Marsh has launched Broker WorkBench, an AI-powered placement platform for London specialty business. The platform is designed to standardise data and workflows across matching market requests, negotiation and binding. It supports lead capacity, digital follow capacity and follow-form capacity. Marsh says a process that can take two to four weeks may in some cases be reduced to days or hours. That is an ambition for the platform, not an independent measure of achieved performance across placements.

From scattered steps to an orchestrated workflow

One important change is faster document production. Specialty placement involves multiple participants, imperfect data, negotiation and judgement about terms and market appetite. The key change here is the introduction of a  shared workflow that can reduce repeated data entry, make status visible and help brokers identify appropriate markets. If implemented well, AI becomes an orchestration layer around professional decisions rather than a substitute for them.

Human approval remains a control, not a slogan

Marsh states that broker approval remains part of the process. The practical value of that control depends on its design. A broker needs to see the source data, understand material changes proposed by the system and be able to reject or amend an output. Approval should be recorded at the decision that matters, not added as a generic click after the work has effectively been completed.

Standardisation also requires careful boundaries. A common data model can improve comparison, but unusual risks should not be forced into fields that hide their distinctive features. The workflow needs a route for exceptions, narrative context and specialist review. Data lineage is equally important: participants should know which information came from the client, which was inferred or transformed, and which version supported the final terms.

For insurers, a more structured submission can improve triage and reduce processing time. For clients, it may shorten the path to executable capacity. Yet speed must be assessed alongside coverage quality, market breadth and negotiation outcomes. A rapid placement that omits a material exposure or produces ambiguous wording would be a poor result.

Lessons for benefits and risk workflows

Although Broker WorkBench is focused on specialty placement, the operating principles apply to global benefits. Many benefit workflows still rely on email, spreadsheets and repeated translation of the same information between employer, adviser, local broker and insurer. Standard fields, controlled document generation and visible approvals could reduce friction in renewals, multinational pooling or captive reporting.

This editor recommends identifying one workflow with enough volume and repetition to test this model. Map the current steps, specify the minimum data set, mark each professional decision and define the evidence needed for approval. The pilot should measure elapsed time, rework, data completeness, exceptions and user satisfaction—not only the number of AI-generated outputs.

The platform also illustrates a strategic choice for brokers. If technology makes routine coordination faster, differentiation moves toward judgement, market access, exception handling and client advice. Firms should therefore redesign roles and service measures at the same time as they deploy automation. The objective is not merely to place the same work more quickly, but to create a traceable process in which professionals can spend more time on the decisions that require expertise.

Data standards create the foundation

The largest efficiency gain may come from agreeing what information is required and how it is represented. A structured submission can reduce questions between parties, but only if definitions are shared and validation catches missing or contradictory fields. Governance of the data standard therefore matters as much as the AI layer.

Market participants should also decide how performance will be compared with the previous process. A pilot needs a baseline for elapsed time, number of touchpoints, quality of terms and client effort. It should record cases that leave the automated path and why. Those exceptions reveal where the standard needs improvement and where specialist judgement should remain deliberately outside automation.

The next review should test the recommendation against fresh operating evidence, identify any unintended consequences and record who owns the resulting action. That discipline keeps the proposal proportionate and allows governance to evolve as market practice, technology and regulation change.