The Strategic Role of Learning Leaders in Scaling AI Across Pharma

By Wendy L. Heckelman, Ph.D. and Tianna Tye, MAIO

AI Adoption Is a Leadership Challenge

Artificial Intelligence (AI) has gained a foothold in the pharmaceutical industry, yet leaders remain wary of how initial AI investments will translate into sustained business value for their commercial organizations. AI pilots for commercial and field force optimization (e.g., AI-powered NBA models, Veeva AI vault assistants, and gen-AI-assisted omnichannel content personalization) across the industry have exposed leadership, capability, and execution gaps that traditional technological transformation approaches have not been designed to address.

Industry research consistently shows that most AI initiatives stall not because tools fail, but because organizations fail to focus on the leadership behaviors and operating norms that support the technology adoption at scale.^1,2

The 5Cs of Transition Leadership for AI®, an explicit extension of WLH’s 5Cs of Transition Leadership framework, aims to address this gap by supporting leaders’ ability to set strategic direction and guide their teams through change. It enables leaders to:

• Commit by owning the AI Transition as a leader
• Construct a mitigation plan to govern risk without stalling learning
• Create a high-performing team that maintains effectiveness through transition to adoption
• Coach individuals through AI-related identity shift and skill development
• Calibrate by identifying what metrics matter in AI adoption

This article outlines how learning leaders can shift from content delivery and skills training to enabling leader-led transitions where managers actively shape behaviors, norms, and confidence in AI-enabled ways of working.

Why AI Is a Different Kind of Technological Transformation

Pharmaceutical organizations operate in environments defined by regulatory scrutiny, with compliance expectations, and high-stakes customer interactions. AI challenges these foundations, not as another incremental digital capability, but as a catalyst for fundamental shifts in how work is done, decisions are made, and value is created. Across the industry, AI integration is moving beyond narrow pilot projects into strategic platforms that reshape workflows, expectations, and organizational identity at large:

• AI-enabled commercial content and engagement hubs are already in operation. For example, Bristol Myers Squibb partnered with Accenture to launch Mosaic, a generative AI-powered medical content hub to support real-time physician education and accelerate commercialization efforts, translating AI insights into strategic market engagement.
• Pharma companies are retooling their workforce and building internal competency networks. Johnson & Johnson mandated generative AI training for tens of thousands of employees to build dual fluency in biology and AI, and Merck instituted an internal AI platform (Petal) to embed AI into routine work such as regulatory documentation and productivity workflows.
• AI platforms are being industrialized across R&D and commercial functions, as evidenced by initiatives like Eli Lilly’s AI-enabled drug discovery platform (TuneLab) and pharma-tech collaborations (e.g., Nvidia-Lilly) to accelerate data-driven workflows.

These examples illustrate that AI adoption in pharma is not limited to simple automation as it actively redefines roles and workflows. AI systems, such as generative models and predictive analytics platforms, often augment expert judgment in ways that require teams to rethink how decisions are generated, communicated, and validated.

• Algorithmic decision support that augments or challenges field and brand judgement
• Rapid learning cycles that conflict with traditional validation and training timelines
• Shifts in professional identity for sales leaders, marketers, trainers, and managers

Research across the industry is consistently signaling that AI transformations differ from prior digital initiatives because they require deep behavioral change, new leadership mindsets, and continuous learning at scale, not one-time adoption events.^3,4

For learning leaders, this means AI adoption cannot be approached as a curriculum checkpoint. It is a transition leadership challenge that requires equipping leaders and managers to guide their teams through uncertainty, experimentation, and identity-level change.

The 5Cs of Transition Leadership® for AI Adoption

Broad AI adoption requires the same disciplined leadership behaviors as other large-scale transformations, but at greater depth and speed. The 5Cs of Transition Leadership for AI® explicitly extends the change leaders’ framework to address the unique challenges posed by commercial environments where adoption is visible and its impact measurable.

1. Commit: Owning the AI Transition as a Leader

In the context of AI, Commit is not about approval or sponsorship. Rather, it refers to a visible personal transitioninhow leaders think, decide, learn, and role-model in an AI-enabled environment. Leaders who demonstrate strong Commit in AI adoption are able to:

• Regulate personal responses to AI-driven uncertainty, including anxiety about relevance, loss of expert advantage, and ambiguity in decision-making
• Evaluate readiness to lead in an AI-enabled environment, including comfort with experimentation, data-informed judgment, and iterative learning
• Articulate the business case for AI, not just the technical or efficiency rationale
• Redefine their expectations regarding decision-making processes and accountability in human-AI hybrid collaboration
• Establish a personal AI learning agenda that is sufficient to lead without requisite technical mastery
• Ensure aligned expectations and commitments upward and across the organization to avoid sending mixed signals
• Construct: Governing Risk without Stalling Learning

