Laying the groundwork for AI transformation in life sciences

The life sciences sector is already managing a crowded and demanding agenda. Between now and 2030, patent expiries affecting dozens of major drugs are expected to put as much as $300bn in revenue at risk. At the same time, drug development failure rates remain high, and attempts to reshape product portfolios through mergers, acquisitions and licensing carry significant risk. The ongoing shift from traditional pharmaceuticals towards biologics, cell‑based and gene therapies is adding further complexity to innovation, manufacturing processes and supply chains. Pricing pressures are intensifying as healthcare systems seek to reduce medication costs, while higher interest rates continue to weigh on investment.

Ongoing geopolitical conflict has also disrupted the sector’s highly interconnected global supply networks.

These pressures are reflected in sentiment across the industry. According to Forvis Mazars C‑suite barometer 2026, only 18% of senior life sciences executives report being very positive about growth prospects in 2026, compared with 37% at the beginning of 2025. Other indicators point in the same direction, including a notable decline in the number of executives who view international expansion or new product launches as top strategic priorities.

In this context, it is increasingly tempting to conclude that parts of the pharmaceutical sector have reached, or are approaching, a “complexity wall”. This concept captures the moment when existing capabilities, processes and operating models are no longer sufficient to sustain growth amid rising disruption and complexity. For many organisations, AI‑enabled digital transformation represents the most viable response, offering a way to accelerate innovation and support the long‑term evolution on which the industry depends.

Improving efficiency across the drug discovery pipeline

Industry estimates suggest that bringing a new drug to market can take more than ten years and cost more than $1 billion. Despite this investment, only around 14.3% of compounds entering the R&D pipeline ultimately receive regulatory approval. This reflects a combination of factors, including scientific and technological complexity, rising compliance costs and the challenge of developing treatments for multifactorial diseases.

Against this backdrop, AI’s ability to shift drug discovery from experimental science towards computational engineering represents one of the few credible responses to Big Pharma’s R&D productivity challenge.

Many pharmaceutical companies have already deployed generative AI tools to scan and analyse vast volumes of scientific literature in search of promising drug candidates. AI systems are particularly well suited to working with noisy, heterogeneous and experimental datasets, identifying patterns and signals that may be overlooked by human researchers, including within data generated by unsuccessful clinical trials.

AI is also increasingly being used to complement laboratory work within silico experimentation. These approaches allow researchers to model and simulate biological processes digitally, accelerating early‑stage discovery. In addition, AI‑based models have the potential to predict how specific compounds will interact with the human body, improving the sector’s ability to identify therapies with a higher Probability Of Success (POS).

The longer‑term ambition is to transform drug discovery into a highly automated, learning‑driven process, where data underpins a continuous cycle of hypothesis generation and validation. To support this shift, management teams should prioritise several actions from the outset of AI transformation:

• Establishing robust technology infrastructure and governance to ensure R&D data is Findable, Accessible, Interoperable and Reusable (FAIR). These principles underpin responsible AI by supporting traceability, explainability, bias management and data privacy.
• Investing in specialist talent to manage and deliver data‑driven R&D, using hub‑and‑spoke operating models that combine centralised governance and infrastructure with local insight into use cases and operational challenges.
• Avoiding innovation for its own sake, instead focusing on clearly defined, measurable objectives such as shortening development timelines and improving success rates.
• Reviewing initiatives quarterly, making informed decisions to scale, pivot or discontinue projects based on updated assessments of measurable return on investment.
• Embedding compliance by design, engaging regulators at an early stage, such as during pilot programmes, and implementing clear guardrails around explainability and auditability.

AI‑driven transformation is unlikely to deliver immediate, large‑scale results in an industry as complex and highly regulated as life sciences. Nevertheless, many companies are already making significant investments in proprietary data, high‑performance computing, robotics, automation and AI. Across the sector, the future of life sciences is increasingly being built on foundations that combine high‑quality infrastructure, advanced data management and multimodal AI capabilities.

Aligning innovation with compliance requirements

There are significant opportunities for AI‑enabled transformation across pharmaceutical manufacturing. Faster and more reliable production processes could deliver double-digit revenue growth for the sector, alongside meaningful reductions in time to market. At the same time, increased collaboration between national regulators offers the potential for globally aligned baseline standards. However, this progress comes with complex regulatory requirements, including the need for AI systems to be explainable, highly validated and tightly controlled.

The move towards real‑time data exchange also introduces new demands. Improvements in data quality and standardisation are essential, alongside interoperable data platforms, local high‑speed connectivity and enhanced cybersecurity capabilities. While these changes will take time to implement, growing regulatory clarity could act as a catalyst for scaling AI‑driven manufacturing transformation across four key areas:

• Optimised production: machine learning and digital twin technologies can support more effective production planning and enable real‑time, multivariate optimisation of manufacturing lines. Modular and flexible production models are increasingly suited to small‑batch and personalised medicines.
• Quality control and quality assurance: traditional QC and QA processes are highly labour‑intensive. By contrast, computer vision combined with deep‑learning algorithms enables continuous, in‑process monitoring of outputs. AI can also analyse deviation data to anticipate quality thresholds and emerging risks.
• Predictive maintenance: AI‑based monitoring tools can enhance inspection regimes, reduce unplanned downtime and extend the operational life of manufacturing equipment.
• Compliance AI: the growing compliance burden within manufacturing facilities can be streamlined through AI systems that use real‑time data to automate documentation and support audit readiness.

