What change management is needed when introducing AI into delegated authority operations?

Most guidance on introducing AI into delegated authority operations focuses on the technical steps: integration, testing, rollback planning. Those matter, but they are not the whole picture.

The operational benefit of AI-assisted bordereaux processing only materialises if the people using it trust the process and know what to do when the system flags something. That depends on how the change is managed, not just on how well the technology performs.

Teams that treat AI adoption purely as a technical project, without preparing the people affected by it, often find that the benefit takes longer to appear than expected, or that staff quietly work around the new process rather than adopting it fully.

Why AI adoption is a people question, not only a technology question

An AI system that maps and validates bordereaux data accurately still depends on people to review exceptions correctly, apply judgement where the system is uncertain, and trust its output enough to rely on it for routine cases.

If staff do not understand how the exception process works, or do not trust the AI's output, they tend to either check everything manually regardless of what the tool has already validated, which erodes the efficiency gain, or accept output without appropriate scrutiny, which erodes the control the process was meant to provide.

Either outcome undermines the point of the implementation, even where the underlying AI technology is working exactly as intended.

How delegated authority teams traditionally managed process change

Delegated authority operations have been through process changes before: new bordereaux import templates, system migrations, changes to reporting cycles.

These changes were typically managed through a mix of documentation, informal knowledge transfer between colleagues, and a period of parallel running old and new processes until confidence built up. This worked reasonably well for changes that altered how data moved through a system without fundamentally changing what staff spent their day doing.

AI adoption is a bigger shift, because it changes the nature of the work itself, not just the mechanics of a template or system. The traditional approach of documentation plus informal knowledge transfer is often not enough on its own.

How roles typically shift when AI is introduced

Where AI removes much of the manual re-keying and reformatting work involved in processing bordereaux, staff time shifts toward reviewing exceptions the system has flagged, interpreting ambiguous or low-confidence output, and applying judgement to cases that fall outside routine patterns.

This is a different skill emphasis. It relies less on speed and accuracy of manual data entry, and more on understanding what a flagged exception actually means, recognising when something looks wrong even if the AI has not flagged it, and knowing when to escalate further.

Not every organisation communicates this shift clearly, and staff left to infer it themselves often assume the change is primarily about reducing headcount, whether or not that is actually the intention.

Practical steps to prepare a team

Preparing a team for AI adoption generally involves communicating early and honestly about what will change and what will not, rather than leaving staff to draw their own conclusions.

Training should focus specifically on the new exception-handling process: what triggers an exception, what information is available to review it, and what the expected turnaround is, rather than generic AI awareness training disconnected from the actual workflow.

Involving experienced staff in reviewing the AI system's early output, before full rollout, tends to build trust more effectively than presenting the system as already proven. It also gives the organisation valuable input into calibrating confidence thresholds and exception rules, since experienced staff are often best placed to judge whether flagged exceptions look right.

Common sources of resistance, such as concern about job security or scepticism about AI accuracy, are addressed more effectively through this kind of direct involvement and transparency than through reassurance alone. Acknowledging genuine concerns, rather than dismissing them, tends to produce a smoother and faster transition.

Example

A managing agent's operations team has spent years manually re-keying premium bordereaux from coverholder spreadsheets into the agent's own systems. The agent introduces an AI-assisted mapping and validation tool intended to remove most of that manual re-keying.

Rather than announcing the change with no preparation, the operations manager runs a short series of sessions explaining what the tool will and will not do, involves two senior analysts in reviewing the tool's early output, and reframes the team's role around exception review and quality oversight. Staff concerns about job security are addressed directly, and the transition proceeds with noticeably less resistance than an earlier, less structured system change.

FAQs

• Does introducing AI usually reduce headcount in delegated authority operations?

  Outcomes vary by organisation. Many teams redirect capacity toward exception handling, oversight and higher-value analysis rather than reducing headcount, though the specific outcome depends on each organisation's own operating model and workload. It should not be assumed automatically in either direction.

• How long does it typically take a team to adjust to an AI-assisted process?

  Adjustment periods vary with team size, prior process maturity and the complexity of the bordereaux involved. A phased introduction that involves experienced staff early tends to shorten the adjustment period compared with a single, unprepared switch-over.

• What is the biggest source of staff resistance to AI adoption?

  Uncertainty about how roles will change, and limited visibility into how the AI reaches its output, are common sources of resistance. Transparency about the exception-handling process and honest communication about role changes tend to address both more effectively than general reassurance.