Survey finds lab orchestration as the strongest near-term opportunity, while trust, legacy integration and uncertain ROI continue to pose questions.
CAMBRIDGE, Mass. and CAMBRIDGE, England, Sept. 23, 2026 /PRNewswire/ -- Last year, the biopharma industry was asking whether its data was ready for AI. This year, the question has changed.
Is the scientific enterprise ready for AI that can act?
A new global survey from Zifo finds that interest in Agentic AI is rising rapidly across biopharma, but practical adoption remains low. Nearly 500 respondents from more than 125 companies participated in the study, offering a grounded view of how scientists, informaticians and technology leaders are approaching autonomous agents capable of reasoning, orchestrating and executing scientific workflows. https://zifo.com/blogs/agentic-ai-survey-early-days-as-industry-focuses-on-roi-and-lab-orchestration/
The headline finding is unmistakable: An overwhelming majority of respondents characterize their organizations as being in the "learning" phase, with teams primarily evaluating the return generated by Gen AI and emerging Agentic AI technologies.
Experimentation is happening. Production adoption, however, is evolving.
The findings represent a notable evolution from Zifo's 2025 survey, which showed that science-focused companies were investing in AI but remained constrained by fragmented data, limited interoperability and integration challenges. That study found that nearly two-thirds of organizations had begun investing in AI and machine learning, although only 32% of respondents expressed high confidence in their ability to use scientific data effectively for those initiatives.
In contrast, the latest Zifo survey suggests that the industry has moved forward conceptually. Organizations are no longer talking only about AI models, copilots or isolated use cases. They are beginning to imagine autonomous systems operating across entire scientific processes.
But imagining the future and operationalizing it are two very different things.
"Right now, the industry hasn't even completely and ubiquitously adopted standard Gen AI into all of its scientific environments. We are waiting to see the true promise of Agentic AI fully realized in workflows: systems that proactively ask, 'You are planning, would you like me to run a Design of Experiments (DOE) or look at previous similar experiments and help define the best conditions?'. That level of seamless background orchestration will dramatically increase the fidelity and speed of the science, but it makes total sense that organizations are currently just learning and evaluating," Paul Denny-Gouldson, Zifo's Chief Scientific Officer said in the report.
Lab orchestration emerges as the leading opportunity
When asked which operational functions hold the greatest potential for Agentic AI automation over the next 12 to 18 months, 64.3% of respondents selected lab orchestration, including instrument control and scheduling.
Cross-departmental knowledge management followed at 50%, reflecting demand for agents that can locate, connect and present relevant scientific information at the point of decision. Pharmacovigilance and safety attracted 42.9% of responses, while 28.6% selected regulatory intelligence.
The attraction of lab orchestration is practical. Scientists frequently work across numerous applications, instruments and data environments. They move information manually. They monitor schedules. They reconcile incompatible outputs. They spend valuable time navigating the machinery around science rather than concentrating on science itself.
Agentic AI could change that.
A trusted orchestration layer could coordinate instruments, applications, data and decisions across a workflow, reducing operational friction while enabling scientists to remain in control. The survey suggests that many organizations see this as one of the clearest routes from AI experimentation to measurable scientific value.
Across the biopharma value chain, respondents identified preclinical research as the area with the highest Agentic AI potential, at 46.2%, followed by omics at 38.5%. Discovery, manufacturing and quality control, and "all of the above" each received 30.8%. Clinical applications received 23.1%, while chemistry, manufacturing and controls received 7.7%.
"This doesn't surprise me at all; it aligns perfectly with the exact demands we are seeing from our customers. Think about Lab Orchestration as a 'brain' or lab assistant sitting at the center of your operations. Currently, scientists carry the heavy burden of interfacing with 20 or 30 different applications, managing manual data migrations, and keeping track of complex instrument schedules. Agentic AI removes that friction, allowing scientists to focus intensely on their actual experiments and analyses rather than administrative overhead. This kind of holistic orchestration has been a dream for scientific informatics for many years, and this technology can finally deliver it. Regarding Knowledge Management, it is deeply linked to process speed and better decision-making. If an agent can surface relevant data from other departments exactly at the point of decision, scientists avoid repeating past experiments and relying on guesswork," Paul said in the report.
Organizations can see efficiency gains, but hard ROI remains elusive
For most companies, the return on AI investment is still being measured through time saved rather than scientific breakthroughs.
About 77.8% of respondents rely on "soft ROI" measures, including productivity and efficiency improvements. At the same time, 44.4% have not formally defined ROI and instead view AI investment as a strategic necessity.
Only 11.1% currently define success through "scientific ROI," such as novel discoveries or accelerated pipeline milestones.
"Time and efficiency metrics are actually measurable, provided you capture your operational baselines first. If you don't know that a task previously took five days, you can't properly measure the success of an agent doing it in one. Right now, individual agents are delivering incredible personal benefits, like cutting a 6-hour task in half. However, organizations ultimately want radical process changes. They are aiming to reduce overall time-to-market by 50% or push process yields up by 40% while holding quality standards high. When AI allows you to reduce bioreactor test runs from ten down to five, the financial ROI is massive, saving half a million to a million dollars. Scientific ROI is inherently tricky because it is a lagging indicator; you won't know if you've brought more drugs to market for a few years. But the underlying truth is vital: seamless tech greatly improves the user experience. Happier scientists are more productive scientists, and more productive scientists inherently deliver better science," Paul said.
To read and download the full report, visit: https://zifo.com/blogs/agentic-ai-survey-early-days-as-industry-focuses-on-roi-and-lab-orchestration/
About Zifo
Zifo is the leading global enabler of AI and data driven enterprise informatics for science driven organizations. With extensive solutions and services expertise spanning research, development, manufacturing, and clinical domains, we serve a diverse range of industries, including Pharma, Biotech, Chemicals, Food and Beverage, Oil & Gas, and FMCG. Trusted by over 190 science-focused organizations worldwide, Zifo is the partner of choice for advancing digital scientific innovation. https://zifo.com
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