COVID-19 highlighted AI’s potential in life sciences, but exponential benefits likely won’t be realized until cultural barriers are removed

By Aditya Kudumala, principal, and Adam Israel, manager, Deloitte Consulting LLP

Artificial intelligence (AI) has already been incorporated into wide range of business functions within the life sciences sector. But unless AI becomes part of an enterprise-wide strategy, companies likely won’t benefit from its full potential.

Rather than being thought of as a possibility, AI is now seen as an inevitability. In a recent blog post, our colleague Kumar Chebrolu explained how AI is being used by hospitals, health systems, and health plans to improve efficiencies. But the life sciences sector is just scratching the surface by applying AI only to automate existing processes and discover new insights. We recently surveyed nearly 150 executives from biopharmaceutical companies and medical device manufacturers to find out how they used AI today, and how they expected it to be used in the future. About 60% of respondents said their companies invested more than $20 million in AI initiatives in 2019, and more than half said they expected to boost those investments by more than 50% this year. Among our survey respondents: 

• 44% successfully used AI to improve process efficiencies
• 28% were using AI to enhance existing products
• 27% were using AI in the development of new products and services

Cultural barriers could limit AI’s potential

AI has the potential to identify and validate genetic targets for drug development, design novel compounds, expedite drug development, make supply chains smarter and more responsive, and help launch and market new products. But convincing researchers and scientists to have faith in AI-generated data can be a difficult obstacle to overcome, interviewees agreed. People who have spent their careers in research and development, for example, might not want to switch to new processes. Moreover, there can be a level of distrust when it comes to the accuracy of AI-generated data. However, we expect researchers and scientists will grow more comfortable using AI in their professional lives as AI plays a bigger role in their everyday lives (e.g., search engines, chatbots, smart speakers, and other smart devices). Rather than thinking about using AI to replace workers, company leaders should instead look at ways it can enhance employees.

Four areas where AI could improve processes

The COVID-19 pandemic appears to have further accelerated the adoption of AI among biopharmaceutical companies and medical device manufacturers that had to quickly figure out a way to conduct many aspects of their business remotely. AI—along with other technologies such as cloud storage and the internet of things (IoT)—have gained momentum since the pandemic began. We have seen increased use of digital technologies in everything from manufacturing and supply chains to virtual clinical trials. 

Here are four areas where we believe AI can improve processes: 

1. Research and development: Clinical development typically involves a wide range of manual and capital-intensive processes that can be automated or augmented through the use of AI. Some biopharma companies have already begun to experiment with AI to accelerate clinical development. Over the next five years, we expect more companies will use AI models to identify and validate targets, design molecules, synthesize and test those molecules in silico, and feed data back into those models to improve their predictive capabilities. This could vastly accelerate the traditionally slow drug-discovery process of identifying new molecules. In addition, AI could also be used to help automate some clinical-trial processes, which could help bring new therapies to market more quickly.
2. Manufacturing: Changes to the manufacturing process typically don’t occur until there is a problem. Addressing a problem, such as a bottleneck or quality-control issue, can affect multiple systems and require a significant level of manual intervention. Companies that apply AI to manufacturing data could predict process bottlenecks, identify quality control issues, and proactively take corrective actions. That tactic could help reduce manual oversight in manufacturing operations and allow for tighter control of quality and operating costs.
3. Supply chain: Life sciences companies tend to respond reactively to supply chain disruptions and are often slow to adjust inventory and production levels. The pandemic exposed the fragility of the supply chain. Self-healing AI supply chain solutions, for example, could make it possible for companies to respond more quickly to sudden changes in market demand and/or supply availability, enable quick recovery from disruptions, and improve decision-making related to product distribution and new product introductions.
4. Commercial: Biopharma companies often market their products to a broad group of consumers across a wide spectrum of channels (e.g., TV, social media, print, web, sales representatives). AI could boost the return on marketing dollars by helping companies identify and reach more targeted customers based on their health condition and preferences. 

We see AI as a potential differentiator, and we are encouraged to see more life sciences companies tap into this technology to improve process efficiency. Companies that are able to adopt and scale AI will likely be able to develop and test new therapies more efficiently and make them available to patients far more quickly. While reengineering an entire organization—and its culture—around AI could be an enormous undertaking, we expect the return on investment could be exponential.

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