I watched enterprises buy AI that solved the wrong problem. So I left Dell and built a startup to fix it

A new drug clears the FDA. Now the health plan has to decide whether to cover it, and that decision sets off a scramble: six or seven specialists, working mostly in sequence, two to three months, and somewhere around $100,000 by the time it’s done. Meanwhile, the patients wait. For some drugs, the wait is an annoyance. For a schizophrenia drug, it’s something worse. When treatment gets interrupted, people end up hospitalized, and each of those admissions costs a plan somewhere between $8,000 and $15,000. A few hundred of them across one large plan and you’re looking at four to seven million dollars in costs that didn’t need to happen. All from one slow decision about one drug. I’ve seen this movie before. Eleven years at Dell — and a few more at startups — taught me how enterprises buy technology that’s supposed to fix exactly this kind of mess, and how rarely it does. The COVID pandemic forced me to stop and reflect. As healthcare systems strained under extraordinary pressure, I found myself asking a simple question: “What am I going to tell my grandkids? That I helped people shop and search more with AI? Or can I do something more?” That question ultimately led me to healthcare. The mission became deeply personal when I later lost a friend to breast cancer, and heard about their struggle to access experimental treatments buried within thousands of pages of medical records. Healthcare doesn’t lack expertise. It struggles to get the right expertise to the right people at the right time. It is the same challenge I have seen play out across enterprises for years. AI is running the same playbook now. MIT researchers analyzed more than 300 enterprise AI deployments last year and found that 95% of generative AI pilots produced no measurable return. Their diagnosis wasn’t that the models were weak. It was that organizations bolted AI onto processes that were never rethought for it. Right now the entire AI convers