Beyond the Algorithm:
How Pharmaceutical Leaders Can Navigate Cultural Transformation in the Age of AI
Section 1:
Executive Summary
At Achieve Breakthrough, we’ve worked alongside pharmaceutical leaders for years, helping them to navigate challenging transformations and cultural shifts. But we’ve never seen anything quite like what’s facing the industry today.
The advent of AI isn’t just another technological development. It is a force that is fundamentally re‑shaping how people think, work, and lead. The numbers speak for themselves: we’re looking at $60 to $110 billion a year in annual economic value. But behind those figures lies another truth that energises all of us at Achieve Breakthrough.
This isn’t really about the technology. It’s about people. It’s about how leaders and individuals choose to respond to a transformation that’s already underway.
This whitepaper isn’t a technical deep dive on AI (there are lots of those already). Instead, it’s an examination of the human side of this transformation. We explore the cultural shifts, the leadership challenges, and the uncomfortable conversations we need to have about how we work together in an AI‑powered world.
Drawing from our work with some of the industry’s most forward‑thinking organisations, we want to share what we’ve learned about navigating this transformation. Because while the technology is incredible, the real competitive advantage will belong to those who can transform their cultures, empower their people, and create environments where human expertise and artificial intelligence amplify each other.
The question isn’t whether this transformation will happen. It’s already happening. The question is whether you’ll lead it or be led by it.
Ric Bulzis Senior Partner –Achieve Breakthrough
Mike Straw Founder and CEO –Achieve Breakthrough
Introduction Section 2:
Traditionally, the pharmaceutical industry has been characterised by inherent and persistent challenges: long development timelines, prohibitively high costs, and the constant pressure to achieve more with less.
These challenges are not new to industry leaders, but they represent fundamental structural challenges that have long defined the sector’s operational reality.
Into this landscape, AI has emerged as a profoundly disruptive force. Its potential to revolutionise all aspects of the sector is vast:
Removing inefficiencies across the entire value chain, from drug discovery to clinical trials and supply chain management.
Accelerating drug discovery and development and expediting the creation of novel therapeutics.
Enabling more efficient clinical trials and supply chain operations, optimising resource allocation and operational flow.
Paving the way for the realisation of personalised medicine, tailoring treatments to individual patient profiles for enhanced efficacy and safety.
While the technical capabilities of AI are widely acknowledged, the pharmaceutical sector has historically struggled to implement new technologies in a seamless and consistent way.
Perhaps the biggest barrier to effective AI adoption is the critical need for a fundamental cultural shift. This is because the successful integration of AI is not merely a technological problem, but overwhelmingly a human and organisational one.
This is a paradox that is emerging across all industries. But the companies capturing real returns from AI aren’t necessarily the ones with the best technology. In fact, a Microsoft‑sponsored study conducted by the International Data Corporation (IDC) found that companies average $3.50 return per dollar invested in AI. Yet a Boston Consulting Group study found finance functions achieving only 10% median ROI. Then again, some studies show organisations realising $8 in returns. This huge variance reveals that technology itself is not the prime differentiator; organisational readiness is.
The aim of this whitepaper is to provide pharmaceutical leaders with a guide to the cultural changes and transformative shifts in leadership and ways of working brought by AI.
Section 3:
How AI is Transforming the Pharma Sector Forever
AI is transforming the pharmaceutical sector and fundamentally reshaping established processes. This transformation extends beyond mere efficiency gains, enabling entirely new capabilities across almost every business unit. To name but a few areas that are transforming:
Drug discovery and preclinical R&D:
• Target identification & validation: rapid analysis of vast biological datasets to pinpoint novel drug targets.
• Molecule generation: design of de novo molecules for specific targets (remains an elusive goal, but progress is being made rapidly).
• Predictive modelling: enhanced prediction of molecule properties, efficacy, and toxicity is streamlining the early R&D pipeline.
Clinical development and trials:
• Trial design optimisation: design of more effective and statistically robust clinical trials.
• Patient recruitment: identification of suitable patients more rapidly and accurately accelerates enrolment.
• Remote monitoring and wearable integration: capability for continuous, real‑time monitoring of patients via wearables is yielding richer and more frequent data.
Manufacturing and supply chain:
• Predictive maintenance: identifying potential for equipment failure, enabling proactive maintenance and reducing downtime.
• Process optimisation: enhanced manufacturing processes for greater efficiency and yield.
• Demand forecasting: more accurate demand predictions, optimising inventory management.
• Intelligent quality control: ensuring product quality with greater precision.
Regulatory and medical affairs:
• Regulatory submissions: the ability to compile and review complex regulatory documents.