Supply chains remain a focus for technology upgrades

According to Forvis Mazars’ global report: Strengthening supply chains, four in ten life sciences executives expect ongoing supply chain disruption to constrain growth this year. In response, companies are increasing investment in digital capabilities that improve operational agility and support more effective responses to geopolitical and climate‑related disruption, including advanced scenario‑planning tools. Many pharmaceutical organisations are working towards end‑to‑end supply chain visibility, although progress is often hindered by incompatible datasets and the continued use of legacy manufacturing systems among suppliers.

Clinical trials represent a highly specialised and complex component of the life sciences supply chain. In the US alone, trials last an average of eight years and can account for up to 70% of the total cost of bringing a new drug to market. They require small‑batch manufacturing, specialist labelling, extensive cold‑chain logistics and robust return pathways for unused treatments. AI is increasingly being applied to improve patient selection and develop synthetic control groups, while physical attendance at medical facilities is no longer always required. Home‑based and decentralised trials, supported by remote sensors and telemedicine, are reducing the need for travel. In certain cases, these models create additional demand for edge computing capabilities, including reliable connectivity and strong data‑security controls that enable more processing to take place locally.

We recommend that organisations consider the following actions when modernising supply chain management systems:

• Establish a single source of truth by integrating fragmented data silos across multiple systems. This includes digitising remaining paper‑based processes, cleansing datasets where required and implementing robust data‑governance frameworks.
• Promote adoption of cloud‑based platforms that support scalability, interoperability and real‑time collaboration, both internally and with suppliers. Organisations should also assess the potential of supply chain “control towers” to enhance visibility and enable faster responses to disruption.
• Apply AI to demand planning, combining predictive insights from epidemiological data, sales performance, demographic trends and other external market indicators.
• Digitise clinical trial data and transition to machine‑readable formats. Explore AI‑enabled patient selection and assess the role of remote monitoring, home care and telemedicine in supporting decentralised trial models.

Navigating real‑time governance and regulatory challenges

When deployed through autonomous or agent‑based systems, generative AI has the potential to significantly reshape compliance activities. By drawing on knowledge bases containing prior regulatory submissions, AI tools can help reduce re‑submission rates by identifying unsupported claims, refining language and anticipating likely follow‑up questions from regulators. Agentic AI may also augment the role of compliance project managers by assessing timelines and estimating the resource demands associated with upcoming submission deadlines.

At the same time, AI introduces new categories of risk that require appropriate oversight. Both the technology itself and the regulatory frameworks governing its use continue to evolve. Greater regulatory clarity will be essential before life sciences companies can commit at scale to solutions that promise substantial efficiency gains. In a highly globalised industry, the potential for regulatory divergence across regions remains a significant concern, as does the risk of retroactive compliance obligations. Questions also persist around the extent to which intellectual property generated by AI can be legally protected, while shortages of AI‑literate expertise within regulatory authorities may further slow approval processes.

We recommend the following initial actions when deploying AI to support compliance workloads:

• Prioritise agentic AI for documentation‑intensive activities, including regulatory drafting, submissions, compliance monitoring and audit preparation.
• Undertake detailed assessment of data sources accessed by AI systems, ensuring the integrity, security and compliance of all data feeds and supporting infrastructure.
• Define clear governance and accountability, including when human oversight is required, which executives are responsible for AI‑driven decisions, and the limits placed on agentic AI’s access rights and decision‑making authority. Governance frameworks should be aligned with evolving regulatory guidance.
• Plan for changes in compliance roles, recognising that the shift from manual compliance processes to human supervision of AI systems will require structured change management and skills development.

Preparing for what’s next: the future of AI in life sciences

According to Forvis Mazars C‑suite barometer 2026, relatively few senior executives in the life sciences sector, just 22%, identify digital transformation as a top organisational priority over the next three to five years. Only around one third of leaders in organisations with a digital transformation strategy believe that AI will play a major role in its success. This contrasts with other sectors, where a higher proportion of executives place transformation at the top of their agenda and more clearly recognise AI’s contribution to outcomes.

In life sciences, this caution is often linked to organisational complexity created by decades of M&A‑led growth. Many companies operate with fragmented structures and rely on siloed, legacy technologies that make transformation more challenging. Introducing emerging technologies into this environment can heighten perceptions of risk rather than reduce them. Regulatory uncertainty around AI further compounds this “complexity wall”. Research from the Pistoia Alliance, for example, highlights ongoing weaknesses in areas such as data quality and governance, AI validation methods, operating models and definitions of value.

These challenges are not unique to life sciences. As a minimum baseline, however, we recommend the following strategic initiatives to support AI transformation over the medium term:

• Review and strengthen data governance, alongside modernising technology infrastructure capable of supporting AI‑ready data, computation, security and privacy requirements.
• Prioritise manufacturing technology upgrades, including closer integration of IT and OT systems and the enablement of real‑time data flows.
• Develop a culture of data and AI literacy, supported by cross‑functional teams operating within a hub‑and‑spoke model that combines centralised expertise with decentralised process knowledge.
• Maintain active engagement with AI regulators, helping to inform policy development while signalling areas where further clarity is required.

Failing to prepare for AI‑driven transformation today will only increase the scale of the complexity challenge facing the sector tomorrow. Simply navigating uncertainty is no longer enough. Even amid ongoing disruption, life sciences companies must focus on creating the conditions needed to deploy transformation at scale in the years ahead.