AI Feature Failure Attributed to Lack of Evaluation
In a recent podcast, AI expert Ankit Shula argued that most AI features fail because of poor evaluation, not poor models. He stated, "If you are shipping AI features without evaluations, your product is lying to you and you have no idea." Shula advocated for a rigorous, non-negotiable evaluation process as a core competency for modern product organizations.
- Industry-wide data suggests high failure rates for AI initiatives, with some reports indicating that between 87% and 95% of data science and generative AI projects never make it into full production. - A primary reason for failure is the disconnect between a model's performance on clean, benchmark datasets and its performance on messy, real-world production data; this is often due to "data drift," where the patterns in live data change over time. - Beyond model accuracy, comprehensive evaluation frameworks test for multiple, often competing, criteria such as fairness, robustness against adversarial attacks, latency, and cost-effectiveness. - Many project failures are attributed to organizational and cultural factors, with one MIT study suggesting these account for 70% of failures, rather than technological limitations. - Leading companies adopt "eval-driven development," an approach where testing and evaluation are integrated throughout the entire product development lifecycle, not just at the end. - A significant portion of AI project work—often cited as up to 80%—is spent on data gathering, cleaning, and preparation, which is a foundational step for any meaningful evaluation. - Failures are often discovered late in the process during the pilot stage, a situation sometimes referred to as "pilot paralysis," which can be mitigated by earlier and more continuous testing. - Post-deployment monitoring is a critical, yet often overlooked, part of evaluation; without it, performance degradation, bias, and unexpected model behaviors can go unnoticed, leading to silent failures.
