Biopharma digital transformation: Gain an edge with leapfrog digital innovation

The pandemic also put biopharma in the spotlight to curb the spread of the virus. Coupled with private and public funding, regulatory support, and extraordinary levels of collaboration, digital technologies provided an advantage as companies raced to develop vaccines and therapies (for more, see our publication, Seeds of change: Measuring the return on pharma R&D). Regulatory agencies also provided flexibility to incorporate digital technologies in research and development (R&D) and other parts of the value chain. For instance, the US FDA and European Medicines Agency (EMA) released multiple guidelines on the use of digital technologies in clinical trials while expanding remote or virtual inspection of manufacturing sites.³

Given the accelerated pace of digital innovation across the industry, Deloitte conducted research to better understand biopharma’s approach to digital innovation.

Biopharma companies are now widely using cloud, AI, data lakes, and wearables 

Survey results suggest that certain digital technologies are widely adopted and incorporated into biopharma operations. More than a third of survey respondents reported using the cloud (49%), AI (38%), data lakes (33%), and wearables (33%) in day-to-day operations.

Cloud: Close to half of the survey respondents said they use cloud computing to facilitate day-to-day work. The cloud has provided scalability and agility for organizations to enable employees to work collaboratively and flexibly from home, securely store and share data, build data lakes, and even run AI and machine learning (ML) algorithms.Companies have also been using cloud to cut costs, improve time to discovery and insight (see sidebar, “Case study 1”), and collate data for greater visibility into manufacturing and supply chain operations.

AI: Our research on Scaling AI adoption across the life science value chain found that COVID-19 put a spotlight on AI. Biopharma investments in AI have risen, and the breadth of AI application has increased.⁵ Companies have used AI to optimize site selection for COVID-19 vaccines and manage the impact of disruptions to their clinical development operations. Novartis, for instance, used AI to analyze data on trial operations housed in data lakes to predict where disruptions (such as staff shortages, enrollment delays) were likely to occur, and intervene early to reduce their impact on trial timelines.⁶ Deloitte’s 2020 Measuring the return from pharmaceutical innovation study found investments in AI and digitalizing trial operations enabled most of the top 20 companies by R&D spend to keep pivotal trials moving without affecting anticipated launch timings.⁷

As biopharma expanded the use of digital channels to engage patients going online for information on their health needs, spending on digital marketing grew significantly.⁸ Implementing AI solutions helped enable better marketing spend analytics and finding the right combinations of digital channels to engage patients and drive conversions (see sidebar, “Case study 2”).

Wearables and AR/VR:Many disrupted clinical trials adopted hybrid study approaches that included delivering medications directly to patients and monitoring and assessing them remotely. Wearables were incorporated into clinical trials to remotely capture metrics such as blood sugar and oxygen levels. As pandemic-related travel restrictions forced manufacturing sites to operate with skeleton crews, wearable and AR/VR technologies were deployed to help on-site staff with configuring equipment, managing processes, and troubleshooting issues.⁹

IoT: As disruptions to logistics and transportation impacted the timely delivery of products, companies rapidly digitalized their supply-chain operations. IoT solutions tracked and traced product shipments in real time and plugged gaps in supply-chain visibility (see sidebar, “Case study 3”).

Others: Our survey shows that biopharma is exploring other digital technologies, such as digital twins, blockchain, and quantum computing. When connected to a live process, such as fermenting, a digital twin can analyze data from sensors and control systems to model the process in-silico and provide feedback to optimize the physical process. In 2021, GlaxoSmithKline successfully piloted the creation of a digital twin to fine-tune its manufacturing process for vaccine adjuvants.¹¹

Given the authenticity and integrity of data stored in blockchain networks, the technology has the potential to support secure data-sharing, connectivity, and auditability for processes across the biopharma value chain. Some companies are already piloting blockchain networks in parallel with serialization to track products and prevent counterfeiting.¹²

While computational approaches, including AI, are being steadily adopted in drug discovery, today’s binary computers can only efficiently simulate simple molecular interactions.¹³ Quantum computers can process data faster to simulate complex interactions between biological molecules, expanding our ability to understand a range of disease mechanisms. Some companies such as Boehringer Ingelheim are experimenting with quantum computing for molecular dynamics simulations.¹⁴

Digital innovation is a burning priority

Large-scale digital innovation projects that became commonplace during the pandemic altered mindsets, freed funding, and created an appetite for digital transformation to continue. In our survey, 82% of respondents agreed that digitalization of activities and operations is likely to continue even after the pandemic ends.