• Compliance monitoring: automated checking for compliance with regulations and guidelines, reducing manual effort.
• Medical writing and content generation: drafting contracts, summarising literature, and creating presentations, freeing up human experts for more strategic tasks.
Commercial and marketing:
• Personalised marketing: hyper‑personalised marketing campaigns based on individual customer data.
• Chatbots: immediate and intelligent customer support.
• Competitive intelligence: rapid processing and analysis of competitor data for strategic insights.
• Content creation: generation of diverse marketing content efficiently.
It is clear that AI is not just a tool for specific functions. It is reshaping every part of the pharmaceutical value chain, from early‑stage research through to commercialisation. Because its impact is so pervasive, no team, function, or leader can afford to operate in isolation.
This demands more than incremental adjustments; it calls for a fundamental shift in how organisations operate. Real transformation will require a unified leadership, management and cultural response that breaks down silos, builds shared understanding across disciplines, and equips people at every level to navigate complexity and change together.
So, where are organisations at now, what are the leadership and cultural shifts required, and how can they be enacted now?
The Cultural Shift: A Leadership Imperative Section 4:
While the technical capabilities of AI are rapidly advancing, the real challenge (and opportunity) lies in how we handle the cultural transformation required to harness it effectively.
One of the AI pioneers at Takeda has said that thinking about a swimming pool is a good way to picture where pharma organisations are with AI. Some are just dipping a toe in. Others are standing cautiously on the steps, unsure whether to commit. A brave few are fully in the water, learning how to swim.
Standing at the edge of the pool: These companies are canvasing the field, observing how AI develops and waiting to see how things shake out before committing. They are cautious, testing the temperature of the water without fully engaging. While the pharmaceutical industry has historically been insulated from change by its long lead times for drug development, this protection is temporary. Leaders adopting this posture risk their organisations becoming extinct if they merely observe.
Standing on the steps: These organisations are attempting to hedge their bets. They may be undertaking a project and assessing it for an extended period, perhaps 12 months, before discussing the next steps. But this cautious model, equivalent to someone lingering at the pool’s edge for hours, is ineffective in a fast‑paced AI environment. These initiatives might yield an interesting prototype, but are unlikely to result in anything built for scalability. This is typically because organisations that only dip their toe in the water lack the foundational data quality to deliver complete and exceptionally high‑quality solutions. At the same time, leadership are not creating the environment to enable people to fully jump in.
Leaping straight into the pool: This group represents the organisations that believe AI will fundamentally change their operations. They have already leapt into the pool and have got a huge head start in learning to how to swim, recognising that the sooner they commit, the better equipped they will be. This approach acknowledges the inherent risks, including the potential for failure, but views experimentation and learning from mistakes as essential for progress. These are the people that are going to win. They are learning, maybe terrifying themselves, but are then recovering.
The core message underscored by this analogy is that companies need to move beyond cautious observation and passivity. Minimum viable products (MVPs) and experiments in silos look like progress, but by their design are difficult to scale across the organisation.
AI is not merely a marginal change but a disruptive force that will blow away or entirely re‑shape various functions and job roles. This means that leaders have no choice but to recognise AI as a forcing function for change.
Individuals and organisations that embrace AI will very quickly replace the people without AI because they will be so much less effective.
Here are the key shifts leaders will need to manage and prepare for:
1. Becoming better collaborators:
To move beyond isolated AI initiatives, organisations will increasingly need to find ways to dismantle traditional industry silos, where functions often control their data and applications in isolation. Data, which naturally wants to flow, is often constrained by very human tendencies to want to control or protect it within specific applications. In other words, by people letting their egos take over and being protectionist over their own parts of the business.
This competitive behaviour can lead to general sense of chaos and the development of prototypes with poor decisions that are difficult to scale. Leaders must actively communicate to build awareness, address misconceptions, and mitigate this inter‑departmental competition. And, even when people are happy to collaborate in theory, we often see people so focussed on their challenges that they are not as open and curious as they could be.
At the same time, now AI enables us to get more done in far less time (and outside our previous areas of expertise). So how can this be managed without things spinning out of control. Samuel Mantle, CEO at Lingaro has a useful analogy to describe the situation:
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?
Samuel Mantle CEO at Lingaro
Ultimately, the challenge is not technological, but rooted in human elements like protectionism, control, and resistance to expanding beyond existing boundaries. This makes collaboration a critical competency for future pharmaceutical leaders. We need to focus on being open, curious, engaged, and connected across the enterprise.
