Laboratory teams have spent the past two years experimenting with generative AI copilots, chatbots, and point solutions. In 2026, that experimentation is expected to give way to something more operational: agentic AI embedded directly into laboratory workflows, data systems, and compliance frameworks.
Across research, quality, and clinical operations, AI agents will no longer sit on the sidelines answering questions. They will begin initiating workflows, coordinating systems, and executing governed tasks across discovery, development, and quality operations.
From chatbots to coordinated AI agents
Enterprise AI is moving beyond single assistants toward networks of specialized AI agents, each responsible for a narrow part of a workflow.
“We are about to see a big change in how teams work,” says Žilvinas Girėnas, head of product at nexos.ai. “The shift from single-purpose agents to coordinated AI teams is fundamental. Businesses are no longer deploying one agent to solve one problem. They’re building teams of specialized agents that work together, each bringing expertise to different parts of a workflow.”
In life science organizations, that shift aligns with a broader push to connect fragmented digital systems, says Kalim Saliba, chief product officer at Dotmatics.
“In 2026, life science organizations will begin taking early but important steps toward creating a digital thread that spans the entire flow of how therapeutics are discovered, developed, and manufactured,” Saliba adds. “This continuous data thread will unify hypotheses, designs, samples, methods, instruments, and results into a single source of truth, strengthening data integrity, reproducibility, and regulatory confidence.”
Agentic AI is positioned to sit on top of that digital thread, orchestrating how data, tools, and people interact across the make-test-decide-analyze cycle.
Rather than replacing scientists, Saliba says, these systems will coordinate complex, multi-step work across wet-lab and dry-lab environments while keeping humans in the loop.
“It’s not about full autonomy—it’s about smarter collaboration between scientists and AI that accelerates discovery while maintaining scientific rigor and oversight.”
Why agentic AI needs orchestration in regulated labs
A new global study of 1,150 large enterprises by Camunda found that many organizations already use AI agents, but few trust them with mission-critical work. While 71 percent of organizations report using AI agents, only 11 percent of use cases reached production last year, largely because of governance, transparency, and compliance concerns.
In regulated environments like laboratories, that trust gap is especially important.
The report found that 80 percent of IT leaders say most of their agents today are still limited to chatbots or assistants, and 48 percent operate in silos rather than inside end-to-end workflows. To move beyond that, organizations need agentic orchestration, which places AI agents inside controlled process frameworks with audit trails, approval steps, and defined decision boundaries.
For laboratories, this model supports the same requirements that already govern quality systems, clinical operations, and manufacturing execution.
Quality and compliance become early use cases
Predictions for 2026 suggest quality control labs may be among the first to move from pilots to operational agentic AI.
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“Labs will move beyond chatbots to embed agentic lab assistants that connect highly specific tasks in a regulated environment,” says Justin Lavimodiere, senior director at Veeva LIMS. He expects AI agents to start workflows, summarize outcomes, and analyze trends across QC operations, enabling earlier risk detection and right-first-time execution.
That same pressure is building in clinical and regulatory operations.
Rik Van Mol, senior vice president of Veeva R&D and Quality, says European regulatory changes will push companies toward continuous inspection readiness under the EU Clinical Trials Regulation, ICH E6(R3), and new structured data requirements. “It is not a scramble at the end; it is a continuous state that depends on clear process ownership, consistent documentation, and a reliable trail of decisions.”
Agentic systems that operate inside governed workflows could help enforce those requirements at scale by ensuring every step is traceable, versioned, and auditable.
The management challenge for lab leaders
As AI agents become operational tools rather than experiments, ownership will shift from IT teams to functional leaders. Team leads will increasingly configure agents, set instructions, test outputs, and scale what works, with engineering stepping in only when needed.
That transition mirrors what many laboratories already experience with LIMS, ELNs, and quality systems, but with a new layer of adaptive software acting inside those systems.
The Camunda global study found that 85 percent of organizations do not yet have the process maturity needed to deploy agentic orchestration at scale. For lab managers, that points to a near-term priority: standardizing data, workflows, and system connections so AI agents can operate safely inside them.
What lab managers should have on their radar
Across the predictions, a consistent theme emerges: agentic AI will not replace scientists or quality professionals, but it will increasingly coordinate the work they already do.
In practice, that means labs should focus less on standalone AI tools and more on whether their data systems, workflows, and governance structures are ready for AI agents to participate.
Organizations that excel in 2026 are likely to be those that treat AI agents as part of their operational infrastructure rather than experimental add-ons. For laboratories, that could translate into faster experiments, more reliable quality execution, and greater confidence in data and compliance as AI moves deeper into everyday scientific work.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
