A study by global professional services firm KPMG has indicated that insurers have high expectations for AI adoption, although there is pressure to demonstrate immediate ROI, particularly from shareholders.
The study found that 66% of the respondents anticipate a moderate to very high return on AI investment, with all respondents believing firms that embrace AI will gain a competitive edge.
Meanwhile, AI spending is expected to increase, with all the respondents planning to allocate a greater portion of their budgets to AI. Of these, 66% expect to spend up to 20% of their global budgets on AI, while 34% plan to invest more than 20%.
Trust in AI remains a challenge, as 46% of leaders express reservations about its reliability, and only 25% fully trust AI within their companies. However, 82% acknowledge the need for robust frameworks, policies and processes to ensure regulatory compliance and responsible AI implementation.
According to the study, AI is impacting insurance processes in several ways. In underwriting, AI automates risk segmentation, integrates electronic health records, and uses predictive analytics to refine policy pricing and assessment.
Meanwhile, life insurers are incorporating wearable data and wellness tracking to personalize policy pricing and incentivize healthy behaviors. AI also enhances claims automation by identifying fraud, analyzing documents such as death certificates, and streamlining payouts using machine learning.
In longevity modeling and compliance, AI helps assess life expectancy, detect disease onset, and optimize risk stratification, enabling insurers to refine pricing and payout structures.
Leadership goals for AI adoption are currently focused more on operational efficiency rather than broader strategic value. To help insurers navigate AI adoption, KPMG outlined three phases of AI value: enable, embed, and evolve.
According to KPMG, the “enable” phase emphasizes building AI foundations by appointing responsible executives, creating AI strategies, identifying high-value use cases, increasing AI literacy, and ensuring regulatory alignment. This phase also involves launching AI pilots, utilizing cloud platforms, and leveraging pre-trained models with minimal customization.
Meanwhile, the “embed” phase integrated AI into the business’ workflows. Under this phase, there will be a senior leader that will drive enterprise-wide workforce redesign, re-skilling and change, embedding AI into the company’s operating models, cautious of ethics, trust and security concerns.
The research also outlined the “evolve” phase, which focuses on transforming business models and ecosystems by leveraging AI alongside emerging technologies such as quantum computing and blockchain. The objective is to address large-scale challenges, enhance enterprise value, and prioritize ethics, trust, and security. Workforce training is also emphasized to foster innovation.
