The Tool Desk
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What does “scaled AI” mean in biopharma?
It helps to distinguish four stages that are often blurred together: trying a model, deploying it in a workflow, extending that workflow or capability across an organization, and demonstrating recurring business results. A pilot can prove that a tool works in a limited setting; it does not by itself show that employees use it reliably, that the workflow has changed, or that the company has captured durable financial value.
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The available figures illustrate why those distinctions matter. McKinsey’s 2025 article reports a late-summer 2024 survey of more than 100 pharma and medtech leaders responsible for generative AI efforts. Every respondent said their organization had experimented, but only 32 percent said it had taken steps to scale gen AI, and 5 percent said gen AI had become a competitive differentiator generating consistent and significant financial value. About 75 percent said their organization lacked a comprehensive gen AI vision or intentionally designed strategic road map with business-linked success measures. These are leaders’ survey responses, not an audit of every company in either sector.
Cross-industry results tell a similar story, but should not be mistaken for biopharma-specific measurements. In its 2024 study, Boston Consulting Group categorized 4 percent of surveyed companies as having cutting-edge AI capabilities across functions and consistently generating significant value, 22 percent as leaders beginning to realize substantial gains, and 74 percent as not yet showing tangible value from AI. BCG also reported that AI leaders had 1.5 times higher revenue growth, 1.6 times greater shareholder returns and 1.4 times higher returns on invested capital over three years. Those are reported associations; they do not prove AI alone caused the differences or that a biopharma company should expect the same results.
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Why do AI pilots fail to become business value?
Many pilots are optimized to demonstrate a model or a local productivity improvement, rather than solve an important business problem end to end. When a tool sits outside the systems and decisions employees already use, it can create extra work instead of removing it. A successful proof of concept may also depend on unusually clean data, expert attention or manual work that cannot be sustained when the use case expands.
Other common obstacles are organizational: unclear ownership, fragmented data access, duplicated platforms, uncertain risk controls, poor integration and no budget or incentive to change the process. McKinsey’s 2025 survey finding that about three-quarters of respondents lacked a comprehensive vision or road map with business-linked success measures points to a strategic gap, not simply a shortage of promising models.
There is potential, but estimates are not realized returns. 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’s 2025 estimate for gen AI potential in biopharma operations was $4 billion to $7 billion annually. These figures describe estimated opportunity, not value already captured by the industry.
How should a pharma company choose AI use cases?
Start with a consequential workflow and assess both the value of improving it and the practical ability to do so. BCG recommends weighing value, feasibility, data availability, complexity, cost and time to value. Add the process’s risk level and whether the work can realistically be redesigned and adopted. A use case with a modest but measurable benefit and strong data readiness may be a better first deployment than an ambitious initiative that depends on unresolved data or governance problems.
- Business importance: Which cost, quality, capacity, service or growth outcome would improve, and who owns that outcome?
- Workflow and data readiness: Are the relevant data accessible, sufficiently reliable and usable in the systems where decisions are made?
- Feasibility and time: What integration, validation, change-management and ongoing operating costs are required?
- Risk and oversight: What harm could an incorrect or delayed output cause, and what level of review is appropriate?
- Repeatability: Could the capabilities, controls or data products support another valuable workflow?
Keep the enterprise portfolio small enough to receive real executive attention. BCG suggests elevating three to five use cases for enterprise focus and recommends selecting two or three functions or business units as test beds. Those are advisory starting points, not universal thresholds. The right count depends on resources, risk and how much change the organization can absorb.
Where can AI assist biopharma operations?
McKinsey’s 2025 biopharma operations article discusses gen AI support for technical troubleshooting, deviation and corrective-and-preventive-action workflows, process design and optimization, and supply-chain planning. These are examples of workflow assistance, not evidence that AI should autonomously make decisions such as batch release or patient-safety determinations.
