Pharma's AI ROI Problem Isn't a Tech Problem but a Qualification Problem

A recent McKinsey analysis of AI adoption in pharma, synthesizing perspectives from R&D and data-science leaders at Johnson & Johnson, GSK, Boehringer Ingelheim, AbbVie, and Genentech who appeared on the firm's Eureka! podcast, opens against a backdrop that should stop anyone in the industry cold.

Global corporate investment in AI reached $252.3 billion in 2024, and the pharma-specific AI market is projected to grow from roughly $4.35 billion in 2025 to nearly $35 billion by 2031, a compound annual growth rate above 41% across the forecast window.1,2 Despite that scale of investment, McKinsey reports that the industry has yet to see meaningfully shorter development timelines or improved preclinical and clinical success rates.3 In other words, the money is flowing and the pilots are launching. But the needle on outcomes that actually matter, like getting therapies to patients faster and more reliably, has not moved.

McKinsey's proposed explanation is a set of six enablers for successful AI deployment: treating AI as part of a broader business strategy rather than a bolt-on tool, using analytics to convert data into decisions, building an integrated technology stack, generating and curating purpose-built data, developing cross-functional talent that blends machine learning expertise with domain knowledge, and adopting flexible, iterative change management.3 The throughline in all six is a call to “redesign” rather than “tinker,” to rebuild workflows around AI instead of layering it onto legacy processes. It is a reasonable framework. None of the six points are wrong. But taken together, they diagnose the wrong disease. They describe symptoms of a much more basic failure: pharma companies adopted AI the way they would adopt a marketing trend, not the way they would adopt anything that touches a regulated process or a patient outcome.

The Rush that Nobody Wants to Name

Here is what actually happened across the industry over the past three or four years, and it is not really a mystery. AI became the defining topic in every boardroom, every investor call, and every competitor's press release. No pharma executive wanted to be the one explaining to a board why their company was behind on “AI transformation” while a competitor announced a flashy partnership with a cloud provider or a splashy new digital-twin initiative. That competitive anxiety, not a well-reasoned strategic thesis, is what actually drove the pace of adoption. Companies approved budgets fast. They greenlit proof-of-concept pilots fast. They selected vendors fast. And in that rush, pharma quietly skipped an entire category of due diligence it applies to virtually everything else in its operations: workforce qualification.

Consider how a pharma company treats a new piece of laboratory equipment, a new assay, or a new manufacturing process. Nothing goes live without a validation protocol. Nobody touches the equipment without documented training. There is a paper trail proving that the people operating the system understand its failure modes, its limitations, and the conditions under which its outputs can and cannot be trusted. This is not bureaucratic overhead for its own sake. It exists because the cost of an unqualified person misreading an assay or mishandling a manufacturing step can mean bad data, a failed batch, or in the worst case, patient harm. The entire culture of the industry is built around the idea that capability without qualification is a liability, not an asset.

I have run environmental monitoring performance qualifications on cleanrooms before those rooms were released for human product. The test is not whether the room holds its specification on its own. You fill it to maximum occupancy, more bodies than the process would ever normally put in there, and you have operators mimic the manipulations the work requires. Nobody is running the batch record end to end. That is not the point. The point is load. EM samples throughout: non-viable counts, viable air, surfaces, personnel. The room passes or fails on what those plates read after incubation. The whole protocol rests on a single assumption, which is that the people are the contamination source. So you put the worst realistic version of them in the room and find out whether sterility survives it. And it does not end at release. EMPQ gets repeated on a defined interval, because personnel are the largest variable in that environment and that variable never stops moving.

Now look at how the industry validates an AI model. Benchmark data set, held-out test set, performance metrics, sign-off. The model is tested by itself, against clean inputs, one time. There is no maximum occupancy condition. Nobody puts the actual users in the actual workflow under actual pressure and measures what comes out the other side. Pharma worked out decades ago that the humans are the challenge condition. It stopped applying that the moment the capability showed up as software.

