Generative AI has quickly progressed from an emerging technology to a strategic priority. In pharma and biotech, we have already seen its potential in drug development, scientific writing, medical review, and content creation. There is an assumption that overcoming the adoption hurdle is a challenge of technology, but we are seeing things differently. The real challenge isn’t technical; it’s behavioural.
Successful adoption depends on how people feel, not just what technology can do. And in an industry where accuracy, regulation, and expert judgement are paramount, bringing people on the journey with us is non-negotiable.
Over the past year or so, I’ve worked with several organisations on rolling out GenAI pilots and larger-scale deployments. While tools differ, the behavioural sticking points are remarkably consistent. Understanding the people on the front line and designing with them in mind can mean the difference between cautious experimentation and meaningful transformation.
Start with how people feel
One of the most common missteps I see is leading with GenAI features. That’s not where people are. For many teams, GenAI raises anxiety about capabilities, compliance, job security and the enormity of change. Starting with a product demonstration can trigger resistance before anything has been explained.
With one client project, we opened with facilitated conversations across regulatory, medical, and commercial teams. The focus wasn’t on what GenAI could do, but on how people felt about it. What had they heard? What were they curious about? What worried them? These conversations shaped everything that followed, from training to risk management.
Clear communication early on helps avoid speculation. Make the benefits personal, dig into the ‘what’s in it for me’. Show how GenAI can take repetitive work off people’s plates, spark new thinking, or improve turnaround times on critical content. Keep the language straightforward and grounded in real tasks.
In your overall change management plan, make sure to keep people at the heart of your programme. Ensure there is a continuous engagement strategy with open communication channels. Listen to real world feedback and be prepared to pivot and adapt.
Involve people before everything is finished
I wholeheartedly believe in the phrase ‘people commit to what they are a part of’. That holds true even when the tech is still in beta. Involving cross-functional teams in design discussions and pilot testing helps build early momentum. It also surfaces blockers that internal rollout teams may not anticipate.
I’ve worked with organisations that brought field medical and market access colleagues into pilot design. The result was sharper prompts, more relevant outputs, and fewer objections later. Creating space for this early input shows that adoption isn’t being imposed from the centre.
Treat questions and concerns as data. They signal where you’ll need to invest more communication or support.
Make it easy to start, and safe to get it wrong
Confidence is often a bigger barrier than capability. People generally want to use GenAI, but they don’t want to get it wrong in a public setting. That’s especially true in high-standards, expertise-driven environments like ours.
The most successful adoption strategies I’ve seen create safe spaces to experiment. For some teams, that’s informal peer sessions. For others, it’s task-based training where people test GenAI on live but low-risk work. I’ve even seen cross-functional ‘prompt challenges’ generate both learning and laughter. Adoption of a healthy test / fail/learn working environment will foster adoption and innovation every time.
Avoid training that’s generic or one-size-fits-all. Tailor it by function, leaning towards personalisation to reflect learning styles and variation in roles and capabilities. Give examples grounded in what people do, whether drafting response letters, analysing stakeholder insights, or reviewing safety data. Make it hands-on, not hypothetical.
Ongoing support matters just as much. Keep help accessible through live clinics, chat-based support, or internal champions who can answer real-time questions without judgment.
Let culture do the heavy lifting
A lot of the real adoption work happens after the launch, and communications go quiet. People look around and ask: is anyone else using this? Is it safe to try? What happens if it doesn’t work?
The teams that make GenAI stick have cultures where experimentation is encouraged, and learning is visible. I’ve seen senior leaders use meetings and videos to share how they’re using GenAI, what helped, what didn’t, and where they’re still cautious. That sends a clear message. This is a tool to explore, not a test to pass.
Recognition plays a role, too. Whether it’s shout-outs in team meetings or informal ‘use case of the month’ style awards, celebrating real examples gives others a reason to follow.
Treat slip-ups as learning moments, not failures. This builds confidence and psychological safety, which are essential when asking people to work differently.
Address ethics as part of adoption, not a side note
Concerns about privacy, bias, and compliance are heightened in our industry. Rightly so, we can’t sideline them or pretend they’re already handled. When employees don’t see evidence that these issues are being taken seriously, they hesitate to engage.
In several programmes I’ve supported, we brought compliance and legal teams into the design phase. Together, we co-created usage guidelines and red flag scenarios. Then we tested those guidelines with actual users, checking for clarity and confidence.
It’s not about shutting down use. It’s about giving people enough clarity to use GenAI responsibly. Open discussion helps people feel part of the solution, rather than being at risk of breaking a rule they didn’t know existed.
Make it ongoing, not one-and-done
This isn’t a tool you roll out and move on from. GenAI is a shift in how people work, and like any shift, it needs reinforcement. Without it, initial enthusiasm fizzles out. Priorities move on. Momentum is lost. We have all seen things come and go quickly in pharma and biotech because implementation wanes.
Some organisations I’ve worked with created internal networks with informal champions across functions who share what’s working. Others embedded GenAI prompts into common workflows or created shared libraries of examples. The format doesn’t matter as much as the consistency.
Think of adoption as something you maintain, not launch. That mindset changes how you resource it, how you talk about it, and how people ultimately use it.
Final thought
We are used to working with complexity in pharma and biotech. We invest in systems, training, and compliance. But with GenAI, the biggest enabler we have is human behaviour.
When it comes to capability development and change management, Uptake’s approaches are rooted in behavioural science. This is particularly crucial during technological shifts, which are often the most challenging to implement without leaving some individuals behind. Behavioural science helps us understand why people hesitate, how they learn and what transforms uncertainty into action. To integrate GenAI into our daily work rather than just discussing it, we must focus on people’s feelings, needs and how they can thrive.
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