A Cross-Functional Guide to AI in Pharma

Charlotte Whitehouse

AI in Pharma has moved from a boardroom aspiration to an operational reality, and it is no longer confined to R&D. Across every function, from commercial and medical affairs to clinical operations, regulatory affairs and supply chain, artificial intelligence in the pharmaceutical industry is fundamentally changing how organisations work, compete and deliver value.

What makes this moment distinct is that the conversations happening in clinical operations look remarkably similar to the ones happening in regulatory affairs or commercial. The specific use cases differ. The underlying questions around adoption, governance, talent, and long-term operating models are the same.

This is a guide to how AI is playing out across every major pharma function in 2026, and what the cross-functional reality of digital transformation in pharma actually looks like on the ground.

The Scale of AI in the Pharmaceuticals Industry

Before diving into the functional detail, the scale of the shift deserves framing.

The AI in pharma market is projected to grow from approximately $1.9 billion in 2025 to over $19 billion by 2034, a compound annual growth rate of around 26%. McKinsey Global Institute (MGI) estimates that generative AI in life sciences alone could generate between $60 billion and $110 billion in annual value across drug discovery, clinical development, operations, and commercial functions.

The more telling signal, though, is a shift in how leaders are framing the question.

A ZS survey of 115 US pharma and biotech technology leaders found that executives are no longer asking “where can AI work?” They are asking “where must AI drive our growth?” Reframing the questions from exploratory to strategic, marks a genuine inflection point for digital transformation in pharma.

It also raises the stakes considerably. Gartner predicts that over 40% of agentic AI initiatives are expected to be cancelled by 2027 unless they are anchored in clear business value and properly governed. The companies that will win are not those moving fastest on technology, they are those building the most rigorous foundation beneath it.

AI in Drug Discovery: Reshaping the Front End of the Value Chain

AI in drug discovery is where much of the industry’s early momentum has been concentrated and where some of the most credible results are emerging. Machine learning models are now predicting drug-target interactions with over 85% accuracy, and AI is reducing drug discovery timelines by up to 25% while cutting preclinical costs significantly.

The 2024 Nobel Prize in Chemistry, awarded for AlphaFold’s use of neural network-based AI to predict complex protein structures, marked a moment of mainstream scientific validation. AlphaFold and its successors have opened target identification approaches that would have been computationally impossible five years ago.

But AI in drug discovery is not only about computational biology. Generative AI models for chemistry, including diffusion models for molecular graphs, are now showing meaningful results in lead generation and optimisation.

Major pharmaceutical companies such as AstraZeneca have integrated AI into their discovery pipelines through a combination of internal capability building and strategic partnerships with technology companies and specialist AI platforms.

The frontier here is moving quickly. NVIDIA and Eli Lilly announced a $1 billion co-innovation lab in early 2026, signalling that the infrastructure investment required to compete at the leading edge of AI-driven discovery is now at a scale few organisations can fund independently.

AI in Clinical Trials: Faster, Smarter, More Representative

AI in clinical trials is delivering measurable results across the full trial lifecycle, from design through to execution and analysis. Industry analysts point to cost reductions of up to 70% in some applications, driven by improvements in patient recruitment, site selection, data management, and safety monitoring.

Precision patient matching is among the most impactful early use cases. Pharma organisations are moving from broad recruitment campaigns toward targeted identification of appropriate trial participants, reducing both time-to-enrolment and screen failure rates.

AI is also being used to select and monitor clinical sites with greater accuracy, improving performance and reducing the variability that has historically made trial timelines so difficult to predict.

Beyond execution efficiency, AI in clinical trials is enabling more innovative trial designs. Adaptive trial methodologies, where pre-specified rules allow modifications to the trial based on interim data, are becoming more practical at scale with AI supporting the analytical demands they require.

Real-world evidence is another growing area. Regulators are increasing pressure for RWE that reflects actual clinical use, driving innovation in continuous data collection via connected devices, electronic health records, and patient-reported outcome tools. AI is central to making sense of data at this scale and diversity.

