Embracing the human challenges in elevating GenAI
Build a learning culture that supports employee upskilling and adaptation
Defining generative AI and early successes
The insurance industry is on the cusp of a transformative shift as generative AI technologies gain traction. Organizations have seen early successes in areas such as knowledge management, training, and coding efficiency. AI-powered chatbots and intelligent search tools have significantly improved the accessibility of information for employees, reducing training time and increasing productivity. Additionally, generative AI has shown promise in automating coding tasks and improving quality assurance processes. However, the adoption of generative AI for cognitive decision-making remains limited due to regulatory concerns and the need for reliable, high-quality data.
The human component is fundamental to any technology conversation. While this may be my own bias, I firmly believe that it will be crucial both in terms of adoption and in the value we can provide to our customers when leveraging this exciting new technology.
Evan Metter
Principal, Advisory, KPMG LLP
Navigating the challenges of generative AI adoption
As adopting generative AI comes with its own set of challenges, organizations must prioritize data quality and integrity to ensure the effectiveness of AI models. Many insurance companies face the challenge of operating with a mix of legacy tech stacks and modern application programming interfaces (API) economies, which can hinder the smooth adoption of generative AI. Running generative AI becomes increasingly difficult without a fully integrated digital substrate, requiring insurance companies to address their data infrastructure and systems before fully leveraging the technology.
Setting up safe, appropriate, and well-controlled environments is crucial before implementing AI solutions. Focusing on problem-driven use cases rather than solution-driven approaches can help mitigate risks and ensure the value-driven adoption of generative AI. To harness the full potential of generative AI, collaboration and focused adoption are key, prioritizing use cases that deliver tangible business value and improve customer experiences.
The impact on the workforce and customer experience
As generative AI takes on mundane and repetitive tasks, the nature of work in the insurance industry is set to change. While there are concerns about job displacement, AI has the potential to augment human capabilities and improve productivity. By automating routine tasks, generative AI can free up employees to focus on more complex and value-added activities, such as customer engagement, risk assessment, and strategic decision-making. However, striking the right balance between automation and human touch remains crucial, especially in sensitive customer interactions.
The "happy robots" anecdote in underwriting illustrates how generative AI is changing the nature of work. As automated systems handle the easy cases, such as approvals and rejections, human underwriters are left with only the most complex and difficult ones. This shift can lead to a more challenging workload for employees. Organizations will need to support their workforce in adapting to these changes.
Insurance companies must carefully consider the customer experience implications of generative AI and ensure that the technology enhances, rather than diminishes, the quality of service delivered. Designing AI systems that complement human expertise and empathy, rather than replacing them entirely, is vital. Framing the technology as a tool to eliminate soul-sucking administrative tasks and empower employees to focus on high-value work is essential.
Building a learning culture for successful implementation
The successful implementation of generative AI in the insurance industry requires a shift in organizational culture towards learning and adaptation. Building a learning culture that supports employee upskilling and adaptation is crucial. Organizations should foster an environment that encourages continuous learning and provides employees with the necessary training to work alongside AI technologies effectively. This includes not only technical skills but also soft skills such as critical thinking, problem-solving, and emotional intelligence.
As insurance organizations grapple with the adoption of generative AI, they face the dual challenges of assessing its business value and managing its impact on the workforce. Determining where to apply these expensive tools requires a methodical approach, with a focus on high-value use cases and clear customer demand. Some companies are forming AI client councils to generate ideas, prioritize focus areas, and ensure proper governance in a regulated industry.
Early adopters have seen challenges in testing and effectively using generative AI tools, but as they progress along the learning curve, pockets of success are emerging. However, enterprise-scale adoption and value realization remain rare. Most insurers continue taking a cautious, targeted approach rather than a broad "spray and pray" deployment. By striking the right balance between business value and human impact, insurance organizations can harness the power of generative AI to enhance productivity and free up workers for higher-value contributions.
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Embracing the human challenges in elevating GenAI
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