What is the rollback plan if an AI workflow fails?

Delegated authority processing runs on fixed reporting cycles. Monthly bordereaux deadlines, binder obligations and downstream reporting commitments leave little room for delay.

When an organisation introduces an AI component into bordereaux ingestion, mapping or validation, a fair operational question follows: what happens if that component fails, produces output that cannot be trusted, or simply stops working partway through a cycle?

Without a defined rollback plan, that question gets answered under pressure, mid-cycle, by whoever happens to be available. With a rollback plan in place, it gets answered in advance, calmly, as part of normal implementation discipline.

This article explains what a rollback plan should cover, how it compares to failure planning in traditional processing environments, and how to make sure a fallback path is genuinely usable rather than theoretical.

Why rollback planning matters in delegated authority

Bordereaux processing is deadline-driven. Coverholders submit on agreed schedules, managing agents and insurers report onward to Lloyd's or other stakeholders, and binder agreements often specify reporting timeframes that carry contractual weight.

An AI component that fails or produces unreliable output mid-cycle does not remove that deadline. The reporting obligation remains, whether or not the technology is working as expected.

The cost of failure, therefore, is not really about the AI itself. It is about what happens next. An organisation with a rehearsed fallback path can absorb the disruption and still meet its obligations. An organisation without one is forced into improvised manual recovery, often under significant time pressure and without the resourcing that recovery actually needs.

Planning for this in advance is what separates a manageable operational hiccup from a missed reporting deadline.

How traditional implementations handle failure

Rollback planning did not begin with AI. Any operational change involving new software, a new rules engine, or a new outsourced process has always required a fallback position.

In traditional bordereaux processing, this typically means:

• Reverting to spreadsheet-based manual mapping if an automated import tool fails.
• Falling back to a previous version of a rules engine if a new configuration produces errors.
• Keeping manual keying capacity available during the early weeks of a new system's use.
• Defining a named individual or team responsible for deciding when to switch back.

These practices exist because operations teams have long understood that no new system, however well tested, should be trusted with full responsibility for a critical process from day one. The same logic applies directly to AI-assisted workflows. The technology changes; the discipline of planning for its failure does not.

What a rollback plan should cover for an AI workflow

A workable rollback plan for an AI-assisted bordereaux or data workflow should address five things clearly.

Failure triggers. Define, in measurable terms, what counts as a failure requiring fallback. This might include a spike in low-confidence mappings, the AI system becoming unavailable, or validation results falling outside agreed accuracy thresholds.

A tested fallback path. This is usually a manual process or a previous system, kept genuinely operational rather than assumed to still work. If the fallback has not been used or tested in months, it is not a reliable fallback.

Parallel running. During early implementation phases, running the AI workflow alongside the previous process, rather than instead of it, keeps the fallback path exercised and gives the organisation real evidence of AI performance before it becomes the sole method of processing.

Clear escalation and sign-off. A named role or small group, typically DA oversight or operations leadership, should hold authority to invoke rollback. This decision should not default to whoever is on shift when something goes wrong.

Stakeholder communication. Coverholders, brokers or internal reporting teams affected by a fallback should know what is happening and why, particularly if it affects turnaround times during that cycle.

A plan covering these five areas gives an organisation a genuine, actionable route back to continuity, rather than a vague assurance that "something would be worked out."

Operational considerations before go-live

Before any AI-assisted bordereaux workflow goes live, a few practical questions deserve clear answers.

Who owns the decision to invoke rollback, and are they available during the relevant reporting windows? How quickly can the fallback path be actioned, and does the organisation still have the people and capacity it assumes it has? Has the rollback plan actually been tested, rather than just documented? And critically, how do lessons from a rollback event feed back into improving the AI workflow, rather than being treated as a reason to abandon it altogether?

A rollback plan is not a one-off document produced for a steering committee. It is an operational capability that needs to be checked, resourced and occasionally rehearsed, in the same way any other business continuity measure would be.

Example

A Lloyd's managing agent is piloting an AI-assisted mapping tool for monthly bordereaux received from an agricultural risk coverholder. Partway through a reporting cycle, the tool flags an unusually high volume of low-confidence mappings on a bordereaux using a template it has not seen before.

Rather than allowing uncertain data to progress, the pre-agreed rollback plan is triggered. The file is routed to the manual mapping process that ran in parallel during the pilot phase, the oversight team is notified, and the reporting deadline is met using the fallback route while the AI tool's handling of the new template is reviewed separately.

The reporting deadline is met without disruption because the fallback path was tested and readily available. The AI tool is subsequently retrained or adjusted to handle the new template, and the rollback trigger thresholds are reviewed to reduce unnecessary fallbacks in future cycles.

FAQs

• Does having a rollback plan mean the AI workflow is not trustworthy?

  No. Rollback planning is standard practice for any operational change, whether it involves AI, a new rules engine, or a new outsourced process. It reflects sound implementation discipline rather than any specific concern about AI's reliability.

• Who should decide when to invoke a rollback?

  This should be a named role or small group agreed in advance, typically within operational or delegated authority oversight leadership. It should not be an ad hoc decision made under pressure by whoever is available at the time.

• How long should parallel running continue before relying solely on AI?

  This depends on the volume and complexity of the bordereaux involved and the confidence built up over multiple reporting cycles. It should be reviewed against agreed accuracy and reliability thresholds rather than fixed to a set calendar period.