Where should organisations start with AI bordereaux processing?

“Automate bordereaux” is too broad to test. It combines different submissions, business purposes, controls, systems and sources of effort.

Starting with a product demonstration can make the available technology define the problem. A credible initiative begins with a specific operational outcome and evidence about the current process.

That makes it possible to decide whether AI is suitable, what must remain controlled and whether a pilot has produced enough evidence to continue.

Begin with a bounded data-flow problem

Choose one workflow with a clear start and end. For example: receive monthly premium bordereaux from a defined group of coverholders and produce validated transaction data ready for a named downstream process.

Identify the business owner, users, control owners and people who perform the work. Map receipt, triage, mapping, validation, correction, reconciliation, approval and hand-off. Include waiting, queries and rework.

Establish a representative baseline: volumes, formats, active effort, elapsed time, exception types, correction rate, backlog, quality and control outcomes. Separate stable submissions from changed or difficult cases.

The baseline should reveal the actual constraint. Manual effort may come from variable field meaning, but it may instead come from missing source data, duplicated approval, a weak integration or an unclear target definition.

Define the intended improvement in operational terms. Faster model output is not an outcome unless it contributes to accepted data, capacity, control or service.

Compare AI with simpler interventions

Improve the source where recurring defects can be prevented. Use reporting standards, clearer guidance or coverholder feedback where they reduce avoidable variation.

Stable formats may suit approved templates. Exact validation, arithmetic, reference lookups, receipt tracking and deadline reminders may be better handled by deterministic rules and workflow automation. A direct interface may remove rekeying more effectively than interpreting another spreadsheet.

Compare these options with AI and with a combined design. Assess their ability to meet the outcome, evidence requirements, cost, implementation effort, support need and operational risk.

AI becomes a stronger candidate when useful information is present but repeatedly needs contextual interpretation across varied formats or terminology. It is a weaker answer when the facts are missing or the task is entirely fixed logic.

This comparison prevents the pilot from proving only that AI can do something, rather than that it is the appropriate intervention.

Use AI where interpretation creates the constraint

Before testing, define the target schema, reporting meanings, correct reference outcomes, validation rules, material fields and human-review points. Decide what the AI may propose and what it may never accept without approval.

Select historical submissions that represent intended operating conditions. Include routine formats, layout changes, poor-quality but usable files and important exceptions. An unrealistically clean sample can produce an impressive demonstration that says little about production.

Agree how source data can be used, stored and accessed. Security, privacy, supplier and retention requirements belong in discovery, not after a successful technical test.

Set measures for accuracy, accepted output, human effort, exceptions, turnaround and control performance. Include the costs and work created by review, monitoring and support.

Design a pilot that can disprove the case

Run a bounded pilot alongside the existing process so outputs can be compared without affecting production decisions. Keep the sample, target, component versions, reviewer decisions and errors traceable.

Define evidence gates before results are known. Proceed may require performance across every material segment, acceptable review demand and a viable operating cost. Adjust may mean narrowing scope or strengthening a rule. Stop may be correct if simpler automation performs better, source quality is inadequate or risk cannot be controlled.

Avoid selecting only the easiest coverholder or the largest volume. A first use case should be manageable, valuable and representative enough to test the intended claim. It should also have an engaged owner and qualified reviewers.

If evidence supports controlled operation, plan parallel running, support, monitoring, fallback and change control. Wider rollout is a separate decision. The first pilot earns the right to consider scale; it does not commit the organisation to it.

Example

A hypothetical managing agent begins with a broad ambition to use AI across all delegated data. Discovery isolates premium-bordereaux mapping from five coverholders as a bounded workflow.

The baseline shows that two stable formats already perform well with templates. Three varied formats create repeated mapping effort and enter a representative parallel pilot. The target schema, material fields, reviewer routes and proceed, adjust or stop criteria are agreed before testing.

The final decision compares AI-assisted results with the existing templates and manual process. The organisation improves only the part of the data flow where evidence shows AI adds useful capability.

FAQs

• Should the first pilot use the cleanest coverholder data?

  Use a manageable but representative sample. An unusually clean case may be useful for setup, but it does not test performance across the conditions the live workflow is expected to handle.

• What needs to be defined before testing AI?

  Define scope, baseline, target schema, accepted outcomes, materiality, validation, review, security, measures and the evidence required to proceed, adjust or stop.

• When should an organisation decide not to use AI?

  When a simpler control solves the problem, evidence is insufficient, source defects need upstream correction, risk cannot be controlled or likely benefits do not justify full implementation and operating costs.