AI & Analytics

Life sciences backs generative AI to drive value in 2024

Dec. 7, 2023 | Research Report | 7-minute read

Life sciences backs generative AI to drive value in 2024

Inside the mind of the life sciences technology leader

The buzz surrounding machine learning, artificial intelligence and particularly generative AI is palpable, especially among leaders overseeing their firms’ technology infrastructure and analytical strategies. 


ZS recently polled 100 of these leaders in life sciences to uncover their current perspectives and outlook for 2024. Here’s what you should know:

Data and technology leaders feel a new urgency to deliver value

Life sciences leaders see 2024 as shaping up to be a year where data, digital and AI investments take center stage across the industry and promise more value to the corporation.


Technology and analytics leaders are optimistic. A whopping 88% are in lockstep, acknowledging that generative AI has dialed up the urgency meter for squeezing every drop of value out of their company’s data reserves.


They’re rallying the rest of the C-suite to prepare for substantial investments aimed at the core value centers of their businesses, from the front lines of commercial operations to the innovation hubs of R&D and the intricate web of manufacturing and supply chain logistics.


Their ambition will test the limits of their ability to show the connection between their work and the specific value metrics that the business seeks. In fact, 48% of them say linking to their business leaders’ expected outcomes is already quite difficult.

Success hinges on the connectedness of data assets, platforms and capabilities

Technology leaders consider data to be the cornerstone of the value puzzle for these investments. How they connect the organization’s reusable data assets, platforms and capabilities will be particularly important in 2024.


These decisions determine how easily ideas scale for value and how functions across the enterprise can seamlessly access high-quality data to use (or build) data products. The goal is to enable continuous insights for various use cases, either for internal teams or new commercial-facing products and services.


Without a connected strategy, a cloud of doubt will loom above this moment of excitement with generative AI. Nearly all (92%) of respondents believe that their success with generative AI is tightly bound to the quality of their data strategy. Any program they put in place, particularly in high-stakes domains like healthcare, must ensure they first have the high-quality data they’ll need.


A clear understanding of how generative AI complements existing AI systems is vital. With this clarity, leaders can use generative AI to make their existing AI-driven systems more contextual and explainable. Leaders are actively formulating their views, with 46% reporting that their companies have set up a vision for the coexistence of legacy AI and generative AI models.

A focus on cost points to a need for top-level support

Technology leaders overwhelmingly agree: Company investments in data, digital tech and AI should deliver better results without significantly increasing costs. Nine out of every 10 of them are firmly on board with this notion.


Yet, here’s the catch: Getting there requires an unwavering commitment from the highest ranks of an organization to foster a mindset that drives business and technology collaboration, innovation and efficiency all at once.


Notably, 54% of respondents identify a need for more people across the organization with this digital mindset.


In another layer to the story, 34% of technology leaders report that their annual plans are often shaped by others in the business who may not necessarily have the same mindset.


When business teams set the agenda for what needs to be done, they often overlook a fundamental tenet of digitally native ways of working: incorporating diverse perspectives when designing solutions to be both valuable to the business and technically feasible.


Instead, when business and technology leaders codevelop the agenda, everyone moves beyond the traditional markers of IT success like budget, on-time delivery and adoption rates. Instead, they explore business questions together such as: How can our solution streamline processes? Improve the overall experience? Boost product sales?

What’s ahead: Priorities for the technology and data strategy

Technology leaders report several actions they plan in the next 12 months to build durable capabilities.


Most plan to show how their work aligns to business measures. Half intend to clarify the company-level strategy for data products that require new business models, such as digital health apps. Half also plan to standardize how teams develop foundational AI models across the organization.

What’s ahead: Priorities for people and skills

Technology leaders also report several actions they plan in the next 12 months to influence the people agenda. Most expect to invest in digital upskilling of the entire workforce (51%) and how their organizations attract and retain digital talent (57%).


Yet they also plan to improve how teams under them work. They plan to step back, evaluate the IT operating model and identify what high-performing teams should look like. Four in ten respondents plan to focus on changing technology roles in the organization toward delivering data products that serve particular use cases for the business.

Takeaways for CDIOs and business leaders

  1. Step up engagement across the C-suite to keep the people and technology priorities in sync. Their commitment is critical to ensuring the company’s people capabilities match or exceed what the technology is already capable of doing and that people stay focused on the few priority business outcomes they need to achieve.
  2. Hype quality data. Success with AI, including generative AI, won’t happen without a shift in most organizations. Today’s focus on model-centric AI, where teams find, build or train models, will need to make room for a more data-centric AI approach, where teams use data strategy to tackle why accuracy in the lab is not predictive of how a model performs in the chaos of a real-world environment. The switch here is to focus on using domain-based knowledge to improve data quality systematically, while the models stay fixed.
  3. Rethink your view of high-performing teams. Consider what high performance looks like in a digitally native model where teams focus on data-centric AI. What elements are missing from your teams now, and how could you set more people up for success? 


The Harris Poll conducted an online survey on behalf of ZS from August 24 to September 8, 2023. The survey targeted 100 technology executives at U.S.-based pharmaceutical, biotechnology and life sciences companies who are decision-makers for their companies’ technology infrastructure, analytics and technology strategy. One-third are executive-level titles (CDIO, CIO, CTO) and the rest are senior-level decision-makers. Percentages may not add up to 100% due to rounding or the acceptance of multiple responses.

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