Preparing for the data-driven future of pharma

Ways for companies to unleash the power of precision medicine

When it comes to advancing technological innovation in healthcare, the tools are here. In fact, most pharmaceutical companies are already using them in some way - whether they are applying artificial intelligence (AI), machine learning (ML), big data processing, and cognitive computing to make advances in the research and development of drugs or to automate operational processes and workforces.

But there’s a problem: The broader adoption of AI in health is still very low. One of the reasons for this is that many business leaders are insecure if they are doing any of it right. According to our recent Future of Health study, the majority of pharma executives reported gaps in their own data analytics capabilities and said that they feel constrained by a lack of access to other data, which they believe is valuable for analyzing business opportunities and threats. They want to use AI and ML to harness the power of data more efficiently and effectively, but they are not sure how.

In this report, we explore the current levels of data and AI maturity in pharma, our expectations for what comes next, and some recommendations for creating long term value. We focus specifically on the expected value from personalized precision medicine and how data and AI can enable scaled value. Now is a critical time for pharma companies to use AI, but first they must reimagine the potential of data - removing it from historical silos, using it instead to connect the dots, and participating in a collaborative ecosystem.

We believe that the leaders of data-driven transformation in pharma will be those that build critical capabilities in two areas, namely:

  • cleaning up their own enterprise data to form a company-wide data value chain

  • establishing strong partnerships in both the public and private sectors, specifically with a focus on collaborations among healthcare providers and tech companies

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Where we are now: Understanding the current landscape in pharma

Although it is becoming outdated, many global pharmaceutical companies today still operate their business according to the so-called blockbuster model. They work to develop mass-produced, mostly chemically developed, drugs for widespread health problems, which can then be prescribed to a huge patient population. But this model is expected to change soon, for two main reasons.


1. Rising costs and changing customer preferences are affecting the industry First, the industry is facing rising costs - across the value chain, from R&D to manufacturing to marketing. Developing new products has always been expensive, but it has become increasingly so over the past two decades. At the same time, the number of products protected by patents has decreased, leading to an increase in imitation products by generic manufacturers. That reality combined with the growing importance of biosimilars and reimports has led to more price pressure in the pharmaceutical market.


Second, the demand for personalized products rises. One reason for this is that the aging population continues to grow, but also because people of all ages are increasingly aware of their health and wellness. They’re looking for advances in medical, chemical, biological, and biotechnological research, as well as for more pharmacological information technology and bioinformatics. Another reason is that the pharma industry is rapidly running out of viable new drug targets that can be intercepted by small molecules or large proteins – and therefore needs to develop completely new modalities. So clearly, data, AI, and ML will play a critical role in this area of innovation.

2. Personalized precision medicine will soon be the “new normal” for pharma and healthcare Cost leaders strive for the greatest possible market share with a cost-effective product portfolio, mostly based on biosimilars and genetics. Only very few of the large research based pharmaceutical companies are following this path, while most big pharma companies have sold their generics businesses over the last years.


Meanwhile, differentiators are focusing on new, precision-based medicines that may apply to smaller niches of the population and to rare diseases. Personalized therapies may be created based on genetic and biological characteristics of an individual patient, and they may even include social and environmental factors.


What comes next: Emerging opportunities for the pharma sector

This is just at the beginning of the age of personalized precision medicine, and already differentiation seems to be the strategy of choice for most big pharmaceutical companies. It is a promising one - if not necessarily easy. Success in personalized precision medicine will require a range of new capabilities, including individual diagnosis of the patient; digital sequencing and analysis of the individual biological characteristics of the patient; mass-customized production (and personalized delivery) of the active ingredient; and the individual follow-up control of efficacy and contraindications.

Today, most pharmaceutical companies are not yet set up for offering such an individual end-to-end care service from diagnosis to control, but they can be if they start focusing on building their enterprise-wide AI and data analytics capabilities:

AI and data-driven transformation will play a bigger role in business innovation




AI and data-driven transformation will reconfigure the pharma value chain





The future of pharma will necessitate an evolution toward platform ecosystems





The future will require different players to work as a community of solvers

Clearly, pharma companies must prioritize building their own data and AI capabilities, but if they want to be successful, they can’t do it alone - or in silos. We expect to see continuous innovation in the converging areas of health, pharma, and tech over the next 20 years. Already now, the potential of AI and data-driven transformation is tangible, and it’s only beginning. This viewpoint focuses on the AI potential in research and development, but there are many more implementable opportunities for AI e.g., in operations, marketing, and sales, which shall be discussed in upcoming Strategy& publications. The conclusion that can be drawn already now is: This is an optimal time for pharma companies to organize their own data, build new capabilities, and form strategic partnerships.

When pharma companies start collaborating to solve several hurdles, it will lead to a new breed of business models in the digital healthcare ecosystem. Likely new roles in the data driven healthcare ecosystem will include

  • solution providers, which can offer personalized solutions for specific diseases and wellness needs

  • orchestrators, which can leverage data analytics to match ideal solutions to an individual customer’s needs

  • and platform providers, which can maintain the physical and technology platforms for orchestrators and solution providers to develop and deliver their offerings

In the end, each pharma company needs to make a deliberate decision about how they will benefit from data and AI-driven innovations. Will you wait for the changes to happen and then collaborate with the new players in the ecosystem?


 

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