Survey reveals data challenges involved in lab digitalisation

Data stands at the centre of transformation in today’s digital labs — both as a major obstacle and a driving force for innovation — according to a survey of more than 150 scientists conducted by Titian Software and Labguru.

The survey, which looked to the future of digital lab operations in the life sciences industry, identified data overload and management as the most significant challenges impacting lab operations. But this same data deluge offers a powerful opportunity: respondents cited AI’s potential to manage and extract insights from the vast volumes of data 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 advances in the lab.

While machine learning and AI are expected to be major drivers of transformation in lab operations, many labs aren’t 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’re interested in adopting. A strong majority of respondents (77%) believe automation will be the primary driver of change by 2026, underscoring the urgent need to address manual processes before the broader adoption of AI and machine learning.

The survey results signal the pressing need to address operational inefficiencies before labs can scale into more advanced technologies. The results were consistent across every type of lab, from big pharma to startup. Despite growing interest in next-generation tools like AI and robotics, only 15% of labs claim to be fully digitised, 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 prioritised before the full promise of AI can be realised.

AI’s greatest promise lies in making sense of 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, it’s clearly 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.

As AI moves from a promising concept to a practical necessity in digital lab operations, many organisations recognise AI’s potential to improve efficiency, accelerate discovery and make sense of complex data. But digital maturity remains uneven — and barriers like data silos and scepticism 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 Chief Executive Officer at Titian Software and Labguru.

“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.”

Image credit: iStock.com/Kobus Louw