Your AI Strategy Needs a Tested Way to Operate Without It

Treat dependence as an operating risk

As you finalize 2027 plans, ask a question that productivity forecasts often leave unanswered: what happens to the business when an AI-supported workflow has to stop?

The interruption may be technical. It may also be a confidentiality concern, unreliable output or a supplier issue that makes continued use unacceptable. A system can remain available while becoming unsuitable for the work.

Consider a finance team using AI to prepare first drafts of cash-flow commentary. If the output becomes unreliable during a reporting cycle, can the team complete the work from source records? Who can identify the errors? How much additional time would the review require?

Those are questions about operating capability, not just technology recovery. The NIST AI Risk Management Framework treats risk management as a lifecycle responsibility and includes safe decommissioning within its management outcomes.^[1] The executive implication is straightforward: stopping a system should not mean abandoning the business obligation it supports.

For each critical workflow, identify the service that must continue, the people who can sustain it and the acceptable period of reduced performance.

Separate a fallback document from real capacity

A procedure that says “return to manual processing” proves very little. The people may no longer have the time, access or practiced expertise to do it.

This matters when productivity gains are built into staffing and service commitments. If a team absorbs more work because AI reduces preparation time, the old process may no longer be a viable fallback. Restoring it could create a backlog that exceeds customer commitments or reporting deadlines.

Require each critical workflow owner to answer three concrete questions:

• Capability: Who can perform and check the essential work without the system?
• Capacity: What volume can that group handle, and for how long?
• Priority: Which obligations continue first, and which work is deliberately deferred?

Do not assume every activity needs full manual duplication. Some can pause. Others can continue at lower volume or with narrower scope. The decision should reflect business impact, not the convenience of the technology team.

NIST’s contingency planning guidance uses business impact analysis to establish recovery priorities and resource requirements.^[2] Apply that discipline to AI-dependent work. Define an acceptable reduced service before a disruption forces someone to improvise one.

Rehearse the loss of trust, not just an outage

An outage makes the problem visible. Plausible but unreliable output is harder to manage. Teams may continue using it because deadlines remain unchanged and no one has clear authority to stop.

NIST’s Generative AI Profile identifies confabulation and human-AI configuration risks, including overreliance.^[3] A continuity exercise should therefore test judgment as well as access.

Choose one consequential workflow. In a controlled exercise, tell the team that its AI output cannot be used for a defined period. Do not disrupt a live obligation. Ask participants to complete a representative task using approved source material and the documented fallback.

Observe what actually happens. Can they find the underlying records? Do they know which checks are essential? Can a reviewer distinguish supported conclusions from assumptions? Does anyone know who can authorize a temporary reduction in service?

Record elapsed time, unfinished work, review effort and unresolved decisions. Use the findings to improve the process, not to label individuals. An exercise that exposes a capacity gap is useful evidence. A polished policy that hides one is not.

Preserve the expertise your fallback requires

Human oversight is weak if reviewers no longer understand the work well enough to challenge it. If AI handles routine preparation, leaders must decide how people will retain the knowledge that preparation previously developed.

That may mean periodic work from source records, rotating responsibility for exception handling or having less-experienced staff explain their reasoning before reviewing system output. The aim is not to preserve every old task. It is to preserve the expertise needed to detect failure and recover.

Before approving next year’s service commitments, review the exercise findings alongside expected productivity gains. If the fallback cannot meet essential obligations, adjust the workload, reserve capacity or narrow the dependency.

The board does not need every procedural detail. It needs clarity on which critical obligations depend on AI, what level of service remains possible without it and where recovery has been tested. That makes continuity a business decision rather than an unsupported assumption.