Creating brand value when clinical information is no longer a differentiator
The Life Sciences industry is undergoing seismic shifts: expanding drug pipelines, compressed launch timelines, and changing healthcare provider (HCP) and human capital expectations. HCPs once relied on field teams for clinical information, but now they can get the latest clinical data synthesized in seconds from OpenEvidence or a large language model (LLM) assistant—faster than any company can push it out.
Under mounting pressure, it’s no longer enough to send out field teams and hope the clinical message resonates. Go-to-market must look radically different, especially for innovative therapies. Every commercial executive with a glaring PDUFA date quickly approaching must ask: when clinical information is no longer a differentiator and access to HCPs is shrinking, how does a brand create value? Pharma leaders need to close the widening human capital gap by empowering their teams for this new era.
Throwing AI at the Problem Won’t Help
AI has been positioned as a solution, with real potential upside. McKinsey estimates generative AI could unlock $60–110 billion a year across pharma and medical products, with $18–30 billion in commercial functions alone. But when deployed poorly, AI adds operational friction, cognitive load, and can contribute to employee burnout.
A recent Harvard Business Review study defined burnout as, “a state of chronic workplace stress consisting of exhaustion, negative feelings about work, and decreased effectiveness on the job”. To ensure AI doesn’t add to workplace burnout, leaders need to reframe AI not as a threat to jobs, but as a trustworthy and helpful solution to augment humans.
Pharma companies are under top-down pressure to ease workforce strain with AI, with 72% of CEOs reporting board pressure to show AI outcomes. Unfortunately, this has resulted in companies deploying swarms of AI agents and a graveyard of failed AI pilots. MIT found that an estimated 95% of enterprise AI pilots fail to deliver measurable business impact.
The potential human capital impact is also worrying. Indeed, the same HBR study found “cognitive exhaustion from intensive oversight of AI agents—is both real and significant.” However, this study made an important distinction between their term “AI brain fry,” i.e. “mental fatigue from excessive use or oversight of AI tools beyond one’s cognitive capacity” and burnout. They found that, “while AI use often predicted mental fatigue, it didn’t seem to predict increased burnout. In fact, use of AI to replace repetitive tasks predicted a decrease in burnout.” This tells us that AI does not inherently lead to burnout, if directed towards the right kind of work.
Trust is the Currency
As with any technology solution used in the highly-regulated Life Sciences industry, the most important hurdle to cross is proving that the solution is trustworthy. In this industry, Trust = Compliance + Capability. When trust is missing from an AI implementation, employees quietly abandon the technology and pilots stall. AI that is compliant but not capable operates within guardrails but doesn’t make an employee’s life easier. AI that is capable but not compliant is a liability, especially when patient outcomes are at stake. At the end of the day, only humans can be held accountable, not AI. Lack of clarity around whether the AI is within compliance guardrails results in distrust of the system, adding another mental burden to employees. When AI is trustworthy, it empowers the workforce and naturally scales.
Think Workforce, Not Workflows
So, how do pharma leaders implement trustworthy AI in a way that augments and doesn’t overwhelm their workforce? Focus on workforce transformation, not workflow optimization. Workflow optimization allows employees to perform certain tasks more quickly and even appear to be “more productive”, but if not directed towards the right kinds of tasks, it can create more work for employees and put them at higher risk of burnout. Moreover, workflows often happen across various systems—both physical and digital—and workflow optimization tends to be restricted to single platforms, which may or may not improve the overall end-to-end workflow.
Role-based AI provides a better alternative. A tangible place to start is with the job description—it’s not a relic of the past, but rather a blueprint for the future of work for that role. When AI is designed around each employee’s role, it has the proper context to make that employee’s life easier and build trust with them. For example, while sales reps and medical science liaisons have some overlap in jobs to be done, there are different compliance guardrails within which the AI needs to operate. If the AI system has this role-based context, it will ensure the actions taken for an employee stay within the compliance boundaries of their role. This demonstrates that the AI is capable: providing the requested information, and compliant: staying within the required guardrails. This leads to trust, which leads to adoption.
The Reframe: Humans at the Center, AI at the Edge
Much of the discourse around AI frames the future of work as a zero-sum game: where the pie chart represents jobs to be done, of which humans and AI will take their separate portions. This narrative suggests AI will be able to replace humans. But there is an alternative vision where AI augments, but does not replace humans. A responsible AI future looks more like concentric circles, with human need and ability at the center and AI at the outer ring designed to kick in at the periphery of human intelligence.
For example, let’s take a day in the life of Gabby, a field sales rep for an innovative therapy. Her day involves pre-call planning, meetings, uploading call notes to a CRM, and various admin tasks like expense reports. Of these jobs to be done, there are core functions that only Gabby can do: building relationships with healthcare providers (HCPs), and bringing texture to conversations by remembering small details about their practice and clinical interests.
When AI takes over Gabby’s dull, repetitive tasks like copying call notes over from her phone to the CRM, she is empowered to spend more time having meaningful conversations with HCPs. When AI can help Gabby surface information across several systems of record—perhaps even during a call with an HCP—Gabby spends less time context-switching and more time building brand value.
Closing the human capital gap has become a critical strategic priority for commercial executives. While AI has multi-billion dollar potential to drive value for pharma leaders, it can overwhelm employees with mental fatigue and possibly burnout if implemented poorly. Trust—composed of compliance and capability—is the key underlying currency for making sure AI augments teams for success. AI is trustworthy when it centers around a given employee’s role and their context. When AI kicks in at the periphery of human knowledge and takes on the dull, repetitive tasks, this empowers employees to focus on what humans do best: human connection, strategy, and judgment.
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