The Piloting-to-Operationalization Shift: Why 95% of Generic AI Tools Fail to Scale in Regulated Life Sciences

Everyone Is Talking About Pilots. Almost No One Is Talking About Scale.

Every life sciences company I talk to has an AI pilot running somewhere.

A chatbot for reps. A summarization tool for medical writers. A copilot for market access teams.

Almost none of them have made it past the pilot.

According to Fortune's reporting on MIT NANDA's "The GenAI Divide: State of AI in Business 2025," "about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L."

Ninety-five percent stall out. In an industry built on evidence, that should stop you.

The 95% Problem Isn't a Technology Problem

The instinct is to blame the model. Not sharp enough. Not fast enough. Not fine-tuned enough.

That's the wrong diagnosis.

MIT researchers point somewhere else. As Fortune reports, quoting NANDA researcher Aditya Challapally: Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don't learn from or adapt to workflows

Read that again. The tools work fine for individuals. They fail inside organizations because they don't learn the specific judgment calls a specific team makes, over and over, inside its own workflows.

That's a behavior problem, not a horsepower problem.

Per Fortune's coverage, "The 95% failure rate for enterprise AI solutions represents the clearest manifestation of the GenAI Divide."

The divide isn't between companies with better AI. It's between companies whose AI actually adapts to how work gets done and companies whose AI just sits on top of it.

Regulated Industries Face a Second Wall

Life sciences has an added complication most industries don't. Even a well-adapted AI tool can't quietly influence a regulatory decision.

The FDA has said so formally. As FDA/CDER states, the agency "published a draft guidance in 2025 titled 'Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products.'"

That guidance isn't optional. It lays out a structured credibility process sponsors are expected to follow. DLA Piper, summarizing the guidance, lists the sequence plainly: Define the question of interest that will be addressed by the AI model. Define the COU for the AI model. Assess the AI model risk.

Question of interest. Context of use. Risk assessment. In that order, every time.

A generic tool built for general enterprise flexibility has no native way to satisfy that sequence. It wasn't designed to.

Governance Isn't the Brake. It's the Chassis.

There's a common assumption in commercial teams that governance slows AI down. Compliance as a speed bump.

I don't think that's right.

Without governance, scaled AI use cases become compliance liabilities. With governance, they become defensible assets that grow. That's the tension: skip governance and you get a fast pilot that can never survive an audit. Build governance in from day one and you get something slower to launch, but built to actually survive contact with regulators, legal, and medical review.

Fast and undefendable isn't actually fast. It's just deferred failure.

The Organizational Gap Is Already Visible

This isn't theoretical. EY's 2024 Life Sciences CIO Survey found that organizations across the industry are managing AI governance through different structural models, from centralized centers of excellence to decentralized approaches distributed across functions.

Many are still running AI governance without a central structure. Different teams making different judgment calls about risk, credibility, and use case documentation. No shared system underneath them.

That's not a technology gap. That's an organizational design gap, and it shows up exactly where MIT's research says enterprise AI dies: at the point where a tool has to adapt to how a real organization actually works.

As the industry moves from experimentation to deployment, life sciences companies face a pressing challenge: how to scale AI responsibly without losing momentum.

That's the whole tension in one sentence.

What Operationalization Actually Looks Like

Piloting is easy. Anyone can stand up a demo.

Operationalizing means the tool has to survive three things at once: adapt to real workflows, not sit on top of them; produce a documented, risk-assessed trail that satisfies a regulatory credibility framework; and run inside a governance structure that's centralized enough to be defensible, not fragmented across the organization doing it differently.

Territory design. Message review. Medical-legal sign-off.

Each one has its own judgment calls, and each one needs a system that learns those calls specifically, not a generic assistant hoping they generalize.

That's the actual shift. Not pilot to enterprise. Pilot to operational, evidentiary, governed.

Most tools never make that jump. The 95% figure isn't a warning about AI. It's a warning about treating a regulated, judgment-heavy industry like every other enterprise use case.

The Real Question

The tools that fail aren't failing because they're not smart enough.

They're failing because nobody built them to learn the specific judgment a regulated commercial team makes every day, and nobody built the governance around them to make that judgment defensible later.

The question worth asking isn't which AI vendor has the best model.

It's whether your organization has decided what "operational" actually means before you scale anything at all.

Sources

Fortune (via AOL), reporting on MIT NANDA's 'The GenAI Divide: State of AI in Business 2025', https://www.aol.com/finance/mit-report-95-generative-ai-105412029.html

Fortune (via AOL), citing MIT NANDA researcher Aditya Challapally, https://www.aol.com/finance/mit-report-95-generative-ai-105412029.html

U.S. Food and Drug Administration (FDA/CDER), https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development

DLA Piper, summarizing FDA draft guidance, https://www.dlapiper.com/en-us/insights/publications/2025/01/fda-releases-draft-guidance-on-use-of-ai

EY (Life Sciences CIO Survey, 2024), https://www.ey.com/en_us/insights/life-sciences/driving-growth-via-commercial-transformation-in-pharma