What is the business case for AI in delegated authority?

Every delegated authority operation eventually faces the same question from leadership: is the effort spent processing bordereaux and oversight data proportionate to the value it delivers, and could technology change that equation?

Answering that question well requires more than enthusiasm about AI. It requires a clear view of the operational costs already being incurred, an honest assessment of how traditional approaches address those costs, and a realistic understanding of where AI changes what is achievable.

This article sets out how to think about that business case, grounded in the operational realities of delegated authority rather than general claims about AI's potential.

The operational cost of processing delegated authority data

Delegated authority operations carry a set of well-understood costs, even if they are not always fully quantified.

Bordereaux arrive monthly from coverholders and MGAs in inconsistent formats, requiring skilled staff time to interpret, reformat and validate before the data can be used for underwriting, oversight or regulatory reporting.

The costs typically show up in several places:

• Cycle time: how long it takes from receipt of a bordereau to validated, usable data.
• Headcount: the number of skilled hours committed to formatting and validation rather than analysis or oversight.
• Error rates: the frequency and cost of mistakes found at reconciliation, often discovered later in the process.
• Capacity limits: the ceiling on how much business a fixed operations team can process without a proportional increase in staff.

These costs tend to grow as binder portfolios expand, as more coverholders are onboarded, and as reporting obligations increase. Any credible business case starts by measuring these costs directly, rather than assuming they exist in some generic form.

How organisations have traditionally addressed these costs

Organisations have developed several ways of managing this operational burden, each with genuine strengths and real limitations.

Manual data entry and review remains common. Skilled staff read each bordereau, interpret its structure and reformat it into a standard layout. This approach is flexible and benefits from human judgement, but it is slow, difficult to scale and highly dependent on the availability of experienced people.

Spreadsheet macros and templated import tools reduce some of the manual burden by automating fixed transformations. These tools work well when a coverholder's format is stable, but they require ongoing maintenance whenever a layout changes, and they typically need a new template for every distinct format encountered.

Outsourcing bordereaux processing to a bureau or third party can relieve internal capacity constraints. This shifts the operational burden elsewhere but does not remove it, and it introduces its own costs, service level dependencies and oversight requirements.

Rules-based validation tools help catch known data quality issues once information has been standardised, but they generally assume the data has already been mapped into a consistent structure, which is often the hardest part of the problem.

Each of these approaches has a place. None of them fully resolves the underlying challenge of format variability at scale.

Where AI changes the economics

AI changes what is operationally achievable primarily by reducing the effort required to interpret varied bordereaux formats.

Rather than requiring a bespoke mapping template for every coverholder, AI can recognise that different terminology, layouts and structures often describe the same underlying business concepts, and transform that information into a consistent format without a new template for every variation encountered.

This has a direct effect on the cost drivers identified earlier. Cycle time can fall because data does not wait for manual reformatting. Skilled staff can be redirected toward exception handling, analysis and oversight rather than repetitive interpretation. Capacity can grow without a proportional increase in headcount, because the constraint shifts from manual effort to reviewing exceptions flagged by the system.

What AI does not change is the need for human judgement. Experienced professionals still need to review exceptions, apply business rules that require interpretation, and sign off on data before it is used for underwriting or regulatory purposes. The business case should reflect a redeployment of skilled effort toward higher-value work, not simply the removal of roles.

Building a credible, defensible business case

A defensible business case for AI in delegated authority rests on three foundations.

First, establish an operational baseline. Measure current cycle time, hours spent per bordereau, error rates identified at reconciliation, and the capacity ceiling of the existing team. Without this baseline, any claimed improvement is unverifiable.

Second, define a measurable pilot. Apply AI to a defined, representative scope of bordereaux, rather than attempting a full rollout immediately. This allows the organisation to compare outcomes directly against the baseline under real conditions.

Third, build governance and oversight into the business case itself, not as an afterthought. Define who reviews exceptions, how confidence thresholds are set, and what sign-off is required before data flows downstream. A business case that addresses efficiency without addressing oversight is incomplete, and is likely to face justified scrutiny from risk, compliance or audit functions.

Taken together, these foundations turn a general belief that "AI helps" into a specific, evidence-based case tied to the organisation's own operational data.

Example

A Lloyd's managing agent oversees a growing binder portfolio spanning marine cargo and agricultural risk, with monthly bordereaux arriving from a dozen coverholders in a dozen different spreadsheet formats.

The operations team spends the equivalent of two full-time roles each month reformatting and validating this data before it can be reported or reconciled. Leadership asks the DA operations lead to prepare a business case for introducing AI-assisted processing.

The operations lead measures the current baseline: hours spent per bordereau, error rates found at reconciliation, and average cycle time. She then proposes a scoped pilot applying AI to a subset of coverholder formats.

The business case presented to the Finance Director focuses on reduced cycle time and the reallocation of skilled staff to exception handling and oversight, rather than headcount reduction alone, with governance checkpoints built into the pilot design from the outset.

FAQs

• Does AI replace the need for DA oversight staff?

  No. AI reduces the repetitive interpretation and formatting work involved in processing bordereaux, but judgement, exception handling and sign-off remain the responsibility of experienced delegated authority professionals. A sound business case reflects a redeployment of skill toward oversight, not simple headcount removal.

• How quickly can an organisation expect to see a return from AI in DA?

  Returns depend on the scale and variability of the bordereaux data being processed. Running a defined pilot against a measured baseline gives a realistic view within a few reporting cycles, rather than an immediate or guaranteed timeframe.

• What is the biggest risk in building an AI business case for DA?

  The most common risk is basing the case on generic efficiency claims rather than measured, organisation-specific operational data. A credible case ties every claimed benefit to a quantifiable baseline drawn from the organisation's own processes.