[AI]How insurance leaders use agentic AI to cut operational costs

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Agentic AI offers insurance leaders a path to scalable efficiency as the sector confronts a tough digital transformation.

Insurers hold deep data reserves and employ a workforce skilled in analytic decision-making. Despite these advantages, the industry has largely failed to advance beyond pilot programmes. Research suggests only seven percent of insurers have scaled these initiatives effectively across their organisations.

The barrier is rarely a lack of interest. Instead, legacy infrastructure and fragmented data architectures often stop integration before it starts. Financial pressure compounds the technical debt. The sector has absorbed losses exceeding $100 billion annually for six consecutive years. High-frequency property losses are now a structural issue that standard operational tweaks cannot fix.

Automating complex insurance workflows with agentic AI

Intelligent agents provide a way to bypass these bottlenecks. Unlike passive analytical tools, these systems support autonomous tasks and help make decisions under human supervision. Embedding these agents into workflows allows companies to navigate legacy constraints and talent shortages.

Workforce augmentation is a primary application.

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, in collaboration with

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, deployed the Sidekick Agent to assist claims professionals. The system improved claims processing efficiency by more than 30 percent through real-time guidance.

Operational gains extend to customer support. Standard chatbots usually answer a query or transfer the user to a ******. An agentic solution manages the process from end-to-end. This can include capturing the first notice of loss, requesting missing documentation, updating policy and billing systems, and proactively notifying customers of next steps.

This “resolve, not route” approach has produced results in live environments. One major insurer implemented over 80 models in its claims domain. The rollout cut complex-case liability assessment time by 23 days and improved routing accuracy by 30 percent. Customer complaints fell by 65 percent during the same *******.

Such promising metrics indicate that agentic AI can compress cycle times and control loss-adjustment expenses for the insurance industry, all while maintaining necessary oversight.

Navigating internal friction

Adoption requires navigating internal resistance. Siloed teams and unclear priorities often slow deployment speed. A shortage of talent in specialised roles, such as actuarial analysis and underwriting, also limits how effectively companies use their data. Agentic AI can target these areas to augment roles that are hard to fill.

Success relies on aligning technology with specific business goals. Establishing an ‘AI Center of Excellence’ provides the governance and technical expertise needed to stop fragmented adoption. Teams should start with the high-volume and repeatable tasks to refine models through feedback loops.

Industry accelerators can also speed up the process. Many platforms are now available with prebuilt frameworks that can support the full lifecycle of agent deployment. This approach reduces implementation time and aids compliance efforts.

Of course, technology matters less than organisational readiness. About 70 percent of scaling challenges are organisational rather than technical. Insurers must build a culture of accountability to see returns on these tools.

Agentic AI is a necessity for insurance leaders trying to survive in a market defined by financial pressure and legacy complexity. Addressing structural challenges improves efficiency and resilience. Executives who invest in scalable frameworks will position themselves to lead the next era of innovation.

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