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Biopharma can learn to scale AI by focusing on a small set of business-critical workflows, redesigning those workflows around AI, and backing deployments with shared data, technology, governance, and training. The important caveat: experimentation is common, but progress to scale and consistent financial value are much less so—and the studies use different definitions of “scaled AI.”
What does “scaled AI” mean in biopharma?
It helps to distinguish four stages: trying a tool, deploying it in a workflow, extending it across teams or functions, and producing recurring business results. A successful pilot is evidence of feasibility, not proof of enterprise scale or financial return.
A McKinsey article published in 2025 reported results from a late-summer 2024 survey of more than 100 pharma and medtech leaders responsible for generative AI efforts. All respondents said their organizations had experimented with gen AI, but 32% said they had taken steps to scale it, and 5% said it had become a competitive differentiator generating consistent, significant financial value. About 75% said their organization lacked either a comprehensive gen AI vision or an intentionally designed strategic road map with business-linked success measures. These are survey responses from leaders in pharma and medtech, not audited results for every company in either sector.
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A separate Boston Consulting Group (BCG) study in 2024 classified 4% of surveyed companies as having cutting-edge AI capabilities across functions and consistently generating significant value, 22% as leaders beginning to realize substantial gains, and 74% as not yet showing tangible value. This is cross-industry evidence, not a biopharma-only measure. The categories also should not be treated as a standardized definition that can be directly compared with McKinsey’s survey.
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Why do AI pilots often fail to deliver business value?
A pilot can demonstrate that a model performs a task under limited conditions while leaving the harder questions unanswered: Will people use it? Can it fit into the workflow? Is the necessary data accessible and reliable? Can the company manage risk and cost? Does it improve a business outcome enough to justify expanding it?
Scattered proofs of concept can leave teams with duplicated tools, fragmented learning, unclear ownership, and no shared way to measure outcomes. A technically impressive system may still have little impact if it adds work instead of changing how work gets done. That is why AI capability, adoption, and operating-model changes need to advance together.
How should a pharma company choose AI use cases?
Start with a business problem, not a model or a mandate to “use AI.” Compare candidate workflows on value, feasibility, data readiness, complexity, cost, time to value, risk, and whether the process can be changed and adopted. The best first use case is not necessarily the most ambitious; it is one with meaningful potential and a credible path to safe, measurable use.
- Business importance: Identify the outcome the workflow must improve, such as cost, cycle time, forecast quality, or staff capacity.
- Data and feasibility: Check whether relevant data are available, sufficiently reliable, and usable in the setting where the work happens.
- Adoption and workflow fit: Identify who will use the system, what work it will change, and who owns that process.
- Risk and controls: Determine how errors could affect quality, safety, privacy, regulatory work, or critical decisions, then design review and escalation accordingly.
- Expansion potential: Consider whether the work can build reusable capabilities, rather than creating a one-off solution with no path beyond its pilot.
BCG recommends elevating three to five use cases for enterprise attention and selecting two or three functions or business units as test beds. Those are recommendations, not proven universal thresholds. The appropriate portfolio depends on a company’s capabilities, risk tolerance, and capacity to support adoption.
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Which biopharma workflows could benefit?
McKinsey’s 2025 article on biopharma operations discusses generative AI support for technical troubleshooting, deviation and corrective-and-preventive-action workflows, process design and optimization, and supply-chain planning. These examples point to workflow assistance: helping people find information, prepare or analyze work, and make processes more efficient. They do not establish that AI should independently make batch-release or patient-safety decisions.
Supply-chain planning
McKinsey reports that certain supply-chain use cases have produced a 2–3% decline in supply-chain costs, a 15% increase in forecast accuracy, and a 20–30% reduction in planner workload. These are reported results for particular use cases, not guaranteed results or an average across biopharma companies. A company evaluating similar work should establish its own baseline and validate results in its operating context.
Technical and quality workflows
AI assistance may help teams search technical material, investigate process issues, or prepare deviation and corrective-and-preventive-action documentation. In regulated work, outputs need appropriate human verification and guardrails; the presence of AI in a workflow does not itself establish that a deployment meets applicable requirements.
