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The life sciences industry has long wrestled with the complexity of its own success: rich datasets, complex regulations, and critical demands for speed and accuracy. In 2025, a new paradigm is emerging to meet these challenges: agentic AI.
Agentic AI combines the raw power of large language models (LLMs) with the ability to plan, orchestrate, and complete tasks by interacting with other tools, data sources, and services. Rather than a single intelligent assistant, agentic AI works via “a team” of specialized agents, collaborating behind the scenes to complete high-value work.
Below, we cover the current state of agentic AI in the life sciences industry and explore three transformative use cases already gaining traction.
The life sciences, with its structured workflows, rich data ecosystems, and pressing need for operational efficiency, is becoming one of the first industries to realize real-world value from agentic AI. In fact, Cape Gemini reports that the pharma and healthcare sector lead in agent adoption, with around 23% of the sector adopting as of 2024.
Agentic AI is gaining more and more footing with organizations as humans’ trust in its abilities builds. The same Cape Gemini report, which surveyed over 1000 executives at organizations with more than $1 billion in revenue, found that a majority (63%) would trust AI agent to analyze and synthesize data. Further, half said they would trust an AI agent to send a professional email on their behalf.
We can expect to see more and more adoption this year as investment in the technology continues to grow. Deloitte reports that over the past two years, investors have funneled more than $2 billion into agentic AI startups, with a strong focus on enterprise-focused solutions. At the same time, major tech firms and cloud providers are racing to build their own agentic AI capabilities with many opting for strategic licensing deals and partnerships.
Agentic AI will be crucial for life sciences companies aiming to stay competitive and innovative in the years ahead. As the industry faces mounting pressure to accelerate drug development, reduce costs, personalize treatment, and navigate increasingly complex regulatory environments, traditional workflows simply can’t keep up.
Agentic AI offers a fundamentally new way to scale expertise by automating multi-step processes, orchestrating data across silos, and adapting dynamically to evolving tasks. Whether it’s streamlining regulatory submissions or optimizing clinical trial design, agentic AI brings the speed, flexibility, and intelligence needed to thrive in this data-rich, fast-moving landscape. Companies that embrace these capabilities will not only gain operational efficiency but also unlock entirely new possibilities for innovation and patient impact.
Regulatory submissions are a cornerstone of the pharmaceutical and biotech landscape, and a notorious bottleneck. Whether preparing Investigational New Drug (IND) applications, New Drug Applications (NDA), or Clinical Study Reports (CSR), teams must gather data from dozens of systems, ensure compliance with rigid standards, and maintain full traceability.
Agentic AI is now streamlining this entire process:
First, one agent retrieves the necessary clinical, non-clinical, and CMC data.
Another generates a draft report using templated regulatory structures.
A third validates the formatting and compliance using agency guidelines.
A final review agent checks for consistency and flags sections needing human review.
This results in submission-ready documents created in a fraction of the time — with fewer manual errors and faster regulatory timelines.
2. Merger and acquisition due diligenceAgentic AI brings speed and precision to merger and acquisition (M&A) due diligence in the life sciences sector. By autonomously orchestrating data collection across clinical, regulatory, financial, and operational domains, AI agents can rapidly surface critical insights, from pipeline overlap and trial risks to IP exposure and compliance flags. This allows deal teams to make faster, more informed decisions while significantly reducing manual effort and oversight risk during one of the most data-intensive phases of a transaction.
Here's a breakdown of the process:
Agents orchestrate data gathering across internal systems and external data sources (e.g. internal CRM, FDA databases, PubMed). They can extract red flags such as IP conflicts, trial failures, adverse event patterns, or regulatory compliance issues.
A reporting agent can then consolidate all findings into a unified due diligence report. It can include source links, agent reasoning chains, and validation status. The outputs are summaries tailored for legal, executive, and operational stakeholders.
From there, a chatbot agent can also be employed to interface with M&A teams via chat or dashboard. It can answer ad hoc questions (“Show me all flagged compliance issues”) and rerun agents on updated criteria.
3. Clinical trial patient recruitmentPatient recruitment continues to be one of the biggest barriers to clinical trial success, often causing costly delays. Identifying the right participants means interpreting complex inclusion/exclusion criteria and searching across fragmented electronic health records (EHRs), registries, and real-world data sources.
Agentic AI can now automate large parts of this workflow:
A planner agent reads the trial protocol and extracts key eligibility requirements.
It dispatches a query agent to search EHR databases or trial matching platforms.
Matched candidates are summarized and scored for feasibility.
Human reviewers are looped in only for ambiguous or edge cases.
This approach results in higher match accuracy, greater diversity in enrolled populations, and accelerated study startup timelines.
Adverse event reporting use case example
In our recent webinar on agentic AI for the life sciences industry, ONTOFORCE’s Product Marketing Manager, Martin, took the audience through an example use case with agentic AI to create a report of adverse events. Check out the recording to see how various agents can be deployed to accomplish this goal. Martin also shares fundamental information on agentic AI and its application thus far in the industry.
Watch the webinar recording now.
You can also join us for part 2 of the webinar series where we'll dive deeper into agentic AI and how organizations can prepare their technology ecosystems accordingly. Registration will be available in May.
