Powerful tools constrained by old habits: The collaboration challenge holding AI back

AI has the potential to transform every part of the pharmaceutical value chain. From drug discovery and clinical development to manufacturing, supply chain, and commercial operations, its impact is already being felt.

Yet many organisations are finding that even with advanced tools in place, progress is slower and more fragmented than expected.

Not because the technology doesn’t work. But because teams aren’t structured in a way to maximise its potential.

This article is part of our series exploring the human side of AI transformation, drawn directly from our whitepaper: Beyond the Algorithm: How Pharmaceutical Leaders Can Navigate Cultural Transformation in the Age of AI. Rather than focusing on algorithms or models, we’re looking at the leadership behaviours and cultural conditions that determine whether AI delivers real value.

One shift stands out as foundational: leaders must help their organisations become better collaborators.

When AI meets old silos

The pharmaceutical industry has long operated through highly specialised functions. Data, systems, and expertise often sit within tightly defined boundaries, shaped by regulation, risk management, and long development timelines.

Historically, this made sense, but AI has rapidly changed the equation.

AI thrives on connection. Data wants to flow and insights compound when discovery, clinical, regulatory, manufacturing, and commercial teams learn together. When information remains locked in functional silos, AI’s impact is immediately constrained.

What often limits progress isn’t infrastructure, but very human tendencies: protectionism over data, attachment to functional ownership, and a reluctance to open work up across boundaries. These behaviours don’t disappear just because new technology arrives.

As a result, organisations see isolated AI initiatives, duplicated effort, and prototypes that never scale beyond the team that created them. On paper, activity is high. In practice, value remains fragmented.

The JCB digger analogy

Samuel Mantle, CEO at Lingaro, captures this challenge with an analogy used in our whitepaper:

“If you think of a metaphor for how we largely work today, we're all working in a field, digging in our own little areas. At the end of the day, we come together, and we all show each other what we've done, and we move on. All of a sudden, we are no longer using a shovel. We are each sitting in a JCB digger that can excavate the whole field on our own in three minutes. But what nobody's figured out is that it's no good if we all sit in our JCB digger and we excavate the field in three minutes and then we all smash into each other. How are we going to re-organise the way that we work together? To collaborate differently and increase our collective collaboration with the new tools?”

AI massively increases individual capability. But without new ways of working together, it also increases the risk of confusion, duplication, and collision.

Why collaboration becomes the constraint

This is why AI initiatives in often struggle to move beyond pilots. When teams work in silos, experiments may look like progress, but they are inherently difficult to scale. Protectionism over data, attachment to functional boundaries, and the lack of shared understanding between teams mean that even promising MVPs rarely translate into enterprise-wide value.

AI magnifies individual capability, but it can also expose limitations in how teams work together. Without attention to collaboration, the full potential of AI remains locked within isolated projects.

As the whitepaper makes clear, the organisations capturing real returns from AI are those with the cultural readiness to let information flow, work across boundaries, and learn together at speed.

From silos to synergy

Effective collaboration in the AI era requires deliberate leadership. Leaders need to move beyond passive encouragement of collaboration and actively address the behaviours that keep silos in place. This means challenging protectionism, surfacing inter-departmental competition, and creating shared understanding across disciplines.

It also means recognising that AI enables people to work faster and far beyond their traditional areas of expertise. Without clarity on how teams coordinate, prioritise, and connect their work, this increased capability can quickly become overwhelming.

The real shift is cultural. Leaders need to now create environments where people are open, curious, engaged, and connected across the enterprise.

The leadership takeaway

AI is reshaping every part of the pharmaceutical value chain. Because its impact is so pervasive, no function or leader can afford to operate in isolation. Powerful AI tools placed into siloed organisations expose fragmentation fast.

The leaders who succeed will be those who recognise collaboration as a critical capability for the AI era, and who are willing to rethink how their organisations work together to unlock it.

Our whitepaper, Beyond the Algorithm: How Pharmaceutical Leaders Can Navigate Cultural Transformation in the Age of AI, explores this shift in depth, alongside the other leadership and cultural changes required to turn AI’s promise into lasting value. Download the whitepaper here.