The Challenges of Capitalizing Generative AI Investments

Gen-AI poses distinct financial planning challenges unseen with traditional systems implementations.

As insurers ramp up investments in artificial intelligence, specifically generative AI, they must recognize these technologies differ greatly from other IT expenditures. While promising major leaps in productivity and business insights, generative AI also poses distinct financial planning challenges unseen with traditional systems implementations.   

Early Stage Investments 

Much pioneering work with generative AI involves experimentation without clearly defined products or tangible deliverables readily meeting capitalization criteria. During these early R&D phases, costs may be more appropriately expensed rather than capitalized as assets. 

Uncertainty in Useful Life   

Useful life estimates for generative AI solutions prove extremely difficult to reliably quantify given the brisk pace of advancement in this space. Whereas conventional policy admin systems typically follow 5+ year depreciation models, AI platforms and their rapidly evolving capabilities often render existing features and functionality obsolete within months. To account for this uncertainty, insurers may find it beneficial to review useful life assumptions annually. 

As Alok Bhargava, a partner at EY commented: “ For Insurance organization to evaluate the useful life of generative AI solutions can be a complex task due to the swift pace of development in this field. Today’s use case, built around a single model, might require the integration of multi-model in immediate future owing to rapid technological advancements. Given the heightened uncertainty regarding the lifespan of these AI solutions, it’s advisable for Insurance carriers to expense, instead of capitalizing their investments in generative AI.” 

For example, in just the year since the introduction of ChatGPT 3.5, numerous enhancements have been added that could obsolete solutions built around 3.5’s original capabilities. Alternatively, future solutions might apply several LLMs working in concert.  

Ongoing Commitments  

Insurance CFOs should also note that relative to conventional on-premise systems, generative AI solutions often require considerably more ongoing investments in maintenance and continuous training. 

In summary, while Datos Insights sees extraordinary value potential in generative AI, we urge insurers to take a prudent financial planning approach when allocating resources to these transformative and rapidly evolving technologies. Please reach out for additional perspectives on AI investment strategies.