Survey Reveals Data Challenges in Lab Digitization

Although data is key to the transformation of digital laboratories, it is both a major obstacle and a driving force for innovation, according to a survey of more than 150 scientists from Titian Software and Labguru.

Data overload and management were identified as the most significant challenges impacting lab operations. This same data deluge offers a powerful opportunity: respondents cited artificial intelligence (AI)’s potential to manage and extract insights from vast data volumes generated by experiments, instruments, and other sources as its most valuable future role. This duality highlights data as not just a pain point, but the foundation for unlocking the next wave of AI-driven laboratory advances.

Titian Software, which delivers the Mosaic sample management solution, and Labguru a lab LIMS, ELN, inventory, and data management platform, have released the results of their latest survey on the future of digital lab operations in the life sciences industry. 

While machine learning and AI are expected to be major drivers of transformation in lab operations, many labs are not quite ready to fully harness its potential. Foundational issues remain, with inventory management and the automation of manual processes taking precedence. In fact, 65% of survey respondents identified inventory management—specifically of reagents and supplies—as the top technology they’d like to adopt. A strong majority—77% of respondents—believe automation will be the primary change driver by next year, underscoring the urgent need to address manual processes before AI and machine learning’s broader adoption.

The results signal the pressing need to address operational inefficiencies before labs can scale into more advanced technologies. The results were consistent across every lab type, from big pharma to startups. Despite growing interest in next-generation tools like AI and robotics, only 15% of labs claim to be fully digitized, and half still rely heavily on manual processes. 

While 45% of respondents plan to implement next-generation lab technologies like AI within the next two years, a significant portion—25%—have no near-term plans or anticipate needing more than five years. This gap highlights a critical period of transition, where foundational improvements must be prioritized before AI’s full promise can be realized.

AI’s greatest promise lies in fathoming the overwhelming volume and complexity of lab data. Nearly a quarter of respondents (24%) identified managing data from lab experiments and instruments as the most significant role AI will play in lab operations over the next five years. With 54% citing data overload and management as a key challenge driving change, the need is clear. It’s not just the quantity of data that’s straining labs—it’s the complexity across diverse modalities, creating added pressure around storage, automation, acquisition, compliance, and regulatory requirements. 

The life sciences industry is poised for a major evolution, as AI moves from a promising concept to a practical necessity in digital lab operations. 

While organizations widely recognize AI’s potential to improve efficiency, accelerate discovery, and make sense of complex data, digital maturity remains uneven—and barriers like data silos and skepticism around AI outputs persist. 

“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, group CEO at Titian Software and Labguru. “Better data practices and smarter sample and inventory management are essential not only for improving day-to-day operations but also for setting the stage for more advanced capabilities. AI cannot deliver real, meaningful benefit without connected and well-managed data. That is where we come in. By helping labs streamline and structure their operations and data management today, we can enable the power of AI to transform the labs of tomorrow.” 

In January 2025, Titian Software and Labguru surveyed the life sciences industry to understand the trends and innovations that matter most in digital lab operations. One hundred fifty-five people completed the survey. The findings represent the current state of automation and data management and the evolution of AI in life science and its potential impact in lab operations. Survey respondent breakdown by organization type:

• 26% from large pharma/biotech (>5,000 employees)
• 12% from mid-size pharma/biotech (500-5,00 employees)
• 29% from small (start-up) pharma/biotech (<500 employees)
• 4% from CRO (Contract Research Organization)
• 21% from academic institution
• 8% from other organizations

Other organization types specified included: research lab for manufacturing, research and consulting lab, chemical company, small startup, carbon capture tech development, and government.

Titian Software and Labguru provide laboratory data management solutions for life science research and industry. Following the recent acquisition of both companies by Battery Ventures, Labguru has joined forces with Titian Software to create a global organization delivering sample management, inventory management, electronic lab notebook (ELN) and laboratory information management system (LIMS) capabilities, as part of the goal to accelerate the future of digital lab operations. Together, the companies already serve more than 45,000 scientists and manage vast repositories of over one billion samples across 900-plus companies and universities.