2. Empowering teams to experiment and take risks (within boundaries)
Successful AI adoption also hinges on empowering employees to effectively collaborate with AI tools. This means equipping teams with secure tools and support, fostering a safe environment for experimentation, and shifting leadership from task supervision to coaching and mentoring. As Bryn Roberts, SVP & Global Head of Data, Analytics & Research at Roche says about his approach:
By setting the guardrails and allowing for (or even expecting) failure, learning happens more quickly.
It’s only by failing that we’re actually going to see what works and what doesn’t.
Bryn Roberts
SVP & Global Head of Data, Analytics & Research at Roche
Beyond our deeper scientific and technical use cases, we encourage ‘everyday AI’ through playful exploration by everybody. Once people appreciate what’s possible, a little training and a safe environment enables them to ‘go and play’ in their daily work.
Bryn Roberts
SVP & Global Head of Data, Analytics & Research at Roche
This experimentation of course needs to be combined with the right guardrails and oversight to manage privacy and legal compliance. For example, to prevent leakage of private information or the model regurgitating data in unexpected ways later, there’s a need to make sure people don’t put sensitive things like HR data into models. However, with the right leadership, communication and governance in place, safe experimentation is possible.
3. Re‑skilling teams and competing for new talent
The evolving roles and new skillsets emerging will necessitate significant investment in upskilling existing employees and recruiting new talent with AI expertise. The talent war for AI capabilities is intensifying, adding to existing talent challenges in the sector. This means leaders will be required to think even harder about attracting and retaining the best talent. So what should leaders look for? As Christian Diehl, Chief Data & Digital Officer at Novartis Biomedical Research sees it:
Those who fail to recruit or develop the talent required to succeed in the new AI‑powered world will struggle. This is because leaders and their teams are not competing against AI itself. They are competing against people who are choosing to work with AI and make themselves more productive.
The implication is stark. People with AI will very quickly replace the people without AI. This reality puts the choice in a leader’s hands. To upskill themselves and their teams, and to recruit the new talent needed, or to fall by the wayside. As Samuel Mantle puts in clear terms:
Hiring someone solely based on their experience with recent inventions and technologies is not enough, and may not be even possible for the most recent innovation. We need to bring in more curious minds. People with a mindset that says, ‘I don’t know it yet, but I will figure it out,’ and who remain open to learning something new every day.
Christian Diehl Chief Data & Digital Officer at Novartis Biomedical Research
I genuinely don’t think that AI is going to replace people, but I think that people who use AI will very quickly replace the people who don’t use AI.
Samuel Mantle CEO at Lingaro
4. The emergence of new roles and responsibilities
As AI becomes embedded across pharmaceutical operations, it’s not just workflows that are changing. Entirely new roles are emerging into business units. Positions like AI ethicists and data governance leads are bringing with them new perspectives, motivations, and definitions of success. These roles require a different kind of thinking.
This shift can challenge a leader’s traditional approaches to performance and purpose. A data ethicist’s “why” – their core motivation – may be rooted in protecting public trust or ensuring fairness, rather than purely scientific discovery.
For leaders, the priority is to quickly understand these new roles and connect individuals
to a meaningful sense of purpose. When people see how their work contributes to something bigger (a clear, shared vision), it sparks ambition, fuels innovation, and drives performance. Purpose‑driven leadership isn’t about control; it’s about cultivating ownership. When people feel accountable to a mission they believe in, they show up differently.
The challenge and opportunity for leaders is to ensure that everyone in these new multidisciplinary teams, whether they are data scientists, ethicists, or commercial leads, see themselves in the future being built. They need to understand the unique contribution they’re making, and how it is helping accelerate the whole organisation toward its ultimate purpose.
5. Leaders may need to shift cultural priorities
The efficiency gains brought by AI in areas such as manufacturing and supply chain – from demand forecasting and inventory management to quality control and predictive maintenance – are already reshaping what success looks like. Leaders may increasingly be measured not just on long‑term outcomes, but on sharper, more immediate KPIs: improved productivity, reduced costs, and greater resilience in operations.
This marks a significant cultural shift, from the traditional “slow and cautious” mindset to one that is far more agile, experimental, and fast‑moving. In many ways, to one that’s much closer to (yet still distinct from) big tech. Rishi Gulati, Vice President, CIO at Otsuka sees this mindset shift as one of the biggest barriers to successful adoption:
and expectations of work and cannot be retrofitted it into old ways of working.
The real returns are not from accessing identical models, but from an organisation’s speed of action. While global GenAI spending is projected to reach $644 billion in 2025, the competitive advantage will go to those who treat this as a structural change (not just a technology deployment) and focus on the speed at which their people adapt.
The technology life cycle is now so fast that it exceeds the capabilities of classical management processes.
