outside, and capable of changing over a weekend. No questionnaire would have scored that risk accurately. What a buyer could manage was its exposure, measured as the cost and time of moving a given use to another model. That cost has three components, which together account for it: what the contracts allow, what the architecture allows, and what the operation can do while a move happens.
What the contracts allow
Organizations reached models by several routes: direct API agreements with developers, cloud platforms hosting those models under the cloud provider’s enterprise terms, and software products with AI features built on someone else’s model. The routes carry different continuity profiles. A model hosted in a cloud provider’s environment, under that provider’s agreement, may keep running even if the developer is in turmoil. A direct agreement depends on the developer itself. An embedded feature depends on a software vendor whose own model supplier the customer may not know.
Procurement and legal teams can map each AI use to its route and read the relevant terms on four points: notice before a model is retired or materially changed, data retention and deletion at exit, transition assistance, and whether the contract restricts using another provider’s model for the same function. For embedded AI, the questions go to the software vendor in writing: which model provider it uses, whether it can switch, and how customers will be told. Interagency guidance on third-party risk, finalized by the banking agencies in June 2023, already expected banks to consider concentration and continuity for critical vendors. A model provider becomes one when a use is important to operations, even if the contract is small.
Contracts also shape the price of leaving. Committed-spend agreements with a cloud provider can make a second provider’s model look expensive even when it performs better. Free credits for a pilot can create a dependency before anyone has decided to accept one. Finance should know which AI commitments run past the next two years and what share of each is tied to a single model family.
What the architecture allows
An application built tightly against one provider’s API, prompt conventions and proprietary features is expensive to move. Compare an application with a thin abstraction layer between business logic and the model, and prompts stored as versioned configuration. If its retrieval pipelines also avoid one provider’s embedding format, it can be pointed elsewhere far faster.
The piece most often missing is an evaluation suite: a set of the organization’s own tasks, with expected outputs or scoring rules, that measures whether a model is good enough for a specific use. For a contact center summarization tool, it might be a few hundred real call transcripts, de-identified, with summaries rated by supervisors. For a document classification workflow, it might be a labeled sample with accuracy thresholds set by the business owner. Without a suite, nobody can say whether an alternative model is acceptable, and switching becomes a leap of faith. With one, switching is a test run that takes days.
The suite also earns its keep in ordinary times. Providers update models regularly, and behavior on a specific task can change without notice. Running the suite on each new version catches regressions before customers or caseworkers do. It also gives banks evidence for model risk management reviews, since supervisory model risk guidance such as SR 11-7 can reach generative AI uses that inform decisions.
What the operation can do during a move
Even with good contracts and architecture, a move takes time, and the third component is what happens in the meantime. For low-stakes uses such as drafting assistance, staff can work without the tool for a while. For uses embedded in customer service, claims intake, prior authorization or case management, the organization needs a defined degraded mode: a second model already approved for the use, a reduced feature set, or a return to manual processing with enough staff who still know how.
Continuity planning for AI can follow the logic used for any important business service. Identify which uses would cause harm if they stopped, set a tolerance for how long each can be unavailable, and test the fallback at least once a year. The test has a useful side effect. If a team cannot describe how its process runs without the model, it may be relying on the model in ways nobody has reviewed.
A simple metric ties the three components together. For each important AI use, record the estimated days to move it to an approved alternative, and report the longest figures to the risk committee. The figure will rarely be precise. It shows where an event like November 2023 would hurt.
With hindsight
The crisis ended within days, and OpenAI’s services were not interrupted. Its corporate structure remained under debate, and in October 2025 it completed a recapitalization that made its for-profit arm a public benefit corporation controlled by the nonprofit foundation. The market moved toward multiple models. Major cloud platforms expanded their catalogs from several developers, and in 2025 Microsoft began offering Anthropic’s models alongside OpenAI’s in parts of its Copilot products. Model gateways and evaluation tooling became standard elements of enterprise AI platforms. In practice the main return on portability was commercial: organizations with evaluation suites could adopt better or cheaper models as they appeared. Supplier disruption did return in a different form in 2026, when federal action against an AI developer forced some agencies and contractors to plan an exit, a case a later article in this series examines.