The pushback here is predictable. An AI model is not a bioreactor. It is software, and pharma does not qualify people on software. So look at what actually triggers a qualification requirement. The output has to be read and acted on by a person. The failure modes do not announce themselves in the output. There are operating conditions outside of which the result cannot be trusted. An assay meets all three. A bioreactor meets all three. A model sitting inside a regulated workflow meets all three. A textbook does not, which is why nobody qualifies anyone on one. Nobody signs a batch record on the strength of what a textbook said. The moment a model's output feeds a decision that ends up in a regulated record, the software argument is finished.

AI escaped that scrutiny entirely. Pharma deployed models into discovery workflows, trial design, and patient identification without applying the same rigor to who was allowed to use them, how their outputs should be interpreted, and what the escalation path looked like when a model was wrong. Scientists and clinicians received powerful tools with a webinar and a slide deck, not a qualification program. That is the actual root cause of the return on investment (ROI) gap McKinsey is describing, and it is conspicuously absent from their list of six enablers.

None of this is hypothetical, and FDA has already acted on it. On April 2, 2026, the agency issued Warning Letter 320-26-58 to a Michigan drug manufacturer, one of the first CGMP letters with a dedicated section on inappropriate AI use.5 The firm had used AI agents to create drug product specifications, procedures, and master production records. When investigators cited it for distributing product without process validation, the firm's answer was that the AI agent it used had never told it validation was required. FDA cited 21 CFR 211.22(c) and stated that any output from an AI agent must be reviewed and cleared by an authorized human representative of the quality unit. Read the fact pattern closely. The failure was not a bad model. It was an operator who did not know what the tool could and could not be trusted to do, and who had nothing in place telling them to check. That is an unqualified user. The firm has since ceased drug production.

Why “Redesign the Workflow” Doesn't Fix What's Actually Broken

McKinsey's framing implicitly assumes that if a company gets the strategy, the tech stack, the data, and the talent mix right, the training problem takes care of itself. It does not. A company can have a beautifully redesigned workflow, a state-of-the-art tech stack, and a strong cross-functional team. It will still fail if the people using the AI day to day do not know how to evaluate whether a model's output is trustworthy in a given context. A pathologist reviewing an AI-flagged tissue sample, a trial designer relying on a digital-twin simulation, or a discovery scientist trusting a molecule-design model all need the same thing: a structured understanding of what the model was validated to do, where its blind spots are, and what a compliant record of that decision-making looks like. It is not something that gets solved by hiring more “trilingual” data scientists, as J&J's approach is described in the piece, or by building a better data pipeline, as GSK apparently has. Those are necessary conditions. They are not sufficient ones.

This is precisely why so many AI pilots in pharma stall out exactly where McKinsey observes them stalling, unable to scale past an isolated proof of concept. Scaling an AI tool across an organization means trusting hundreds or thousands of employees to use it appropriately, under real regulatory scrutiny, without a data scientist standing over their shoulder. If those employees were never actually trained and qualified on the tool in a documented, auditable way, scaling is not a workflow problem but a trust and compliance problem. No amount of iterative change management resolves that.

What Genuine Qualification Actually Requires

If pharma wants to treat AI the way it treats every other capability that affects patient outcomes, a few things need to happen. None of them are new. Role-based training needs to exist for every function that touches an AI system, from the bench scientist using a molecule-design tool to the clinical operations lead relying on a patient-matching algorithm to the regulatory affairs team responsible for explaining that algorithm to the FDA or the European Medicines Agency (EMA). That training needs to be specific to how each role actually uses the tool, not a generic “intro to machine learning” course that treats a clinician and a data engineer identically.

Validation needs to extend beyond the model itself to the humans operating it. It is not enough to validate that a model performs well on a benchmark data set; someone needs to validate that the people using that model in production understand its intended use, its limitations, and the conditions that would trigger escalation to a human expert or a fallback process. That is the qualification layer that mirrors how pharma validates equipment and processes today, and it is the layer that has been almost entirely missing from AI rollouts across the industry.

