{"id":1815,"date":"2026-06-20T08:37:59","date_gmt":"2026-06-20T08:37:59","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=1815"},"modified":"2026-06-20T08:37:59","modified_gmt":"2026-06-20T08:37:59","slug":"i-watched-enterprises-buy-ai-that-solved-the-wrong-problem-so-i-left-dell-and-built-a-startup-to-fix-it","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=1815","title":{"rendered":"I watched enterprises buy AI that solved the wrong problem. So I left Dell and built a startup to fix it"},"content":{"rendered":"
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For some drugs, the wait is an annoyance. For a schizophrenia drug,\u00a0it\u2019s 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\u2019re looking at four to seven million dollars in costs that didn\u2019t need to happen. All from one slow decision about one drug.<\/p>\n

Read more The highest-paid hospital CEO made $43 million last year all while Americans hold $220 billion in medical debt<\/a><\/p>\n

I\u2019ve seen this movie before.\u00a0Eleven years at Dell \u2014 and a few more at startups \u2014 taught me how enterprises buy technology that\u2019s supposed to fix exactly this kind of mess, and how rarely it does.<\/p>\n

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The COVID pandemic forced me to stop and reflect. As healthcare systems strained under extraordinary pressure, I found myself asking a simple question: \u201cWhat am I going to tell my grandkids? That I helped people shop and search more with AI? Or can I do something more?\u201d That question ultimately led me to healthcare. The mission became deeply personal when I later lost a friend to breast cancer,<\/s>\u00a0and heard about their struggle to access experimental treatments buried within thousands of pages of medical records.<\/p>\n

Healthcare doesn\u2019t lack expertise. It struggles to get the right expertise to the right people at the right time.\u00a0It is the same challenge I have seen play out across enterprises for years.<\/p>\n

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\u2019t that the models were weak. It was that organizations bolted AI onto processes that were never rethought for it.<\/p>\n

Right now the entire AI conversation is about agents \u2014 how many you can deploy, how autonomous they are, how fast they move. The race is to build more AI workers. Almost nobody is building the thing that manages them. That\u2019s the part that actually matters, and it\u2019s the part nobody\u2019s racing for.<\/p>\n

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The analysts have already priced in what happens next. Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027,<\/s>\u00a0and\u00a0cites inadequate risk controls \u2014 alongside cost and unclear value \u2014 as one of three primary causes.<\/p>\n

We\u2019ve been here before. When the cloud showed up, the pitch was transformation. What most companies actually did was pick up their existing workflows, move them off their own servers, and set them down in the cloud. Same work, same thinking, bigger bill.\u00a0AI is heading down the same road. Companies are automating one step here, one queue there, and calling it a win. But speeding up a broken process just gets you to the wrong answer faster.<\/p>\n

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Healthcare\u00a0is hard for AI in exactly the ways that make it useful to learn from. The regulations are brutal. The decisions matter. The data sits in systems that were never built to talk to each other, and the people who can actually read that data \u2014 clinicians \u2014 are in short supply and getting shorter. The AAMC projects a shortage of up to 86,000 physicians by 2036, and nurses are stretched just as thin.<\/p>\n

Read more Meet the SpaceX insiders Elon Musk trusts to run his $1.25 trillion empire<\/a><\/p>\n

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One health enterprise we work with has 600 nurses whose main job is prior authorization and payment integrity. Six hundred people who trained to care for patients, spending their days inside paperwork. Technology was supposed to fix that. Mostly it just turned the filing cabinet digital.\u00a0The AMA\u2019s 2024 physician survey found that prior authorization alone consumes an average of 13 hours of physician and staff time every week, and 93% of physicians say it delays patient care.<\/p>\n

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Healthcare simply makes the problem visible sooner. Financial services, insurance, energy, and government face the same challenge. Expertise is trapped inside workflows, regulations are increasing, and every decision requires traceability. The question isn\u2019t whether AI can perform a task. It\u2019s whether organizations can understand, govern, and trust the decisions AI helps produce.<\/p>\n

We ran the math and found that for a pharmacy benefit manager or health plan,\u00a0a new drug approval\u00a0can trigger a process involving six or seven specialists \u2014 pharmacists, coders, actuaries, compliance counsel \u2014 all working largely in sequence over 60 to 90 days, at a cost of roughly $100,000 per drug. A large PBM runs 200 to 300 of these assessments every year. While that process runs, patients are in coverage limbo.<\/p>\n

The same assessment, done with coordinated AI, takes four to eight hours. A clinical pharmacist reviews the output rather than producing it;<\/s>\u00a0, the\u00a0coverage gap closes, and direct labor cost drops by 97%. And everything the agents did is documented, so when a compliance question comes up later, you can trace exactly what informed\u00a0decision.\u00a0No one is hunting for answers buried in an email chain or locked inside someone\u2019s memory.<\/p>\n

The goal shouldn\u2019t be automation. It should be changing what\u2019s possible.\u00a0Most enterprise AI deployments are invisible\u00a0\u2014\u00a0accelerating steps a human would otherwise take without changing what that human does. But deploying a thousand agents that can\u2019t be coordinated, governed, or audited is the same thinking that gave enterprises a thousand disconnected point solutions a decade ago,<\/s>\u00a0and produced the same result.<\/p>\n

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Healthcare didn\u2019t volunteer to go first. The pressure simply arrived there earlier\u00a0\u2014\u00a0people are leaving because of the administrative burden, patients aren\u2019t being cared for, and coverage processes are failing. It is a broken system that urgently needs help. That urgency is forcing the industry to confront something it hasn\u2019t had to yet: not just how to deploy AI, but how to govern it, audit it, and make it work for the people it\u2019s supposed to serve\u2013<\/s>\u00a0\u2014\u00a0not just the systems processing data in the background.<\/p>\n

Every other regulated industry will face that same pressure eventually. The MIT and Gartner numbers suggest many already are.\u00a0The question is whether they learn from the lessons of others now or wait until they\u2019re at the same breaking point.\u00a0What this work has taught me: you don\u2019t need more AI agents. You need an operating system to manage them \u2014 and the clarity to change what your people actually do.<\/p>\n

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of\u00a0<\/em>Fortune.<\/em><\/p>\n

Read more Cape Verde\u2019s Roberto Lopes was working at a bank when he was recruited on LinkedIn to play soccer\u2014he thought it was spam, now he\u2019s at the World Cup<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

The healthcare industry’s broken prior authorization process isn’t just a patient problem \u2014 it’s a warning sign for every regulated industry deploying AI without the infrastructure to govern it.<\/p>\n","protected":false},"author":1,"featured_media":1814,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54],"tags":[],"class_list":["post-1815","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-venture-capital"],"yoast_head":"\nI watched enterprises buy AI that solved the wrong problem. 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