However, the industry is now at an inflection point. Organizations, which are already juggling several priorities, now face a critical choice—either decelerate the current pandemic pace of innovation or raise the bar even higher by accelerating the adoption of digital technologies (see figure 2).

Our research and experience with clients point to the likelihood of the latter. Leaders are now willing to invest, take risks, and digitally innovate to stay ahead of the competition: Seventy-seven percent of survey respondents said their organization treats digital innovation as a competitive differentiator. There is a sense of urgency among executives to double down on digital investments to realize long-term business goals as quickly as possible. More than half of our survey respondents view their organizations as fast followers, failing to capture the first-mover advantage of early adopters of digital technologies. Of these executives, most (80%) believe that their organization needs to be more aggressive and adopt digital technologies faster to win in the market.

However, merely increasing investments isn’t enough. Organizations should move beyond random and duplicative digital technology investments across the enterprise to pursue agenda-driven leapfrog digital innovation. This involves building a portfolio of digital technology investments that come together to achieve innovation north stars within each functional area, tied to an enterprise value stream. A “value stream” is a collection of processes that helps achieve goals; for example, the molecule to market value stream focused on creating and launching new drugs or the procure to pay value stream focused on purchasing materials and services. Innovation portfolios architected in this manner create capabilities that improve key performance indicators (KPIs) for activities across the value stream to generate enterprisewide impacts.

Leapfrog digital innovation opportunities across value streams

We now outline examples of potential digital innovation north stars across functional areas tied to an overarching value stream, enabled by leapfrog digital innovation (see figure 4).

Drug discovery 

North star aspiration: Lab of the future

In the R&D lab of the future, an interconnected ecosystem of data, platforms, instruments, and advanced analytical tools supports scientists across teams and geographies to rapidly discover breakthrough therapies. Such a lab could optimize and expedite value-stream processes from target identification to preclinical development—all through leapfrog digital innovation. 

Realizing this potential north star ambition will likely require companies to build a purposeful portfolio of digital innovation (see figure 6) while bringing about a series of key transformational shifts within the discovery organization, including: 

• Empowering researchers with machine intelligence: Changing traditional human-led scientific methods by embedding machine intelligence into research processes for faster analysis of molecular structures and identification of promising compounds.
• Building interoperable research ecosystems: Shifting from the current landscape of isolated scientific instruments, manual data-collection techniques, and fragmented databases to create an interconnected research ecosystem for fluid data generation, sharing, and analysis. 
• Breaking down research barriers: Moving away from information siloes created from differing organizational priorities and geographical barriers to create a collaborative culture where research inputs and outputs are frictionlessly shared among teams and research partners.

The research leaders we surveyed said their organizations are currently prioritizing investments in AI (81%) and cloud (71%). A much smaller percentage believes that over the next five years their organizations are likely to invest in AR/VR (19%) and IoT (24%), both of which are essential for the lab of the future. Additionally, research leaders see improving research productivity (95%), reducing drug discovery costs (76%), and improving pipeline diversity (67%) as the top value levers realizable through digital innovation.

Drug development

North star aspiration: Patient-centered and seamless clinical trials

Next generation clinical trial protocols will be built to address participants’ diverse medical and behavioral needs, with participant engagement extending beyond the life of the clinical trial. At the same time, every aspect of the trial from protocol development, clinical observation to developing a dossier is seamlessly executed digitally. Such a north star could accelerate value-stream processes from study design to closeout, making it easier to attract and retain trial participants and reduce development costs and cycle time.

