Insurers have plenty of experience with disruptive technologies and can apply lessons from the past to chart the right course forward. The immediate priorities are to systematically identify and pilot use cases, while simultaneously establishing the necessary governance, infrastructure and operating models to scale adoption successfully. As both these tracks are critical, leaders must strike a balance in managing them.
Findings from EY research confirm our experience working with insurers around the world – that it’s critical to both take quick action to start on the GenAI journey and methodically build a stable foundation for future success. Insurers that move too slowly risk being leapfrogged by the competition. And those that fail to build core infrastructure, governance and capabilities will struggle to consistently develop and scale GenAI applications and to maximize returns on their investments. Firms that embrace a dual-track approach will be well positioned to unlock immediate value even as they set the stage for sustainable success at scale.
1. Encourage experimentation by equipping your people with GenAI tools
Accelerate grassroots innovation by deploying GenAI tools, including both internally and externally developed copilots and sandbox environments, and empowering users to explore and develop potential use cases. Appropriate guardrails can promote a safe and secure environment, such as protections on using internal data with public tools. Start with talent upskilling and foundational training, then prioritize quick-win use cases that can be tested and launched efficiently and with low risk, such as data summarization and report generation across near-term time horizons, for example eight to 18 months.
- Take immediate action but prepare to fail fast: Leaders of strategy and innovation teams can champion a grassroots culture and spearhead initial pilots as they understand where targeted GenAI applications can be applied to address specific business problems. Encourage all staff levels and functions to test use cases autonomously with minimal oversight in sandbox environments. EY teams are working with a speciality insurer that has formed cross-functional teams to identify unmet business needs and assess how GenAI solutions can help, for example automating the policy renewal process.
- Prioritize and pilot quickly: “Lite” frameworks can help determine the viability of GenAI solutions for starting-point use cases. To identify quick wins, leaders can weigh business value against the ability to execute. EY teams recently supported a life insurer in evaluating and prioritizing a lengthy backlog of use cases; identifying “clusters of value,” for example enhanced underwriting, ease of doing business, was critical to short-listing use cases aligned to enterprise goals.
Rapid piloting throughout the organization can quickly validate assumptions and hypotheses. For other insurers, EY teams are conducting six- to eight-week sprints to streamline the development and launch of high-priority use cases.
- Set up guardrails: Determine the organization’s risk appetite and develop clear guidelines on what is and is not allowed for employee use of GenAI. Deploy off-the-shelf models in a sandbox environment to accelerate innovation through “test-and-learn” iterations. For a leading global insurance carrier, EY teams helped in creating a dedicated, fit-for-purpose development environment to promote experimentation and testing with innovative ideas.
Compliance with the EU AI Act will be a baseline expectation. Other EY research shows senior leaders are focused on establishing the right guardrails and policies. In a recent survey of insurance chief risk officers (CROs), 81% said their firms had already enhanced or were in the process of enhancing policies around development, validation and use of GenAI. A similar number, 82%, said they had established a governance framework, policies and procedures for LLM and GenAI adoption or were working to establish them. You can see the full results here.
2. Develop the enterprise strategic and technical infrastructure for GenAI
Even as they pursue quick wins and seek to build confidence with AI, insurers should craft the overall vision and determine how GenAI can transform the business over the long term. Key elements of the GenAI strategy include the delivery model, data governance, computing infrastructure (i.e., on-premise vs. cloud) and a prioritization framework to support desired business outcomes.
Ultimately, success with GenAI will be determined by innovative, externally oriented use cases that provide tangible long-term benefit such as, revenue growth and more accurate risk pricing. For many insurers, conducting a thorough assessment of organizational readiness and the maturity of current capabilities will be a sensible first step.
- Mobilize cross-functional teams and centralize management: Multidisciplinary teams with different competencies, such as business, IT, security, compliance, should be charged with designing and delivering end-to-end solutions. We have seen first-hand how centralized management, coupled with enhanced capabilities and select competencies, provides a strong foundation for successful GenAI programs.
As GenAI adoption expands across the value chain, cross-functional teams should have the support of senior-level sponsors. Increasingly, we expect more heads of dedicated GenAI teams will report directly to the C-suite, including the CEO.
Most firms will need to fill key competency gaps with external talent. Engaging with third parties and tapping into an ecosystem of GenAI players can further accelerate adoption; large insurers may establish a framework to inform build-buy-partner decision making.
- Establish data infrastructure and governance practices: As insurers mature their GenAI use, they should enhance governance and policies in line with their own needs and shifting regulations. Continuous monitoring of the regulatory landscape is essential.
Strong data governance starts with understanding the source of data, where it resides and who has access to it. Identify critical data that is used to generate judgements and establish clear controls for decision-making processes. Conduct regular reviews of AI-generated content. By maintaining data transparency and accountability, insurers can avoid “garbage-in, garbage out” scenarios.
Insurers should establish a strong data environment, such as closed- or open-source LLMs, to create, train and deploy GenAI models and solutions, and plan to enhance it on an ongoing basis. In parallel, they should design a capability for processing and managing unstructured and semi-structured data that supports reusability in line with strategic priorities.