Decision-Making and Real Options
This dynamic interplay between technological progress and co‑innovation informs how decisions should be made. Gen AI adoption is iterative, involving continuous learning and deployment in new workflows and organizational models. The pace of this feedback loop has accelerated from steam to electricity to semiconductors – and is expected to be even faster with Gen AI, placing the insurance industry on a path of rapid learning and continuous innovation.
Each adoption decision carries risk: an error of omission (failing to pursue a valuable project) or an error of commission (pursuing a project that should have been avoided). Not pursuing a project is fully irreversible, while executing one often involves sunk costs – expenditures that cannot be recovered if the project is abandoned or altered.
This creates value in waiting. Delaying a decision preserves the option to act later with better information. This option value reduces the traditional net present value (NPV) of a project. However, waiting also has a cost: missed opportunities and reduced future flexibility. This cost of waiting increases the project’s value.4
Real options theory emphasizes learning and reversibility. It favors technologies that enable reversible decisions and expand future choices – both factors that support low-cost complementary innovation and early adoption. While real options are difficult to quantify in complex environments, their practical value lies in guiding decision-making: prioritize technologies that allow reversibility and broaden future opportunities.
The J‑Curve in Measured Productivity
Adopting a GPT requires complementary investments. For Gen AI, foundational investments include cloud infrastructure, data architecture, and organizational capabilities in data and AI engineering. At the workflow level, co‑innovation requires building competence in human‑AI collaboration and in coordinating workflows with AI agents.
These investments create intangible assets that initially function as outputs – products of the organization’s efforts. Over time, they become inputs to production. Traditional productivity metrics often fail to capture these assets, underestimating productivity during their formation and overestimating it once they are deployed.5
For insurers, a rough productivity measure is the expense ratio. While intangible assets are being developed, they increase expenses without contributing to output. Once deployed, they enhance output without adding to expenses. This dynamic can create a J‑Curve in measured productivity: initial decline followed by long‑term gains, as illustrated below. Recognizing this effect is essential for accurately assessing Gen AI’s impact on productivity in both the short and long term.
J‑Curve in Measured Productivity