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Numerical monitoring and control
McKinsey cautions that generative AI can hallucinate, particularly with numerical data, and may be unsuitable for some high-volume, time-sensitive monitoring and control. Use a tool only where its capabilities fit the task, and keep human review and established controls where errors could have serious consequences.
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What foundations make scale possible?
Successful use cases become easier to extend when teams can rely on shared, secure foundations rather than rebuilding basic capabilities each time. These include accessible, high-quality data; security and privacy controls; data governance; application integration; model flexibility; cost tracking; and clear accountability for intended use.
McKinsey’s 2024 pharmaceutical report describes a product-platform approach: an enterprise team provides core infrastructure, such as security, data governance, and cost tracking, while teams build use cases on top of it. This aims to combine local speed with shared capabilities. A fully decentralized approach can create silos and inconsistent quality or cost control; a wholly top-down platform can constrain local problem-solving. The operating model should give teams room to address real workflow needs while preserving enterprise standards and shared learning.
How should biopharma balance build, buy, and partner decisions?
The choice depends on strategic importance, existing capability, implementation speed, cost, customization needs, and control of data and intellectual property. Building may make sense where a capability is differentiating and the organization can sustain it. Buying or partnering may speed implementation when suitable capabilities already exist. In each case, tie the decision to the business goal and assess security, privacy, integration, performance, model flexibility, total cost, and governance of intended use.
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Partnerships can fill gaps in expertise or infrastructure, but they do not transfer accountability for workflow design, data use, or outcomes. BCG and McKinsey both emphasize aligning technology and partnership choices with business strategy rather than treating them as stand-alone procurement decisions.
Why do people and change management matter as much as technology?
Users need to understand what a system can and cannot do, when to verify its output, and how to raise problems. Technical teams also need skills to integrate, maintain, and monitor systems. In regulated and high-consequence work, training should reflect the actual task and its risks rather than offer generic AI awareness alone.
BCG says that, based on its experience, about 70% of gen AI value depends on how well a company manages change. This is BCG’s guidance, not a measured universal law. Its practical implication is that adoption deserves explicit ownership, time, and resources: redesign responsibilities, fit the tool into the work people already do, and address barriers after deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can a company tell whether AI is working?
Set a baseline and a target before rollout, and connect both to the business priority that justified the use case. Track whether the intended users adopt the tool, whether the output is reliable enough for its purpose, and whether the workflow or business outcome improves. Select measures that fit the job—for example, quality, cycle time, cost, workload, forecast accuracy, or revenue—and document the conditions under which they are measured.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchDo not substitute model performance, number of pilots, or user activity for business impact. BCG’s 2024 cross-industry study associated AI leaders with 1.5 times higher revenue growth, 1.6 times greater shareholder returns, and 1.4 times higher returns on invested capital over three years. These are associations reported by the study; they do not prove AI alone caused the differences or show that a particular biopharma deployment will produce the same results.
Best Value
That same BCG study reported that 62% of AI value came from core business functions and that R&D accounted for 27% of AI value in biopharma, using the report’s definitions. These figures do not mean that 27% of biopharma companies’ value came from R&D, nor do they establish the outcome of any individual company’s program. They reinforce the importance of examining where value is actually produced rather than assuming it comes only from one function.
How large is the opportunity—and what do estimates show?
McKinsey Global Institute estimated in 2023 that generative AI could create $60 billion to $110 billion in annual value across pharmaceutical and medical products. McKinsey estimated a $4 billion to $7 billion annual opportunity for gen AI in biopharma operations in 2025. Both figures describe potential, not realized industry-wide gains. They should not be used as forecasts for an individual company without assessing its workflows, data, costs, and ability to implement change.
What evidence can—and can’t—show about scaled biopharma AI
The available evidence supports a practical case for prioritizing business-linked use cases, changing workflows, building shared foundations, and investing in adoption and measurement. It includes consultancy surveys, cross-industry associations, recommendations, potential-value estimates, and reported operational use-case results. It does not establish an independently audited ranking of named biopharma companies with attributable, durable AI returns. Treat each result according to what it measures: survey-reported status, an observed use-case metric, an estimate of potential, or an association across companies.
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