In our recent post, Scaling AI in biopharma for transformation and growth, we outlined the case for scaling the Artificial Intelligence (AI) across the biopharma value chain, through drug discovery, clinical and drug launch, product supply, commercial and core enterprise functions. We identify some of the main barriers biopharma’s face along the transformation journey, including a lack of culture for change, lack of buy-in from employees and the lack of a clear roadmap. We know that these barriers exist, yet 83% of life sciences executives from our AI: Built to Scale report believed AI was critical in achieving their growth objectives. So, what are the pathways for success?

In this post, we’ll explore strategies for successfully scaling AI in biopharma and unlocking its value to drive strategic priorities.

Pathways for successful AI scaling

Nearly all C-suite executives view AI as an enabler of their strategic priorities – and an overwhelming majority believe achieving a positive return on AI investments requires scaling across the organization. With the stakes higher than ever, we’ve identified five pathways for successful scaling:

1. Drive an intentional strategy:

Throwing everything at the wall to see what sticks may work in some scenarios, but certainly not with scaling AI. For true success, an intentional strategy is required – one that is aligned to the strategic goals of the company, has the appropriate focus on scaling vs. piloting, and has a clearly defined AI operating model.

The biopharma industry is, on average, piloting the same number of AI applications but scaling less of those applications as their cross-industry peers. Further, biopharma’s take longer to scale as they are 45% more likely to report a timeline of one to two years to move from pilot to scale while cross-industry peers are more likely to do so in less than a year. Part of the reason for this may be due to an industry-wide tolerance for developing prototypes and pilots, with limited focus on how to scale those ideas if they prove successful.

In addition, we have observed cultural divides between biopharma functions of R&D, commercial and supply chain. Biopharma’s often do not have a single leader accountable for driving the AI agenda and therefore it is critical to have a clear operating model, governance and culture in place to drive company-wide support. Establishing roles and responsibilities across the functional business leaders (Chief Information Officer, Chief Data Officer, Chief Analytics Officer and Head of Digital) is also crucial.

2. A strong data foundation:

90% of the data in the world was created in just the past 10 years – and an astonishing additional 175 zettabytes of data will be created by 20251. We see biopharma’s very focused on ingesting new data sources, participating in data consortiums and producing larger and larger volumes of data not traditionally used in the past (e.g. medical device, fitness data). Yet after years of collecting, storing, analyzing and reconfiguring troves of data, most organizations struggle with how to cleanse, manage, maintain and consume it. They need better foundational solutions including a data architecture that is cloud based, secure and discoverable. Open and modular platforms like the Accenture INTIENT Platform are serving as the foundation that allows for faster, more efficient access to research and trial data, accelerating the development pipeline and helping deliver therapies to market in shorter timelines. Programs that build high quality, accessible data across the biopharma enterprise and break down data silos organizationally and technically are also a critical step in enabling AI at scale.

AI in action

We recently worked with a large life sciences company to help coordinate a new approach for using data and making more strategic investments in digital capabilities. Using a clear strategy, we designed and enabled a new model for scaling the company’s digital and analytics capabilities, building an AI-driven platform that helps drive value throughout the journey.

Key steps:

  • Defined an operating model and rapid mobilization plan to identify, test and scale data and analytics services.
  • Enabled the new data and analytics technology environment, with a marketplace for FAIR (findable, available, interoperable, reusable) data.
  • Created new data capabilities and mindsets, with data talent development programs and engaging enterprise-wide communications.
  • Delivered new data science and data services capabilities in a new way of working, with value-focused use cases delivered by agile multi-disciplinary teams.
3. Treat AI as a team sport:

Strategic scaling requires support from leadership and the C-Suite. These multi-disciplinary AI teams are comprised of functional experts, data scientists, data modelers, machine learning, data and AI engineers, visualization experts and data experts. It is becoming more and more critical that technical and data roles develop functional specializations. For example, applying and scaling AI in research and clinical requires a strong understanding of R&D processes and the uniqueness of research, clinical, and real-world data domains. Furthermore, it is important that everyone in the organization build their data and analytics IQ.

As companies continue to scale AI, new ‘soft’ skill sets also emerge as critical to success. Things like human-centric design and social and behavioral sciences to drive responsible AI will help to ensure a more sustainable and ethical result.

The most successful teams have clear sponsorship from the top ensuring alignment with the C-suite vision. These teams not only cultivate and drive AI opportunities, but also enable faster culture and behavior changes.

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79%

of life sciences Strategic Scalers leverage multi-disciplinary teams (compared to 92% cross-industry).

24%

conduct formal AI training (compared to 35% cross-industry).

18%

fully understand how AI applies to role (compared to 34% cross-industry).

21%

understand and implement responsible AI (compared to 37% cross-industry).

41%


have a Chief AI Officer as part of dedicated team/champion (compared to 49% cross-industry).

4. Operate at multiple speeds:

As with any transformational work, there are varied requirements and approaches for scaling AI. Certain problems such as financial process automation – where the value may be known, and the risk is low – require delivery models for rapid execution to scale and therefore won’t have the ability or need to go through a prolonged pilot period. Other problems, where there is a lack of certainty in the outcome such as deploying machine learning models to improve target identification, require experimentation/prototype development to prove value before making larger investments in moving straight to scale. Upfront diligence should be taken to clearly define the scope of the idea and assess what the best approach should be to balance risk and speed to scale.

5. Hyper-focus on value:

Key to strategic success is a rigorous process around identifying how AI can enable the strategic priorities of the enterprise. This requires a deep understanding of ROI and the value of AI adoption. When the 1,500 companies in our report were analyzed collectively, $306 billion was spent on AI applications in the past three years – but the ROI gap amongst them was significant. On average, it spanned $110 million over three years between companies stuck in Proof of Concept and those who have progressed to becoming Strategic Scalers.

We also discovered a positive correlation between successfully scaling AI and three key measures of financial valuation from our cross-industry analysis:

  • 35% increase between enterprise value & revenue ratio
  • 33% increase between price & earning ratio
  • 28% increase between price & sales ratio

In our experience, the most successful biopharma’s define metrics to measure the value of AI initiatives, ensure metrics are tied to overall business objectives and KPIs, evaluate AI investments based on the value potential, and establish a rigorous process to track value realized.

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Companies strategically scaling AI have nearly 2x the success rate and 3x the return from AI investments versus companies pursuing siloed proof of concepts.

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Scaling to new heights of competitiveness

Scaling to new heights of competitiveness with AI requires understanding the “how.” And at times, eschewing conventional wisdom that continues to emerge as AI evolves. It’s about moving deliberately, in the right direction, while aligning your investments to the right places with the intention of driving large-scale change.

By building multi-disciplinary teams that bring the right capabilities, investing in a data foundation to drive the right insights and operating at multiple speeds with a hyper focus on value, companies can better capitalize on the growing opportunities of scaling AI.



 

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Scaling AI in biopharma for transformation and growth

Explore strategies for successfully adopting transformation in biopharma where AI is the key for survival and growth. 

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Source:

1 AI: Built to scale: From experimental to exponential

 

Chad Vaske

Senior Manager – Analytics Strategy, Applied Intelligence


Kingston Smith

Managing Director – Applied Intelligence

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