Why Learning and Development in Life Sciences Needs to Stay Human

The Promise of AI and What’s Holding It Back

Let’s be clear, the life sciences industry is moving fast! And for learning and development teams, the pace of change can be hard to keep up with. AI is already playing a major role by helping teams accelerate drug discovery, personalise treatments, and even predict trial outcomes with startling accuracy. The numbers are impressive too! McKinsey estimates that generative AI could create between $60 to $110 billion in annual value for pharma and medical products companies.

And yet, with all this, something is missing.

Despite the breakthroughs, many organisations still struggle to get real returns from their AI investments. Why? Because the real differentiator isn’t the algorithm, it’s the people. The human beings who interpret the insights, make the decisions, and lead the transformation.

HR and Learning and Development professionals in the life sciences industries will know this all too well. The technology is evolving faster than the workforce can keep up. And unless we equip people with the right skills, not just technical, but deeply human, we risk leaving a lot of potential on the table.

The Skills That Make the Difference

The future of learning and development in life sciences isn’t just about teaching people how to use AI. It’s about helping them to thrive alongside it.

Let’s break it down.

Leadership That Inspires Confidence

Talk of AI can often trigger a level of anxiety, with individuals questioning – will it replace me? Will I understand it? Good leadership helps cut through that noise. But not every leader is naturally equipped to lead through digital change.

This is where L&D comes in. We need to support leaders in becoming better communicators, better coaches, and better at creating a vision that people can believe in. If leadership doesn’t buy in, or worse, sends mixed messages, AI adoption will stall.

Collaboration Across Functions

AI is changing the makeup of teams in life sciences. Data scientists are working with lab technicians, IT is embedded in commercial strategy, and global teams must align across disciplines. It’s no longer about handoffs, it’s about co-creating in real time.

This approach requires more than meetings, it takes active listening, translating complex ideas, and building trust across functions. These human skills are critical to making AI work in practice. Leading organisations are now prioritising collaboration-focussed learning, ensuring teams not only have the tools, but the behaviours to innovate and problem-solve together.

Digital Confidence for Everyone

Here’s a stat that should make us pause, BCG found that 86% of employees believe they’ll need AI training, but only 14% have received any.

This is a huge gap.

We’re not talking about turning every employee into an AI expert. We’re talking about building a level of AI literacy where people feel comfortable using new tools, asking smart questions, and understanding the basics. When that confidence clicks, resistance will drop and innovation will pick up.

Clear Human Communication

We can’t ignore the role of communication. With AI feeding us more data than ever, the real challenge is to translate it into action.

People need to know how to tell the story behind the data. For example, how to explain AI decisions to stakeholders, or how to listen to concerns without shutting down the conversation. These are soft skills, yes, but they have hard business value.