Achieving the north star described above likely requires creating a targeted portfolio of digital innovations (see figure 7), accompanied by a series of transformational shifts, including: 

• Cultivating deeper patient relationships: Moving away from short-term relationships with patients focused on trial completion to treating patients as longitudinal partners to better understand their disease, collect long-term safety and efficacy data, and improve care outcomes. 
• Decentralizing trial environments:Changing the definition of a clinical site from a physical medical center to a virtual or preferred local setting (doctor’s office or alternative sites) to ease trial participation.
• Infusing digital agility into study deliverables:Rethinking the nature of study deliverables (e.g., protocols, dossiers) from static documents that need to be rewritten for every trial to living collections of digital elements that can be assembled and dissembled based on the needs of the trial.
• Digitizing trial processes: Shifting away from inefficient processes that clinical researchers struggle with today by creating end-to-end digital workflows across the trial life cycle using digital tools, automation, and machine intelligence.

Survey results suggest that organizations are currently prioritizing investments in the cloud (80%) and AI (76%) for drug development. Future investments in wearables and IoT will likely be needed to shift toward trial decentralization and modernizing the trial data environment. Reducing time to market (76%), decreasing time to analyze data (65%), and reducing study execution costs (57%) are the top value levers surveyed clinical leaders believe can be realized through digital innovation.

Manufacturing 

North star aspiration: Smart factories

Smart factories seamlessly connect disparate manufacturing systems and processes for enhanced visibility into shop floor operations, as machine intelligence monitors processes and provides actionable insights for floor staff to reduce errors, deviations, and production losses. Such smart factories could streamline processes from raw material procurement to quality control to improve yield and asset uptime, reduce manual oversight costs, and bring greater efficiency to manufacturing processes. Show-and-tell centers such as Deloitte’s Smart Factory@Wichita, with smart production lines, prototyping simulators, and experiential labs enable exploring possibilities through smart factory investments.¹⁷

Our research on the biopharma factory of the future shows building smart factories not only requires enabling digital innovations (see figure 9) but also transforming the infrastructure and culture within the manufacturing organization including: 

• Building connectivity: Shifting away from siloed manufacturing systems and processes to create a connected manufacturing ecosystem for free flow of information, data, and actionable insights. 
• Changing innovation mindsets: Adopting a think-digital-to-be-digital mindset to consider digital technologies to augment human capabilities and change execution of processes.
• Encouraging the art of the possible: Changing the innovation-averse DNA within the manufacturing organization by encouraging digital innovation pilots to convince executives about the tangible value of plant floor innovations. 
• Productize and scale digital innovation: Transition from disparate digitalization efforts and viewing digital innovation as an in-house engineering problem to productize or standardize digital innovation across manufacturing sites by accessing external capabilities through ecosystems and alliances.

Surveyed manufacturing leaders said their organizations are prioritizing investments in IoT (75%) and data lakes (50%) that could lead to greater connectivity across the manufacturing ecosystem. However, less than 45% expect their organization to invest in VR/AR and digital twins over the next five years. Improving asset efficiency (100%), yield rates (67%), and safety and sustainability (50%) are the top value levers manufacturing leaders say could be realized through digital innovation.

Supply chain

North star aspiration: Predictive and autonomous supply chain management

Adaptive and flexible biopharma supply chain networks can enable real-time visibility into material and product flow, allowing for the prediction of issues and disruptions and autonomous mitigatation of risk. Such predictive and autonomous supply chain management can improve processes across the value stream from forecasting demand to tracking products in transit. This could reduce lead times, processes, and oversight costs, and optimize supply chain planning. 

Enabling such a north star requires targeted investments in digital technologies such as AI, IoT, and others (see figure 11), coupled with a series of transformative shifts including:

• Championing proactivity: Enabling free flow of information, data, and insights across supply chain processes to shift away from linear and reactive supply chain management and create proactive and adaptable supply chain networks.
• Enhancing data usability: Moving toward an environment where digital tools and solutions enable data accuracy, latency, and relevancy for cross-correlation, insight generation, and decision-making.
• Embracing machine intelligence:Replacing human effort in managing supply chain operations by using machine intelligence to augment human decision-making and autonomously mitigate risks. 
• Building connectivity to other functions:Moving away from siloed supply chain operations by connecting supply chain data to data from other functions to synchronize business planning and decision-making for greater business resilience.

