How Do We Integrate AI into Existing DA Systems?

Most delegated authority organisations already run systems that work reasonably well: bordereaux portals, policy administration platforms, oversight reporting tools and data warehouses built up over years of operation.

When AI is proposed as a way to improve bordereaux processing or oversight, the first question is rarely whether AI is useful. It is how it fits into a landscape that already has established systems, established workflows and live reporting cycles that cannot afford disruption.

This is fundamentally an integration question, not a replacement question. Understanding the available integration patterns, and the organisational considerations around them, is the practical starting point for any DA team evaluating AI.

Why integration, not replacement, is the real challenge

Delegated authority operations depend on continuity. Monthly bordereaux cycles, regulatory reporting deadlines and coverholder relationships all rely on systems that are already embedded into daily work.

Replacing a bordereaux portal or policy admin system outright is a significant undertaking, involving data migration, retraining and often a lengthy procurement process. Few organisations can justify that scale of change purely to introduce AI capability.

The more realistic question is how AI can be introduced alongside what already exists, adding capability at specific points in the workflow without requiring the underlying systems to change. This reframes AI adoption as an integration exercise rather than a transformation project, and it significantly lowers the barrier to getting started.

Traditional approaches to system change in DA

DA organisations have long experience of managing system change, even before AI entered the conversation. Three approaches are common.

Phased rollouts introduce new functionality gradually, often starting with a single class of business or a small group of coverholders before expanding further.

Parallel running keeps the existing process operating alongside the new one, comparing outputs before switching over. This is standard practice when replacing policy admin systems or introducing new reporting tools.

Vendor-led migrations rely on a supplier to manage the technical transition, with the organisation focusing on data validation and user acceptance testing rather than the underlying build.

Each of these approaches shares a common principle: change is introduced in a controlled, reversible way, with the existing process available as a fallback until confidence is established. That same principle applies directly to AI integration.

Where AI changes the integration equation

What is different with AI is not the need for careful change management, but the range of integration patterns available and the type of data AI can work with.

Four patterns are commonly used in DA environments:

• API connections. AI tools connect directly to an existing bordereaux portal or policy admin system, reading and writing data through defined interfaces without altering the core platform.
• Middleware layers. AI sits between incoming data (such as coverholder bordereaux) and the existing downstream system, performing validation or transformation before the data reaches the platform teams already use.
• Side-by-side pilots. AI processes a copy of incoming data in parallel with the existing process, with no changes made to production systems until outputs have been compared and validated.
• Embedded features. AI capability is added directly within a tool the organisation already uses, often through a vendor update rather than a separate integration project.

Unlike traditional software, which typically requires data to already be in a consistent, predefined format, AI can often work with the inconsistent, unstructured bordereaux data that DA teams handle every month. This means AI can frequently be introduced at the point where data first arrives, rather than only after it has already been standardised by another system.

Operational considerations for a safe integration

Regardless of which pattern is chosen, several considerations apply consistently.

Running AI outputs in parallel with the existing process for at least one or two reporting cycles allows discrepancies to be identified and understood before any cutover decision is made.

Ownership of exception handling and sign-off needs to be agreed before go-live. AI can reduce the repetitive interpretation work involved in reviewing bordereaux, but decisions on exceptions and final validation should remain with experienced DA professionals.

Data security and access controls must be maintained wherever AI tools connect to existing systems, particularly where coverholder or policyholder data is involved. This includes confirming how data is stored, who can access it, and how long it is retained by any AI service.

Finally, phasing the rollout, starting with a single coverholder, class of business or reporting cycle, keeps the scope of any issues contained and gives the organisation a natural point to pause and review before expanding further.

Example

A Lloyd's managing agent currently receives monthly bordereaux from twelve coverholders into an existing bordereaux portal, which feeds its policy administration system. The oversight team wants to introduce AI to help validate and reconcile incoming bordereaux, but is wary of disrupting the existing monthly reporting cycle.

The managing agent introduces an AI validation layer as middleware that sits between the coverholder submissions and the existing bordereaux portal. For the first two reporting cycles, the AI output runs in parallel with the existing manual validation process, with discrepancies reviewed by the oversight team.

Once confidence is established, the AI layer becomes the primary validation step, with the existing portal and policy admin system unchanged.

FAQs

• Do we need to replace our existing bordereaux system to use AI?

  No. Most AI integrations work alongside existing systems through API connections, middleware layers or embedded features, rather than requiring a full system replacement. The existing bordereaux portal or policy admin system can typically remain unchanged.

• How long does a typical AI integration take?

  Timelines vary depending on scope and existing infrastructure, but a contained pilot running alongside existing processes can often be established within a matter of weeks. Full integration usually follows a longer period of parallel running to validate accuracy before cutover.

• Who should own exception handling once AI is integrated?

  Exception handling and final sign-off should remain with experienced delegated authority professionals. AI reduces the repetitive interpretation work involved in reviewing bordereaux, but judgement on exceptions and oversight decisions stays with the team.