Artificial intelligence has moved from promise to practice in the laboratory. At Lab of the Future USA 2026, the opening plenary made that clear. Speakers from pharma, technology, and scientific instrumentation describe a future in which AI no longer sits at the edge of laboratory work. It is starting to shape how experiments are designed, how data are interpreted, and how scientific decisions are made.
The question now is less about whether AI will enter the lab, and more whether laboratories can build the data foundations, workflows, and trust needed for scientists to act on AI-driven recommendations.
As Julie Huxley-Jones, VP Research, Pre-clinical and Manufacturing, Data, Technology & Engineering at Vertex, remarks during the opening comments: “The tipping point has passed. There’s absolutely no turning back on AI.”
From Faster Experiments To Better Decisions
Much of the discussion focuses on drug discovery, but the implications reach across analytical science. Laboratories already face pressure to generate more data, process more samples, handle more complex workflows, and deliver results with fewer resources. AI and automation promise to help. Yet the plenary challenges the idea that speed alone should define progress.
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Speakers argue that the lab of the future must improve the quality of scientific decisions, not just accelerate existing processes. In pharma R&D, this means reducing uncertainty earlier in the pipeline. Poor early decisions can cascade into wasted time, higher costs, and late-stage failures. Better use of integrated data, AI models, and experimental feedback loops could help teams select stronger targets, prioritize better hypotheses, and design more effective studies.
This marks a shift from using AI to explain what happened toward using AI to recommend what happens next. That shift changes the role of both the laboratory and the scientist. AI systems are no longer limited to passive analysis. They are starting to influence which compounds move forward, which targets receive investment, and which experiments deserve resources.
That creates opportunity, but it also raises new questions. Who owns the final decision? Where does AI recommend, and where does it decide? What assumptions sit behind the model? Can a scientist defend the output if the decision later fails?
Data Foundations Come First
The plenary returns multiple times to a practical point: AI cannot compensate for poor data foundations. Before laboratories can benefit from advanced models, they need connected instruments, standardized data, consistent metadata, and reliable governance.
Emanuele de Rinaldis, VP, Global Head of Target, Disease & Systems Biology at Sanofi, describes this as the work that happens before AI: building a FAIR data ecosystem in which data are findable, accessible, interoperable, and reproducible. That foundation allows teams to connect experimental results, biological context, disease models, and clinical outcomes in ways that support more systematic decision-making.
For analytical scientists, this is one of the clearest takeaways. The value of AI depends on the quality and structure of the data flowing into it. Instruments, informatics platforms, electronic laboratory notebooks, automation systems, and cloud environments must work together. Data must carry enough context to remain useful beyond the original experiment.
This is especially important as laboratories move toward closed-loop systems. In these workflows, AI may help design experiments, automation might execute them, analytical systems can generate results, and models could learn from the output. Each step depends on clean, traceable, reusable data.
As Mark Fish, VP & GM of Digital Lab Solutions at Thermo Fisher Scientific, remarks, “Data is the product of the lab. It’s the fuel for scientific innovation and it’s the crown jewels for our organizations.”
Trust Becomes The Productivity Constraint
The most compelling theme from the plenary is trust. AI may compress analysis from weeks to hours, but that does not guarantee faster decisions. If scientists still need days or weeks to gain confidence in an AI recommendation, the bottleneck has simply moved.
Nicole Crane, Senior Principal at Accenture, illustrates this with a scenario: a scientist arrives in the morning to find that an AI system has integrated new data, run predictive models, flagged a subtle safety signal, and recommends advancing one target while deprioritizing another. The data look clean. The dashboard looks complete. Yet the scientist pauses.
That pause matters. It reflects the questions scientists must ask before acting: Do I trust this enough to move forward? Do I understand it well enough to defend it? What assumptions should I challenge experimentally? Who is accountable if the decision proves wrong?
“The lab of the future doesn’t break when technology fails,” Crane warns. “It breaks when trust hasn’t kept up with capability.”