Most (76%) supply chain leaders we surveyed report that their organizations are already prioritizing investments in AI today and are likely to continue to do so. However, less than 50% see investments in IoT and data lakes—likely essential for enhancing supply chain visibility—as a priority over the next five years. Supply chain leaders also highlighted lowering customer cycle time (67%), reducing lead times (57%), and improving delivery time accuracy (52%) as the top value levers realizable through digital technology investments.

Commercial

North star aspiration: Precision experiences for patients and partners

AI-driven engagement recommendations, connected patient, and health care provider (HCP) platforms provide patients and partners (e.g., payers, HCPs) timely access to relevant content and treatments. Such a north star could hyperpersonalize value stream processes from drug launch to postmarketing surveillance. This could increase patient conversion and long-term adherence, support virtualized sales forces, and maximize care outcomes. 

Bringing to life precision experiencesfor patients and partners (HCPs and payers) requires biopharma companies to undertake purposeful digital innovation (see figure 13), accompanied by a series of transformative shifts within the commercial function, including: 

• Tailored engagement: Moving away from high-frequency engagement models that bombard patients/partners with information to providing customized information based on distinct needs and behavioral and health characteristics. 
• Expanding access and affordability: Pivoting from the current environment, where patients that could benefit the most from a treatment lack timely access to value-added information, to one where information and care can be accessed at the speed of need.
• Proactively sensing marketplace dynamics:Shift from a retrospective understanding of market dynamics such as competition, patient sentiment, and marketing impact, to a predictive approach founded on proactive sensing and continuous learning.

Within the commercial function, survey results suggest that most organizations are already prioritizing investing in AI (82%) and cloud computing (68%), key digital innovations to create a foundation for precision experiences. Surveyed leaders from this function believe digital could positively impact sales team effectiveness (80%), increase customer conversion rates (76%), and broaden access channels (64%).

Surveyed companies face challenges in executing leapfrog digital innovation 

Our research shows that, before attempting leapfrog digital innovation, organizations should solve fundamental issues around funding, strategy, and talent. Today, funding for digital innovations comes from sources such as organizational leadership, functional area budgets, innovation groups, or a mix of those. Close to 60% of survey respondents say dedicated funding is needed to accelerate their organization’s digital innovation efforts. Also, most respondents (55%) say their organization lacks a centralized group to remit funds and to proliferate digital innovation. Simply investing in individual projects or sustaining existing investments is likely to only power incremental innovation. There is a need to change the old ways of thinking and put in place new budgeting processes to help enable leapfrog digital innovation.

Almost half of our respondents believe their organization needs a better strategy to support digital innovation. Leaders need to shift away from viewing technology investments as part of five-year strategic road maps and think more cohesively about these technologies as they can facilitate enterprisewide digital transformation (for more, see Deloitte’s research on enterprise digital transformation a competitive necessity).

Close to half (47%) believe that their organization needs to acquire the right talent to accelerate digital innovation—especially, data engineers, cloud specialists, data scientists, and other technology experts. Biopharma companies are competing not only with innovative technology brands but incumbents in other industries—which are also undertaking digitalization—to attract and retain such talent. A few organizations have set up digital innovation hubs in nontraditional locations to access digital innovation capabilities, solutions, and talent. Takeda, for instance, set up an innovation hub in Helsinki to support startups and companies creating AI and digital health solutions.¹⁸

Deloitte’s 2020 Global Human Capital Trends cross-industry survey found that while 74% of organizations recognize that reskilling of the workforce is important for their success, only 10% are ready to address it.20 Like organizations across other industries, biopharma companies may also be struggling with the significant effort involved in reskilling the workforce as digital technologies are integrated into business operations.

Call to action: Winning with leapfrog digital innovation

Pursuing leapfrog digital innovation, weaving it into the fabric of business operations, and using it to gain an edge in the market is easier said than done. While there is no one-size-fits-all answer, we have captured some insights from our experience in helping biopharma organizations with digital innovation, which we believe could help organizations win with leapfrog digital innovation.

