CIOs Anchor AI Resilience In Answer Quality As Provider Outages Mount

An approved second AI provider and a usable fallback are not the same thing. Real readiness means testing that the whole business process keeps running, not just checking that a backup endpoint answers. And an outage is only the obvious failure. A provider can stay up and still break you: it retires the model you tuned against, cuts the compute behind it, or ships a new version that shifts latency and behavior. The API keeps returning success while the actual output falls apart.

Denis Kondratev is Chief Technology Officer of the HR Tech Stream at MTS Group, where his teams run hiring, onboarding, and HR reporting systems for a workforce of more than 64,000. He spent two decades building enterprise systems for large operators and banks, and now also runs AI research through his ventures NeuralVM and Smena.AI, the latter testing whether many small distributed models can outperform a single large one. He approaches provider resilience the way a continuity engineer approaches any critical system.

"For every AI process you define a recovery time objective, as in classic disaster recovery," Kondratev said. "The difference is that you are not restoring a server. You are restoring answer quality." That reframing changes what a resilience plan has to protect. The target isn't a live endpoint but the level of output the business depends on, which means the plan has to survive a provider that stays online while its quality drifts.

• When a provider fails without failing: The clean-outage scenario is the easy one to picture and the least likely to be the problem. A provider can deprecate the exact model a workflow was built around, pull back the compute allocated to it after a new release, or change default inference settings in ways that surface as rare but costly differences in output. The industry has already watched a capable model pulled with little notice under a government directive, and platform disruption days have climbed sharply across the major providers this year. "A provider may keep operating but discontinue access to an older model or cut the compute behind it. The API will still respond, but quality, latency, or behavior may change, and for the business, that is also a failure," Kondratev said.

• Test the fallback before you need it: A backup provider earns the name only after it clears the same checks as the primary, and the sequencing is the whole point. "If you only start testing it after an incident, the primary provider will probably recover sooner." So he runs the fallback through the levels in order, safest first. "The first level is selective traffic mirroring: some real production requests are duplicated to the fallback provider, but the user still receives the response from the primary one. The responses are then compared offline. This gives a realistic view of quality and latency with zero risk," said Kondratev. "You do not need to duplicate the entire flow. A representative sample is enough, and no one wants to pay for inference twice." Offline replay of past production scenarios comes next, then controlled failover drills that take the primary down on purpose to see whether the automation holds. The check that gets skipped most is load testing, which measures the capacity behind your service tier rather than the provider in the abstract. "A tier sized for normal traffic may simply fail under production load." Only once those levels pass should live traffic move, and for high-cost systems it shifts over gradually, with an immediate path back to the primary.

• Most of the rebuild is the prompts: Swapping providers while keeping the same model rarely breaks much, since connector contracts don't depend on who serves the inference. Moving to a different model is where the work concentrates. For a product with large prompts and a well-isolated integration layer, Kondratev estimates a rough 95/5 split between prompt work and everything else, with most of the larger share going into quality retesting rather than plumbing. The layer around the model tends to survive the change; the timeline is set by how mature the testing is. "If automated runs on golden datasets, prompt versioning and other supporting processes are already in place, changing the model can fit into days or weeks," Kondratev said. Without them, quality gets judged by hand and the timeline slides into months.

A larger provider count sounds like more governance overhead, and Kondratev argues the opposite is often true. The load a governance team carries doesn't track the number of providers on the list. It tracks the number of integrations nobody owns, and those are what a single-provider policy tends to produce.

• Govern the pool, not the provider: When the one approved provider can't do what a team needs, and eventually it can't, the team wires in its own and tells no one. The company keeps a tidy diagram and accumulates a shadow stack of integrations underneath it. Offering several vetted providers up front removes the reason to go around procurement in the first place, which is why a wider list can mean fewer ownerless data paths, not more. Those paths are where the legal exposure sits: without processing terms, storage rules, and security review agreed for each provider in advance, an incident-time switch can route data to another legal entity in another jurisdiction. "Several managed providers can create less complexity than trying to force everyone onto one," Kondratev said.

The tooling market is converging on AI gateways that sit in front of the models and make switching easier, and Kondratev expects most companies to adopt and customize one rather than build the abstraction themselves. The catch is that the gateway becomes a more critical single point of failure than any provider behind it, so for anything critical it needs its own redundancy and monitoring. That is the real work of the next couple of years, and it starts from the business service rather than the tool.

"The task is to move critical AI dependencies from hidden product-level decisions into the company's operational resilience model," Kondratev said. The starting point is the service itself, its criticality, its acceptable degradation, and the cost of recovery, which is the same question disaster recovery has always asked, now pointed at the quality of an answer.