Agentic AI in Life Sciences: A Quiet Shift with Real Momentum

• Agentic AI improves workflow across discovery, clinical development, regulatory work, manufacturing, and quality.
• The biggest gains come from reduced friction in routine tasks and earlier access to relevant information.
• Studies from Accenture, Wharton, and McKinsey highlight strong operational value and shorter development timelines.
• These changes influence how scientific teams use their time and how organisations structure roles and processes.

The shift aligns with wider sector trends, including increased competition for AI-literate scientific talent.

Across life sciences, teams are noticing the same quiet shift. Work that used to drag now moves more smoothly, and information that once required days of searching surfaces in minutes. This is where agentic AI is making its mark. It sits inside existing processes and absorbs the repetitive work that fills scientific, clinical, and regulatory days. The effect is a more consistent flow of work and fewer points where progress slows.

 

What is Agentic AI?

Agentic AI refers to systems that can plan, act, and adapt across tasks with limited human prompting. Instead of responding to single instructions, agentic AI works toward defined goals, managing sequences of steps and making decisions about what to do next.

In life sciences, agentic AI typically operates inside existing tools and workflows. It does not replace scientific or clinical judgment. It supports it by handling preparation work, coordination, and information flow.

 

Key characteristics of Agentic AI include:

• Goal oriented behaviour
  The system works toward an outcome, such as preparing a regulatory draft or monitoring trial progress, rather than completing one-off tasks.
• Autonomous task management
  It can decide which steps to take next, pull information from multiple sources, and update outputs as new data appears.
• Continuous context awareness
  Agentic AI retains context across activities, so work does not reset with each interaction.
• Human oversight by design
  Experts remain responsible for decisions, interpretation, and accountability. The system supports, it does not decide.

This is why agentic AI fits naturally into discovery, clinical development, regulatory work, and manufacturing. These areas rely on long chains of interdependent tasks where delays often come from coordination and information gaps, not from lack of expertise.

By absorbing routine steps and keeping work moving in the background, agentic AI reduces friction without changing the scientific intent of the work.

 

A more streamlined way of working

The most significant change created by agentic AI is the reduction of friction. Research activities begin with the right information already gathered. Clinical tracking happens without teams needing to chase data. Regulatory drafts take shape before writers begin shaping the narrative. None of this replaces scientific judgment. What it does is remove barriers, so expertise is applied where it matters most.

Large studies confirm what teams are describing. A joint analysis by Accenture and the Wharton School found that agentic systems could support around 55% of the hours worked in a typical biopharma company, unlocking 180 to 240 billion US dollars of annual value in the United States if used widely. McKinsey has shown that these tools can shorten clinical development by up to twelve months, and in some settings allow organizations to run twice as many trials using the same teams and budget.

Gains may feel incremental day to day, but they accumulate. Work becomes steadier, pressure eases, and teams spend more time making decisions rather than preparing for them.

 

Discovery: Faster starts and fewer delays

In discovery, the value of agentic AI shows up early. Scientists often begin projects by revisiting previous data, reading background literature, and chasing information across systems. Agentic tools step into this stage by reviewing the material quickly and presenting researchers with the pieces most relevant to the question at hand.

This early clarity strengthens scientific decision making. The review process itself doesn’t change. What changes is the time spent reaching it. Administrative load softens, and teams begin with a clearer view of where their effort should go.

 

Clinical Development: More stability and fewer surprises

In clinical development, timing is everything. Delays in site activity, enrolment, or data review can ripple across an entire program. Agentic systems support teams by tracking these signals in the background, surfacing issues earlier, and presenting progress in a form that is easier to act on.

Clinical teams describe the change as more stability rather than more automation. They receive the information they need when they need it, helping studies stay on track. The work becomes less reactive and more deliberate, which is exactly what clinical programs need to maintain pace.

 

Regulatory work: Less assembly, more interpretation

Regulatory writing has always required a mix of precision and patience. Much of the effort goes into assembling structured content before writers can focus on interpretation. Agentic systems help by preparing early drafts, identifying inconsistencies, and pulling forward information from previous submissions. The scientific argument still depends entirely on human judgment. What changes is the speed at which teams reach that stage, reducing administrative burden and freeing time for work that defines regulatory quality and clarity.

 

Manufacturing and Quality: Earlier awareness, fewer blind spots

Manufacturing and quality teams are also seeing early benefits. Agentic tools can review batch data, highlight unusual patterns, and alert teams to areas worth checking. These early warnings give specialists more control and clearer visibility of where problems may be forming.
The aim is not to automate manufacturing decisions. It is to provide timely information so experienced teams can act before issues disrupt production.

 

Where this shift leaves life sciences teams and organisations

As agentic AI settles into routine workflows, teams are finding that the shape of their day is shifting in small but meaningful ways. Scientists have more time for interpretation because the early preparation work is already in place. Clinical groups move through studies with fewer gaps in information, which helps them focus on decisions rather than data chasing. Regulatory teams begin their review from structured drafts instead of spending the first hours building the foundation.

Individually, these adjustments seem small, but together they create a smoother pathway through research, development, and regulatory activity. Expertise is applied at the right moments rather than being absorbed by repetitive preparation.

This shift also influences how organizations think about their scientific environment. When roles become more focused on interpretation and decision making, people place greater value on purpose, clarity, and strong technical context. These themes align with the wider competitive pressure across life sciences, particularly as biotech and technology companies draw from similar talent pools. Readers interested in this broader dynamic can find a deeper exploration in our Biotech vs Big Tech article, which examines how scientific organisations differentiate themselves in a market shaped by modern technical expectations.

 

A sector moving with intention

Agentic AI is not redefining the purpose of life sciences. It enables teams to pursue that purpose with fewer barriers. When routine steps become lighter and progress is less fragmented, scientists and clinical specialists have more time for critical thinking. The result is steady, meaningful improvement rather than dramatic change.

 

How EPM Scientific can support your next steps

Agentic AI is reshaping how work unfolds across scientific, clinical, and regulatory teams. It also informs the skills organisations see and the environments and teams they need to build. At EPM Scientific, we support companies adapting to this shift, strengthening specialist functions, hiring people who can operate confidently in data supported environments, and redefining team structures to maximise new tools.

Request a call back to discuss how these changes affect your organization and what capabilities will be critical in the year ahead. We help plan a practical, actionable way forward.