How insurers can implement GenAI in insurance actuarial operations

GenAI, richer data and intelligent operating models are empowering actuarial teams to deliver greater value across insurance.

In brief

• Prioritize GenAI actuarial use cases (reporting, reserving/valuation, model modernization) to speed insight and cut cycle times. 
• Scale GenAI with governed data, secure APIs and controls that keep outputs transparent, reviewable and auditable. 
• Redesign operating models and upskill teams so actuaries orchestrate AI-enabled workflows and apply judgment where it matters most.

The technological advancements of the last several years have reshaped what’s possible for actuarial teams across the insurance industry. Generative AI (GenAI) and richer data sets are now in production at many insurers. As a result, actuaries are delivering more granular, actionable insights at greater speed. Questions that once took days or weeks to answer can now be addressed in hours or minutes. Many manual tasks have been reduced or eliminated. That shift frees actuaries to focus on strategic activities at a time when market volatility and intensifying competition demand faster decisions.

If you lead actuarial at an insurer, the mandate is to move faster while strengthening governance and decision confidence. This article outlines where GenAI is already delivering impact and the practical steps to scale it responsibly across data, controls, talent and operating model.

Actuaries orchestrate AI-enabled workflows

As these tools become standard, actuarial work shifts further. Actuaries increasingly orchestrate work across human teams and AI-enabled workflows. They define objectives, sequence tasks, select tools and datasets. They review outputs and apply professional judgment, which ensures results are interpretable and decision-ready. AI-enabled workflows also connect processes end-to-end. They reduce handoffs and compress cycle times while preserving actuarial control over assumptions, governance and sign-off. 

Governance and controls for trusted AI-supported actuarial decisions 

Strong governance and controls are essential. As AI-generated insights are used more frequently in decision-making, trust becomes critical. Clear standards, transparency and accountability underpin that trust. Actuaries are well positioned to define standards of practice and shape governance models that allow leaders to rely on AI-supported insights. 

AI also reframes the business case for transformation, based on lower costs and higher rates of efficiency. But efficiency gains do not reduce the actuary’s importance; they reallocate capacity. Automating repeatable work shifts effort from process cycles toward more frequent scenario analysis, governance and enterprise decision support. When paired with redesigned processes and modern platforms, this shift delivers meaningful returns. Cloud-native infrastructure and elastic computing further enhance stress testing, forecasting and capital projections. 

The payoff, however, is not automatic. Data remains the foundation of actuarial judgment. Insurers need clean, governed and accessible data, supported by modern platforms and application programming interface (API) connectivity. Without this foundation, AI will not reach its full potential and returns will be constrained. 

Democratized access to trusted data helps actuaries move faster and reduces dependence on technical intermediaries. It is a prerequisite for capturing value at scale.

Actuarial talent and skills for the GenAI era

How does GenAI in insurance change actuarial talent needs?

The role expands as execution becomes more automated. Reinvention places new demands on actuarial talent. This is not because actuaries lack capability today, but because the scale and scope of their contribution are growing. As actuarial work intersects more deeply with data, product, and finance decisions, actuaries are doing more of the work that differentiates the function. They apply judgment in uncertain situations, shaping the business questions that matter most and influence decisions that drive growth, profitability and resilience.

AI accelerates this shift. By reducing time spent on repeatable execution, actuaries have capacity for higher-value work, including strategic insight, collaboration and leadership. This combination brings technical rigor and business acumen together to elevate the quality of decisions across the enterprise. 

How AI is transforming actuarial roles 

Market dynamics increase demand for actuarial judgement across disciplines. Faster product development in response to new risks, new distribution models and non-traditional capital strategies are increasing demand for actuarial judgement across disciplines. Many actuaries are operating as multidisciplinary specialists, embedded in innovation programs and playing larger roles in product lifecycle management, FP&A, reinsurance and asset-liability management. 

Will AI replace actuaries? The value shifts toward judgement and accountability

There has been speculation about AI replacing actuaries. Advanced technology matters, but talent and leadership determine whether AI translates into real business value. By automating repeatable execution, AI actually increases the importance of judgment, governance and accountable decision-making. In other words, high-performing actuarial teams remain the bridge between the potential of advanced analytics and the impactful actions that will lead to value for the business. 

Operating models that scale AI-enabled actuarial performance

What operating models can scale AI-enabled actuarial performance?

Realizing the full potential of AI-enabled actuarial transformation requires more than new tools or incremental reskilling. It requires a deliberate operating model designed for speed, integration and accountability. When paired with refreshed talent and modern technology, a strong operating model amplifies performance gains.

Operating models define how organizations structure processes, deploy talent and use data and technology to make decisions. They clarify roles and responsibilities, establish governance and enable collaboration across functions.

There is no single best-practice template. Effective operating models reflect organizational strategy, culture and maturity. Still, the most successful transformations consistently share five elements.

Five elements of operating models that work 

Iteration should be treated as resilience, not rework. As technologies and market demands shift, operating models must evolve deliberately – guided by metrics, decision quality and business outcomes. 

Unlocking the full strategic potential of the actuarial function

AI’s most profound impact may be in unlocking the full strategic potential of the actuarial function. AI accelerates insight generation and strengthens judgment in the face of uncertainty. It embeds analytics more directly into everyday decisions. Together, these changes allow actuaries to move beyond execution and fully step into their role as value creators. 

Technology alone is not enough. Insurers that capture the most value modernize talent, operating models and technology together. They align judgment, governance and accountability with AI-enabled capabilities. Those that act decisively will strengthen decision quality and improve performance across the enterprise. In an increasingly complex and volatile environment, this integrated approach can become a durable source of competitive advantage.

The following individuals also contributed to this article: 

• Sherry Chan, Managing Director, Ernst & Young LLP
• Jennifer Haid, Principal, Ernst & Young LLP
• Ben Yahr, Managing Director, Ernst & Young LLP
• Daniel Jonas, Manager, Ernst & Young LLP