AI can create growth capacity, and that is where the financial gains will show up

Agentic AI has the potential to give insurers capacity to write more business with the teams they already have, while freeing scarce underwriting, broking and claims expertise from routine work, mea Platform’s Max Richter tells Monte Carlo Today.

Key points:
AI could reshape peak renewal capacity
Claims offers significant scope for agentic AI
Insurers need new measures across workflow

Insurance has moved from AI pilots into deployments at scale. The gains are starting to show up in how much business a team can handle, how fast it responds and where scarce expertise gets spent, and those have a clear path through to financial performance.

For Max Richter, EMEA CEO and global growth leader at mea Platform, that shift from experimentation to implementation is where the value gets created.

“There are a lot of pilots and a lot of experimentation in the industry. There are also deployments at scale now in different areas,” Richter told Monte Carlo Today. “Where organisations have gone to scale, we are starting to see a real delta in how much business a team can handle and how fast they respond, and that has a clear path through to the financials.”

“The prize is enabling the same people to handle materially more business and respond faster.”

“That is something insurance executives should be pressing their teams and their technology partners to show them, and that line of sight is there to be built,” he said.

That matters more as softer market conditions put pressure on margins. Richter sees the opportunity in how much profitable business existing teams can handle.

“The prize is enabling the same people to handle materially more business, respond faster, and make better use of what we know is scarce underwriting, broking and claims expertise,” he said.

A decision-ready risk

In underwriting, that means removing much of the work surrounding the actual risk decision.

Agentic systems can receive and classify submissions, extract and validate information, perform checks and populate rating and exposure systems before a risk reaches the underwriter.

“In practice the underwriter receives a decision-ready risk rather than an inbox full of documents,” Richter said. 

“That can create growth capacity in several ways,” he explained. “More in-appetite submissions can be reviewed, quotes can be returned while the opportunity is still live, and underwriters can spend more time with brokers, structuring and selecting the risks they want to write. Operations can also absorb more seasonal volume without building permanent capacity around peak renewal season.”

Richter describes it as releasing capacity for growth. 

“The practical test is simple,” he said. “It is how much administrative work reaches the underwriter at all.” 

Similar changes could extend across the insurance chain. Brokers could spend less time assembling submissions and comparing quotations and more time advising clients and negotiating placements. In claims, agentic systems could maintain chronologies, summarise new documents and surface information relevant to coverage, liability, reserving and recovery.

Beyond another AI tool

Before joining mea in March 2025, Richter spent more than two decades at Accenture across two spells, giving him a close view of earlier insurance technology and transformation programmes. 

He said previous technology initiatives often delivered less than promised because insurers were trying to automate processes that were too complex and variable for rigid, predefined workflows.

Agentic systems can navigate more of that complexity, Richter argues, but the operating model around them must change too.

That includes the workforce. Removing routine processing could leave people handling a greater concentration of exceptions and difficult decisions. 

“If people see only unusual, complex cases, they need stronger judgement and broader training, and they have to be able to cope with a consistently high mental workload,” Richter said.

Organisations will also need clear authority limits, escalation routes and quality controls defining what AI can do and where people remain accountable.

“This is as much a leadership and workforce transformation as it is a technology implementation,” he said.

From tokens to business outcomes

How insurers measure AI therefore needs to change with the scale of its role.

Measuring usage or the accuracy of an isolated task may make sense at an early stage, Richter said. But once AI is executing larger parts of a workflow, the focus should shift towards end-to-end turnaround times, human effort and the quality of the transaction.

“You move from tokens through to business outcomes,” he said.

For insurers, that provides a harder test than the number of pilots launched or tasks automated. It measures how much business existing teams can handle, how quickly they respond and where scarce expertise gets spent.

Richter says the organisations measuring it that way are beginning to see the answer.

Max Richter is the EMEA CEO and global growth leader at mea Platform. He can be contacted at: max.richter@meaplatform.com

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