In AI adoption, Construct is not about avoiding or eliminating risk. Rather, it refers to designing governance, guardrails, and decision pathways that allow responsible learning without disrupting core business performance. Leaders who demonstrate strong Construct in AI adoption are able to:

• Sustain core business outcomes during the AI transition without compromising brand integrity, customer trust, or regulatory obligations
• Identify AI-specific risk categories relevant to their focus of influence
• Develop mitigation strategies that balance compliance with adoption velocity
• Communicate expectations around governance, accountabilities, experimentation, and course correction
• Create: Building High-Performing Teams

In AI adoption, Create is not about assembling a project team or launching a tool. Rather, it refers to intentionally assessing the overall team adoption for new ways of working as AI becomes part of everyday work. Leaders who demonstrate strong Createin AI adoption are able to:

• Evaluate the team’s readiness beyond technical skill and identify and size the presence of uncertainty or identity disruption
• Reset the team vision and performance expectations in an AI context
• Establish norms for responsible AI usage while preserving human judgment
• Activate the team through early, visible wins
• Coach: Supporting Individual Transitions in an AI-Enabled Model

In AI adoption, Coach is not about improving performance within stable roles. Rather, it refers to guiding individuals through uncertainty, skill evolution, and identity-level change while maintaining engagement and trust. Leaders who demonstrate strong Coach in AI adoption are able to:

• Coach individuals through uncertainty, loss of confidence, and shifting professional relevance
• Identify if/where AI adoption may trigger disengagement or lead to attrition
• Adapt coaching approach to reinforce experimentation and encourage informed judgment
• Co-create a personal learning plan for AI skill adoption as appropriate
• Calibrate: Identifying What Metrics Matter in AI Adoption

In AI adoption, Calibrate is not about confirming plan adherence. Rather, it refers to continuously sensing what is working, what is stalling, and what must continue to evolve. Leaders who demonstrate strong Calibrate in AI adoption are able to:

• Establish clear metrics across behavioral adoption, capability, and value/impact
• Design ongoing review and recalibration mechanisms
• Communicate progress, insights, and adjustments openly to build trust and momentum
• Recognize teams and leaders who demonstrate effective AI-enabled behaviors early

Learning Leaders as Change Agents: Ingraining the 5Cs at Organizational, Team, and Individual Levels

Learning leaders play a decisive role in whether AI adoption becomes embedded or stalls. While AI technologies are advancing rapidly, their impact will be determined less by technical sophistication and more by how effectively organizations navigate the human transition that accompanies them. For learning and capability leaders, this moment presents a unique opportunity to step into a broader role: not simply enabling AI skills but shaping and optimizing the conditions under which AI adoption succeeds.

Learning leaders have influence at multiple levels of the organization, and AI adoption requires intentional action at each.

At the Organizational Level

Learning leaders should serve as advocates for change readiness ensuring that leadership behaviors, governance models, and success measures reflect and communicate what is actually required for people to adopt AI responsibly.

Key questions include:

• Are leaders visibly demonstrating Commit through learning, experimentation, and changed decision-making practices?
• Where are people, behavior, and readiness risks underrepresented in AI strategy, planning, and governance discussions?
• Do success metrics include capability maturity and behavior change measures, not simply ROI?

At the Team Level

Because AI adoption succeeds or fails in teams, learning leaders must play a critical role in equipping people leaders to translate enterprise ambition into everyday practice.

Key questions include:

• Are leaders equipped to Create clear norms for human-AI hybrid collaboration?
• Do managers have the tools and confidence to Coach teams through uncertainty, experimentation, and evolving expectations?
• Where might existing team routines or performance measures be reinforcing legacy ways of working?

At the Individual Level

AI adoption often introduces a fundamental shift in professional identity. Learning leaders are uniquely positioned to address this dimension directly, rather than allowing it to surface as resistance or disengagement.

Key questions include:

• Are we explicitly acknowledging how AI changes what individuals are valued for?
• How are we helping people redefine their contribution in an AI-enabled role?
• Have learning experiences been designed to build confidence and enhance judgment and adaptability skills, in addition to technical proficiency?

Conclusion

By leveraging the 5Cs of Transition Leadership for AI® and carefully considering these questions, learning leaders are able to move beyond content delivery and toward enabling leader-led AI transitions. In doing so, they become essential architects of responsible, sustainable AI adoption, ensuring that investments in technology are matched by leadership behaviors, team norms, and individual engagement and capabilities necessary to realize AI’s full potential.

Endnotes

1. McKinsey & Company. The State of AI and Generative AI in the Pharmaceutical Industry.
2. Boston Consulting Group. The Leader’s Guide to Transforming with AI.
3. McKinsey & Company. Scaling Gen AI in Life Sciences.
4. Harvard Business Review. Why AI Transformation Is a Leadership Challenge, Not a Technology One.