The Pistoia Alliance, a global, not-for-profit that advocates for greater collaboration in life sciences R&D, has released new poll data from its annual European conference, revealing that while AI use is becoming widespread across life sciences organizations, its measurable impact on drug R&D remains limited. Almost a third (30 %) of life sciences professionals say their organization has implemented an enterprise-wide AI platform, yet 69 % lack clear metrics to assess AI’s impact on reducing the cost or time required for drug R&D, and just 4 % report tangible benefits of AI for R&D leadership. The poll, sponsored by Thoughtworks, was conducted at the event attended by 300 industry leaders at the Royal Society of Medicine in London.
“A key focus at this year’s conference was understanding why the gap between AI investment and value persists. The polls show AI success depends on getting both the data and people elements right – 59 % say their companies need to prioritize data quality and accessibility, while 22 % highlight AI adoption and change management,” comments Dr Becky Upton, President of The Pistoia Alliance. “The success stories shared on stage show these barriers can be overcome by combining technology, scientific and regulatory expertise. In essence, progress depends on collaboration, which is where the Alliance plays a critical role, bringing organizations together through a portfolio of projects and communities that continues to evolve in line with industry needs.”
The survey also revealed that AI’s impact is uneven throughout the R&D lifecycle. More than half (54 %) of respondents said teams focused on regulatory submissions and reporting are seeing the greatest benefits from AI, alongside research analysis teams (21 %). By contrast, just 13 % cited value in automating scientific workflows and experiments while only 1 % report value in the wet lab.
“Many leaders are asking, 'how much AI are we using?' when they should be asking, 'how much faster can we move because of it?’ The real value of AI lies in increasing the speed of the entire R&D pipeline, but today it is often confined to isolated projects,” comments Ammara Gafoor, Head of Life Sciences Data & AI, Thoughtworks. “Different teams are applying AI to individual use cases, such as target identification or molecule generation. But without a joined-up view of how these efforts work together, AI is stagnating at the level of local gains. To move beyond this plateau, the industry must rethink how humans and AI work together, including how to build reusable agent capabilities and integrate them across the enterprise.”
Alongside plenary sessions, conversations from the conference were split into four themes:
- Semantic Data in Pharma: Speakers included Roche, AstraZeneca, AbbVie, Pfizer, Rancho Biosciences, Elsevier and Sigmatic Sciences, exploring how semantic data and ontologies improve interoperability and ground AI in reliable knowledge.
- Accelerating Late-Stage R&D: Experts including the FDA, Parexel, Boehringer Ingelheim, Roche, Amino Data and Our Future Health discussed digital twins, social media data, omics and patient cohort engagement.
- Practical AI and Machine Learning: Sessions featuring Bayer, IBM, Thoughtworks, UCB and Sapio Sciences covered multi-agent systems, literature analysis and trial forecasting.
- Exchange of Data and Regulatory Submissions: Speakers from GSK, Eurofins, AstraZeneca, AbbVie and Zifo examined how data standards can improve in vivo data and non-animal model submissions.
It’s a contradiction that organizations report adopting AI enterprise-wide, when its value is not reaching specialized R&D activities. Instead, AI is largely being used for traditional Natural Language Processing tasks like searching literature and writing reports. If AI were truly embedded into R&D, we would see impact in more workflows,
Progress in areas such as regulatory submissions is encouraging, but there is more to be done in environments like the lab. The responsibility for this does not sit with pharma alone. Regulators have a role to play, and are also keen to move quickly while ensuring that patient safety is maintained. We encourage closer collaboration between the Alliance and regulators to shape the data and standards needed to scale AI.”
Dr Christian Baber, Chief Portfolio Officer, The Pistoia Alliance
The conference also saw the introduction of the Pistoia Alliance’s Data Scientist Team of the Year Award, won by Roche, and for the second year running, the Young Data Scientist Award, won by Shruti Kaushal of AbbVie.