Build an AI fallback plan around the business process—not just the model or provider. Start by identifying what each AI-supported workflow does, how disruption affects the organization, and how long work can safely pause. Then define a tested response for outages, slow or throttled service, poor-quality outputs, and safety or security incidents. Depending on the workflow, that response may be a vetted alternate model, a limited degraded mode, a human-led process, or a deliberate shutdown.
1. Identify and rank the workflows that depend on AI
Begin with a business impact analysis (BIA). Map each AI-supported process to the business function it serves, the people responsible for it, and the consequences if it stops or produces unusable work. The U.S. Centers for Medicare & Medicaid Services (CMS) describes a BIA as a way to correlate system components with the business processes they support, assess the impact of unavailability, identify resource needs, and set recovery priorities in its Information System Contingency Plan (ISCP).
Make the inventory specific enough to use during an incident. Record the AI function, business and technical owners, users, dependencies, impact of interruption, and the maximum tolerable interruption. Include dependencies beyond the model itself: provider and cloud services, model or version, identity and access systems, data sources, integrations, and staff.
| Workflow | AI function | Impact if unavailable | Dependencies | Owners | Maximum tolerable interruption |
|---|---|---|---|---|---|
| Fill in per organization | What the model or service does | Customer, employee, revenue, compliance, or operational consequences | Provider, model/version, cloud, identity, data, integrations, and people | Named business owner and technical owner | Set from the BIA |
Rank workflows by business impact, not by how prominent or technically sophisticated the AI system is. A customer-facing assistant, an internal drafting tool, and an AI-assisted safety decision may have very different tolerance for delay, error, or manual substitution.
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2. Set recovery objectives from business impact
Use the BIA to establish workflow-specific recovery objectives rather than adopting a generic downtime target. CMS identifies recovery time objective (RTO), recovery point objective (RPO), maximum tolerable downtime (MTD), and work recovery time (WRT) among the metrics addressed by a BIA in its ISCP guidance.
- RTO: the maximum time a system resource can remain unavailable before unacceptable impacts arise.
- RPO: the point in time to which data must be recovered after an outage; it helps define how much data loss the process can tolerate.
- MTD: the maximum period the business can tolerate disruption before the impact becomes unacceptable.
- WRT: the additional time needed to resume normal work after the system has been recovered, such as reconciling queued requests or validating records.
Set objectives for the business workflow, including the time needed to switch to a fallback and the work required to return to normal. The right values depend on the process, obligations, data, and impact analysis; the CMS material is federal contingency-planning guidance, not a universal target for every organization.
3. Choose what the workflow does when AI is unavailable or unsafe
Define the response by failure condition. A provider outage is not the same as an output-quality problem or a suspected security incident. AWS guidance for AI systems and financial-services operations discusses responses to service failures, hallucinations, inappropriate outputs, security events, bias, data leakage, prompt injection, and regulatory violations.
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Use an alternate model or provider only after assessing it
Failover can preserve a path to service, but it does not automatically preserve output quality, privacy, compliance, or availability. Assess the alternate model’s capabilities, data-handling rules, safety controls, and workflow-specific acceptance criteria before routing work to it. AWS’s financial-services guidance describes circuit breakers that can switch to alternative models or fallback logic when thresholds are breached; that is a design option, not certification of a particular failover setup.
Offer a safe degraded mode
Decide which essential functions can continue with reduced capability and which must be unavailable. For example, a workflow might accept requests but defer AI-generated recommendations, or provide a limited status response instead of a full automated result. Set acceptable degraded service levels and tell affected users what is impaired.
Prepare a human-led process
Write down how staff will take over essential work: where requests go, who handles them, what instructions and approvals apply, what capacity is available, and how completed work is recorded for later reconciliation. AWS recommends safe fallback systems and staff to maintain essential operations when business-critical AI is offline.
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Pause, roll back, or shut down when needed
For unsafe or high-risk behavior, define who can disable the affected feature, move it to a safe mode, or roll back to a stable version. A deliberate shutdown is a valid continuity decision when continuing or rerouting work would create unacceptable risk.
Compare fallback choices against the same practical criteria before selecting one:
- Time to activate and maximum capacity
- Output quality and how it will be validated
- Safety, security, privacy, and data-handling constraints
- Reliance on the same provider or other shared dependencies
- Customer impact and staff readiness
- Recovery, reconciliation, and operating cost
4. Define detection, activation, and communications
Specify how the team will recognize that a fallback is needed. Monitor both availability and whether outputs remain within agreed quality and safety bounds. Set measurable thresholds, identify alert recipients, name who has authority to activate the plan, and document escalation routes. AWS recommends mapping business outcomes and metrics to workloads and support teams, setting baseline alert thresholds, and documenting communication channels in its FSIOPS6 guidance on cloud provider service events.
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Document a primary and secondary communication path in case the usual channel is unavailable. Assign who will notify staff, affected users, customers, and other stakeholders; explain what information will be shared and how often updates will be sent. AWS recommends stakeholder updates on an established cadence during provider events, as well as a post-event operational review.
Keep the runbook usable under pressure. For each incident, record:
- The observed problem, time detected, and affected workflow and users
- Provider status checked and relevant availability or quality signals
- The active fallback mode and the decision-maker who authorized it
- Communications sent, recipients, and time of each update
- Actions taken, unresolved risks, and the steps required to restore normal operation
5. Recover service, validate it, and revise the plan
Define what must happen before work returns to the AI-enabled path. CMS’s ISCP structure includes recovery procedures, assigned responsibilities, and validation of recovered data and system functionality. Depending on the workflow, recovery may also require reviewing queued work, reconciling records created manually, checking for duplicates, or confirming that quality and safety checks are operating.
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Test the procedures rather than assuming that a documented route will work. Exercise the relevant outage, degraded-mode, human-handoff, and restoration steps with the people who would use them. CMS’s template calls for annual BIA review in its federal context; it is an example of a formal review cadence, not a universal requirement for every organization or AI workload. Update the plan when the workflow, provider, model, dependencies, owners, or business impact changes.
6. Treat governance guidance as context, not a substitute for the plan
NIST describes its AI Risk Management Framework as voluntary guidance. Its framework page says AI RMF 1.0 was released January 26, 2023, the Generative AI Profile (NIST-AI-600-1) was released July 26, 2024, and AI RMF 1.0 is being revised. These materials can inform risk-management work, but they do not determine your workflow’s recovery targets or fallback architecture.
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