Bryn Roberts SVP & Global Head of Data, Analytics & Research at Roche
Some people get consumed with the thought that ‘we’re a science company, not a technology company’. What they are failing to understand – and something I hope changes soon – is that the technology is in service to the science they are going after.
Rishi Gulati Vice President, Chief Information Officer at Otsuka
Making cultural and mindset leap demands a genuine breakthrough in thinking and behaviour. It requires leadership bravery and a deep commitment to grapple with unfamiliar strategic and operational decisions. AI is not just another tool to manage. It fundamentally changes the pace, possibilities,
Organisations increasingly need to move away from rigid, centralised models and enable smaller, more autonomous teams that can test, learn, and iterate rapidly. The traditional model of long planning cycles, multiple rounds of review, and tightly controlled approvals is increasingly out of sync with the fast‑moving AI landscape. Gartner projects that by 2029, one in ten boards will use AI guidance to challenge executive decisions on strategic matters. This level of AI‑informed governance requires a new kind of institutional agility and highlights why organisations must move faster. Companies that remain too cautious will find themselves outpaced by competitors willing to embrace discomfort, risk, and speed.
To support this shift, some leaders are adopting a “care and dare” approach:
Care: Operational excellence remains critical for core business functions, where mistakes carry significant cost or compliance risk. Here, precision and control are essential, and mistakes should be rare exceptions.
Dare: In AI‑enabled projects and innovation efforts, experimentation need to be encouraged, including the possibility of failure. It is through rapid prototyping, iteration, and adaptation that new breakthroughs are found. Crucially, this is not about tolerating sloppiness, but about creating space where smart risk‑taking is celebrated and learning is prioritised over perfection.
As already discussed, this cultural shift also requires leaders to focus on removing barriers to experimentation and trying to remove any protectionism, fear of job erosion, and territorial thinking.
Ultimately, this is not just a technological transformation, but a human one. The defining leadership challenge is cultural. The need is to create environments where curiosity, initiative, and cross‑functional collaboration become the norm. Organisations that succeed will be those where people feel safe to try, motivated to contribute, and clear on the purpose behind what they’re doing. As Christian Diehl sums it up:
The real proof in the pudding with AI will come when we open our minds and ask: ‘Let’s fundamentally challenge how we’re operating today. Is there a way to completely re‑engineer our business processes?’
Christian Diehl Chief Data & Digital Officer at Novartis Biomedical Research
6. Getting this right comes down to communication and psychological safety
These profound changes open up challenging conversations for leaders, particularly the crucial need to communicate to teams that AI is about augmenting human capabilities rather than replacing them.
The message should be that: you plus this capability means we can do so much more. For example, AI can automate frustrating, non‑job‑description tasks, enabling skilled pharma teams to focus on higher‑value activities. This has the potential to dramatically boost productivity, with immediate paybacks. For example, who wouldn’t love to create a slide deck in half an hour instead of an entire day? In short, when managed effectively, AI can empower employees and foster a culture of increased ownership and faster decision‑making.
But to ensure that this message is actually heard, leaders must also actively cultivate an environment of psychological safety and openness. In other words an environment where individuals feel secure enough to voice concerns, challenge assumptions, ask questions, and admit mistakes without fear of judgment or reprisal.
Leaders should create spaces where fears about change can be safely explored, allowing for open and honest discussions that address real concerns and potential resistance. This is crucial, because if people don’t feel safe to express their concerns, they won’t fully engage in transformation efforts.
Ultimately, this psychological safety helps to unearth underlying issues and enables people to move through change with clarity, support, and strategic intent.
It’s the human side of things that will become even more in demand. Our ability to communicate, our ability to influence, our ability to negotiate. They are going to be even more powerful human skills in the future.
Samuel Mantle CEO at Lingaro
Section 5:
What do Leaders Need to do to Support AI Transformation in the Pharmaceutical Sector
The pharmaceutical industry is facing a wave of AI driven transformation. And the choice is clear: learn to ride it or risk being swept away.
But this shift won’t be achieved through technology alone. Real progress requires bold leadership, deep cultural evolution, and a human centred approach to change.
Whether you’re dipping a toe into AI, lingering on the steps of the pool, or ready to dive in, it requires leadership transformation to succeed.
Driving a culture shift and enabling change
Many transformation efforts falter because they focus on structures and processes while overlooking the most vital element: their people. We understand that success, especially in the age of AI, hinges on human adaptability, beginning with how people think.
This is because the context we hold is decisive. It tells us what is possible, what’s not possible, and informs the actions we take.
Think of context like the water in a fish tank: it’s critical to existence but often unseen. Just as a fish would gain a new perspective by momentarily leaving the water, it is important that organisations learn to “jump out of the tank” to recognise their existing, often limiting, contexts and uncover what’s truly holding them back.