Where AI is delivering value in clinical trials:

AI-powered patient recruitment and diversity monitoring
Adaptive trial design and protocol optimisation
Site selection and performance management
Safety signal detection and pharmacovigilance
Regulatory-ready data analysis and dossier preparation

AI in Medical Affairs: Managing Scientific Complexity at Scale

AI in medical affairs sits at a particularly complex intersection: the function is under pressure to process and communicate an ever-growing volume of scientific evidence, while operating within strict regulatory and compliance boundaries that govern every communication.

The most immediate value AI delivers to medical affairs teams is in managing scale. Scientific query management, literature monitoring and evidence synthesis are time-intensive processes that AI tools can accelerate significantly, surfacing relevant data faster and helping medical science liaisons prepare for HCP conversations with greater depth and precision.

The boundary question, however, is critical. There is an important distinction between what AI can assist with, evidence synthesis, publication management, query routing, internal knowledge management and what still requires human expert judgement: clinical interpretation, label discussions, off-label communication governance.

Most organisations are still developing the frameworks to manage this boundary responsibly and getting it wrong carries significant regulatory and reputational risk.

For medical affairs leaders, the opportunity is real but the governance architecture needs to be built in parallel with capability deployment, not after it.

AI in Regulatory Affairs: From Compliance Burden to Strategic Advantage

AI in regulatory affairs is one of the most under-discussed applications in the industry, and potentially one of the most consequential. Regulatory teams manage an enormous volume of documentation, dossier preparation, label management and ongoing compliance work. The efficiency gains available from AI are substantial.

The FDA published a draft framework on AI model credibility for drug submissions in 2025, an important signal that AI-assisted regulatory processes are increasingly accepted by the agency when properly validated and documented.

The FDA has also been developing AI tools for use internally, which further normalises AI as part of the submission and review process.

AI in regulatory affairs pharma teams specifically involves submission preparation, variation management, labelling automation, pharmacovigilance signal detection, and increasingly, the monitoring of evolving global regulatory requirements across multiple markets simultaneously.

The regulatory environment for AI itself is also evolving rapidly. The EU AI Act, progressing through legislative processes early in 2026, is expected to classify many healthcare AI systems as “high-risk”, requiring mandatory risk management, technical documentation, and potentially third-party conformity assessment. The MHRA in the UK published its own AI regulatory strategy in 2024 with five pillars covering safety, transparency, fairness, accountability, and contestability.

For regulatory affairs teams, this creates a dual mandate: using AI to improve the efficiency of their own function while simultaneously developing expertise in the governance frameworks that will govern AI use across their entire organisation.

Predictive Analytics in Pharma: The Engine Behind Every Function

Predictive analytics in pharma deserves its own attention because it underpins AI applications across every function discussed in this piece, even when it is not labelled as such.

In commercial functions, predictive analytics drives next-best-action recommendations, patient identification, and territory planning.
In clinical, it powers adaptive trial designs and safety signal detection.
In supply chain, it enables demand forecasting and predictive maintenance.
In regulatory, it supports risk-based monitoring and submission prioritisation.

What makes predictive analytics distinctive as a capability is that it compounds in value over time and models improve as more data flows through them. Organisations that invest in the data infrastructure and governance frameworks to support predictive analytics now and build trust in model outputs through transparent validation, are building a capability that becomes more valuable with each year of use.

The critical dependency is data quality. Predictive analytics in pharma is only as good as the data it is trained on.

Siloed systems, inconsistent data standards across clinical and commercial datasets, and legacy architecture that prevents data sharing remain the most common barriers to realising this value.

AI in Supply Chain Pharma: From Reactive to Anticipatory Operations

AI in supply chain pharma is undergoing a shift that mirrors what is happening across the rest of the industry: moving from a tool that generates insights for human decision-making to an active participant in operational workflows.

Early adopters are reporting 30–50% reductions in equipment downtime through predictive maintenance, one of the most concrete ROI cases in pharmaceutical AI. Yield optimisation, demand forecasting, cold chain management and supplier risk monitoring are all areas where AI is delivering documented value.

