Why AI Pilots Burn Out
- Catherine Del Vecchio Fitz
- Oct 1, 2025
- 5 min read
Models write emails. Systems run a company.
TL;DR
MIT reports that 95% of generative AI pilots fail to deliver business return. The models aren’t the issue—they’re more than fine for emails and summaries. The issue is that they tend toward the average, which is useless when workflows demand precision, compliance, and internal data no foundation model has ever seen. Too many pilots generate an output but never wire it into the workflows that make it a usable system. I know, because for years I stitched this together manually—pulling outputs into spreadsheets, patching workflows by hand. AI and no-code tools have finally caught up, but the real edge is still the combination of smart integrations and deep domain expertise.
The Pilot Problem
In August 2025, MIT’s report on the “GenAI Divide” made headlines: 95% of generative AI pilots fail to deliver measurable return (Fortune). The issue wasn’t model performance but integration failure: demos looked great in isolation but collapsed when plugged into messy company data, compliance, and operations.
That number stuck with me—and made me really question my business model. A few days later, on a run while listening to the Practical AI Podcast, I heard the same theme laid out in plain language. Hosts Daniel Whitenack (Prediction Guard) and Chris Benson (Lockheed Martin) put it bluntly: “Simply accessing a powerful model isn’t enough.”
It resonated because it perfectly captured what I’ve seen firsthand—and what we’re tackling at Savvyn. A model might classify a variant or generate a draft report, but unless it’s tied into the lab’s systems, aligned with compliance checks, and trusted by the end user, it doesn’t matter. The pilot never leaves the runway.
That run helped crystallize the real problem: it’s not the models. It’s the gap between a clever demo and a functioning system. Closing that gap requires orchestration, stitching, and domain expertise—not just bigger models.
Everything Is Not a Nail
This brings me to a children’s book.
My aunt, who is always sending me the latest kids’ stories, gave me David Shannon’s Mr. Nogginbody Gets a Hammer. Mr. Nogginbody fixes one nail in his floor—success. Then he starts hammering everything: picture frames, flowers, even a stop sign. Until he tries to hammer a bee on his head and learns—painfully—that not everything is a nail.
That’s how many companies treat AI. Discover GPT-whatever, and suddenly every problem looks solvable with the same tool. Need reports? Summarize. Need insights? Summarize. Need workflows automated? Summarize.
This is how you get pilots that look clever in a demo—an LLM spitting out a mock clinical report—but collapse the moment they hit the rest of the workflow:
Not integrated with classification frameworks
Never connects to the systems the business actually runs
Doesn’t feed back into quality management
The pattern is predictable: without that integration, it’s just another disconnected output.
The System, Not the Model
A foundation model on its own is… fine. (Oh don’t worry, I’m just being cheeky—I love ChatGPT as much as anyone.) It can draft emails, summarize articles, or clean up slides. Useful, but not enough. These models also tend toward the average—acceptable for boilerplate, but dangerous in regulated domains.
Diagnostics makes the point clear. One model can call a variant, but that doesn’t make a clinical report. To get there, you need a system that layers multiple algorithms, classification frameworks, and compliance rules before a single word is shown to a physician. Skip those steps, and you end up with an output that looks convincing but can’t be trusted or used.
For early-stage companies, the stitching often looks deceptively simple: pulling raw lab outputs into spreadsheets, connecting analyses with external knowledge sources, or turning scattered files into a standardized dataset. But without that foundation, the work never scales. You’re left with a demo that impresses once but can’t be repeated or extended.
That’s the orchestration gap MIT described in its report: the distance between adoption and transformation. The root cause is a stitching problem—the hidden work of wiring models, data, and workflows into something that functions end to end.
For years, I did this stitching manually—pulling outputs into spreadsheets, wiring early commercial trackers by hand, patching workflows one painful handoff at a time. Now AI and no-code tools have caught up. The same stitching that once took months can finally be built with speed and scale.
Most companies already have the ingredients—models, data, infrastructure. What they lack are the recipe and the cook. (Shameless plug: that’s us.) That’s why their pilots collapse.
Where the Moat Actually Is
Executives love to talk about AI as a moat. But the model itself isn’t a moat—anyone can call GPT-whatever.
For early-stage companies, the moat comes from credibility and repeatability. Can you take raw data and produce the same, trusted output every time? Can you show investors, partners, or customers that your science and operations hold up beyond a single demo?
That’s where stitching becomes the moat. It’s building workflows that turn scattered results into clear, consistent narratives. It’s showing how scientific progress translates into commercial traction. It’s proving your systems, even in prototype form, are coherent enough to trust.
The edge isn’t having the biggest model. It’s showing your system is repeatable and reliable—even when the company is still small and scrappy. That’s the difference between a flashy demo and a foundation that can scale.
Closing Thought
MIT quantified the problem. Practical AI explained it in plain language. And I’ve lived it: with advanced training in oncology and years of building workflows, I’ve spent too much time stitching by hand what should have been systems. The bottleneck has never been the model—it’s the orchestration. AI in biotech, healthcare, and diagnostics won’t be won by whoever has the biggest model. They’ll be won by the companies that can integrate messy data, fragmented workflows, and deep domain expertise into systems that are scientifically rigorous, operationally durable, and trusted by physicians, regulators, and payers. That’s the difference between a clever demo and a company that actually changes outcomes.
When I first saw MIT’s 95% failure stat, it made me question my own business model. The run that followed helped crystallize the answer: the work isn’t about chasing the next big model, it’s about closing the orchestration gap. For our part at Savvyn, we’re starting with the “Docs” and “Data” modules—because they solve immediate pain points. But they’re not the endgame. We can already see that Docs and Data won’t be solved with the same approach. They’re different problems, requiring different recipes. And that’s the point: they’re not meant to stand alone. They’re modules in a larger system, stitched together into something coherent and scalable.
Thanks for reading,
—Savvyn (your partner in ruthless efficiency)
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