Beyond the hype: How insurers can use AI to deliver lasting business value

This white paper, authored by Zinnia’s chief data officer Daniel Gremmell, director of technology for AI solutions Chris Eve-Levesque, and managing editor Pat Howard, explores actionable insights and strategies that leaders in the life & annuities insurance space can use to drive efficiency, innovation, and growth.

Executive summary

Artificial intelligence (AI) is revolutionizing the life and annuities (L&A) insurance sector, with significant implications for operational efficiency and customer engagement. Yet AI is also the subject of immense hype, with many applications amounting to little more than a flashy interface. 

As AI technologies evolve, the insurance industry is well positioned to capture substantial value from these innovations by focusing on practical applications that align with operational goals. Traditional AI applications in machine learning have been enhanced by the integration of generative AI (GenAI) and large language models (LLM), streamlining processes throughout the business value chain, including risk assessment and underwriting, claims processing, operations, and customer service. Meanwhile, GenAI has the ability to unlock new applications in areas such as product development, leading to faster time-to-market with less risk and more predictive precision.

The past few years have seen a significant uptick in funding toward emerging technologies, particularly GenAI, which is increasingly perceived as a strategic asset beyond mere technological advancement. As recently as 2017, just 1.33% of insurance companies had invested in AI, according to Deloitte. As of 2024, 29% of insurers indicated that they’re using AI, and 51% are looking to implement AI technologies into their businesses. 

By placing business objectives at the heart of any AI implementation, promoting a data-forward culture in their organizations, and embracing ethical AI, L&A insurers can move beyond the hype and deliver long-term value to their business.