Beyond the Hype: Not Every Problem Requires Agentic AI

Explore how life sciences organizations can identify the outcomes and workflows that truly benefit from agentic AI

---

Why the Hype Is Outpacing Reality

Agentic AI represents a meaningful evolution beyond today's generative AI tools. Unlike traditional AI systems that generate output after receiving a prompt, agentic systems can plan, take actions, evaluate outcomes and adapt based on changing conditions. That capability is powerful, particularly when work spans multiple systems, decision points and stakeholders.

The problem is that many organizations are beginning to view agentic AI as the answer to every operational challenge. That mindset creates risk.

When every workflow is treated as an agentic AI opportunity, teams often introduce unnecessary complexity, governance requirements and operational overhead into processes that could be solved more efficiently through conventional approaches. In reality, many business processes do not require autonomy. They require reliability.

Not Every Problem Requires an Agent

One of the most consistent lessons emerging from early implementations is simple: Some workflows are already optimized for traditional automation.

Structured, repetitive and highly deterministic processes often perform best when governed by clear rules rather than autonomous reasoning. Consider pharmacovigilance intake.

Large volumes of safety reports arrive through multiple formats and channels. The challenge is not usually deciding what action to take but executing a well-defined process consistently, accurately and in compliance.

In these situations, organizations often achieve greater value through structured automation, workflow orchestration and governed AI assistance than through fully agentic decision-making. The objective is to maximize outcomes, with the right level of autonomy for the work involved.

What Makes a Good Agentic AI Use Case?

A practical way to evaluate whether a workflow truly requires agentic AI is to test it against three conditions. The more a workflow exhibits these characteristics, the more likely autonomy can create meaningful value.

Together, these three conditions form a practical use-case test for agentic AI:

1. Goals evolve as work progresses
   • Some workflows cannot be completed through a single instruction or predefined rule set
   • Clinical study planning, enrollment management and operational risk management often require continuous reassessment as new information emerges
2. Multiple decisions must be coordinated
   • Long decision chains tend to benefit from agentic coordination
   • Rather than executing a single task, the system must evaluate information, determine next actions, gather additional context and adjust subsequent decisions
3. Conditions change frequently
   • Dynamic environments create opportunities for agentic systems to outperform static automation
   • When market conditions, study performance, enrollment trends or operational risks change continuously, adaptability becomes valuable

When all three are present, autonomy can often create value. When they are absent, traditional automation may remain the better choice.

Applying the Three-Condition Test

The three conditions above can be translated into four practical questions before deploying agentic AI:

Where Agentic AI Is Already Creating Value

The strongest examples today tend to involve the coordination of complex work rather than isolated task execution.

Example #1 - Medical Writing AI

Generating patient narratives, plain language summaries and clinical study reports require coordinating data extraction, structured authoring, validation workflows and human review. In this environment, agentic capabilities have contributed to more than 50% reduction in narrative writing effort and a 60-70% reduction in CSR draft generation effort.

Example #2 - Predictive enrollment intelligence

The value of predictive enrollment intelligence goes beyond identifying enrollment risk. The agentic capability emerges when the system interprets changing enrollment conditions, evaluates the likely impact on study timelines, determines appropriate response options and coordinates the next actions for operational teams. In one program, AI identified a projected 15-20% enrollment shortfall attributed to a competitor drug launch, before it became visible through traditional reporting, helping preserve study timelines despite standard-of-care disruption.

Example #3 - Site activation orchestration

Because workflows span multiple teams, systems and approvals, agentic coordination can help standardize execution, reduce preparation effort and improve first-pass quality outcomes. Early implementations have demonstrated a 15% reduction in median site activation time and improvements in first-pass success rates from 36% to 54%.

All of these examples share a common characteristic. The value does not come from automation alone. It comes from interpreting what should happen next and coordinating actions toward the desired goal.

The Real Goal Is Better Decision-Making

The conversation around agentic AI should focus less on technology and more on outcomes. The most successful organizations will ask:

• Which decisions create the greatest operational bottlenecks?
• Which workflows require adaptation rather than repetition?
• Where can intelligence improve outcomes without introducing unnecessary complexity?

In many situations, the best answer may still be traditional automation. And that is perfectly acceptable. AI maturity is not measured by how much autonomy an organization deploys. It is measured by how deliberately organizations decide where autonomy creates value. The objective is not more autonomy. It is more value through the right level of intelligence, oversight and automation.

Contributors