In an essay, the CEO of Anthropic, Dario Amodei, has hailed the AI-driven "compressed 21st century" where 50 to 100 years of innovation might happen in a fraction of that. One thing is certain: the pace of innovation is outstripping the capacity of traditional laboratory tools to keep up. As new data types, modalities, and analytical techniques emerge, lab research is facing a critical inflection point.
Today’s laboratories generate data at unprecedented volumes. High-throughput sequencing, automated imaging, IoT technologies, and instrument-rich experimental platforms have transformed research environments into data factories.
This data explosion can be a double-edged sword. According to our 2025 Future of Digital Lab Operations survey, scientific organizations now face ‘data overload’. The challenge isn’t just in the collecting of data, but the making sense of it. Over half of respondents said that the problem of data overload was driving change in their lab. A quarter also believed the most significant role of AI over the next five years would be in managing the massive amount of data generated from experiments, instruments and other sources. Valuable insights may be trapped in formats that resist easy analysis, buried under a deluge of raw output - a picture may be worth a thousand data points.
Compounding the problem is the issue of data silos. Across organizations, different labs, teams, and instruments store their data in disconnected systems. File servers, spreadsheets, vendor-specific databases, and internal APIs don’t talk to each other. These silos can stifle collaboration, obscure visibility, and inhibit the full potential of AI.
Scientists often spend more time finding, cleaning, and transforming data than analyzing it. Indeed, a survey by Deloitte and AWS found that “70% of lab workers’ time is wasted on admin, preparation work, data handling and reporting”. Connectivity is essential for deeper automation and AI adoption and the current landscape of many different silos is severely limiting both the intelligent use of data and the opportunity to leverage these new tools. It’s clear that through better collaboration and a unified data strategy, software partners must work even closer together; unburdening lab workers and ensuring that innovation is not lost in the gaps between systems.
Adding to the complexity is the accelerating emergence of new modalities; next-gen peptides, single-cell multi-omics, gene therapy and more. These innovations often require new hardware, handling and are not easily integrated into unprepared existing lab systems. In short, each modality brings its own data structures, processing pipelines, and nuances. Our survey found that only 15% of respondents felt very well prepared for increase in sample types; others were experiencing challenges.
When speed of innovation accelerates there grows a need to manage that scale with comprehensive tools, more analysis and deeper insight. Lab groups are transitioning from developing processes with a few components to the orchestration of thousands. Lab software must now support flexible data models, adaptive metadata handling, and seamless connectivity, all while ensuring universal FAIR principles.
We know that when it comes to automating manual processes and increasing digital maturity labs have trodden carefully. 50% still have significant manual processes. The way forward is not to resist complexity but to embrace it with the right tools. AI can play a transformative role in this landscape, provided the underlying data infrastructure is ready.
AI and data thrive together; AI extracts structure from unstructured content, suggests metadata, flags anomalies, and identifies hidden patterns across experiments. Machine learning models can predict experimental outcomes, optimize workflows, and even generate hypotheses.
That’s why to unlock the power of AI, modern lab operations software must do more than be a digital representation of the lab - it must be a robust data platform. A platform that enables Agentic AI to operate on data as a first-class citizen, that welcomes diverse modalities, embeds domain knowledge, manages complex lineage and supports collaboration. We theorize that Documentation Agents, Inventory Agents, Data Analysis and Reporting Agents, as well as Robot Orchestration and Management Agents, will thrive on this platform. To progress, we need to move beyond diligently recording what happened but also enlighten on what should happen next.
We don’t have a crystal ball to see where science will be five or ten years from now – adoption of technology often goes down unexpected paths. But we do know that research will be more automated, more data-driven, and more complex. AI won’t replace scientists, but it will supercharge them, accelerating insights, enhancing precision, and empowering creativity across all areas of lab research.
At the core of this transformation is data. How we collect it, clean it, connect it, and learn from it. Cenevo is executing a data strategy and developing a next-generation data lake for supporting lab operations using Labguru and Mosaic. It’s clear to us that thoughtfully deploying these novel tools can help our customers with the problems they are facing right now. With scientists we are building a rich data platform from which they can launch the next generation of innovations.
We don’t know exactly what the future of science looks like. But with the right data foundations, total connectivity and AI-enabled technology, Cenevo is not just preparing for the future - we are building it.
Want to turn your lab data into actionable insights? Book a demo today and discover how.