<![CDATA[Generative Artificial Intelligence (AI) is reshaping retail investment strategies, yet traditional evaluation tools such as Net Present Value (NPV) and Internal Rate of Return (IRR) struggle to capture uncertainty and flexibility. This study applies a binomial lattice real options model to assess Generative AI investments in retail, demonstrating that real options provide a more adaptive framework than conventional methods. The model evaluates multi-stage decisions pilot testing, regional scaling, and enterprise adoption and incorporates sensitivity analyses to account for adoption probabilities and volatility scenarios. Results indicate that real options modeling captures strategic flexibility by valuing managerial discretion, phased rollouts, and intangible benefits, which static NPV models overlook. This highlights its relevance for addressing retail-specific challenges such as data integration and organizational readiness. The study concludes that real options offer a superior framework for evaluating AI investments, supporting adaptive planning and long-term strategic value for retailers.]]>
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