“Deloitte’s 2020 State of AI in the Enterprise Study, 3rd Edition” by the Deloitte AI Institute and Center for Technology, Media and Telecommunications, uncovered how organizations are adopting, benefiting from, and managing AI technologies by industry, including life sciences. While the “State of AI in the Enterprise” survey was conducted before COVID-19 significantly impacted the U.S., its findings are ever more relevant as life sciences companies look to reduce costs, increase product development and better engage with ecosystem partners in the “Age of With,” a world where humans work alongside machines to enable greater outcomes.
The “Scaling up AI across the life science value chain: Enhancing R&D, creating efficiencies and increasing impact” report summarizes key findings from that survey, and offers recommendations for how life sciences companies can gain immediate returns on investment and experience a competitive advantage over the longer term.
From R&D to manufacturing, supply chain to commercial functions, AI is beginning to have an impact on increasing efficiencies across the biopharma value chain, especially as a result of the COVID-19 pandemic. In addition, increased remote work environments helped life sciences leaders realize how effective digital solutions can be in helping their businesses run smoothly, transforming mindsets and enabling executives to lean into a future grounded in digitization, data and AI. While today AI is primarily applied to automate existing processes, additional potential lies in accelerating insights and discovery to expedite access to new therapies and products. AI’s potential stretches across the value chain from identifying and validating genetic targets for drug development, designing novel compounds, expediting drug development, making supply chains smarter and more responsive, to helping launch and market products. Life sciences companies that deploy AI strategically across the entire enterprise will likely be best positioned to maximize their AI investments and achieve a competitive edge.
“The life sciences industry has only begun to scratch the surface of AI’s potential but the good news is biopharma and life sciences leaders see the potential and are willing to make the investments necessary to realize what’s possible. They should be cautious, though, and carefully plan and strategize so those investments are used wisely and result in the desired outcomes. By spending time on a solid strategy, putting the building blocks in place for success and leveraging relationships with relevant partners, AI can help transform the life sciences industry as we know it and get the necessary products to market more quickly,” said Aditya Kudumala, principal, Deloitte Consulting LLP.
Investing in AI
Biopharma and medtech leaders surveyed agree that life sciences organizations are investing in AI, but “how” they’re investing varies widely. When asked about their company’s investments in AI projects and technologies in the past year:
- Forty-one percent invested $20-50 million; followed by 26% that invested $10-20 million; and 21% that invested more than $50 million in AI initiatives.
- More than half of survey respondents expect investments in AI will continue to increase throughout 2020 as AI moves from tactical improvements in efficiency to larger-scale, organization-wide projects.
- Forty-six percent of respondents said it takes longer than expected to receive payback from investments in AI initiatives.
Enhanced efficiency
When it comes to the top outcomes life sciences companies hope to achieve by using AI, three primary objectives emerged:
- Enhance existing products (28%)
- Create new products and services (27%)
- Make processes more efficient (22%)
The top three outcomes where respondents said they’ve seen the most success with AI include:
- Making processes more efficient (43%)
- Improving decision making (41%)
- Enhancing existing products and enabling new business models (both 40%)
Understanding risks
As with any new technology, survey respondents pointed to multiple challenges associated with AI implementation, including poor-quality data, siloed data systems, integrating AI into legacy systems and risk management. The top challenges life sciences companies cited when implementing AI were:
- Identifying use cases with the greatest business value (30%)
- Integrating AI into the organization (28%)
- Data challenges (28%)
- Challenges implementing AI technologies (28%)
Moving AI forwardÂ
In order to take AI to the next level, Deloitte recommends life sciences leaders consider the following three step process:
- First, company leaders should define their vision and strategy for how they want to take advantage of AI and what they seek to achieve from their investments in data, analytics and AI.
- To carry out that strategy, companies should then assemble the building blocks to help set themselves up towards success. The core building blocks Deloitte advises leaders to consider include: technology and data, technology architecture, ecosystems and alliances, talent and aligning on risk management.
- Third, leaders should evaluate ways to scale up internal investments or partnerships that can help their companies go from short-term to longer-term success in the market.
