This article first appeared on LinkedIn on March 31, 2026

AI has enormous potential to create efficiency and enhance the effectiveness of commercial roles in life sciences; yet, despite significant investment, most organizations are struggling to capture that value. The gap between AI’s promise and its actual impact has become a recurring theme in industry coverage and a growing frustration for commercial leaders.

The problem isn’t the technology. It’s that AI adoption requires changes in how people work, make decisions, and execute. Traditional implementation approaches weren’t designed to address these changes.

Why AI Is Different

AI isn’t another CRM upgrade. Across the industry, AI is moving beyond pilots into platforms that reshape how commercial organizations operate both internally, in how work gets done and decisions are made, and externally, in how companies engage HCPs and accounts. Bristol Myers Squibb launched a generative AI-powered content hub for real-time physician education. Johnson & Johnson mandated AI training for tens of thousands of employees. Eli Lilly industrialized AI across R&D and commercial functions. These examples signal a common shift: AI is becoming embedded in the commercial operating model, not bolted on at the edges.

For your commercial organization, this may mean:

• Next-best-action engines influencing field deployment and customer engagement
• AI-generated insights shaping brand strategy and omnichannel execution
• AI-enabled coaching tools integrated into field learning and skill development
• Field leaders, brand directors, and market access teams working with tools that augment or challenge their judgment in real time

The commercial implications are significant: faster execution cycles, higher expectations for personalization, and competitive pressure from organizations that figure out how to make AI work at scale. The risk isn’t that AI won’t deliver value. It’s that your competitors will capture that value first.

A One-Size-Fits-All Approach Won’t Work

What often gets overlooked is that not all AI changes demand the same level of transition. The scope of the AI application (i.e., task automation, insight generation, decision-making, etc.) directly determines how much people must change how they work, think, and lead. An internal productivity tool or insight engine may simply require new skills. An AI coach, next-best-action system, or HCP-facing application can challenge role identity, decision authority, and compliance norms. The deeper AI reaches into judgment, customer interaction, or regulated decisions, the greater the transition burden on leaders and teams. This is why AI adoption cannot be led with a one-size-fits-all approach. The leadership effort must scale to the nature of the change itself.

The CCO’s Role in AI Adoption

AI adoption across your commercial organization won’t happen because IT deployed the tools or Learning delivered the training. It will happen because you create the conditions for it through your own leadership, the alignment of your leadership team, and the operating environment you establish.

The 5C’s of Transition Leadership for AI^® provides a framework for leading this transition:

• Commit: Signal that this is a commercial priority, not an IT initiative. Your field leaders, brand teams, and market access directors are watching whether you’re personally engaged with AI or delegating it downward. Commit means articulating why AI matters for commercial performance, not just efficiency but competitive positioning, customer engagement, and execution speed. It means ensuring your direct reports are aligned and sending consistent signals across the organization.
• Construct: Establish governance that enables execution. AI introduces real risks including compliance, customer trust, and brand consistency. Your job is to shepherd guardrails that manage those risks without paralyzing adoption. Define what experimentation looks like. Clarify decision rights. Create pathways for teams to learn and adjust without waiting for perfect answers. Governance that eliminates all risk also eliminates speed.
• Create: Set the operating conditions for your teams. Your field leaders and brand directors need clarity on what’s expected in an AI-enabled model. What decisions should AI inform? Where does human judgment remain primary? How will performance be measured? Reset expectations, establish norms for working with AI, and create early wins that demonstrate what effective adoption looks like across the commercial organization.
• Coach: Hold your leaders accountable for their teams’ adoption. You won’t personally coach every rep or brand manager through this transition, but you are accountable for ensuring your leaders do. Make AI adoption part of how you evaluate field leadership and brand leadership performance. Address resistance directly. Ensure your leaders have the capability to guide their teams through uncertainty and changing expectations.
• Calibrate: Measure commercial impact, not just adoption rates. Track whether AI is improving the outcomes you care about: field productivity, customer engagement, launch execution, time-to-insight. Build review mechanisms that surface what’s working and what’s stalling. Recognize the leaders and teams who are demonstrating results. This signals what success looks like and accelerates adoption across the organization.

Questions to Pressure-Test Your Approach

• Is AI adoption positioned as a commercial performance priority, or has it drifted into an IT or Learning initiative?
• What level of behavior, judgment, or decision-making is this AI actually changing and are you leading the transition at the depth that change requires?
• Are your field leaders, brand directors, and market access leads aligned on what’s expected and how they’ll be measured?
• Does your governance model enable speed and learning, or is risk avoidance slowing adoption?
• Are you holding your leadership team accountable for adoption within their organizations?
• Are you measuring commercial impact or just tool usage?

The Bottom Line

Your competitors are making the same AI investments you are. The difference will be which organizations figure out how to translate those investments into commercial performance: faster execution, better customer engagement, and more effective field deployment.

That’s not a technology problem. It’s a leadership problem. And it’s yours to solve.

How WLH Can Help

WLH Consulting & Learning Solutions (“WLH”) partners with commercial leaders to close the gap between AI investment and AI adoption. Drawing on 30 years of experience in pharmaceutical commercial organizations and our proprietary 5C’s of Transition Leadership^™ framework, we help CCOs and their teams build the leadership alignment, operating conditions, and accountability structures that turn AI tools into commercial results.

To discuss how your organization can accelerate AI adoption across your commercial team, please email wendy@wlhconsulting.com.