By Laura Farmer, Opus Strategy

Recently, I attended the AI in Medical Affairs Conference in Philadelphia, and came away with a clear impression: the conversation around AI in #MedicalAffairs has entered a more serious phase. The focus is no longer on potential or experimentation alone. It’s on execution: how organizations integrate new capabilities responsibly, sustainably, and in ways that truly support scientific and business objectives.

Here are the five lessons that stood out most, and that I believe matter most for leaders navigating this transition.

1. The challenge is no longer technological—it’s organizational

Most Medical Affairs organizations are past the point of asking whether AI belongs in their function. Tools have been piloted, use cases explored, and early value demonstrated. Yet many teams remain stuck in pockets of experimentation rather than moving toward scaled impact.

What’s holding them back is rarely the technology itself. Instead, the real work begins with aligning workflows and understanding decision rights, governance, and expectations. AI introduces new ways of working, but organizations often try to layer those capabilities onto existing structures and workflows without rethinking how work actually gets done. Leaders who recognize this as an organizational #transformation (not a digital add-on) are the ones seeing progress.

2. Quality of scientific exchange is becoming a deliberate capability

Several discussions focused on the evolving understanding of Medical Affairs interactions, particularly between MSLs and HCPs. These conversations were framed not as isolated events, but as cumulative experiences shaped by listening, empathy, credibility, and relevance over time.

Here’s what’s shifting: The ability to support this type of development around the interactions between MSLs and HCPs systematically. AI-enabled tools are beginning to provide structured feedback and insights that help teams reflect on how they engage, identify patterns, and improve consistency. When used thoughtfully, this kind of support elevates practice across a team without reducing interactions to scripts or metrics. It enables professional growth while preserving scientific judgment.

3. Adoption depends on leadership clarity and change discipline

Adoption emerged repeatedly as the differentiator between organizations making progress and those stalled in repetitive pilot modes or adoption purgatory. Tools alone do not change behavior. What does change behavior is clarity and connections with end users, understanding and educating teams on why a tool exists, how it fits into daily work, and what success looks like.

Organizations seeing momentum have invested in change management fundamentals: training grounded in real workflows, internal champions who model new behaviors, and leadership messaging that signals both permission and expectation to adopt. Without this foundation, even well-designed tools struggle to move beyond early adopters.

4. Governance is enabling trust, not slowing progress

A consistent theme across sessions was the importance of verification. AI-generated outputs are widely treated as inputs that require review, context, and traceability, particularly in regulated environments.

Rather than creating friction, clear governance and guardrails have helped teams move faster. When guardrails are explicit, users know where automation is appropriate and where human oversight remains essential. This clarity builds confidence, supports compliance, and reinforces scientific integrity. Governance, when designed intentionally, becomes an enabler rather than a constraint.

5. Value is being measured in capability, not just efficiency

Conversations around ROI reflected a more mature understanding of value. While time savings still matter, leaders increasingly emphasized outcomes such as improved quality, greater consistency across teams, and the ability to scale best practices.

These measures resonate because they connect #AI investments to long-term capability building. Leaders are asking whether AI helps teams make better decisions, engage more effectively, and operate with greater confidence—not just whether it speeds up existing tasks. This shift in measurement is helping organizations build more durable business cases and align investments with strategic goals.

What does this mean for Medical Affairs?

The organizations that will stand out in the next phase of Medical Affairs transformation are not those with the most advanced tools, but those that approach adoption deliberately. Success depends on leadership alignment, thoughtful change management, clear governance and communications, and metrics that reflect real value.

AI is becoming part of the operating fabric of Medical Affairs. The opportunity now is to shape how it’s embedded—intentionally, responsibly, and in service of both science and strategy.

What Leaders Should Do Next

For leaders, the next phase of AI adoption success by the Medical Affairs team will be more about intentional design than continual experimentation. A few actions can help organizations move from pilots to sustained impact.

1. Anchor AI initiatives in clear business and scientific priorities. Teams should be explicit about the problems they are trying to solve—whether improving consistency of scientific exchange, accelerating insight synthesis, or strengthening decision-making. Clarity at the outset prevents tools from becoming disconnected from real needs.
2. Invest in change management as deliberately as in technology. Adoption requires training that reflects actual workflows, visible leadership support, and internal champions who help translate strategy into practice. Without this foundation, even strong tools will struggle to gain traction.
3. Define governance early and communicate it clearly. Establishing expectations around verification, oversight, and acceptable use builds confidence and trust across teams. When guardrails are understood, users can engage more freely and responsibly.
4. Rethink success metrics. Move beyond short-term efficiency gains and track indicators that reflect capability building—quality, consistency, scalability, and decision support. These measures better capture the long-term value of AI in Medical Affairs.
5. Plan for how work will shift. AI changes not just how quickly work is done, but where time and expertise are required. Leaders who anticipate this shift and adjust capacity accordingly will be better positioned to realize meaningful returns.

The organizations that act with intention now—aligning leadership, people, and processes—will be the ones that turn AI into a durable advantage in Medical Affairs.