Why the insurance industry can’t get past the AI testing stage

The insurance industry remains largely in its pilot phase for AI adoption

Most insurers are still testing AI rather than scaling it. The experiments happening today focus on small, safe areas, customer service chatbots, document summarization, and limited claims support. These pilots help organizations explore potential efficiency gains but rarely move beyond controlled environments. Scaling these projects into business-critical functions like underwriting or claims automation remains limited. The result is predictable: low productivity impact and minimal effect on the bottom line.

For executives, this signals a maturity issue, not a technology problem. The tools are available and proven. The challenge comes from embedding them deeply enough into operational workflows to yield measurable financial outcomes. Staying in pilot mode creates the illusion of progress but doesn’t shift performance metrics in a meaningful way. Leaders need to focus on end-to-end integration, moving from “trial and learn” to “deploy and optimize.” This means aligning technical, strategic, and regulatory considerations right from the start to ensure projects don’t stall.

The industry data supports this cautious pace. Simplifai reports that fewer than half of insurers have implemented AI in even one functional area. Full workflow automation, particularly in underwriting or claims, remains rare. The pattern is consistent: active exploration yet slow execution. Closing this gap demands stronger leadership direction, better internal coordination, and a clear link between experimentation and real performance indicators.

There is misalignment and confusion in defining and measuring AI return on investment (ROI)

Across industries, the gap between technical teams and executive leadership is widening when it comes to understanding AI’s financial impact. Many executives want to see tangible outcomes, faster processing, cost savings, higher revenue, but technical teams often frame success in experimental or process terms. This division makes it difficult to build a shared vision of what ROI actually means in practice.

Executives need clarity, not complexity. Without a solid definition of value, measurement becomes arbitrary, and scaling slows down. Most organizations still treat AI ROI as a theoretical metric instead of a performance-based one tied to real business results. This problem isn’t about AI itself, it’s about communication and expectations. Bridging this divide requires consistent metrics that reflect both business and technical success.

The 2026 Industrial Technology Index from TE Connectivity found that only 19% of executives have “full clarity” on AI ROI. That low figure captures the core problem: strong interest in AI but weak alignment on what successful implementation looks like. For leaders, the path forward involves creating enterprise-wide ROI frameworks that integrate technical performance with financial outcomes. Without this structure, investment decisions risk being driven by hype rather than measurable impact.

C-suite executives must move the conversation from “Can this work?” to “How do we define and prove its value?” The difference is executional discipline, setting clear metrics, validating early results, and scaling only what demonstrably works. When organizations master this alignment, AI moves from being a cost center to a core business driver.

While investments in AI are increasing rapidly, scalable returns remain elusive

AI investment is accelerating across industries, driven by confidence in its transformative potential. In the United States, companies plan to invest $207 million in AI within the next year, nearly double the amount from the previous year, according to KPMG. However, this rapid spending surge isn’t yet producing proportional returns. About two-thirds of companies report some business value from their AI efforts, but only a small share have scaled projects enough to produce measurable ROI.

This mismatch shows that capital alone doesn’t guarantee successful transformation. Many organizations are discovering that scaling AI requires more than models and data, it demands strong architecture, workflow integration, and a consistent vision across leadership and technical teams. Without these elements, large budgets often lead to fragmented, siloed initiatives that fail to translate into broad operational or financial gains.

For executives, the key takeaway is that the market’s confidence in AI far exceeds its demonstrated efficiency. Investment is important, but execution discipline is what determines impact. The most successful organizations are moving beyond proof-of-concept experiments and prioritizing large-scale deployment strategies with well-defined metrics for performance and ROI. This shift transforms AI from a promising test project into an operational asset.

The implication for leadership is clear: spending is not the differentiator; scalability is. Achieving meaningful returns requires building systems that can adapt, expand, and sustain value creation at enterprise scale. Investment strategies must now evolve from enthusiasm to precision, where every dollar spent accelerates measurable impact rather than driving experimentation alone.

