While artificial intelligence has become a staple of corporate strategy, its impact on the bench remains low. According to new poll data from the Pistoia Alliance, a global non-profit advocating for life sciences collaboration, only one percent of industry professionals report seeing tangible value from AI in the wet lab.

The survey, conducted at the Pistoia Alliance’s annual European conference attended by 300 industry leaders at the Royal Society of Medicine in London, highlights a growing divide. While 30 percent of organizations have implemented enterprise-wide AI platforms, 69 percent lack clear metrics to assess whether these tools actually reduce the time or cost of drug R&D. For a lab manager, this suggests that while the "front office" may be digitized, the actual experimental environment is still struggling to integrate these advanced tools effectively.

The uneven impact of AI across the R&D lifecycle

The poll results indicate that AI benefits are currently concentrated in administrative and data-heavy roles rather than experimental ones. More than half of the respondents noted that regulatory submissions and reporting teams see the greatest advantages from AI. Research analysis teams followed at 21 percent, while only 13 percent cited value in automating scientific workflows.

The near-total absence of perceived value in the wet lab—just one percent—points toward a significant bottleneck. Ammara Gafoor, head of life sciences data & AI at Thoughtworks, noted that AI is often confined to isolated projects like molecule generation or target identification. Without a "joined-up" view of how these individual successes work together, AI stagnates at the level of local gains. For those managing a laboratory, this means the challenge isn't just buying the right software, but ensuring the software communicates across the entire R&D pipeline.

Overcoming the barriers of data quality and change management

Why is the gap between investment and value so persistent? The survey identified two primary hurdles: data and people. Fifty-nine percent of respondents stated that organizations must prioritize data quality and accessibility, while 22 percent pointed toward AI adoption and change management.

Dr. Becky Upton, president of the Pistoia Alliance, emphasized that success depends on getting both elements right. The conference explored several themes aimed at solving these issues, including:

• Semantic data: Utilizing ontologies to improve interoperability and ensure AI is grounded in reliable, structured knowledge
• Late-stage R&D acceleration: Using digital twins and omics data to bridge the gap between experimental results and clinical outcomes
• Multi-agent systems: Moving toward "reusable agent capabilities" that allow humans and AI to work together more fluidly within the enterprise

Christian Baber, PhD, chief portfolio officer for the Pistoia Alliance, noted that AI is largely being used for "traditional Natural Language Processing tasks like searching literature and writing reports" rather than being embedded into specialized R&D. He argued that if AI were truly integrated, the industry would see "impact in more workflows," including those in the wet lab.

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Strategies for improving lab digitization and AI readiness

AI cannot be a top-down mandate; it must be built on a foundation of high-quality, accessible data generated at the bench. To move beyond the one percent value threshold, managers should consider the following steps when evaluating their digital strategy:

• Focus on data "hygiene" at the point of collection to ensure that experimental results are AI-ready from the start
• Collaborate with regulatory and research analysis teams to see how they have successfully leveraged NLP for reporting and apply those lessons to lab documentation
• Invest in change management training for bench scientists to reduce the friction of adopting new digital workflows
• Prioritize interoperability between different lab instruments to prevent the "isolated project" stagnation mentioned by industry leaders

By focusing on these operational realities, a lab manager can help ensure that AI moves from a corporate buzzword to a tool that actually accelerates the R&D pipeline. The focus should shift from asking how much AI is being used to asking how much faster the lab can move because of it.

This article was created with the assistance of Generative AI and has undergone editorial review before publishing.