AI in Life Sciences - Agentic vs. Generative or from "Drafting" to "Doing"
The "AI revolution" in Life Sciences is entering its next phase, and the rules of engagement are changing once again.
For the last two years, the industry has been consumed by Generative AI (GenAI). We have seen a flood of investments in Large Language Models (LLMs) to summarize clinical notes, draft marketing copy, and synthesize research papers. While valuable, these are fundamentally passive tasks. GenAI creates content.
The next frontier is Agentic AI. Unlike its predecessor, Agentic AI does not just passively draft. Rather, it acts. It possesses the ability to reason, plan, execute multi-step workflows, and correct its own course without constant human micro-management.
For Life Science leaders, the distinction is deeply strategic. Understanding the difference between "creating" and "doing" is the key to shaping a benefits case that delivers real ROI rather than just "efficiency theatre."
The Distinction: The "Writer" vs. The "Worker"
To choose the right tool, we must first strip away the marketing hype and look at the functional architecture.
Generative AI is a knowledge engine. A huge step forward from predecessors, GenAI includes the ability to “self”-predict, -extrapolate and -generate some of the source-data required for its operations It excels at retrieval and synthesis. If you need to summarize a 200-page FDA guidance document or draft a patient consent form, GenAI is your tool. It is reactive: it waits for a prompt and delivers an answer.
Agentic AI is an execution engine. It is goal-oriented. You do not ask it to "write an email"; you ask it to "manage the supply chain deviation." It will autonomously check inventory, identify alternative suppliers, draft the purchase order, and then ask for your signature.
How to Choose: A Strategic Decision Framework
The most common mistake we saw in 2025 was organizations trying to force GenAI to do Agentic work. This led to multiple “hallucinations" in complex workflows and subsequent frustration among users.
To determine which technology to deploy and when, apply this simple filter:
1. Use Generative AI when:
The task is informational, not operational.
The output is a draft that a human will review (e.g., medical writing, marketing content).
The goal is speed of production.
Example: "Summarise the adverse event reports from the last quarter."
2. Use Agentic AI when:
The task involves multi-step reasoning and dependency (Step B depends on the result of Step A).
The system needs to interact with other software (e.g., querying a LIMS, updating a CRM, sending an email).
The goal is autonomous resolution.
Example: "Monitor the cold chain sensors; if a temperature excursion occurs, flag the batch in the ERP, notify the Quality Director, and draft the deviation report."
The Benefits Case: Efficiency vs. Outcome
The business case for these two technologies requires fundamentally different metrics. If you apply a GenAI benefits framework to an Agentic investment, you will miss the true value.
The GenAI Case: Efficiency (e.g. FTE Hours)
The ROI for Generative AI is typically measured in time saved. It is an efficiency play.
Metric: Reduction in hours spent writing clinical study reports.
Value: Cost reduction and operational throughput.
The Agentic Case: Effectiveness (e.g. Strategic Outcomes)
The ROI for Agentic AI is measured in outcomes achieved. It is a capability play, such as
Metric: Reduction in clinical trial protocol amendments (because the Agent simulated the trial beforehand).
Metric: Reduction in batch rejections (because the Agent intervened proactively).
Value: Revenue protection and competitive speed.
Conclusion
We are moving from a world of "Human-IN-the-Loop" (where AI helps the human) to "Human-ON-the-Loop" (where the human supervises the AI).
For Life Science companies, the "safe bet" is to continue investing in GenAI for document processing. But the next competitive advantage will belong to those who are brave enough to deploy Agentic AI to close the loop between data and action.