By Mark Steedman, PhD, Manager, Centre for Health Solutions


This week, we have launched the first in a series of reports on artificial intelligence (AI) and its potential impact in driving the digital transformation of biopharma. This overview report, Intelligent biopharma: Forging the links across the value chain, explores the challenges and opportunities in AI adoption and the potential ways that AI might impact the different segments of the biopharma value chain (see Figure 1).1

Figure 1. The biopharma value chain


The pace and scale of medical and scientific innovation, together with increasing competition, lengthening R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny are challenging the existing biopharma business and operating models. These challenges have also had a massively negative impact on the expected return on investment that large biopharma companies expect to achieve from their late-stage pipelines. Consequently, companies are looking to digital transformation as a key differentiator and essential part of their change management strategy. AI technologies are some of the most anticipated of these digital technologies.

What is AI?
AI refers to any computer programme or system that does something we would normally think of as intelligent in humans. AI technologies extract concepts and relationships from data and learn independently from data patterns, augmenting what humans can do and interacting with humans in a natural way. These technologies include machine learning (ML), deep learning (DL), supervised learning, unsupervised learning, natural language processing, computer vision, speech and robotics. For definitions of these terms, please see Figure 2 in the full report.

How can AI technologies enable digital transformation?
The fundamental role of digital technologies is to improve the quality of data and information flow and the robustness of insights derived from this data. Biopharma companies see data that is generated, captured, analysed and utilised in real-time, as their new currency. As a result, they are increasing their commitment to adopting digital technologies, including ML, which sits at the core of many AI technologies. Indeed, recent advances in ML algorithms are driving much of the excitement around AI.

Until recently, many companies lacked the expertise to use AI, but this is changing rapidly as tech giants and start-ups offer AI-based development tools and applications as products and services. As data is vitally important for AI implementation, biopharma’s access to large data sets and the resources to access the required computing power and top technical talent gives them an inherent advantage compared to other industries. However, only those willing to invest early in AI are poised to capitalise on its potential.

The market for AI in biopharma
According to MarketsandMarkets, the AI biopharma market is expected to increase from $198.3 million in 2018 to $3.88 billion in 2025, at a compound annual growth rate (CAGR) of 52.9 per cent. These values vary across four geographical regions: North America, Europe, Asia-Pacific (APAC) and Rest of World (RoW), which includes South America, Africa and the Middle East (see Figure 2).

Figure 2. Expected growth in the AI market in biopharma, 2018-2025

Expected growth in the AI market in biopharma 2018-2025

Source: MarketsandMarkets.

The three factors driving this growth are the exponential increases in data volumes massive improvements in computing power and the decreasing costs of computing. Real–world data (RWD) accounts for a large part of the increase in the volume of data that is now available. RWD is derived from electronic health records, medical imaging, insurance records, wearables and health apps, social media, clinical trials, genomic sequencing and many other sources. However, RWD often exists in silos and needs to be analysed and processed into real-world evidence (RWE) to generate new insights. This is where AI, and ML in particular, come into their own.

The challenges and opportunities in AI adoption
We have identified five critical themes that cut across the biopharma value chain and are influencing the pace and scale of AI implementation (see Figure 3).

Figure 3. Five critical themes cut across the biopharma value chain (top) and are influencing the pace and scale of AI implementation

Five critical themes cut across the biopharma value chain (top) and are influencing the pace and scale of AI implementation

These themes represent both challenges and opportunities for the industry:

  • The race for data: if biopharma companies are to capitalise on AI technologies, they will need to maximise the utility of the huge datasets at their disposal and identify, access and integrate new sources of RWD. They need robust, reliable and curated data that is interoperable across their systems but is also private and secure. Acquiring data through partnerships, collaborations, mergers and acquisitions (M&A) or developing internal capabilities are all current strategies.
  • Updating IT infrastructure: while AI technologies rely on data to learn, improvements in computing infrastructure that perform the algorithms, including the hardware, software and services, are key factors enabling the rise of AI. Moving these data to the cloud also offers potential advantages as long as security, privacy and regulatory requirements are met.
  • Navigating regulation: in the past few years, the biopharma industry has seen a proliferation of regulatory changes and a plethora of new regulations. Biopharma companies should consider taking steps to standardise global operations to facilitate regulatory convergence; increase transparency to highlight to regulators the benefits of partnerships and improve public trust; and transform culture, processes and operating models to embrace the newfound interconnectivity of regulation.
  • Ethical AI implementation: as AI technologies become more powerful, so does the potential for unintended or adverse outcomes. Biopharma companies need to build in ethical considerations as they design, build and deploy AI-powered systems, including testing for and remediating systems that unintentionally encode bias, and treat users or other parties unfairly. Companies can also build trust by being transparent about their use of AI.
  • The future of work: traditional thinking and long-standing organisational cultures are holding biopharma companies back. AI technologies and digital transformation will result in major changes to roles and responsibilities within biopharma companies, requiring companies to rethink the workforce experience, adapt to employing a more diverse workforce and transform their approach to leadership development. AI will create opportunities for biopharma companies to redefine roles, activate and reskill their workforce and generate broad and valuable enterprise-wide benefits.

Biopharma’s AI-fuelled future
Digital transformation will enable biopharma companies to use data and technologies to accelerate innovation, streamline processes and eliminate barriers. AI technologies will be at the forefront of this transformation, helping improve efficiencies, power new products or services, enable new business models and create a biopharma industry that will survive and thrive. Over the next few years, AI’s impact will be felt across the entire biopharma value chain, initially by aggregating and synthesising information from data (see Figure 4).

Figure 4. Some of the main applications of AI across the biopharma value chain

Some of the main applications of AI across the biopharma value chain

Stay tuned for more insights
Biopharma’s use of AI across the value chain is still in its infancy, but the time to invest in its future is now. Indeed, AI is increasingly seen as a critical capability of the digitally transformed biopharma company. Maximising the potential of AI technologies will help drive biopharma companies toward full digital transformation, which – if done right – could catapult biopharma companies into a world where they are able to develop more effective and efficacious drugs, and where these drugs are discovered, tested and approved faster and cheaper than is currently possible.

Over the next few months, we will examine each link of the biopharma value chain in more detail, examining specific case studies and applying insights from interviews with senior leaders from major biopharma companies to innovative start-ups, including many companies that are poised to disrupt the traditional biopharma landscape.


Dr Mark Steedman (PhD)- Research Manager, Deloitte UK Centre for Health Solutions

Mark is the Research Manager for the Deloitte UK Centre for Health Solutions. Until November 2016, he was the Institute Manager and a Policy Fellow at the Institute of Global Health Innovation at Imperial College London, where he supported research on palliative and end-of-life care, maternal and child health, design, philanthropy and electronic health records. Mark has a PhD from the UC Berkeley - UCSF Graduate Programme in Bioengineering, where he worked with Professor Tejal Desai on retinal tissue engineering and drug delivery. He also completed a Whitaker International Postdoctoral Fellowship with Professor Molly Stevens in the Departments of Materials and Bioengineering at Imperial College London.

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