Cloud-based platforms are enabling companies to unify manufacturing, quality and supply data in ways that were not previously feasible. The efficiency gains are significant: 42% of pharma firms now use cloud-based platforms, with organisations reporting 52% faster trial timelines and 48% improved data integration efficiency as a result of this infrastructure investment.

The challenge is legacy and the average operational technology asset in pharmaceutical manufacturing has been in service for eleven years with many lacking the embedded security and AI integration required for more autonomous operations.

Upgrading this infrastructure is a prerequisite for capturing the AI opportunity in supply chain, not a downstream consideration.

Digital Transformation in Pharma: The Cross-Functional Reality

All the functional applications described above exist within a broader context of digital transformation in pharma, a transformation that is creating both the opportunity and the pressure that every function is currently navigating.

What is increasingly clear from conversations across the industry is that the most significant barriers to AI value are not functional but structural. Data fragmentation, governance gaps, talent shortfalls, and legacy architecture are challenges that no single function can solve independently.

The organisations making the most meaningful progress on digital transformation in pharma share a common characteristic: they are treating AI as an enterprise capability question, not a series of functional technology projects. That means:

Data Infrastructure Before AI Tools

A unified, cloud-based data core is not a technology preference, it is a prerequisite for AI that actually works across the organisation. The fragmented, siloed systems that characterise most pharma enterprises will limit the value of any AI investment regardless of how sophisticated the models are.

Governance as a Strategic Asset

Decision-grade AI in GxP environments such as pharmacovigilance, clinical documentation, quality control, requires validated, auditable, explainable systems. Building governance frameworks that meet current and anticipated regulatory requirements is not a compliance exercise. It is a competitive advantage.

Talent as a Transformation Lever

The pharma AI skills gap is real and growing, and compliance officers need AI literacy. Medical science liaisons need to understand what AI-generated content does and does not represent.

Data scientists need GMP training. New roles including AI governance officer and algorithmic auditors are emerging without established talent pipelines. The organisations investing in workforce capability now will be materially better positioned to scale.

Cross-Functional Learning as an Accelerant

The insights that accelerate AI adoption most quickly are often those that travel across functional boundaries. What clinical operations has learned about model validation is directly relevant to regulatory affairs. What commercial teams have discovered about managing AI-generated content applies directly to medical affairs. The most valuable conversations in pharmaceutical AI right now are cross-functional ones.

What Pharma Leaders Should Prioritise

Across every function and every organisation, a consistent set of priorities is emerging for leaders serious about AI:

Invest in data foundations before AI tools. Data quality and infrastructure determine the ceiling on AI value. This is the most important investment most pharma organisations can make right now.
Define governance before you scale. Build validation frameworks, model documentation processes, and accountability structures before regulatory requirements force the issue. Early movers on governance will be faster to scale.
Connect every AI initiative to measurable business value. Pilots that don’t connect to outcomes are the ones that get cancelled. Every use case needs a clear hypothesis and a measurement mechanism.
Build cross-functional AI fluency. Commercial, Medical, Clinical, Digital, Regulatory, and Supply Chain leaders need shared frameworks and shared language. Siloed AI strategies produce siloed results.
Start the talent conversation now. The skills required to operate effectively in an AI-enabled pharma environment are different from those required today. Workforce planning that ignores this will create capability gaps at exactly the moment they matter most.

The Conversation Across the Industry

AI in life sciences is at a genuinely consequential moment. The technology is maturing, the regulatory environment is crystallising, and the competitive dynamics are beginning to shift toward organisations that are getting implementation right, not just moving fast.

What is also becoming clear is that the most valuable insights about how to navigate this shift are emerging from our cross-functional conversations at our roundtable events.

Leaders from clinical, commercial, medical, digital, and regulatory functions come together to compare notes on what is working, where the real challenges lie, and what the talent and operating model implications look like over the next three to five years.

If you are a leader working through these questions, in any function within pharma or life sciences, we would genuinely value your perspective.

This blog is part of an ongoing series exploring AI in life sciences and digital transformation in pharma across all functions. If you are interested in participating in our roundtable discussions with leaders from across the pharmaceutical industry, please get in touch.

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A Cross Functional Guide to AI in Pharma