Discussion in the insurance industry has often centered on whether AI will replace people, but the more pertinent question is how it is changing broking and underwriting roles.

AI is redefining the role of the insurance broker, as it moves the function away from transactional activity and toward more strategic, client-led advice built on better use of data. Used well, it allows brokers to operate more effectively by reducing repetitive tasks, structuring information and improving the quality of insight available at the point of decision.

Across larger broking firms this is already visible with AI applied to benchmarking, coverage design and portfolio analysis, enabling brokers to assess client risk positions with greater speed and consistency while supporting more informed engagement and strengthening client retention. Distribution therefore becomes less about the annual renewal cycle and more about continuous advisory, where conversations are influenced by up-to-date information rather than periodic reviews.

The effect is clear in specialty lines, where placements are often complex and involve multiple insurers. AI can streamline how these risks are structured and placed by analyzing underlying characteristics, identifying appropriate capacity more efficiently, and supporting decisions on how risks are layered or shared to better align risk with capital. This reduces friction in the placement process and shortens the time needed to bind coverage, while still requiring broker judgment in how placements are ultimately constructed.

There is also a direct link to revenue growth, as improved visibility of client exposures allows brokers to identify gaps in cover and areas of underinsurance with greater consistency. Some firms are also using AI to support prospecting, drawing on structured data to prioritize targets and focus outreach so that growth activity becomes more targeted and less dependent on manual processes.

Data remains a constraint in traditional broking, where information is often held across multiple systems with varying formats and levels of completeness, challenging how risks can be assessed and compared. AI can organize and standardize this data so that exposures are evaluated on a more comparable basis, supporting more consistent underwriting discussions and creating scope for product development as firms gain a clearer view of where existing solutions fall short.

Beyond broking, managing general agents are adopting similar approaches, in some cases more quickly. Augmented UW Ltd. provides one example, with a model built around automated underwriting and end-to-end risk placement, showing how processes can be streamlined when data, decision-making and placement are more closely integrated. It also points to how newer entrants can scale without the same operational constraints as more established firms.

There are still challenges to address, particularly where legacy systems limit data quality or restrict integration. Regulation continues to affect how AI can be applied in areas where decisions affect customer outcomes and firms must ensure appropriate oversight. There is also a skills gap, both in terms of technical capability and in how brokers interpret and apply outputs. In addition, client trust depends on transparency where decisions are supported by automated processes.

AI is not replacing the broker, but it is changing how brokers compete. Firms that combine data, technology and advisory capability are better placed to strengthen their position, while those that remain reliant on manual processes and fragmented information will find it harder to differentiate in a market where the quality of insight and advice increasingly determines value.