A new industry survey has revealed that while life sciences labs are eager to adopt AI, many are still struggling with basic operational challenges — particularly data management and inventory control.

The survey, conducted by Titian Software and Labguru, gathered responses from over 150 scientists across pharma, biotech, CROs, and academic institutions. It found that data overload and the complexity of managing diverse lab datasets are the biggest barriers to efficiency and digital progress. Just 15% of respondents said their labs are fully digitized, with nearly half still reliant on manual processes.

Despite widespread enthusiasm for AI — with 45% planning to implement advanced technologies within two years — foundational issues persist. 65% identified inventory management, especially of reagents and supplies, as their top technology priority. And 77% said automation would be the main driver of change by 2026, suggesting that most labs are still focused on streamlining basic workflows before adopting more complex AI tools.

“Labs today are generating more data than ever before, but without the right systems in place, that data becomes a burden instead of a benefit,” said Keith Hale, CEO of Titian Software and Labguru. “AI can only deliver meaningful results when it’s built on clean, connected data and efficient lab operations.”

Respondents were also clear on the long-term value of AI. 24% said the most important role for AI will be managing and interpreting experimental data, particularly as labs deal with growing regulatory pressure and data complexity across modalities.

The survey included labs of all sizes, from large pharma to early-stage startups. While adoption timelines varied, a quarter of respondents said they had no near-term plans for AI or would need more than five years to get there — underscoring the uneven pace of digital transformation across the industry.