This article is by Alexandra Mousavizadeh, CEO at Evident
When the world’s biggest bank claims to be on course to achieve $2 billion in AI-enabled savings, it’s hard to argue against the reality that AI is reshaping financial services.
In insurance, however, AI doesn’t yet have the same prominence from an external vantage point. Looking at the financial results of 30 of the largest insurers in Europe and North America, for example, AI was notable for its absence. Just five of the 10 most AI-mature insurers from the recent Evident AI Insurance Index made any reference to AI in their H1 results statements or press releases.
From a communications standpoint, insurance can feel like an industry preparing for an AI future – at a varying pace – rather than one that is surging ahead with adoption. Silence, however, should not be mistaken for inactivity. Concrete examples of AI applications are emerging. But the work towards operationalising AI at scale is taking place – some of the biggest players in global insurance are laying the groundwork for organisation-wide transformation.
Laying the groundwork
While the insurance industry is still relatively early in its adoption curve, leading companies are recruiting people, building infrastructure, and testing applications – all of which will unlock returns in the years ahead and act as steps towards scaling up AI activity that delivers ROI.
Our recent Insurance AI Index found that across the sector, insurers have collectively hired more than 20,000 AI specialists. Allianz employs the largest pool, while USAA, Liberty Mutual and Aviva lead on “talent density”, meaning these companies have the highest proportion of AI roles as part of total headcount. Likewise, at least 13 insurers have published AI-specific research papers, and 14 now hold AI-related patents.
Furthermore, several firms, including Aviva, Zurich and Intact Financial, have already begun publishing evidence of financial value from AI use cases.
This mirrors what we’ve seen in banking over the past few years. At the start of 2023, most banks were still debating where to place their AI bets; few grasped the technology with both hands. Today, the banks that went early on AI are reporting significant ROI and describing AI as integral to future performance.
AI transformation happens in stages. First comes capability-building – including identifying and prioritising potential use cases, as well as putting the internal structures in place to make wider rollout possible – then disclosure follows once use cases mature. That journey takes years and requires patience. The challenge for insurance leaders is getting to a point where they can measure and show progress to investors, regulators and employees.
Why talking about AI matters
Our data has consistently shown that visibility into AI transformation efforts and continuing implementation can be a competitive advantage. The organisations that speak most clearly about AI strategy tend to have the strongest governance, talent and infrastructure already in place – and having access to benchmarking accelerates the successful adoption of AI in sectors as a whole.
What’s more, as businesses tentatively move towards wider use of agentic AI, being able to clearly show how the AI works and prove that it delivers as intended is a major step towards getting the tech beyond trial and into widespread deployment.
In banking, the number of institutions publicly reporting on AI use cases has doubled over the past year. Leaders like JPMorgan Chase’s Jamie Dimon and Bank of America’s Brian Moynihan frame earnings calls around AI outcomes, signaling the value they’re already seeing from these technologies. This is particularly crucial as the mood shifts and talk of an AI bubble grows. For all of the hype surrounding the AI industry itself, there’s a wealth of evidence to show that AI is delivering for the banks.
Insurance, though traditionally more conservative in approach, is beginning to move the same way. Manulife talked about its AI strategy in its recent quarterly earnings press release, Aviva has described how its GenAI platform, Oasis, is supporting over 150 live use cases across claims and customer service, while Zurich has referenced responsible AI principles in investor comms. The shift towards more open discussion has begun, even if it’s not universal across the sector.
Greater openness around AI progress will ultimately accelerate adoption. Clear communication about how AI is used, governed and measured builds confidence among the public, investors and with regulators too – vital in a market underpinned by trust. Crucially, types of insurers can learn from one another too, with property and casualty insurers facing differing challenges in implementation but also broadly working towards similar goals in driving efficiency.
From potential to proof
Of course, the next big test for the insurers currently leading the tightly contested AI race is whether their early investments can translate into measurable performance improvements in key business lines.
One area with strong potential is claims processing. Computer vision and large language models can automate damage assessment and streamline processing, and the number of real-world deployments is steadily growing. Allianz Technology’s Insurance Copilot assists adjusters by summarising cases and flagging inconsistencies while keeping humans in the loop. Aviva’s AI-driven claims function has reportedly reduced complex-case handling times by over three weeks and cut customer complaints by nearly two-thirds.
AI also holds great promise in both pricing and underwriting, integrating new data sources like geospatial, telematics and wearables. Travelers is using AI-powered recommendation engines to classify businesses for coverage more accurately, cutting onboarding times by up to 70%. More accurate risk segmentation boosts competitiveness and can expand coverage access – growing the customer base too.
Predictive analytics can use AI to model climate events or fraud patterns with unprecedented detail, helping manage capital allocation and reinsurance strategies dynamically. MunichRe, for example, has developed proprietary AI models for pricing, portfolio optimisation, and event prediction through its aiSure and REALYTIX ZERO platforms, using machine learning to guard against these risks.
Insurers looking likely to be bolder
For all these reasons, AI is set to take a far more prominent place in insurers’ public communications over the coming year. It may not happen by the next financial reporting cycle, but in the near to mid-term we should expect deeper disclosure on talent investment, productivity gains, and measurable financial returns.
Insurers should not underestimate the value of building a compelling AI narrative around this data. If they can explain, with evidence, how AI is improving efficiency, accuracy, and customer outcomes, they’ll reassure key stakeholders while at the same standing out from the competition. Regulators and investors will be watching closely for evidence that automation is explainable and fair.
Insurance could become one of the clearest demonstrations of how AI can responsibly transform a complex, tightly regulated industry – showing how to operationalise the technology at scale. The infrastructure is in place, the data pipelines are flowing, and the early results are already starting to appear. It’s too early to say whether winning the AI race will be ‘winner takes all’, but the insurers that lead from the front and share their AI progress publicly could gain a vital edge over their rivals.
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