[Part 1 of 3] Foundations of Trustworthy AI: A Guide to Mitigating Systemic Bias and Reducing the Pilot-to-Production Gap

Author(s): Suresh Dhamapurkar

Originally published on Towards AI.

Part 1 of 3: Introduction; The Anatomy of a System Failure; Biases Related to Time and Data Persistence

A July 2025 report from MIT’s Project NANDA reveals a stark reality: despite billions in enterprise investment, a staggering 95% of organizations fail to achieve any business returns from their AI initiatives. This failure is starkly illustrated by the “Pilot-to-Production Chasm”, where only 5% of AI pilots successfully transition into valuable, operational systems.

The article discusses the challenges organizations face in successfully implementing AI initiatives, highlighting the significant gap between successful pilot projects and their full-scale deployment. It indicates that while advanced AI models may promise enhanced outcomes, failures often stem from systemic biases in training data and improper risk management. The author stresses the importance of understanding these biases to build trustworthy AI systems that can deliver reliable value, concluding with a call to mitigate risks that hinder AI’s effective integration into business processes.

Read the full blog for free on Medium.

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Published via Towards AI