Embracing AI to future-proof insurers’ operating models

Today, insurance businesses are at a crossroads. They can either fully embrace AI to transform their operating models, or cede market share to more agile competitors willing to make this leap.

Many insurers have experienced intensifying margin pressures and erosion of technical equilibrium. Meanwhile, flood-driven losses reached $10 billion in Europe alone, against an estimated $135 billion worldwide¹. France, for example, faced a 14 % rise in large commercial property claims² during 2024 and a 9% increase in the cost of professional and corporate insurance claims³.

These trends have been compounded by a volatile macroeconomic environment, marked by inflationary pressures, shifting interest rates, and slower growth. For many insurers, costs are rising faster than premiums, making structural cost reduction a matter of survival.

In this context, insurers face a clear imperative to optimize operational costs while safeguarding financial solidity and customer relevance. In fact, insurance businesses are streamlining their product portfolios, revising contract clauses, and adopting more selective underwriting practices. At the same time, they are pursuing market share growth, creating a delicate balancing act between risk management and competitive positioning. Operational excellence is being redefined, encompassing not just cost discipline, but also customer self-service, digital distribution, and agile product innovation.

AI offers a clear path forward, enabling real-time risk assessment, granular customer insights, and fully integrated value chain management. Executives who prioritize AI adoption are seeing accelerated decision-making, improved accuracy, higher productivity, and new sources of differentiation - outcomes critical for navigating contemporary industry headwinds.

The adoption of AI is one of the top ten priorities for insurance companies within the modernization of their core insurance systems according to the recent BearingPoint Kaleidoscope study.⁴ 

AI's capacity to process large datasets also transforms risk assessment. By combining historic claims data with external signals such as climate trends and cyber activity, predictive analytics enable more accurate, data-driven pricing. 

These operational gains translate directly into better customer experience. Faster decision-making accelerates quote generation, policy approvals, and claims resolution. Meanwhile, AI chatbots can deliver 24/7 service and personalized policy recommendations, driving self-serve resolution rates and reducing operational costs. To leverage AI efficiently, insurers must focus on 4 high-Impact AI use cases. 

Insurers are struggling to scale AI efficiently given rising costs and eroding margins, AI integration should be a strategic priority for European insurers. Yet BearingPoint data⁹ shows adoption is progressing at markedly different rates: while 33% of insurers have deployed multiple business-wide AI initiatives, 22% are still exploring the technology or have yet to implement any projects. 

More concerning is the strategic gap in how AI is being deployed. Only 58% of firms have both a clear AI transformation strategy and roadmap. Without a roadmap, insurers risk remaining locked out from the technology's full benefits.     

According to the BearingPoint Kaleidoscope study 63% of the interviewed insurers decision makers at insurance companies state that AI will be a central component of core insurance solutions.¹⁰ 

Many insurers – even early adopters with clear AI strategies – are struggling to realize the technology’s game-changing potential, with deployments stalling in pilot programs or experimentation. Only 17% of insurance AI projects have been scaled in line with original business case expectations, according to the BearingPoint study.¹¹ 

AI: the time to act is now 

The speed and diversity of innovation within the AI space mean it will be an engine for sustainable growth in the insurance market for decades to come. With current challenges to scale AI successfully, insurance leaders must invest boldly in high-impact projects and commit to scaling AI across the organization to fully unlock the value of AI-led transformation. Pushing past pilot programs requires significant transformation – from breaking organizational silos to improving data hygiene. Only by doing so can insurers bring operational excellence, cost optimization, and enhanced customer experience on the next level.