Even before 2025, AstraZeneca had been an early mover in applying AI across discovery workflows—from target identification to candidate selection — via collaborations with AI-native partners and internal platforms. 

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In 2025, AstraZeneca further expanded AI-enabled discovery through new alliances that explicitly highlight the role of Agentic AI in compressing discovery cycles and lowering failure rates.  The results were staggering. Their ROI in medicine discovery shot up by 70%.

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What “agentic” looks like in drug discovery

Most AI in pharma began as passive analytics. Agentic AI adds autonomous behavior: systems that plan experiments, fetch and fuse evidence (omics, literature, SAR, real-world data), propose next actions, and continuously refine hypotheses. 

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Contemporary research describes agentic AI as systems designed to act autonomously and make decisions, distinguishing them from traditional “tools” or single-prompt copilots. This is critical in life sciences, where discovery is a long, multi-step, branching process that benefits from agents that reason, act, and learn across steps—not just generate summaries. (ScienceDirect)

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Where the cost savings accrue

For SMEs in biopharma, the economics of discovery hinge on cycle time, cost per qualified target/lead, and the attrition curve. Industry analyses forecast material ROI upside from AI because better early-stage decisions cascade downstream, reducing wasted assays and failed programs. 

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Reviews covering AI in pharma highlight ROI lifts of >45% in certain scenarios when AI is meaningfully embedded in the pipeline, particularly when it influences target selection and design tasks that have high downstream leverage. (ScienceDirect)

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Why off-the-shelf AI products won’t deliver the same results

Generic copilots can summarize papers and draft protocol text, but they don’t internalize your program logic, scoring frameworks, and experiment-selection criteria—or the guardrails required for regulated science. These factors are crucial in the research and development of new medicine products.

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Research and practitioner guidance consistently stress that strategic ROI comes from agents that are customized and orchestrated around your own data, ontologies, and decision checkpoints, not from one-size-fits-all chat interfaces. 

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How an SME can build its own agentic discovery system (a practical mini-playbook)

1. Narrow the first mission. Pick one chokepoint with measurable cost/time pain: e.g., target triage for an inflammatory program.
2. Unify evidence. Centralize structured (assays, omics) and unstructured sources (papers, patents). Data quality matters: “garbage in, agentic out.” (McKinsey & Company)
3. Compose agents. Start with three: a Literature Agent (retrieval and claims extraction), a Target-Scoring Agent (implements your weighted evidence model), and a Planning Agent (proposes the next assay or in-silico experiment). Use an orchestrator so the agents can call each other.
4. Encode domain logic. Translate inclusion/exclusion criteria, prior knowledge, and risk rules into the agent’s policy so suggestions reflect your science and tolerance for false positives.
5. Human-in-the-loop (HITL). Require sign-off at decision gates; log all agent rationales for audit and reproducibility.
6. Run a 90-day pilot. Track: manual hours saved, time-to-shortlist, hit rate uplift, and avoided experiments.
7. Scale to adjacent steps. After target triage, extend to hit-to-lead hypothesis ranking or protocol refinement.

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The Agentic AI ROI Engine 

The AstraZeneca story signals where the ROI comes from for medical companies: AI agents that change decisions early in the pipeline. 

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By 2026, SMEs in the medical field that build custom, workflow-aware agents will cut weeks off discovery cycles and meaningfully reduce cost per qualified candidate. The alternative — generic chatbots — will help researchers read faster, but they won’t rewire the decisions that drive your economics. Choose wisely.

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