A former Dell executive turned healthcare startup founder argues that enterprise AI deployments fail because companies automate broken processes instead of redesigning them—and that healthcare's crisis in prior authorization and coverage decisions reveals why proper AI governance, not just more agents, is essential.

What happened: The author, drawing on 11 years at Dell and subsequent startup experience, left enterprise technology to start a healthcare company focused on coordinating AI systems. MIT researchers found that 95% of generative AI pilots produced no measurable return, and Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027, citing inadequate risk controls and unclear value as primary causes.

Why it matters: In healthcare, a single drug approval decision currently requires six or seven specialists working in sequence over 60 to 90 days at a cost of roughly $100,000 per drug—while patients wait in coverage limbo. The author shows that enterprises across financial services, insurance, energy, and government face the same problem: expertise trapped inside slow, rule-bound workflows. AI cannot solve this by simply speeding up broken processes; it must be governed, audited, and coordinated to produce traceable decisions that clinicians, compliance officers, and regulators can trust.

What to watch: A large pharmacy benefit manager runs 200 to 300 drug approval assessments every year. The same assessment done with coordinated AI takes four to eight hours with direct labor cost dropping by 97%, and every decision is documented for audit. This gap—between current six-to-eight-week timelines and four-to-eight-hour potential—illustrates the scale of efficiency and patient care at stake across regulated industries.