For instance, if an organisation’s ingrained context is one of extreme risk aversion or siloed data ownership, even the most advanced AI tools will struggle to gain traction. Similarly, if the prevailing context fosters a fear of job erosion rather than augmentation, people won’t truly engage with the technology.
Leaders need to work to shift this underlying context, making it fertile ground for AI adoption. By changing what people believe is possible or safe within their context, you unlock the potential for AI to truly deliver across an organisation.
Part of the approach for helping companies adapt in the face of rapid change is built around understanding and harnessing the following equation. We find this useful to really explain what is required:
Impact and results
This equation highlights that achieving real results with new technology like AI isn’t just about the tech itself. It’s about:
• Embracing technology: Moving beyond simply understanding AI to actively reskill, stay curious, and fully integrate it into workflows. In other words, jumping into the pool. Only 20% of success is about the technology itself. The rest is about a willingness to embrace it.
• Leadership mindset: Cultivating leaders with an open, curious, and engaged approach. Leaders who are willing to experiment and model this for their teams.
• Chosen action: Taking deliberate steps into the unknown, demonstrating a willingness to learn by doing and fostering a culture of curiosity and adaptability. Acting with a direction in mind is an antidote to passivity.
Conversely, progress is diminished by:
• Overcontrol: Micromanagement or dictatorial approaches to tech use that stifles innovation.
• Fear/over-compliance: Hesitation and that leads to missed opportunities.
• Unhelpful environment: A lack of psychological safety that prevents open dialogue and learning.
Ultimately, we use this thinking to help organisations and leaders to reframe their culture, empowering people with the mindset, skills, and confidence to embrace and drive meaningful change. By working with your leaders as catalysts, you can activate the conditions for lasting change and in turn shift mindsets to unlock human potential and deliver results.
At the heart of this method are seven core mindset shifts for you to lean into for success. Each is designed to spark breakthrough change. Together, they shape a new, compelling narrative that’s driven by your people and built to last.
1. Letting go of the past: Challenge ‘the way things are done around here’ and empower people to create a different future.
2. Developing breakthrough ambition: Ensure your change is truly breakthrough and inspires you to take on the impossible. This gives a fundamentally different vantage point.
3. Creating a bold new vision for the future: Setting an ambition is one thing, declaring it is another. Equip your team with the confidence to declare a powerful and believable future reality. Shifting reality with words.
4. Engaging the players in the bold new future: Develop your leadership style to inspire and engage others, coaching the concerns expressed along the way.
5. Cutting through the DNA: Allows you to recognise and challenge organisational habits, testing your comfort zone to find innovative and efficient change pathways.
6. Keeping the organisation future-focused: The conversations happening around a change determine its life span. By embedding a ‘can do’ coaching culture, you can keep everyone’s eyes on the future.
7. Gaining energy from setbacks: Overcoming these is at the heart of making breakthroughs happen, using setbacks as a catalyst to develop and an opportunity to learn.
Whether applied to a critical project, cross functional initiative, or your entire operating model the above perspectives create a truly agile organisation where people think beyond their function, act with shared accountability, and build partnerships that deliver outcomes no one could achieve alone.
Final Thoughts Section 6:
The pharmaceutical industry stands at an inflection point. AI is not simply another technology to be managed or incrementally adopted. Instead, it represents a fundamental shift that will redefine how the sector operates, competes, and delivers value to patients. The technical capabilities are advancing rapidly, but the real determinant of success lies in an organisation’s ability to navigate the profound cultural transformation that AI demands.
The leaders who will thrive are those who recognise that AI transformation is fundamentally a human challenge. It requires the willingness and ambition to get off the sidelines and start making changes at speed. This requires the ability to break free from past assumptions and past ways of doing things, and being open and curious enough to experiment and take bold action.
The choice facing pharmaceutical leaders today is stark: dive into the AI transformation pool and learn to swim, or risk being left behind by competitors who have already embraced the discomfort of change. Those who choose to leap will find that the water, while initially challenging or uncomfortable, offers unprecedented opportunities to accelerate innovation.
The transformation ahead is not just about adopting new tools, it’s about fundamentally reimagining what’s possible when human expertise is amplified by AI. For organisations ready to make this leap, the potential rewards are transformational.
So, where are you right now? On the side looking in? Testing the water from the steps? Or in the pool and learning to swim? If you want support diving in, let’s talk about how we can help you build the cultural, leadership, and human capabilities to thrive in an AI‑powered world.
mike.straw@achievebreakthrough.com
ric.bulzis@achievebreakthrough.com