From AI Rollout to Business Impact: What Leaders Must Get Right

By Wendy Heckelman, Ph.D., Tianna Tye, MAIOP

Look at nearly any 2027 biopharma commercial or medical budget and you will find a line for AI field tools: next-best-action engines, note capture, pre-call intelligence, engagement analytics. The technology case for improving efficiency, decision-making, and field execution has been made and largely won.

Access to AI does not guarantee transformation. AI-enabled transformation succeeds when organizations integrate operational change, cultural readiness, and prepare leaders through a coordinated top-down and bottom-up approach.

When organizations bring us into these transformations as change strategists, four questions come up more than any others.

What is in scope for a typical biopharma AI-enabled transformation initiative? 

The scope of the organizational change required depends on the technology and how significantly it affects daily work. A tool that accelerates one task creates a different transition than one that changes how employees make decisions, apply expertise, interact with customers, or define the value of their role.

Consider 3 field applications we are consistently seeing:

1. An AI writing assistant that supports field coaching reports (FCRs) may streamline a defined workflow and reduce administrative effort.
2. An AI-generated pre-call brief may change how field professionals prepare, exercise judgment, and integrate technology-generated insights with their domain expertise.
3. A next-best-action tool may introduce new decision expectations, coaching practices and even incentives.

Before implementation, organizations need to understand what the technology does and what must change around it. From there they can accurately forecast a plan to account for key elements of the change initiative such as:

1. Workflow and process design
2. Change leadership capability building
3. Structured implementation and adoption support
4. Measurement and business impact tracking
5. Communications strategy with top-down and bottom-up feedback loops

The scope, therefore, extends beyond deploying the technology. It must account for the organizational work required to translate access into adoption and transformation at scale.

Should AI transformations be top-down or bottom-up?

Both. Successful AI transformation combines clear direction and resourcing from the top with applied intelligence from the people closest to the work.

The organization must define:

• The business opportunity and intended outcomes
• Expectations for how the technology should be used
• Resources for workflow integration and capability building
• Appropriate governance and guardrails

Leaders must model and reinforce:

• A grounded understanding of the technology and business case for transformation
• Behavioral changes aligned to workflow implications and proper usage
• Visible support for experimentation, feedback, and course correction

Employees must contribute lived experience:

• Participate in MVP rollouts and pilots to provide real usage feedback
• Share operational detail around process, exceptions, friction points, judgement calls 
• Apply their domain expertise to interpret, challenge, and appropriately use the tool’s output 

This is why executive sponsorship and grassroots enthusiasm should not be treated as competing approaches. The organization and leadership provide direction, resources, and support; employees provide the information and lived-experience needed to make the transformation workable. AI transformation scales when both directions of change continually inform and reinforce one another.

What exactly do leaders need to be equipped for in a technology rollout? Can’t we just send in a trainer to explain how to use the tool?

Understanding tool functionality and appropriate use is a core element of any technology transformation, but most organizations have broader aspirations for AI-enabled change. This is especially true for organizations that intend to evolve alongside the technology and establish themselves as innovative competitors.

Executives, functional leaders, and managers have different responsibilities in the transformation and should be prepared accordingly.

At WLH, we approach transformation with an emphasis on change leadership, anchored by the 5Cs of Transition Leadership^®. Within that framework, we equip leaders to support the four human conditions that influence whether employees will adopt AI appropriately and consistently:

1. Business clarity: Do employees understand the opportunity, expected outcomes, and implications for their roles?
2. Trust: do they have confidence in the technology’s security, fairness, transparency, and level of human oversight?
3. Capability: do they have the AI literacy, domain expertise, and critical-thinking skills required to use its outputs responsibly?
4. Transition support: Are leaders providing the communication, coaching, reinforcement, and measurement needed as new ways of working take hold?

Organizations must prepare leaders to establish these four conditions, diagnose where they are weak, and guide their teams through any operational and behavioral changes introduced by the technology.

I need a change management partner for a field technology rollout. What should I look for?

A strong partner should help the organization embed AI into how work gets done, not simply communicate that a new tool is coming. This requires experience at the intersection of operational integration, cultural readiness, leader enablement, and field execution. 

Look for a partner that brings:

1. A focus on operational integration and cultural readiness. The approach should extend beyond communications and training to address the workflows, behaviors, leadership practices, and human conditions that influence adoption.
2. The ability to assess the full organizational impact. The partner should examine how the technology affects roles, workflows, decision-making, coaching, performance expectations, and cross-functional coordination.
3. A structured approach to human adoption. The work should deliberately build business clarity, trust, capability, and transition support rather than assuming these conditions will emerge after launch.
4. Leader enablement at every relevant level. Executives, functional leaders, and first-line managers have different responsibilities in the transformation and should be prepared accordingly.
5. Depth in biopharma. The approach must reflect the realities of regulated, matrixed commercial and medical organizations, where AI-enabled recommendations must be integrated with human judgment, domain expertise, and appropriate guardrails.

WLH brings more than thirty years of biopharma change leadership to this work, anchored by the 5C’s of Transition Leadership^®. If a field technology change is in your 2027 plan, we would welcome a conversation.