The AI Proposal That Needed a Comprehensive Reality Check

A technically impressive AI concept receives strong internal enthusiasm. The prototype performs well in demonstrations, but the team has not yet established how the output fits into professional workflows, how errors would be identified, or what level of evidence users would need before trusting it.

MIT Sloans Artificial Intelligence in Pharma and Biotech programme reinforced that AI adoption in health-related environments cannot be evaluated only through model capability. Data provenance, workflow integration, human accountability, regulatory expectations, and the consequences of failure are equally important.