Agentic AI in Life Sciences: How AI Agents Are Changing Scientific Workflows

Updated May 2026

Key Insights

• Agentic AI is beginning to improve workflows 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.
• Recent launches such as Google’s Gemini for Science show how quickly AI-enabled research tools are moving into scientific workflows.
• Studies from Accenture, Wharton, and McKinsey highlight potential operational value and shorter development timelines.
• These changes influence how scientific teams use their time, how organizations structure roles, and which skills may become more important.
• The shift aligns with wider sector trends, including increased competition for AI-literate scientific talent

 

Across life sciences, AI is moving from isolated tools into more connected scientific workflows. Teams are using these systems to review information, support documentation, monitor progress, and reduce the repetitive work that can slow discovery, clinical development, and regulatory activity.

This is where agentic AI is becoming increasingly relevant. Unlike traditional AI tools that respond to single prompts, agentic AI can work toward defined goals, manage sequences of tasks, and adapt as new information appears. For life sciences organizations, the value is not in replacing scientific judgment, but in helping specialists apply that judgment faster and with better context.

 

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 one instruction at a time, agentic AI works toward a defined goal, deciding which steps to take next and updating its output as new information becomes available.

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 behavior
The system works toward an outcome, such as preparing a regulatory draft, reviewing literature, 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.

 

How Gemini for Science reflects the shift toward agentic AI

Google’s launch of Gemini for Science reflects how quickly agentic AI is moving into scientific research. The collection includes experimental tools for hypothesis generation, computational discovery, and literature insights, designed to support researchers across different stages of the research process. Google describes Gemini for Science as a collection of tools and experiments designed to expand the scale and precision of scientific exploration.

For life sciences organizations, the significance is not just the launch of another AI product. It shows where scientific workflows may be heading: toward systems that can review literature, structure information, test computational approaches, and support research planning with greater speed and consistency.

These tools do not remove the need for scientific expertise. Instead, they increase the importance of people who can combine domain knowledge with data literacy, AI fluency, and strong judgment.

 

A more streamlined way of working

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

Industry research suggests this shift could have significant operational value. A joint analysis by Accenture and the Wharton School found that agentic systems could support a large share of the hours worked in a typical biopharma company, creating potential annual value if adopted widely. McKinsey has also suggested that AI-enabled tools could shorten clinical development timelines and improve operational efficiency across trial design, recruitment, and data management.

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

n 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 does not 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.

Recent advances such as AlphaFold, AlphaGenome, and Gemini for Science show how AI is becoming more embedded in the research environment. These tools are helping scientific teams explore biological data, review literature, and test ideas more efficiently. As adoption grows, discovery teams may increasingly need specialists who understand both scientific context and AI-enabled research methods.

Clinical development: More stability and fewer surprises

In clinical development, timing is everything. Delays in site activity, enrollment, 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 may experience 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 can help by preparing early drafts, identifying inconsistencies, and pulling forward information from previous submissions.

The scientific argument still depends 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 organizations

As agentic AI becomes more embedded within routine workflows, many life sciences teams are finding that the structure of daily work is beginning to change in small but meaningful ways. Scientists can spend more time on interpretation because preparation work is already in place. Clinical teams can move through studies with fewer gaps in information, helping them focus on decision making rather than chasing updates across systems. Regulatory specialists can begin their review from structured drafts instead of spending the first stage assembling baseline content.

Individually, these changes may appear incremental, but together they can create a more efficient pathway through research, development, and regulatory activity. Expertise is applied at the right moments rather than being absorbed by repetitive preparation and administrative coordination.

This shift is also influencing how organizations think about scientific talent. As AI becomes more embedded in research and development workflows, life sciences companies may need more professionals who can work confidently across science, data, and technology. Demand is likely to grow for computational biologists, bioinformaticians, AI-literate research scientists, clinical data specialists, regulatory technology specialists, and leaders who can guide responsible AI adoption.

Recent developments such as Google’s Gemini for Science also suggest that AI-enabled research support tools may become increasingly common across scientific environments. Systems designed to assist with literature analysis, hypothesis generation, and computational modeling could change how research teams access information and organize work, increasing the importance of technical fluency alongside scientific expertise.

For employers, the challenge is not simply adopting new tools. It is building teams that can use them effectively, interpret outputs responsibly, and maintain scientific accountability. These themes also connect to wider competition for technical life sciences talent, particularly as biotech, pharmaceutical, and technology companies increasingly draw from overlapping talent pools.

 

A sector moving with intention

Agentic AI is not redefining the purpose of life sciences. It is changing how scientific work is organized, prepared, and supported. When routine steps become lighter and progress is less fragmented, scientists, clinical specialists, and regulatory teams have more time for interpretation, decision making, and oversight.

The result is not instant transformation, but a steady shift in how life sciences organizations operate and the skills they need to compete.

 

How EPM Scientific can support your next steps

Agentic AI is reshaping how work unfolds across scientific, clinical, regulatory, and technical teams. It is also influencing the skills organizations need as research and development workflows become more data-supported and AI-enabled.

At EPM Scientific, we support life sciences organizations as they adapt to these changes, from strengthening specialist functions to hiring professionals who can operate confidently across scientific and technology-led environments.

Request a call back to discuss how AI-enabled workflows could affect your hiring strategy, team structure, and future capability needs.