Artificial intelligence has become one of the most discussed technologies in the modern laboratory. Across life sciences, organizations are testing generative AI tools, exploring automation, and beginning to imagine what agentic AI could mean for scientific work.
Yet the path to AI-enabled lab operations does not start with AI. It starts with the systems, data, workflows, and governance that allow AI to produce reliable results.
Cenevo’s 2026 Lab Operations Report makes that gap clear. The report finds strong interest in AI across the life sciences, but it also shows that many labs still operate with fragmented data, partially manual workflows, and limited integration between core systems. In other words, labs may want AI-driven operations, but many still need the digital foundation to support them.
AI Interest Is Growing, but Production Use Remains Limited
AI adoption has moved beyond early curiosity. According to the report, 60 percent of respondents are exploring or piloting generative AI, while 25 percent use generative AI in production. Agentic AI sits at an earlier stage. 27 percent are exploring or piloting it, but only 5 percent use it in production.
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That difference matters. Generative AI tools can help summarize information, support natural language search, or assist with reporting. Agentic AI raises the stakes. These systems aim to carry out multi-step tasks with more autonomy, such as creating data processing workflows, generating reports, coordinating resources, or supporting protocol creation.
For analytical laboratories, that shift could bring real value. Many labs manage complex sample flows, instrument data, reporting requirements, and compliance expectations. AI agents could help reduce repetitive work and improve coordination across systems.
But greater autonomy requires greater trust. It also requires cleaner data, clearer workflows, and stronger control.
Digital Maturity Still Lags Behind AI Ambition
The report shows that fully digitalized labs remain rare. Only 9 percent of respondents describe their labs as fully digitalized, while 43 percent report moderate digitalization and 33 percent describe partially digitalized environments with significant manual processes.
This creates a practical challenge. AI performs best when data is accessible, structured, and connected. Many laboratories still depend on a mix of instruments, spreadsheets, local files, LIMS, ELNs, chromatography data systems, and reporting tools. Each system may serve a purpose, but poor connectivity limits what scientists can do with the data.
In analytical science, this issue reaches beyond convenience. Fragmented data can slow investigations, complicate method transfer, delay reporting, and make it harder to trace decisions. These pressures grow in regulated environments, where data integrity, auditability, and reproducibility shape every workflow.
AI cannot solve those issues on its own. In some cases, it may expose them faster.
Data Integration Is the Real Bottleneck
The strongest message from the report is that data readiness now defines AI readiness. 55 percent of respondents cite lack of integration between systems as a barrier to effective lab data use. 47 percent report data spread across instruments, and another 47 percent cite unstructured or inconsistent data.
These findings reflect a common lab reality. Data often exists, but scientists cannot use it at scale. Instrument outputs can remain locked in proprietary formats. Results may sit in disconnected systems. Metadata could lack consistency. Historical data might prove hard to search, compare, or reuse.
For AI, that creates a ceiling. A model or agent can only act on what it can access and interpret. If a lab cannot connect sample context, instrument data, methods, results, and reporting requirements, AI remains limited to narrow tasks.
This is where analytical labs need to focus. The immediate opportunity lies in making lab data more findable, accessible, interoperable, and reusable. FAIR data principles may sound abstract, but they have direct operational value. They help labs build the conditions needed for automation, analytics, and AI-supported decision-making.
Automation Comes Before Autonomy
AI attracts attention, but the report suggests that labs place automation first. 66 percent of respondents expect to invest in automation over the next 12 months, making it the leading operational investment priority. By contrast, only 14 percent report that their lab processes are predominantly automated today.
This gap points to a useful distinction. Labs do not need to jump from manual processes to autonomous AI. They need to standardize and automate the workflows that create friction today.
For many analytical labs, that may include sample intake, reagent and consumables tracking, data analysis, data clean-up, reporting, and workflow orchestration. These areas often involve repetitive steps, handoffs, and documentation demands. They also create the structured process environment that agentic AI would need later.
Automation builds discipline into lab operations. It clarifies inputs, outputs, rules, exceptions, and responsibilities. Those same elements help AI systems operate within defined boundaries.
The future lab will not replace scientists with agents. It will use automation and AI to reduce avoidable work, improve consistency, and help scientists focus on interpretation, problem-solving, and scientific judgment.
Trust, Skills, and Governance Will Shape Adoption
AI adoption does not depend on technology alone. The report identifies privacy and security concerns as the top barrier to AI adoption, cited by 58 percent of respondents. Lack of skills or training follows at 51 percent, while 42 percent cite low-quality or inconsistent data.
These barriers carry special weight in regulated analytical environments. Labs need confidence that AI-supported workflows protect data, preserve traceability, and support compliance. They also need staff who understand when AI can help, when human review must remain central, and how to validate AI-assisted outputs.
Governance must develop alongside adoption. That includes clear policies for acceptable AI use, data access, model validation, audit trails, version control, and human oversight. Without these controls, AI can introduce risk instead of reducing workload.
The report’s findings suggest that organizations no longer need convincing that AI has potential. They need a practical framework for using it safely and effectively.
The AI-Ready Lab Starts With Operational Readiness
The most successful AI strategies will likely start with operational pain points, not technology showcases. Labs should ask where fragmented workflows slow scientists down, where data loses context, where manual handoffs create risk, and where reporting consumes time that could support higher-value work.
From there, AI readiness becomes a staged process.
First, labs need connected data. Then they need standardized workflows. Next comes automation in high-value operational areas. Only then can more advanced AI and agentic systems deliver meaningful value at scale.
This sequence may sound slower than the current AI conversation. In practice, it creates a faster path to results because it targets the real constraints inside lab operations.
A More Practical Future for AI in Analytical Science
The next phase of lab AI will depend less on novelty and more on usability. Analytical scientists do not need abstract promises about autonomous labs. They need tools that help manage complex data, reduce repetitive work, maintain compliance, and support better decisions.
Cenevo’s report shows that the industry has entered a critical transition. Labs are exploring AI, but many still need stronger digital infrastructure, better integration, cleaner data, and clearer governance. That may seem like a limitation. It is also an opportunity.
The labs that invest in these foundations now will place themselves in a stronger position to use AI with confidence. They will move beyond isolated pilots and toward connected, scalable, and trusted digital operations.
AI-ready labs will not emerge from AI alone. They will emerge from better lab operations.
