Can life sciences companies unlock the full value of GenAI?

By Vicky Levy, Global Life Sciences sector leader, and Pete Lyons, US Life Sciences sector leader, Deloitte Consulting LLP

Generative artificial intelligence (GenAI) may be an inevitability. The question is no longer whether life sciences organizations will adopt it. The question is, will GenAI be adopted to generate incremental improvements, or will it be a catalyst for a radical business transformation?

Many life sciences companies are experimenting with GenAI to test ideas and to build use-cases. They are exploring ways the technology could automate repetitive back-office functions, reimagine supply chains, or support compliance and regulatory affairs. While initial productivity gains are likely, they could quickly become common across the life sciences sector. And as the technology becomes commoditized, software vendors will likely include GenAI tools as part of their offerings.

We are urging our clients to look beyond individual use-cases and consider how GenAI could be part of an enterprise-wide transformation that not only fundamentally changes the way work is done and value created, but also addresses compliance, privacy, regulation, and trust. Trust in this emerging technology could be critical as companies work through the regulatory and cultural challenges that are beginning to emerge. (See the Deloitte AI Institute’s Generative AI Dossier to explore use-cases across six industries, including life sciences and health care).

A string-of-pearls strategy

While each individual GenAI use-case could generate some improvements, stringing together multiple use-cases—along with other digital tools like machine learning and Internet of Things (IoT)—could transform entire processes. This string-of-pearls strategy could be applied to everything from research to clinical development to customer engagement and patient experience. Each individual use-case connects to another use-case, and another. For example, instead of helping an employee save an hour a week on a task, interconnected GenAI use-cases could be strung together to streamline entire processes and improve efficiencies by weeks or months. Combining GenAI with other digital platforms such as machine learning and predictive analytics could create an end-to-end business value stream (e.g., clinical study startup through clinical study closeout).

Consider this: While a GenAI use-case could help a company achieve significant productivity gains and cost savings, it is still part of the traditional, rigid customer-engagement ecosystem. Some companies are likely to take on a more disruptive approach. They might string use-cases together across the end-to-end lifecycle—from customer segmentation to content tagging, content generation, digital-rights management, and closed loop measurement. These companies will likely be well positioned to deliver on the promise of hyper-personalized omnichannel engagement.

Four areas where GenAI could drive leapfrog domain plays

Life sciences companies that use GenAI as a catalyst to transform entire end-to-end processes could quickly find themselves with a competitive advantage. In this early stage of GenAI adoption, speed matters. Companies should consider implementing GenAI solutions to extract immediate value, learn, and ladder-up to bolder transformation. Bold end-to-end transformations, using a string-of-pearls strategy, could help companies leapfrog competitors that did not move as quickly or make the same investments. Consider the potential of GenAI in the following areas: 

• Research and clinical development: GenAI is already transforming the way life sciences organizations decide which disease areas to invest in. It is also being used to identify targets, develop molecules, accelerate clinical trials, and submit findings for regulatory approval. In 2023, several biotech companies have announced the development of AI-designed drug molecules.^{1, 2}
  
• Quality management in manufacturing: The future of quality management will likely be a fully integrated approach that is built on a foundation of data and analytics. Historically, quality management has been reactive. GenAI, alongside traditional AI techniques, will be highly proactive interventions. Costly and labor-intensive tasks such as documentation and reporting could be completed within minutes.
  
• Marketing excellence: GenAI could be used to generate hyper-personalized content (e.g., message, graphic) at scale that appeals to customers’ emotional needs. It might be used to tag content at scale to enable measurement of content effectiveness at a granular level with precision. It can also help marketing teams develop a deeper understanding of customer needs by mining customer data at scale and speed. Moreover, it can help drive an insight-driven brand strategy that can respond to evolving customer, content, and market insights.
  
• Application development and testing: Gen AI has the potential to transform virtually all IT functions and services. In application development, testing new products, features, and designs can be faster, cheaper, and easier, making creativity a better long-term predictor of success. As activities become automated, integrating outputs into consuming applications could unlock new productivity improvements.
  

The cost of inaction

Unlike any other breakthrough technology in recent memory, there has been a remarkable openness to the adoption of GenAI in a relatively short time. It offers a tremendous opportunity to significantly improve productivity, experience, and capabilities and make processes more efficient. This technology could forever alter the way work is done. At this point, however, communications seem to be ahead of what is actually happening in the adoption of GenAI in life sciences. This is understandable. Many executives are publicly broadcasting their aspirations with the anticipation that employees are listening and taking action. Some of our clients are organizing teams to identify and map out the value potential of GenAI use cases. This will help ensure investments made today will lead to sustained value once the use cases are implemented.

We are encouraging leaders to empower their employees with GenAI solutions that can summarize information quickly, democratize knowledge, and take on wide-spread repetitive tasks. They should give employees plenty of space to experiment, reimagine their business processes, and challenge the adages about how work used to be done. The cost of inaction at this stage, particularly in a data-intensive sector like life sciences, could be significant.

Closing thoughts

The life sciences sector is in the early stages of what will likely be a revolutionary change to traditional business operations. Like with other disruptive technologies, we are already seeing public decrees of importance and sometimes substantial investments. We suspect there will be some failures and successes along the way. There will likely be a wide range of strategies and approaches, and we expect some companies will emerge with critical capabilities and advancements that give them a competitive edge. GenAI is positioned to be integrated into bold but well-thought-out approaches to solving many of the known challenges our industry faces. This is what excites us. We will work to drive our activities beyond the incremental, operate at a previously unimaginable level of speed, maintain a ruthless focus on value and patient impact, and do what we can to deepen trust in the power of this new tool. We will try to learn from our experiences embracing and globally scaling other disruptive technologies, but we are also clear minded that this technology is different than other technologies. GenAI is an inevitability, and we are in this industry-shaping moment together.

Endnotes:

¹_{Generative AI Drugs Are coming, Forbes, September 5, 2023}

^{2_{AI-generated drug begins clinical trials in human patients, CNBC, June 29, 2023}}

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