Establish your leapfrog innovation value streams and north stars 

What is your winning aspiration? To navigate in the changing environment, it is important to identify value streams and functional north star aspirations to serve as building blocks for an enterprisewide digital innovation blueprint. 

Key actions

• Assess industry, technology, regulatory, and patient trends affecting your business to identify enterprise value streams that can benefit from leapfrog innovations.
• Create north stars across every function in the value stream that describe aspirational future states and potential impact. 
• Break down north stars into a series of transformational operational shifts that business and IT will need to address. 

Develop a purposeful digital innovation portfolio

What will it take to achieve your north stars?

Every north star aspiration is different. It is critical to understand the myriad of operational, environmental, and technological factors at play, to develop a successful portfolio of digital innovation investments. Our experience suggests that successful portfolios do not reflect random acts of innovation that result in countless proofs of concept, but a series of purposeful investments that cohesively build on each other to create transformational experiences and value to realize north star aspirations. 

Key actions

• Outline how individual capabilities combine to support a transformational operational shift, such as interconnected information flows, processes, or business applications.
• Perform a fit/gap assessment to identify existing capabilities that can fulfil requirements versus gaps that need new investments to fill.
• Develop a unified view of common digital capability needs across value streams, north stars, and enterprise functional areas.
• Build a comprehensive business case highlighting investment needs, enablement of north stars, and traceability to enterprise value opportunities. 

Determine your innovation archetype

How will you develop the innovation portfolio?

Most technologies and talent behind the latest digital innovations are nurtured by startups, academic institutions, big technology companies, and consultancies. Biopharma companies should rethink their IT approach to access innovation at its source. They could employ one or more the archetypes to help drive speed and scale of execution.

1. DIY innovator: Builds digital innovation capabilities within the organization (either centrally or at a functional level) to take innovative ideas from concept to prototype to full-scale deployment. 
2. Crowdsourcer: Solicits the best thinking and solutions from an ecosystem of startups, academics, and technology companies through design challenges, hackathons, etc.
3. Venture capitalist: Invests in early-stage companies to access or acquire emerging digital technology capabilities and new business solutions.
4. Incubator: Provides mentorship and funding and enables sharing of expertise to help entrepreneurs, academics, and others refine and launch their ideas.
5. Accelerator: Guides startups to scale up their minimum viable products through proofs of concept, pilots, and other targeted experiments.

Key actions

• Evaluate innovation archetypes, understand their advantages/disadvantages and implications on enterprise strategy. 
• Select the combination that best suits your needs.
• Develop an ecosystem engagement plan to build relationships with innovation partners in specific technological, therapeutic, and geographic domains.

Design your operating model 

How will you execute and scale? 

Weaving digital innovation into the fabric of the business requires lockstep coordination between IT, business, and innovation partners. This necessitates an operating model built on the chosen innovation archetype or archetypes. 

Many successful organizations have established operating models where dedicated digital innovation resources are situated in between business and IT, often in a center of excellence. This has helped translate business north stars into technological needs, agilely manage investment portfolios, and rapidly collaborate with external partners. Organizations may also need to make important trade-offs on business priorities to help ensure adequate funding for digital innovation while also reimagining traditional IT budgeting processes.

Key actions

• Design an innovation development life cycle that describes all incubation processes from digital capability sourcing and funding to prototyping and scaling. 
• Outline roles and responsibilities within this life cycle across IT, business, and dedicated innovation resources. 
• Move away from legacy budgeting models to iterative project-based financing to ensure adequate funding for digital innovation.
• Establish success factors to ensure that portfolio operation is driven toward north star aspirations.
• Focus on enabling areas that will be critical determinants of portfolio success such as stakeholder buy-in, data access, digital talent recruitment, and retention.

Conclusion: Choosing your path at the digital innovation inflection point

The pandemic forced biopharma companies to prioritize digital innovation, instill it into every aspect of work, and use it to transform the experiences of patients and partners. However, the momentum and importance of digital innovation isn’t slowing down—it’s accelerating to become a new competitive advantage for organizations. 

The industry is at an inflection point where enterprises can either choose to meet the moment and double down on digital innovation investments through leapfrog digital innovation or decelerate and accept the risk of digital inferiority.