What's Happening?
Chris Raimondo, Global and U.S. Insurance Consulting Leader at EY, observes that the insurance industry is moving into a more demanding phase for Artificial Intelligence (AI) adoption. While production
deployments of AI are increasing, consistent enterprise-wide adoption remains a challenge. Insurers are now facing harder questions regarding enterprise governance, financial returns, and workflow redesign. Raimondo notes significant progress over the past year, with more operational applications of AI, including workforce productivity assistants, generative AI, and agentic capabilities in underwriting, claims, and actuarial workflows. Enterprise software vendors have also advanced their embedded AI offerings. However, a coherent enterprise model for AI across the industry has yet to emerge, with security and financial performance now carrying greater weight in client conversations. Insurers are also gaining a clearer understanding of the costs associated with AI investments and are scrutinizing whether these investments are yielding anticipated effects on revenue, expenses, and shareholder value.
Why It's Important?
This shift indicates a maturation of AI adoption within the U.S. insurance sector, moving beyond initial experimentation to a phase where tangible business value and robust governance are paramount. The increased scrutiny on financial returns means that AI initiatives must demonstrate clear profitability and a positive impact on the bottom line, which is crucial for securing continued investment. The emphasis on enterprise governance, security, and responsible use reflects growing concerns about data privacy, regulatory compliance, and the ethical implications of AI. This will likely lead to more structured and controlled deployments of AI, ensuring that the technology is integrated safely and effectively across an organization. For systems integrators like EY, this means evolving their role from mere platform implementation to strategic partners who can help insurers reimagine workflows and build differentiated capabilities, ultimately shaping how insurance services are delivered and managed in the future.
What's Next?
Looking ahead to 2027, Raimondo anticipates that insurers will continue their transition from AI experimentation to full-scale enterprise implementation. This will involve a heightened focus on developing comprehensive governance frameworks to manage AI deployments securely and effectively. The financial test for AI investments will become more concrete, with companies demanding clear evidence of return on investment. This could lead to a consolidation of AI solutions that prove their value and a re-evaluation of those that do not. The role of systems integrators will continue to evolve, with a greater emphasis on helping insurers redesign their core workflows to fully leverage AI's potential, rather than simply plugging AI into existing processes. Insurers will also increasingly consider what AI capabilities they should build in-house versus what they should purchase, focusing internal development on areas that provide competitive differentiation.
Beyond the Headlines
The move towards a more demanding phase for AI in insurance highlights a broader industry-wide challenge: how to scale innovative technologies while maintaining control, ensuring profitability, and addressing ethical concerns. The tension between 'build' and 'buy' decisions for AI capabilities reflects a strategic dilemma for insurers, balancing the need for proprietary differentiation with the efficiency of off-the-shelf solutions. This evolution could lead to a two-tiered market, where larger insurers with significant capital can invest in extensive custom AI development, while smaller and middle-market insurers rely on prepackaged solutions from integrators. Furthermore, the focus on workflow redesign suggests a fundamental re-imagining of how insurance operations function, potentially leading to more agile, data-driven organizations. This transformation will not only impact business models but also the skills required of the insurance workforce, necessitating continuous learning and adaptation to new AI-powered tools and processes.
