The insurance sector is, understandably, quite risk-averse. Insurance companies don't endure and thrive by taking unnecessary risks in an environment defined by stringent regulation and nuanced decision-making. This is why it is so concerning to see companies within the sector taking unnecessary risks as they face up to the mounting pressure of demonstrating progress with generative AI.
It's easy enough to understand the urgency behind the industry-wide scramble to leverage new tools like autonomous agents and AI-powered applications. AI isn't new to the insurance business — use cases revolving around risk modeling and data forecasting were becoming commonplace in the sector before the deep learning and LLM boom in 2023. However, those technologies took years to find a place in insurers' technology stacks. They were heavily tested with strong, clear use cases. The race to adopt generative AI tools is not the same.
EIOPA's 2024 Digitalisation report found that 50% of non-life insurers and 24% of life insurers were already using AI in various areas of the insurance value chain, with applications including pricing and underwriting, fraud detection, and claims management. When it works, it really works. Research from McKinsey found that the insurance sector's AI leaders have created 6.1 times the total shareholder return compared with AI laggards. That figure doesn't just make the case for adopting AI. It makes the case that, if generative tools can be successfully integrated with insurers' tech stacks, the results are outstanding. McKinsey's report found that, in other sectors, AI leaders were generating at most two to three times the shareholder return.
Across the insurance space, there is an increasingly common fear that everyone else is somehow ahead. Companies see their competitors announce new AI pilots and products, vendors make new promises of transformative AI tools, and employees are already experimenting with agents and chatbots. No one wants to be caught standing still while the rest of the market races ahead.
The result is a growing wave of AI FOMO. Insurers are launching pilots, funding multimillion-dollar integration plans, and generally jamming AI into any perceived gap in their workflows. The problem is, many are doing this before interrogating and identifying where AI will actually create any meaningful impact for their business.
The dangers of putting AI before the business case
Last year, a study from MIT found that 95% of AI pilot projects "failed to deliver any discernible financial savings or uplift in profits," the data from which supports an earlier report released by Capgemini in 2023 that found 88% of AI pilots never reached production.
A more recent report from Simplifai found that, while 99% of insurers now have some form of generative AI in place and 83% of carriers are spending more than £3.75 million every year on tokens, subscriptions, and infrastructure, just 42% of insurers had taken the next step towards actually deploying AI into active business functions.
There is an undeniable appetite in the insurance sector for AI, but not a great deal of understanding when it comes to what the technology can do, or where it fits within existing business systems (not to mention the tech stack, which for an insurer is more likely to be some form of legacy system). This issue presents symptomatically as an abundance of AI pilot programmes that never mature into real world business solutions. Insurers know they need to act on AI, but they don't have a clear idea of where to start, which processes to prioritise, or how to evidence the value it creates.
How insurers can distinguish AI opportunity from AI hype
This raises an important question for insurers: what will it take to shift AI from isolated innovation projects to something with tangible business applications?
Scaling AI effectively can lead to substantial business value — the data supports it — but for insurers mired in expensive pilot programmes that never seem to translate into finished products, or who feel as though they're in danger of being left behind, it's essential to approach AI from a business perspective, not a technological one.
Successful AI adoption in insurance might just mean embracing more of the risk averse, methodical behavior for which the sector is sometimes criticized. It means making informed decisions regarding where AI can create real, sustainable impact. Success depends on identifying the use cases with the strongest commercial and productivity outcomes, rather than increasing the volume of AI initiatives in hope of reaching a magical, unspecified tipping point.
Insurance industry-specific AI tools are accessible across the market, doing away with any competitive advantage gained by adopting a particular platform or model. As access to the technology becomes more uniform, competitive differentiation will depend upon how organisations apply it. Successful approaches start with workflows, operating models, and business outcomes. They deploy AI across complete business processes instead of isolated point solutions and establish governance from the outset so that risk, compliance, and accountability are embedded throughout implementation.
The insurance industry doesn't have an AI problem. It has a strategy problem. FOMO is pushing insurers into suboptimal decisions, when the real challenge lies in executing a coherent transformation strategy. The next phase of insurance AI adoption won't be about buying and using more AI. It will be about making better decisions about where AI belongs.
