Are you a swimmer or a spectator? Navigating AI transformation in the pharmaceutical sector

Pharmaceutical leaders have long understood AI’s enormous potential. The technology has been dominating headlines for years, and most organisations are running pilot projects or proofs-of-concept.

Yet, a critical disconnect remains. Many leaders still treat AI as merely another tool (a system to install or a project to tick off) instead of embracing it as a force that's totally reshaping how organisations think, work, and lead.

The crucial barrier to successfully navigating the AI transformation lies not in the code, but in the willingness to change mindsets and organisational habits. The question now is whether your organisation is ready to its adapt mindset, behaviours, and culture to fully harness AI's transformative power.

This blog is the first in a series exploring the human side of AI transformation. Not the algorithms, but the leadership behaviours, organisational habits, and cultural conditions that determine whether AI delivers breakthrough value, or fizzles out in siloed, unscalable experiments.

And to begin, we want to start with a deceptively simple question: Are you a swimmer or a spectator? Because when it comes to AI, the position you take (both individually and organisationally) will define your trajectory more than any tool ever will.

The swimming pool analogy: Where are you standing?

In our new whitepaper, we explore a metaphor that tries to capture the state of AI adoption across pharma today.

Imagine a swimming pool. Some organisations are standing on the edge, observing the water. Some are lingering on the steps, cautiously dipping a toe. A few have already dived in and are quickly learning how to swim. So what does each position really mean, and what can it teach us?

1. The spectators: Watching from the edge

These are the organisations studying the pool. They’re researching AI, monitoring competitors, attending conferences, perhaps tasking a small working group to explore possibilities. None of this is wrong, per-se. But, paradoxically, this conservative, risk-averse approach could be the most risky.

The pharma sector has historically benefited from long development cycles that shielded it from the pace of external change to some degree. But AI doesn’t follow 10-year cycles. It shifts week by week. And companies who wait to see how things shake out don’t just risk delay. They risk irrelevance.

Spectators tell themselves they’re being responsible. But in reality, they’re often paralysed by uncertainty, legacy assumptions, or fear of getting it wrong. And the cost of that hesitation compounds every day.

2. The edge-waders: Lingering on the first few steps

These organisations are engaging, but only cautiously. Perhaps they’ve launched a proof-of-concept in one function. Maybe a data science team is experimenting quietly with generative AI. They may even have an AI strategy on paper (though rarely one that is truly enterprise-wide).

This is progress, but because siloed experiments rarely scale, it’s often not enough. These experiments rely on incomplete or poor-quality data. They sit outside core workflows. And critically, they exist in environments where leaders haven’t yet created the psychological safety or empowerment for teams to fully experiment, fail, and learn.

This is the organisational equivalent of someone standing waist-deep in the water, still clutching the rail, telling themselves that a gentle entry is safer. Meanwhile, others are already doing laps of the pool. Organisations in this position may yield interesting prototypes, but lasting value is unlikely.

3. The swimmers: Fully in the pool and learning fast

These are the organisations that accept a core trade-off. They recognise that AI isn’t a side project, but a structural transformation that requires full commitment, even without all the answers.

Swimmers understand that you can’t master something by analysing it from a distance. You learn by being in motion. They embrace experimentation, even when it’s uncomfortable. They build cross-functional bridges because they know data, processes, and talent must flow. And they invest in upskilling not as a training exercise, but as a cultural commitment. Their advantage isn’t necessarily in knowing more, but because they are learning faster.

In a world where the pace of technological change outstrips traditional management processes, the fastest learners win.

Why the difference matters now

So what are we trying to say? Well we argue that the distinction between spectator, edge wader, and swimmer is existential. AI is already reshaping roles, decision-making, collaboration, and what “good” looks like in leadership. Those who haven’t leapt in yet are likely to be left behind.

That’s because people who work with AI will quickly outperform those who don’t. Teams that embrace experimentation will outpace teams waiting for perfection. And organisations that build cultural readiness will outlast those that focus solely on tools. AI won’t replace people, but people who use AI will replace people who don’t. The question for leaders is whether their current approach and culture is enabling this shift or quietly suffocating it.

The next steps

If you’re honest with yourself, where does your organisation sit in the pool today? On the edge? Testing the water from the steps? Or already swimming and learning?

Understanding where you are is the first step toward meaningful change. But knowing how to move forward safely and effectively is another matter.

In our whitepaper, Beyond the Algorithm: How Pharmaceutical Leaders Can Navigate Cultural Transformation in the Age of AI, we dive deeper into this metaphor and provide practical guidance for leaders on how to leap in, foster collaboration, build psychological safety, and develop the skills and culture needed to thrive in an AI-powered world.

Download the whitepaper to explore how to move from spectator to swimmer, and start leading the transformation today.