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The article reports that certain supply-chain use cases have produced a 2 to 3 percent decline in supply-chain costs, a 15 percent increase in forecast accuracy and a 20 to 30 percent reduction in planner workload. These are reported results for particular use cases, not a sector-wide average or a guaranteed outcome. A company considering a similar deployment should establish its own baseline and define how it will measure costs, forecast performance and workload.
Gen AI also has limits that matter in regulated operations. McKinsey cautions that generative models can hallucinate, particularly with numerical data, and may not suit some high-volume, time-sensitive monitoring and control tasks. Users need training, outputs need human verification where appropriate, and guardrails must reflect the consequences of error. These general recommendations do not establish that any particular system or deployment meets regulatory requirements.
How should teams balance local experimentation with enterprise control?
Biopharma needs room for teams close to the work to identify problems and test solutions, but isolated pilots can multiply cost, inconsistent quality and data silos. A fully centralized, top-down platform can create the opposite problem: slow delivery and solutions disconnected from day-to-day needs.
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McKinsey’s 2024 pharmaceutical report describes a product-platform approach intended to balance the two. An enterprise team provides shared capabilities such as secure infrastructure, data governance and cost tracking; product teams then build use cases on that foundation with business and technical expertise together. The platform should enable local teams rather than dictate every workflow. Shared components, clear ownership and common controls make it easier to reuse what works without preventing teams from adapting solutions to their processes.
Build, buy or partner decisions should follow the business need. Building may make sense where proprietary data, intellectual property or a differentiated workflow is central. Buying or partnering can accelerate implementation when suitable capabilities already exist. In each case, assess integration, customization, total cost, security, privacy, model flexibility and who controls data and outputs. BCG advises anchoring partnerships in business strategy and goals, rather than treating technology access as the strategy itself.
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What foundations make successful use cases repeatable?
Scaling requires more than model access. The shared foundation should make it possible to use data safely, integrate applications, monitor costs and performance, and assign responsibility for decisions and outputs. BCG and McKinsey guidance emphasizes secure infrastructure, accessible and high-quality data, governance, application integration, model flexibility and cost controls.
- Data and access: Define data quality expectations, permissions, classification and stewardship so teams know what can be used and for what purpose.
- Technology and integration: Provide a secure environment, connect tools to the workflow, and preserve flexibility to select or change models when needs evolve.
- Accountability and governance: Assign owners for the use case, the underlying data, model oversight and business outcomes; set controls proportionate to risk.
- People and adoption: Train end users and technical teams, involve workflow experts in design, and make adoption part of delivery rather than an afterthought.
BCG’s 2023 guidance says about 70 percent of gen AI value depends on how well a company manages change. That is the firm’s experience-based guidance, not a measured universal law. Its practical implication is still useful: a technically capable system cannot deliver its intended benefit if people do not trust it, know how to use it or have a reason to change established work.
How can leaders tell whether AI is working?
Set the baseline and target before deployment, and connect both to the business outcome the use case is meant to affect. Track adoption and quality alongside operational or financial results. Depending on the workflow, useful measures can include cycle time, error or rework rates, forecast accuracy, workload, service levels, cost or revenue. Model accuracy alone is not proof of business impact, and a count of pilots says little about recurring value.
Report results at the level the evidence supports. A survey response about taking steps to scale is not a documented deployment; a metric from one use case is not an enterprise-wide financial return; and an estimate of potential value is not realized benefit. BCG reported that 62 percent of AI value in its cross-industry study came from core business functions, and that 27 percent of biopharma AI value was in R&D under the report’s definitions. Those figures describe the study’s allocation of AI value, not the share of all biopharma value that R&D can capture.
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What the evidence does—and does not—show
The available sources provide consultancy survey findings, cross-industry associations, recommendations and selected operational use-case results. They do not establish a comparable, independently audited list of named biopharma companies with attributable, durable AI returns. That distinction matters: it is possible to learn from reported approaches and use cases without claiming that the sector has already demonstrated broad, consistent financial gains from scaled AI.
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