Scale Your Top Digital Stars in 2024—Not Just Generate New Ones

Jessica Jarvis and Daniel Blessing

One prediction for 2024 that may go unnoticed but should not is that the top players in pharma will begin to scale up the results they’re getting with data, digital, and artificial intelligence (AI). The idea of scale—taking the innovations that only happen in some parts of an organization and helping them happen elsewhere—is not a novel one, but it’s still an essential concept when it comes to generating value for an organization.

When we consider the idea of scale from the viewpoint of operating income, for example, we estimate that growing seven tested digital programs could generate $1.4 billion to $2.2 billion in five years for an average top 10 pharma company. This number could go higher, especially as generative AI is combined with existing approaches. But a sensible way to think about value is to understand how these programs already prove their worth and then mix generative AI with them to see how you can take things further.

The programs to scale

In our work, seven programs stand out as crucial to boosting digital value. Each one strengthens the pharma value chain, impacting the core areas of clinical development, supply chain, and manufacturing and commercial engagement.If you haven’t developed these, you are missing out on quick and significant ways to show how digital, technology, or AI drive measurable outcomes.

Figure: Essential digital programs that create meaningful value

Clinical development programs

The goals for the programs to scale in clinical development are to reduce study completion times, improve and diversify patient recruitment and retention, and lower costs to bring products to market.

A good test of your company’s ability to create value here is to ask how well your teams can:

• Reveal the flaws of past studies to remove future costs and delays. A recent focus on unifying underlying operational data has been helping sponsors understand the cause-and-effect relationships of the choices made in study designs. Armed with easy-to-query data, researchers can conduct retrospectives to answer questions such as the upstream causes of amendments, which sites have consistently better track records for recruitment or how budget parameters should inform design decisions.
• Make it easier for participants to get and stay enrolled by reducing the burden of participation.Sponsors have traditionally focused solely on recruitment in clinical trials. And while recruiting participants can be costly ($6,533 per person on average), the average cost to replace a participant if one is lost to noncompliance is three times greater. Using data and digital innovations, sponsors can identify how to provide a better experience, lower the overall patient burden, and have fewer dropouts. This capability is a key differentiator for any team working toward more inclusive and diverse participation in clinical trials and patient engagement.
• Link the use of digital health and decentralized tools to value metrics. These tools allow patients to complete steps virtually throughout the recruiting, participation, and closeout stages. Early estimates have quantified today’s expected return for using digital health and decentralized tools in mid- and late-stage trials at a 21% and 26% reduction in trial duration, respectively. Large companies have proven it can work: In 2016, Sanofi used digital health and decentralized tools for a 56% faster recruitment rate in a diabetes phase IV trial and Novartis enabled a 30% higher patient retention rate in a 2018 musculoskeletal study.

Supply chain and manufacturing programs

Today’s best opportunities for scaling value exist in manufacturing and they are steps along the journey to optimize operations to support frequent nano and micro product launches—and eventually adopt autonomous manufacturing. All this work supports one goal: getting the right medicines to the right patients.

Good performance from your teams here means they regularly:

• Use data proactively to improve output across manufacturing plants. While manufacturing has always produced massive amounts of data, these data have primarily been used to resolve issues for single sites retroactively. Taking an approach to optimize yield on a larger scale across manufacturing plants and use predictive analysis can lead to increased revenues, minimize inventory stock-outs, and achieve overall cost reductions.
• Reduce unplanned scrapping of raw materials. Upstream decisions that lead to the scrapping of raw materials can have a significant impact on the bottom line, yet this cause-and-effect relationship often goes unanalyzed. For example, procurement patterns such as long lead times often force companies to purchase raw materials years in advance based on the best-known supply plan at that point in time. As supply plans change, the procured materials of cancelled products are often scrapped. Focusing analytics attention on why supply plans change, rather than how to reduce the final scrap figure, can dramatically shift the value equation.
• Maximize production schedules. Effective production scheduling can offer multiple benefits to manufacturing, including maximizing plant utilization, reducing downtime, and, ultimately, increasing the run rate to open additional manufacturing capacity. But plant utilization is often constrained by various subdimensions, such as equipment or personnel availability, making it essential to utilize digital tools to integrate all these aspects and create a unified model for efficient manufacturing planning.

Commercial programs

For commercial engagement, field teams know the importance of remaining relevant through more personalized, engaging experiences. Today’s test for scaling value here depends on how well commercial teams can:

• Enable the field force and marketing team with a true picture of each customer’s needs. Digital leaders equip the company with insights about customers and suggestions for actions that would benefit everyone. They use data and AI to facilitate collaboration across sales roles and digital channels, raising productivity, sometimes by as much as 40%.
• Build personalization capabilities. Personalization is not one activity, but several activities that build for value, including data analysis to uncover preferences, channel innovation, omnichannel orchestration of marketing and sales touch points and the development, optimization, and delivery of hyper-personalized content. In our work with pharma, personalization activities have a clear margin upside, up to 20% of sales.
• Mine for more value-added insights. Research shows that the disconnect between physicians and patients is stark. Pharma can help close these gaps by looking for more value-added insights in patient-level data, such as never starts or those who stop their treatments midflight. These become powerful, tailored messages that provide relevant value to HCPs about their specific patient population.

These programs need everyone to work together to grow, but with a clear strategy, they can show their worth in real dollars fairly quickly. And beyond the financial upsides, they offer significant opportunities for talent growth and help you build maturity for all future digital programs.

About the Authors

Jessica Jarvis and Daniel Blessing are principals with ZS, where they help organizations create meaningful value with data, digital and AI programs.