Beyond the First Bite: Scaling AI in Healthcare Communications
By Thomas Nisters, Duncan Arbour, and Andreas Reinbolz
Everyone’s nibbling AI like it’s candy — testing tools, launching pilots, experimenting with workflows. But tasting is not transformation. In medical communications and across the healthcare industry, the early excitement around generative AI has led to a surge of innovation. Yet, the gap between experimentation and scaled adoption remains wide.
The uncomfortable truth? Your AI experts might be the wrong people to drive that change, just as a clinical research team isn’t best suited to commercialize a drug, AI specialists may not always be positioned or equipped to drive complex organizational transformation on their own. Expecting them to carry the full burden can unintentionally set initiatives up for struggle.
The real question isn’t whether we use AI. It’s whether we’re ready to scale it across scientific workflows, regulatory environments, and stakeholder touchpoints. In our industry, speed and accuracy aren’t nice-to-haves — they’re business-critical. Pilots are easy. Embedding AI into the day-to-day reality of cross-functional teams, global medical strategies, and content lifecycles is harder. But it’s also where the value lies. The organizations that take the leap and operationalize AI at scale will gain a lasting competitive edge.
1. Pilots Are Not Progress
AI is already demonstrating value in medical communications, from content creation and personalization to data insight extraction. Every major pharma company is piloting something, somewhere. But the hard truth is that pilots don’t scale easily.
“At Syneos Health Communications, we’ve moved beyond exploring AI’s promise into delivering concrete and replicable, AI-powered solutions rooted in technology and human expertise across medical affairs.”
— Thomas Nisters, Medical Director
Most organizations are stuck in what we might call the pilot paralysis phase. AI tools remain disconnected from daily workflows, team structures, or strategic KPIs. Without full integration, their value is capped.
2. The Real Work: Scaling AI Means Scaling Change
To scale AI, you have to scale change. This isn’t just about tools or technology — it’s an organizational transformation. Teams, culture, and leadership need to evolve alongside the technology.
“Generative AI isn’t an IT or technology issue, it’s a talent issue. Hearing directly from our clients and partners around the real and immediate needs of their teams and talent is the best possible input into the evolution and direction of our capabilities.”
— Duncan Arbour, SVP Insights and Innovation
Here’s where many organizations get it wrong: they put the entire burden of change on their AI experts. But these experts are often researchers, engineers, or platform owners. They’re brilliant — but they’re not built to drive broad transformation. That’s a different skillset. Expecting AI specialists to reshape structures, align incentives, retrain teams, and communicate vision is like asking your statisticians to handle product launch.
At the same time, placing full ownership with business stakeholders can be just as limiting. They often approach AI as a way to optimize existing workflows, not rethink them. Without a clear understanding of what AI can and can’t do, innovation risks being shaped by familiar patterns rather than new possibilities.
At Syneos Health, we’ve seen that lasting transformation depends on combining both perspectives: technical teams who understand AI’s capabilities, and business leaders who understand the strategic context. Neither can succeed alone. Scalable AI adoption requires structured collaboration around three critical pillars, each demanding focused and coordinated effort.
Rationality: Focus Where It Matters
Avoid the temptation to roll out AI everywhere at once. Identify the three to six workflows where AI can deliver fast, visible impact — such as publication planning, modular content generation, or medical insight summarization.
•Use benchmarking and operational data to prioritize areas with process gaps or resource strain.
•Align AI use cases with unmet needs in scientific teams, content functions, or field engagement. Start with real problems, not just tool features.
Commitment: Resourcing for success
Change doesn’t happen in the margins. One of the most common reasons AI initiatives stall is lack of clear ownership and under-resourcing.
•Assign full-time staff to manage transformation efforts.
•Temporarily free high-potential employees from their day-to-day roles to lead AI adoption internally.
•Bring in external experts where needed to support delivery and speed up implementation.
•Separate technical build from change execution — both need focus, but not necessarily from the same people.
This sends a strong message that AI isn’t just a pilot — it’s a priority.
People-Centricity: Win Hearts, Build Trust
Tools don’t drive change. People do. That’s why adoption hinges on motivation, confidence, and the right support.
•Show teams how AI will help them do their jobs better, not replace them.
•Pilot communications strategies and timing to find the most effective engagement approach.
•Use peer-led learning, success stories, and real-life demos to inspire buy-in.
•Offer training that builds understanding of how AI works and where human judgment still matters.
A clear people strategy, backed by regular communication and leadership support, makes all the difference.
3. Put AI to Work: Key Use Cases
AI can do the heavy lifting, but only when integrated into real workflows. That means moving from strategy decks to everyday impact.
“We’re using AI to accelerate insights, shifting teams from low-value tasks to high-impact strategy while raising the bar on quality and innovation.”
— Thomas Nisters, Medical Director
Key areas to embed AI in MedComms:
•Insight Generation: Integrate scientific data with field intelligence, MSL reports, and omnichannel engagement feedback. With next-gen large language models supported by retrieval-based technology, teams can access and act on insights more effectively.
•Content Acceleration: Use AI to draft, repurpose, and adapt scientific content in multiple formats, supported by human review and medical oversight.
•Medical Writing Hubs: Assign repetitive or low-complexity content to AI-enabled writing teams. Freeing senior writers to focus on strategic deliverables.
•Patient-Centric Storytelling: Combine customer insights with behavioral data to craft content that resonates with patients, HCPs, and advocacy stakeholders alike.
In any event, all AI applications shall be used in accordance with applicable regulations and subject to human oversight and scientific validation.
“AI can help with the big strategic tasks and the little annoying everyday tasks.”
— Andreas Reinbolz, Healthcare Agency Lead
4. Scale with Guardrails: Governance, Not Guesswork
The future of AI in healthcare communications depends on the trust it earns. That means putting governance and compliance front and center — not as afterthoughts, but as enablers of scale.
Build safety into the system by:
•Embedding AI-supported reference validation tools within standard workflows
•Ensuring models only use medically sound, scientifically credible data
•Defining what AI can and cannot generate under existing regulatory and ethical frameworks
•Prioritizing the quality and curation of input data sources, since flawed, outdated, or non-reviewed content will degrade the reliability of AI-generated outputs
When compliance and content quality are built in from the start, teams can move faster with confidence. Vendors like Syneos Health Communications can play an important role in ensuring this quality.
Final Word: Beyond the Candy Shop
We’ve had our taste. AI works. But moving from taste to transformation means embedding it into how we think, operate, and deliver value. It’s not about tools alone. It’s about vision, structure, and leadership.
“We are enabling our clients to unlock new, measurable value that strengthens their strategies and ultimately enriches patient outcomes.”
— Thomas Nisters, Medical Director
Just remember: the people building AI aren't always the people who should lead your change. Don’t ask them to do both. Scale requires the right structures, and the right roles.
Now is the time to move AI from pilot to practice, from hype to habit, from tasting to transformation.