This point should resonate across regulated and high-stakes laboratory environments. Analytical scientists build careers on defensible results. Trust is not a soft concept. It depends on traceability, reproducibility, explainability, and clear accountability. As AI takes on more decision-support functions, those principles become more important.
The Scientist’s Role Is Changing
The plenary also challenges a common assumption: that the future lab requires every scientist to become an AI expert. Instead, speakers describe a model built on deliberate pairing. Scientists bring domain knowledge, mechanistic understanding, experimental judgment, and accountability. AI engineers and technologists bring model knowledge, system architecture, drift detection, and awareness of technical constraints.
This partnership is important because AI outputs rarely speak for themselves. Scientists need to understand where a model works, where it fails, and where judgment must override a recommendation. Technologists need to explain model behavior in terms that connect to experimental risk and scientific decision-making.
“The goal here is not bilingual unicorns,” Crane remarks. “That would be lovely though, but it’s actually a deliberate pairing.”
The panel discussion extends this idea. De Rinaldis notes that AI forces scientists to make implicit judgment more explicit. When teams translate workflows into AI-readable rules, thresholds, and criteria, they must define what they are doing and why. That can expose assumptions that previously sat inside experience or intuition.
In this sense, AI may act as a forcing function. It pushes teams to codify hypotheses, decision points, validation criteria, and rejection thresholds. That process can sharpen scientific thinking, even before the technology delivers its full promise.
Workflow Redesign, Not Tool Adoption
The speakers also warn against treating AI as a plug-in technology. Laboratories will miss much of its value if they attach new tools to old workflows. The larger opportunity lies in redesigning how work moves through the lab.
Christopher Arendt, CSO, Head of Research at Takeda, describes an approach centered on embedding AI and automation into real programs rather than running isolated pilots on the side. The aim is to connect wet lab and dry lab work, redesign workflows, and measure near-term impact on cost, cycle time, and program progression.
This theme is significant for analytical laboratories. Many labs still operate in brownfield environments, with legacy instruments, fragmented data systems, and established processes that cannot stop while transformation happens. Modernization must therefore address two problems at once: building the future while unwinding parts of the past.
That work requires leadership, investment, and cultural change. It also requires practical proof points. Scientists and laboratory managers need to see that new systems reduce friction, improve confidence, and support better outcomes.
The Agentic Lab Needs Guardrails
The plenary also introduces the idea of the agentic lab, where AI does more than advise. In this model, agents may operate within governed workflows, helping to design experiments, coordinate execution, adapt testing, and feed new data back into models.
This vision depends on human oversight. Fish emphasizes that agentic orchestration requires defined boundaries, auditability, compliance, and safety controls. AI may act within the workflow, but scientists must remain in the lead.
This distinction will become critical as laboratories adopt more autonomous systems. Closed-loop workflows may improve speed and consistency, but they also increase the need for governance. Data integrity, cybersecurity, model drift, bias, and accountability cannot be afterthoughts.
Responsible AI in the lab will require more than technical performance. It will demand clear decision rights, transparent assumptions, reliable data pipelines, and a shared understanding of where automation should stop.
A More Human Lab Of The Future
Despite the focus on AI, the plenary did not present the lab of the future as a replacement for scientists. It describes a more connected, data-rich, and decision-oriented environment in which human judgment becomes more important, not less.
Automation can reduce manual friction. AI can surface patterns and trade-offs. Simulation can expand the range of options scientists can explore. But decisions still require context, creativity, risk assessment, and accountability.
That may be the most useful message for analytical scientists. The lab of the future will not be defined by automation alone. It will be defined by the ability to turn trusted data into better decisions.
Speed matters. But trust, data quality, and scientific judgment will determine whether AI changes laboratory work in a meaningful way.
To attend the next Lab of the Future Congress in Europe, visit the website at: https://www.lab-of-the-future.
