AI frequently fails by solving irrelevant problems, driven by available data instead of genuine needs. Many healthcare AI tools, like radiology segmentation, are accurate yet impractical; crucial problems like triage are neglected. AI struggles with misdefined problems or ignored context. Successful AI addresses real practitioner pain points. The text advocates an inverted development: practitioners must define desired outcomes, guiding AI specialists to build solutions that meet actual needs, rather than letting data dictate the problem.