Navigating the GenAI Revolution

Why Adoption Has Lagged

Despite GenAI's clear promise, adoption within the insurance industry has been more cautious than in other sectors.

This hesitation stems from key limitations inherent to the technology, including responses that seem accurate but are fabricated (hallucinations), training bias introduced from skewed data sets, and complex models that make it difficult to understand the decision-making process fully.

Additionally, insurance companies must navigate complex regulatory landscapes that include data privacy laws such as CCPA and GDPR and industry-specific regulations like NAIC guidelines. This adds operational complexity, reinforcing the industry's careful approach to adopting GenAI.

However, these limitations are manageable. For example, careful scrutiny of data sets to determine what might be over- or underrepresented before input can help eliminate training bias.

Careful planning and risk assessments are beneficial in minimizing the impacts of hallucinations and ensuring ethical alignment.

Three Areas of GenAI Value

Despite these challenges, growing adoption is evident in three primary categories with the potential for significant revenue impact: boosting productivity, enabling growth, and optimizing acquisition costs.

1. Boosting productivity: GenAI can supercharge productivity through process automation and task streamlining. Companies such as Gong have incorporated GenAI to extract actionable insights from call recordings, enabling advisors to concentrate on client interactions without being burdened by note-taking.

At Bestow, our Advisor Assist tool allows agents to find information from extensive field underwriting guides effortlessly. Instead of combing through content pages, agents can pose questions in natural language and receive precise answers.

On the underwriting side, early results with our product, UW Assist, show promise in revolutionizing how we conduct post-issue audits, potentially reducing manual workload and improving accuracy.

2. Enabling growth: Growth comes from enhanced customer service and streamlined service delivery.

GenAI can be used to automate initial touch points and reduce response times to elevate customer satisfaction. Lemonade, for example, has leveraged AI to expedite claims processing.

Mass personalization is another game-changer enabled by GenAI.

Platforms like Tavus.io allow agents to create personalized video outreach at scale. An agent can record a single video message, and Tavus can modify it to address hundreds of customers individually without additional effort.

3. Optimizing acquisition costs: From a marketing perspective, GenAI offers powerful capabilities for enhancing customer acquisition and lifecycle management by identifying and prioritizing high-value prospects, significantly reducing acquisition costs.

AI-driven analytics can optimize channel performance, ensuring marketing and sales efforts are directed toward the most effective platforms.

Additionally, GenAI can power personalized outreach campaigns, increasing customer engagement and boosting conversion rates through tailored data-driven interactions.

Starting Small — But Not Slow

Insurers must integrate this new technology with strategy and caution. Moving too fast can lead to operational vulnerabilities or compliance missteps while moving slowly might result in competitors outpacing (and outselling) them.

Get started on this journey by defining the use cases that apply to your business and are most likely to be approved by your compliance department.

Engage your risk and compliance teams early. They are the gatekeepers for your opportunity to engage with GenAI, and you'll enjoy the process with an enthusiastic compliance partner.

Cross-functional collaboration beyond compliance is necessary, too. Collaboration between human experts and AI systems leads to the best outcomes. Insurers must foster strong cross-functional teams where data scientists, actuaries, and partner-vendor leaders work together to ensure that AI solutions align with strategic goals and are implemented responsibly.

Decide how to implement GenAI capabilities by evaluating the build, buy, or partner approach. Each option has unique benefits and challenges. Building an in-house solution provides complete control and customization but requires substantial resources and technical expertise. Buying pre-built solutions offers speed but can be costly and may come with limited flexibility. Partnering with specialized vendors, especially for insurers exploring GenAI for the first time, is often the fastest and most cost-effective way to launch. Partnerships can enable quicker deployment, offer ongoing support, and provide access to established expertise, helping insurers navigate initial complexities and scale more confidently.

Finally, drive value iteratively. The key is starting with a small, focused pilot. In this contained environment, insurers can experience and refine without widespread risk.

The insurance industry is poised for a GenAI revolution that promises significant benefits.

By adopting a strategic approach that balances innovation with caution and leverages pilot programs to scale AI solutions, insurers can unlock new opportunities for growth while managing the risks inherent in a highly regulated environment.

Lena Chukhno is the chief revenue officer at Bestow, a life insurance distribution technology firm. Earlier, she spent about a decade in management consulting at McKinsey & Co. and PwC.

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