Measuring ROI for enterprise GenAI projects is challenging because traditional financial models don't account for variable usage, evolving prompts, and iterative development. Hard ROI includes quantifiable financial returns like cost savings and reduced cycle times, while soft ROI captures strategic benefits like faster decision-making and improved customer interactions. Common pitfalls include relying on vanity metrics (model accuracy, adoption counts), measuring too early before production integration, and overlooking hidden costs like inference expenses at scale. Organizations struggle to prove value due to missing baselines, attribution challenges in complex systems, and inability to isolate AI's causal impact. Successful measurement requires portfolio-based frameworks, realistic time horizons, and connecting AI initiatives to defensible business outcomes rather than technical benchmarks.
