Ask most lab managers what they need to get more value from AI, and the conversation quickly turns to data. But dig a little deeper and a more specific answer emerges, it is not just data they need. It is connected data, flowing through automated workflows, across integrated systems. That distinction matters more than it might seem.
Our 2026 Lab Operations Report, based on insights from over 110 life sciences professionals, puts numbers to something many people working in lab operations already sense in their day-to-day work. AI ambition is not the problem. The infrastructure is.
55% of labs cite lack of integration between systems as their biggest barrier to making effective use of lab data. Nearly half report data spread across instruments. A further 47% describe unstructured or inconsistent data as a major challenge. These are not new problems, but they are becoming more urgent as AI moves from an aspiration to an operational expectation.
The picture on automation tells a similar story. Only 14% of labs describe themselves as primarily automated, with more than 75% of their processes running without manual intervention. A third still rely predominantly on manual operations. These are labs where scientists are spending significant time on tasks that could and should be automated, time that could be spent on the work that genuinely requires scientific judgment.
What the data shows is an industry that has made real progress but is still operating in a mix of digital, manual and semi-automated environments. The gap between where labs are and where AI requires them to be is, in most cases, an automation and integration gap.
The good news is that lab leaders know this. When asked where they expect to invest most over the next 12 months, 66% identified automation as their top priority. System integration came in at 50%, ahead of standalone AI tools.
This is a meaningful shift from previous years, when investment conversations were often dominated by specific tools or platforms. Labs are now thinking more foundationally, asking not which AI tool to buy but what infrastructure is needed to make AI work reliably at scale.
For smaller and mid-sized organizations, the integration priority was even more pronounced, with almost two thirds identifying it as a key area for investment. This makes sense. Larger organizations have more resources to absorb fragmentation. For smaller labs, disconnected systems create proportionally greater operational drag.
One nuance worth holding onto: automation is not a single thing. For some labs, the priority is physical device and instrument automation, robotic liquid handling, sample processing and analytical equipment. For others, it is process and workflow automation, digitizing and standardizing the steps and handoffs that connect systems and people.
The survey findings in the report reflects this. Sample intake and registration (53%), data analysis and clean-up (42%) and reagent tracking (40%) were the processes most commonly identified as candidates for greater automation. These are high-volume, repetitive, data-intensive activities where the operational case for automation is clear and the AI opportunity is significant.
Large pharma organizations showed a stronger focus on end-to-end workflow orchestration. Smaller organizations and academic institutions tended toward more targeted automation of specific high-friction activities. Both approaches are valid. What matters is that automation investment is matched to the actual bottlenecks in each lab environment rather than applied generically.
The link between automation, integration and AI is not incidental – it’s structural. Agentic AI, the kind that can carry out complex multi-step tasks with real autonomy, requires data that is connected, accessible and trustworthy. It requires workflows that are standardized enough for an agent to act on. It requires systems that can communicate with each other in real time.
Right now, only 5% of labs have agentic AI in full production. 60% are still exploring or piloting generative AI. That gap exists not because labs lack ambition but because most are still building the connected, automated environments that make agentic deployment possible.
The labs that invest now in automation and integration are not just solving today's operational problems. They are building the infrastructure that will determine how quickly and effectively they can deploy AI tomorrow.
For lab managers and operations leads thinking about where to focus, the report suggests a practical order of priority. Start with the integrations that will have the greatest impact on data quality and accessibility. Identify the manual handoffs in your workflows that create the most friction and address those first. Build a data foundation that is structured, consistent and queryable before layering AI on top of it.
None of this is straightforward. Cost constraints, legacy systems, process complexity and instrument integration challenges are all real barriers that the report captures in detail. But the direction of travel is clear, and the organizations making the most progress are those treating automation and integration as foundational investments rather than line items to be deferred.
Download the full From Digital to Agentic: The 2026 Lab Operations Report to explore the complete findings on automation, integration, AI adoption and what the connected lab of the future looks like in practice.