May 22, 2026

The hidden flaw in insurance AI adoption for advisors and carriers

Many insurers are still in a phase where AI is being applied to existing underwriting and claims workflows rather than fundamentally redesigning how those workflows operate.

“That’s understandable to an extent, as these environments are deeply interconnected, highly regulated, and built on operational models that have evolved over decades,” said Manuel Rodriguez Vera, business unit head of insurance, WNS, part of Capgemini.

For many carriers, the immediate focus has been on improving efficiency within current systems before undertaking broader operating model transformation.

However, simply layering AI onto fragmented processes often creates incremental efficiency gains rather than meaningful transformation. The bigger opportunity lies in rethinking how underwriting, intake, triage, and servicing work together end to end, experts maintain.

“The insurers who approach AI with a wide-open view will create competitive differentiation and in turn, long-term resilience,” explained Jaime Henry, vice president of product at Origami Risk.

‘AI can improve speed’

When AI is integrated into outdated models, insurers often end up accelerating inefficiencies instead of eliminating them.

“If workflows remain fragmented, overly manual, or dependent on disconnected data environments, AI can improve speed without necessarily improving decision quality or operational effectiveness,” Rodriguez Vera said.

Additionally, existing QA controls and audit processes may not fully translate to a new AI-centric model and fail to proactively identify potential red flags.

Gap between AI investment and true change

Many organizations these days invest heavily in AI models and pilots without equally rethinking workflows, decision-making processes, or data integration.

Transformation happens when AI becomes part of how underwriting decisions are orchestrated across the enterprise and not when it exists as a standalone technology layer.

“Organizations seeing stronger outcomes are typically the ones integrating AI into end-to-end underwriting workflows, combining AI-led triage, workflow orchestration, and human underwriting oversight to reduce manual touchpoints and improve responsiveness,” Rodriguez Vera said.

Henry encourages taking a simple, incremental approach to building AI workflows.

“Like anything, start simple. It doesn’t have to be a big ‘wow’ workflow to have impact. The ‘wow’ moments come through cumulative efficiencies and outcomes,” he added.

Where advisors can make the greatest impact

Current workflows were often designed in the context of legacy limitations of machine and human interaction. Many of these limitations have shifted or no longer exist.

“For example, in an AI-native process design, information can be leveraged in real-time, across multiple specialized agents,” Rodriguez Vera explained. “Parallel processing through properly orchestrated AI agents can scale without the same limitations that exist on a human-centric process design.”

Similarly, the traditional focus on balancing how to leverage specialized knowledge for specific tasks against the friction caused by multiple handoffs can be looked at in a completely different light.

These workflow limitations ultimately impact the broader advisor and customer experience. Fragmented operations can slow underwriting responsiveness, limit personalization, and create inconsistencies in client interactions and servicing experiences.

In contrast, insurers that modernize workflows more holistically are better positioned to deliver faster decisions, improved service experiences, and more contextual product recommendations while still keeping human judgment at the center.

“Advisors should recognize that AI transformation is increasingly shaping not just operational efficiency, but also customer expectations around speed, transparency, and responsiveness,” Rodriguez Vera said.

At the end of the day, leveraging AI in areas where it performs exceptionally well allows people to spend more time in areas where human expertise is essential.

Supporting clients amid AI change

Agentic AI, in particular, has catapulted the insurance industry to the edge of transformative change. With carrier systems that can independently reason, dynamically learn from outcomes, and adapt in real-time, the industry is moving forward rapidly.

To stay ahead of these evolving capabilities and position themselves effectively in client conversations, advisors must proactively adapt their approach.

“Block out and prioritize time to innovate,” Henry said. “It’s easier said than done, but we are witnessing firsthand that those who prioritize and accept that AI isn’t going away will learn more quickly and help lead future innovations.”

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