Pharma must document all of this in a way that would satisfy a regulator asking, after the fact, how a given AI-assisted decision was made and by whom. Compliance cannot be retrofitted onto a tool that has already been in production for two years. Pharma must build it into the qualification process from day one, the same way it builds compliance into every other validated system it operates.

The Cost of Skipping it

The consequences of skipping this step are exactly what McKinsey's podcast guests are describing, even if they are not naming it directly. Pilots that produce promising results in a controlled setting fail to scale because the broader workforce was never brought along. Clinicians and scientists who were never given the tools to evaluate outputs treat them as suspect or ignore them, so the model's insights sit unused. Governance becomes an afterthought, bolted on only after a near-miss or an audit finding forces the issue, rather than being built in from the start. And perhaps most damaging long-term, trust in AI as a category erodes internally, making the next rollout even harder, because employees remember that vendors handed them a powerful, poorly explained tool and told them to figure it out.

None of this is a strategy failure or a talent-mix failure in the way McKinsey frames it. It is the predictable result of skipping the qualification step that every other regulated capability in this industry goes through as a matter of course. Qualification is not the only constraint on AI returns in pharma. It is the binding one, because it is the only one the industry skipped entirely while it went to work on the others. Close it and a company finds out whether the technology it bought was worth buying. Skip it and it never will.

The evidence outside pharma points the same way. It does not settle anything yet. YouGov surveyed more than 500 US and UK business leaders for DataCamp in early 2026. Just over one in five reported significant positive ROI on their AI investment. Among organizations running mature data and AI literacy programs, that number was 42%.4 Treat that as a signal, not a finding. It is self-reported. It is correlational. The company that paid for it sells upskilling. General AI literacy is also not qualification in the way 21 CFR 211.25 uses the word. But the direction holds, and pharma should clear that bar faster than anyone, because it already runs the qualification infrastructure every other industry is now trying to build from scratch.

A Solution that Treats this Like the Compliance Problem it Is

The fix is not complicated. It requires discipline that the “move fast” instinct around AI has made unfashionable. Pharma needs to build AI workforce qualification programs with the same seriousness it applies to equipment validation and process training: role-specific curricula, documented competency assessments, and governance frameworks that are explicitly mapped to FDA and EMA expectations rather than invented internally on an ad hoc basis. The goal is to build a qualified workforce that actually lets AI-driven redesign, the kind McKinsey is calling for, succeed instead of stalling at the pilot stage.

Pharma does not have a problem with AI's potential. The technology clearly works, and the use cases McKinsey describes, from prescreening trial candidates to simulating patient outcomes with digital twins, are genuinely valuable. What the industry has is a self-inflicted training and validation gap, born from the pressure to move fast on the hottest technology of the decade instead of treating it like any other internal capability that requires rigorous, compliant qualification before it is trusted with real decisions. Close that gap and the industry finds out what its AI investment was actually worth. Keep skipping it, and no amount of strategic redesign will save the next round of pilots from the same fate as the last one.

References

1. Economy. In: The 2025 AI Index Report. Chapter 4. Stanford Institute for Human-Centered Artificial Intelligence; 2025. Accessed August 7, 2026. View source

2. Artificial intelligence (AI) in pharmaceutical market size and share analysis: growth trends and forecast (2026-2031). Mordor Intelligence. Updated August 7, 2026. Accessed August 7, 2026. View source

3. Devereson A, Nagra N. How pharma is rewriting the AI playbook: perspectives from industry leaders. McKinsey & Company. November 6, 2025. Accessed August 7, 2026. View source

4. AI training efforts give ROI a boost. CIO Dive. March 2, 2026. Accessed August 7, 2026. View source

5. Warning letter 320-26-58: Purolea Cosmetics Lab. US Food and Drug Administration. April 2, 2026. Accessed August 7, 2026. View source