In pharmaceutical organisations, functions have traditionally operated with a strong degree of independence. R&D, clinical, regulatory, manufacturing, commercial, each with its own processes, data, systems, and measures of success. Coordination exists, but is often structured around handoffs rather than genuine integration.
That model made sense in a world where change was slower and more contained. But AI changes this dynamic entirely.
This article is part of our series exploring the human side of AI transformation. We're less concerned with the mechanics of models and more interested in what happens organisationally when AI begins to reshape multiple functions at once. Because once AI starts making an impact in one area, it creates a domino effect across the enterprise.
When the first domino falls
Think of an AI model designed to accelerate target identification in early-stage R&D. Suddenly, the volume and speed of potential candidates increases. That impacts preclinical teams, who must process more data and make faster go/no-go decisions.
Clinical development likely feels the next shift. Trial design becomes more data driven and patient recruitment accelerates. Then remote monitoring generates richer streams of information and regulatory teams are required to review and compile increasingly complex submissions at speed.
Meanwhile, across the organisation, other dominos are falling. Manufacturing and supply chain teams may be using predictive maintenance and demand forecasting tools that change production rhythms and inventory models. Commercial teams could be deploying personalised marketing and real-time insights that reshape engagement strategies.
These aren’t downstream consequences of the same cascade. They are parallel transformations, unfolding at different speeds, driven by different priorities. Each of these changes may begin within a single function, but none of them stay neatly contained there.
AI's impact in one area alters expectations, timelines, data flows, and decision-making in others. And when functions respond at different speeds, friction builds. This is what we mean when we talk about the AI domino effect.
The danger of a fragmented process
Many organisations are still approaching AI as a series of discrete pilots. A proof-of-concept in clinical, or an automation initiative in regulatory. Individually these efforts can yield results, but collectively they struggle to deliver sustained impact.
This is because AI is reshaping the entire value chain at speed. If one team is learning to move at speed, experiment, and iterate, while another remains cautious, siloed, or protectionist with data, misalignment becomes inevitable.
We see this particularly around data ownership. AI thrives on data flow, yet traditional structures encourage functions to control and protect their own systems and datasets. When egos, territorial thinking, or fear of exposure creep in, collaboration slows and prototypes fail to scale. Minimum viable products created in isolation rarely become enterprise capabilities.
Why unified leadership matters
Because AI touches every stage of the pharmaceutical value chain, no leader can afford to treat it as someone else's agenda. A CIO can’t carry this alone. Nor can a data science team, an innovation hub, or a single business unit.
What's required is a unified leadership response that breaks down functional silos rather than reinforcing them. Leadership needs to build shared understanding of how AI is reshaping interconnected workflows, and align incentives so that collaboration is rewarded rather than penalised. That means creating the conditions for open, cross-functional dialogue about risks, priorities, and pace throughout the organisation.
This demands a cultural shift away from incremental adjustments and siloed experimentation, toward collective ownership. Leaders must actively communicate that AI adoption is an enterprise-wide transformation. That means addressing misconceptions, surfacing concerns, and creating psychological safety so teams feel able to raise tensions early rather than defend territory.
From ego-led to enterprise-led thinking
One of the most common barriers we see is subtle protectionism. A sense that "our data", "our systems", or "our processes" must remain tightly guarded. In an AI-enabled organisation, that mindset becomes increasingly costly.
Data naturally wants to flow and insight compounds when it connects across boundaries. This requires leaders to challenge inherited habits and ask harder questions. Are we optimising locally or collectively? Are we aligned on the speed at which we're willing to learn, and honest about where that differs across functions?
Building a coordinated response
So what does a unified organisational response look like in practice? It starts with treating AI as a structural transformation, not a side project. That means creating empowered teams that can test and learn rapidly, while staying visibly connected to enterprise priorities.
It also means being explicit about where the organisation must maintain rigorous operational control, and where it needs to actively encourage experimentation. In pharma, that tension is ever-present: patient safety and regulatory integrity are non-negotiable, but the functions surrounding them must be willing to move, adapt, and learn at a pace the industry hasn't historically demanded.
Underpinning all of this is investment in people. Re-skilling can’t be limited to specialist teams. For a unified response to work, people across functions need to engage confidently with AI and understand enough to collaborate, challenge, and contribute. And as new roles emerge, leaders have to make the connections visible: how does the work of a data scientist intersect with that of a regulatory affairs lead or a commercial strategist?
Once one domino falls, others will follow. The question is whether they fall in a coordinated sequence that accelerates performance, or in a scattered pattern that creates confusion.
Our whitepaper, Beyond the Algorithm: How Pharmaceutical Leaders Can Navigate Cultural Transformation in the Age of AI, explores the cultural conditions, collaboration models, and leadership behaviours required to make that unified response real. If the dynamics described in this article feel familiar, it's worth a read.