What Is the Best First Step in Automating a DA Workflow?

Most delegated authority teams are eventually told to "automate bordereaux processing" with little further direction.

The instruction sounds simple, but the workflow behind it is not. Bordereaux arrive from dozens of coverholders, in dozens of formats, on different cycles, with different levels of data quality. Faced with that variety, teams often default to picking a technology first and deciding where to apply it later.

That sequence is usually backwards. The organisations that get value from automation quickly are the ones that choose the right starting point in their workflow before they choose any tool at all.

This article sets out how to identify that starting point, what traditional project sequencing already tells us about doing this well, and where AI genuinely changes what is achievable at the pilot stage.

Why the First Step Matters More Than the Technology

When an automation initiative stalls, the cause is rarely the technology itself. It is usually the scope.

Teams are often tempted to prove the value of automation by tackling the hardest problem in their workflow first: the coverholder with the messiest bordereaux, the class of business with the most exceptions, or the process with the most manual steps. The logic seems sound. If automation can handle the worst case, it can handle anything.

In practice, this approach tends to slow initiatives down. Complex cases take longer to scope, produce less reliable early results, and make it harder to demonstrate clear success to stakeholders who are watching for evidence the initiative is working.

The choice of where to start determines how quickly an organisation can show measurable value, build internal confidence and learn what needs to change before scaling further. That decision has to be made before any technology is selected.

Traditional Approaches to Sequencing DA Automation

Long before AI entered the picture, operations teams used established methods to decide where to focus process improvement efforts. These methods remain sound and should not be skipped simply because AI is involved.

Common approaches include:

• Process mapping. Documenting each step in the current bordereaux workflow to identify where time and effort are actually being spent, rather than relying on assumption.
• Volume and frequency analysis. Prioritising processes that run often and at scale, since improvements there compound quickly across many cycles.
• Risk assessment. Identifying which processes carry the least operational or regulatory risk if something goes wrong during a pilot, so that early mistakes are contained.
• Stakeholder consultation. Speaking with underwriters, oversight teams and operations staff to understand where manual effort is genuinely burdensome, as opposed to where it merely appears that way from outside the team.
• Phased rollout planning. Treating the first process as a deliberate pilot with a defined scope and end point, rather than an open-ended project that quietly expands.

These methods take time, but they consistently produce a defensible answer to the question "where do we start", regardless of which technology is eventually used.

Where AI Helps Identify and Execute the First Step

Once a candidate process has been identified through the methods above, AI can change what is realistically achievable within the pilot itself.

Traditionally, even a well-chosen pilot required significant upfront configuration: building a mapping template for the specific bordereau format, agreeing field definitions, and handling any variation in layout largely by hand. This setup work could take weeks before any data was actually processed.

AI-based approaches can interpret bordereaux based on the meaning and context of the data, rather than relying solely on a fixed template. This means a pilot can often begin sooner, and can accommodate more format variation from the outset than manual mapping alone would allow. It also means the pilot is more representative of the wider coverholder population, rather than being tailored so tightly to one format that it tells you little about scaling further.

This does not remove the need for the sequencing decisions described above. AI changes how quickly and how well the chosen first step can be executed, not which first step should be chosen. Oversight teams should still review outputs, agree exception-handling rules and sign off results before the pilot is considered a success.

Operational Considerations When Starting the Pilot

A handful of practical decisions determine whether a promising first step turns into a genuine template for wider rollout.

• Define success measures before starting, such as a target reduction in manual processing time or a specific data accuracy threshold, so that success is judged against something agreed in advance rather than debated afterwards.
• Keep oversight and sign-off responsibilities clearly defined, even for a small pilot. Automation changes how data is interpreted, not who is accountable for the results.
• Set a realistic timeframe. A first pilot should typically be measured in weeks, not months. An open-ended pilot with no defined end point tends to lose momentum and stakeholder attention.
• Involve the right people early, including underwriters and oversight staff who will need to trust the output, not just the operations team running the pilot.
• Plan the exit criteria. Decide in advance what result would justify expanding the approach to further coverholders, and what result would mean revisiting the scope or approach instead.

Example

A London market managing agent oversees several binder agreements across marine cargo and agricultural risk. Leadership asks the DA operations team to "automate bordereaux processing" but gives no further direction.

The team reviews their coverholders and identifies one MGA that submits a monthly bordereau in a consistent format with generally good data quality. They select this bordereau as the pilot, define success as reducing manual processing time by a set amount while maintaining data accuracy, and use an AI-assisted tool to interpret and map the bordereau, with the oversight team reviewing outputs before sign-off.

Within a few weeks, the team has a working, measurable pilot that demonstrates time savings and maintains oversight standards. This becomes the template used to prioritise and expand automation to other, less standardised coverholders over time.

FAQs

• Should we automate our most complex bordereau first to get the hardest problem out of the way?

  Starting with the most complex bordereau usually increases risk and delays results, since messy formats and exceptions take longer to scope and produce less reliable early evidence of success. A simpler, high-volume starting point builds confidence more quickly and creates a reusable template that can then be applied to harder cases.

• How long should a first automation pilot take?

  A first pilot should typically be measured in weeks rather than months. Setting a defined end point and success criteria in advance helps keep the scope contained and gives stakeholders a clear point at which to assess results and decide on next steps.

• Do we need AI to take this first step?

  No. Traditional manual mapping and validation can support a first pilot, though it typically takes longer to set up. AI can meaningfully speed up the interpretation of varied bordereaux formats and reduce upfront configuration work, but it is an accelerant rather than a prerequisite for getting started.