Enterprise AI Fails When Deployed on Broken Business Processes

Many enterprise AI projects begin by selecting a model rather than first examining the business process the technology is meant to improve. Poorly defined workflows — marked by unclear responsibilities, inconsistent data, and unwritten business rules — do not get fixed by AI; they get amplified. A human operator working with bad data may produce isolated errors, but an automated AI system can replicate those same errors across thousands of records at scale. The article argues that teams often spend weeks refining prompts while the root problem, flawed process design, remains unaddressed. Before choosing any AI model, organizations should clearly define process boundaries, data ownership, and measurable success criteria.