How much manual work can AI remove?

Manual bordereaux work is rarely one activity. It includes receiving files, finding the data, interpreting headings, mapping fields, correcting formats, applying checks, reconciling totals, investigating exceptions and obtaining sign-off.

AI may reduce several of those tasks, but automating a step can also create new review or support work elsewhere. A percentage quoted without a workflow boundary says little about the capacity that an organisation will actually release.

The useful question is how much net human effort changes for accepted, usable outputs.

Manual work is spread across the full data flow

Start by observing the present workflow. Record the activity, role, elapsed handling time, waiting time, volume and outcome. Separate routine submissions from changed formats, poor-quality files and material exceptions.

This exposes where work really occurs. A team may spend little time entering data but substantial time understanding a new worksheet, querying missing fields or correcting output after a failed load.

The boundary should run from receipt to an agreed accepted outcome, not stop when an AI component returns a result. Otherwise review, correction, resubmission and reconciliation disappear from the estimate.

Case mix matters as much as average effort. Stable high-volume bordereaux may support greater automation than unusual low-volume submissions, while a small number of difficult cases may consume a large share of specialist time.

Rules and workflow automation already remove some effort

Not every repeatable task needs AI. File naming, receipt logging, exact code validation, arithmetic, deadline reminders and routing based on known conditions are often better handled by deterministic rules or conventional workflow automation.

Templates also remain efficient for stable formats. A tested mapping can process a known workbook with strong predictability. Replacing it solely because AI is available may add cost without improving the outcome.

The baseline should identify these existing controls so the benefit is not credited to AI twice. It should also show where rigid templates create maintenance demand when layouts change or where manual interpretation remains the bottleneck.

This task-level view supports a mixed design: simple automation for fixed logic, AI for variable interpretation, and human review for uncertainty and judgement.

AI can reduce repetitive interpretation

AI can help classify incoming files, detect tables and record boundaries, propose field mappings, standardise supported formats, compare descriptions with approved concepts and prepare exceptions with relevant source evidence.

It can also summarise validation failures and group similar issues, reducing the time reviewers spend locating context. These activities are strongest where the work is repetitive but the presentation varies.

People still need to approve ambiguous mappings, establish the meaning of incomplete fields, investigate material inconsistencies and decide whether data is fit for use. Contract interpretation, underwriting or claims judgement, authority decisions and final accountability do not become AI responsibilities.

An exception-led model changes the shape of work. It may remove routine touches while concentrating human effort on harder cases. Teams therefore need suitable queues, skills and escalation routes rather than an assumption that every processed record is labour free.

Measure net effort on accepted outputs

Compare the old and new process over representative periods and segments. Useful measures include total human minutes per accepted bordereau or record, percentage accepted without correction, review time, exception rate, rework, backlog and time to trusted downstream availability.

Include data preparation, quality assurance, monitoring, support and change maintenance. During a pilot, reviewers may duplicate checks deliberately to build evidence. Report that separately, but do not pretend it consumes no capacity.

Volume growth can also mislead. Total hours may rise while effort per accepted output falls. Conversely, a high processing count can hide an expanding exception queue.

Finally, record where released time goes. Repeatable hours may become additional throughput, reduced backlog, better coverholder oversight or less external spend. Scattered theoretical minutes do not automatically become cash savings or removable roles.

The defensible answer is therefore local and measured: which tasks changed, for which cases, with what control outcome and what usable capacity followed.

Example

A hypothetical managing agent measures its monthly premium-bordereaux process across stable, recently changed and complex coverholder submissions.

An AI-assisted pilot reduces file classification and mapping effort for stable and changed layouts. During the first cycles, review time increases for complex submissions because analysts are checking unfamiliar suggestions and refining exception criteria.

The benefits analyst reports net human minutes per accepted bordereau for each segment, including review and rework. Once the evidence stabilises, the operations manager assigns the repeatable capacity released from routine cases to an aged exception backlog. No headcount saving is claimed from theoretical processing time.

FAQs

• Can AI make bordereaux processing fully straight-through?

  Some low-risk, well-understood cases may be accepted automatically under approved controls. Material fields, ambiguity, validation failures and new conditions still need defined review and escalation.

• Should time saved be reported as a headcount saving?

  No. First demonstrate repeatable net time after review and support effort, then show whether it becomes throughput, backlog reduction, stronger oversight, avoided cost or another evidenced outcome.

• What is the best baseline for manual effort?

  Use observed activity and outcomes across representative volumes, formats and complexity. Measure from receipt to accepted output and include correction, exceptions and reconciliation.