AI has moved beyond proof-of-concept on the clinical side of pharma, driving rapid discovery of new molecules. But post-market commercialization teams tell a different story: their boards are asking them to use AI to solve the problems caused by multiple rounds of layoffs, mounting pressure to do more with less, and a human capital fabric stretched thin. When boards tell leaders to “figure out AI”, they’re really asking: What is our human capital strategy in an agentic AI era?
This pressure has spurred a wave of AI pilots across Commercial and Medical Affairs teams. And yet, according to McKinsey, fewer than 10% of high-impact vertical generative AI use cases move past the pilot stage. In Life Sciences, where organizational inaction can delay therapies reaching patients, stalled pilots are not just an operational frustration—they are a competitive and ethical liability.
Most pilots fail to scale because of misalignment between how AI is being deployed and people’s ways of working. Many initiatives focus on optimizing isolated workflows. What’s needed instead is a role-based approach—one that empowers the workforce and designs AI to be context-aware, connected, and compliant. Those three qualities are foundational to a change management process that holds.
The Real Barriers to Scaling AI
Over several months, our team hosted a roundtable series where we spoke with over 150 post-market commercialization leaders across eight cities in North America and Europe about responsible AI in Life Sciences. Three concerns surfaced in nearly every conversation:
Does the AI do what it claims? Will it stay within regulatory and compliance guardrails? Will the people who need it use it?
These questions reveal why so many pilots fail to scale. Technology is advancing faster than the organizational infrastructure required to support it. Companies are discovering that deploying AI responsibly in a highly-regulated industry like pharma demands institutional and infrastructural readiness, governance, and a long-term commitment to change management.
The obstacles are both institutional and human. Compliance is a persistent concern. Indeed, 55% of pharma leaders say compliance is a top challenge to deploying Agentic AI. The pharma industry has spent decades building an extensive compliance infrastructure, including rigorous onboarding, competency certification, and ongoing monitoring. Now, AI agents are entering those same customer-facing workflows without clearing the same bars.
Equally persistent and arguably harder to solve are the change management barriers: insufficient training, widely varying levels of AI fluency across teams, and a pervasive lack of trust in what the technology can deliver. People will quietly stop using a technology if they do not trust it or it fails to make their lives easier. Even the most sophisticated technology will fail to scale if companies neglect change management.
Perhaps most telling is the focus of many pilots. Many AI initiatives are built around automating stand-alone workflows: compressing a manual step here, speeding up a data pull there. These efforts produce considerable efficiency gains, but they are sequestered to specific systems. In reality, a workflow crosses many systems, so automating tasks in one system still leaves gaps in efficiency across the complete workflow.
Rethinking the Design Criteria
The pilots that scale tend to share a common characteristic: questions about technology performance, compliance, and user adoption were not addressed after launch. They were built into the design from day one.
Treating compliance and change management as post-deployment problems—something to solve once the tool is live—almost guarantees failure. The pharma industry cannot afford the luxury of learning by doing when patient outcomes are involved. And organizations cannot retrofit trust onto a system that was never designed with the end user and existing SOPs in mind.
What separates successful deployments from stalled pilots is a shift in design philosophy: from workflow-centric to workforce-centric. Instead of asking, “Which tasks can we automate with AI?”, the more productive question is, “How does this role change with AI—and how do we support the person in it?”
For example, if AI is workflow-centric, a Medical Science Liaison (MSL) still needs to jump across several systems to weave together a clinical narrative using training material, the latest study, and notes from her last call with a KOL. If the AI is role-based, it will orchestrate work across multiple systems on her behalf based on her role and other factors, to more quickly deliver a stronger clinical narrative.
A Role-Based Approach to Agentic AI
The most effective framework for scaling AI in Life Sciences treats the job description, not the software stack, as the primary design blueprint. Every role in a Commercial or Medical Affairs function has a defined set of responsibilities, pain points, and high-value activities. AI pilot design should begin there.
This role-based approach is what we call the TRUST framework—a structured method for deploying AI that supports the human professional rather than replacing them. The goal is context-aware agentic AI that absorbs the dull, difficult, and data-intensive work, freeing up the person to focus on what humans do best: strategy, judgment, and human connection.
Building toward that vision requires a structured approach that moves through five stages:
Target the role. Design for a specific function—a sales representative and MSL have different boundaries, freedom, and guardrails when it comes to HCP conversations. Broad, role-agnostic AI agents lack this specificity, which drives real trust and utility. Start with one role and anchor the design to the job description you already have.
Raise empathy. An AI agent without context is just automation. Equip the agent with training history, performance data, and coaching insights so it can provide more personalized support. The goal is an agent that understands the individual’s professional situation, not just the general workflow.
Unify activity. Connect the agent to the systems of record that the role depends on—CRM, LMS, email, content repositories, and more. The ability to orchestrate work across data streams is what allows an AI agent to deliver coherent, actionable support rather than isolated outputs, saving the end user time.
Safeguard and compliance. In a highly regulated industry like pharma where patient outcomes are at stake, guardrails cannot be an afterthought. Build existing compliance guardrails, SOPs, and governance into the technology and keep humans in the loop for critical verification points.
Test and certify. Before any agent goes live, validate it with the same rigor you would apply to a human new hire. Use assessments, scenario-based evaluations, and ongoing observability to ensure that performance meets the standards your organization and your regulators require. If it cannot pass your own bar, it is not ready for deployment.
Trust Is the Metric That Matters
Ultimately, the gap between AI pilots and AI at scale is a trust gap. People quietly abandon—or circumvent—tools that feel unreliable, opaque, or disconnected from the realities of their work. Building trust in the technology requires leadership that is willing to invest in role-based design, rigorous change management, and continuous governance.
Life Sciences organizations that treat AI as workforce empowerment rather than workflow optimization will find that adoption follows naturally. When AI is connected to the right systems of record, compliant with the right guardrails, and context-aware about the person it is supporting, field teams will use it. Not because they are required to, but because it was designed with their role in mind and makes their job easier.