Agentic AI is anticipated to be a driver of future productivity and ROI, especially in insurance

Agentic AI, AI capable of acting autonomously within defined boundaries, is beginning to capture executive attention. It’s not about faster calculations or content generation. It’s about applying intelligence directly within workflows to manage ongoing tasks with limited human oversight. For industries like insurance, this evolution means automating large parts of claims handling, underwriting, and compliance operations, unlocking significant productivity improvements.

Simplifai’s research shows that organizations deploying agentic AI in these workflows report 30% to 40% productivity gains in claims and underwriting operations. PagerDuty’s findings reinforce this confidence: more than 60% of IT decision-makers expect agentic AI to deliver over 100% ROI, with nearly 45% believing its impact will surpass that of generative AI. The data signals a clear shift in executive priorities, from theoretical innovation to measurable, integrated performance.

For C-suite leaders, agentic AI demands a different mindset. Success depends on seamless integration, governance, and real-time monitoring. Deployments that treat AI as part of the operational core, not an experimental overlay, create lasting value. The insurers achieving the strongest returns are those embedding agentic systems directly into workflows, ensuring accountability, compliance, and flexibility as the technology evolves.

The takeaway for executives is straightforward: agentic AI represents a step-change moment. It brings AI closer to practical, sustained ROI by automating repetitive functions while maintaining control and transparency. The opportunity is significant, but it must be guided by disciplined governance and a clear understanding of business goals. Alignment between strategy, systems, and people is what turns agentic AI from potential into realized performance.

A strategic, workflow-first approach, rather than mere technology access, is crucial for AI success in insurance

Every insurer today has access to the same AI tools, models, and cloud platforms. The difference in outcomes comes down to execution. Success lies in how these tools are embedded into business processes and managed with the right governance and accountability structures. Simplifai’s report makes this point clear: organizations focusing on workflow-first deployment, where processes are redesigned around AI rather than layering AI onto existing ones, achieve far stronger results.

This distinction matters to executives. A workflow-first approach ensures AI doesn’t operate in isolation. Instead, it becomes part of the operational foundation, integrated with data management, risk control, and performance monitoring. These integrations set the stage for scalability and consistency, turning AI from a technical experiment into a stable operational system. Insurers that pursue model-first pilots without such structure often find themselves stuck in limited trials with little measurable financial benefit.

For leadership, this is a call to rethink priorities. The objective isn’t just to adopt AI; it’s to integrate it into the company’s operating system. That means re-engineering workflows, embedding governance from the start, and aligning teams across business and technology. Done properly, this alignment allows AI to deliver compounding value over time, reducing costs, improving accuracy, and expanding capacity without undermining control or compliance.

Simplifai’s findings highlight the gap: carriers with integrated, governance-driven AI deployments report measurable productivity gains, while those following a model-first path see minimal P&L impact. The message is simple and actionable, technology parity exists, but operational integration defines performance. For executives, the challenge is to move beyond adoption and focus on alignment, execution, and long-term scalability. That’s where the real competitive advantage is created.

Key executive takeaways

• AI in insurance remains stuck in pilot phase: Most insurers continue to test AI in narrow areas like chatbots and document processing. Leaders should move beyond pilots and focus on integrating AI into full workflows to achieve measurable financial and operational results.
• ROI confusion slows progress: Misalignment between executives and technical teams on what defines AI ROI limits enterprise-wide adoption. Leadership must establish unified ROI frameworks tied to real business metrics to guide strategic investment.
• Rising investment isn’t delivering scale: AI spending is doubling year over year, but few firms are realizing proportional returns. Executives should pair increased funding with structured scaling strategies to convert experimentation into enterprise impact.
• Agentic AI shows strongest ROI potential: Early deployments of agentic AI in insurance show 30–40% productivity gains in claims and underwriting. Leaders should prioritize agentic AI with proper governance to unlock transformative automation and performance efficiency.
• Workflow-first strategies determine success: Access to AI tools is no longer the differentiator, execution is. Decision-makers should adopt workflow-first, governance-embedded deployment models to scale AI across operations and achieve lasting competitive advantage.