Where to find your first agentic automation use case

If you are managing an automation pipeline, your intake queue is likely overflowing with requests from business leaders who want to suddenly "add AI" to their departments. But your next agentic process automation use case is probably already sitting in your existing backlog.

When new technologies emerge, Center of Excellence (CoE) teams and citizen developers often feel pressure to invent entirely new workflows to prove the technology works. In practice, the best opportunities for agentic process automation are the projects you already know about, specifically the ones that were simply too messy for standard rules-based automations to handle.

I have seen firsthand that finding your first agentic use case requires a structured audit of your past rejections, your present bottlenecks, and your future intake pipeline.

The Past: Mining the Graveyard

Robotic process automation (RPA) is a powerhouse for executing highly structured, repetitive tasks at incredible scale, but it inherently requires predictable inputs and rigid flowcharts. Because of those strict technical constraints, every automation team maintains a graveyard of use cases they walked away from. You likely passed on these projects because the data inputs were too variable, the decision trees were too complex, or the scope was too difficult to map out step-by-step.

Those rejections were the right engineering call at the time. Today, those same challenges are the defining characteristics of an agentic process.

AI agents thrive on ambiguous input and contextual decision-making. By looking backward through your backlog, you can identify workflows where human interpretation was previously the definitive roadblock and re-scope them for agentic automation.

The Present: Finding the Handoffs

If your backlog feels light, examine the automations actively running in your environment right now. Standard automations often run into manual handoffs. A bot successfully moves data from system A to system B, but a human operator must step in to review the output, synthesize the context, and decide what happens in system C.

These transition points are prime targets for AI agents. Look closely at the friction that occurs at three specific operational moments:

• Before a process begins (interpreting an unstructured incoming request).
• Midway through execution (handling a system exception or a missing data point).
• Immediately after the final step (synthesizing and summarizing outcomes for human review).

When a workflow becomes too complex for a standard bot to navigate independently, an agent steps in. The agent acts as the connective tissue, managing the human-level reasoning required at the handoff while preserving the value of your existing automation investments.

The Future: Rethinking the Intake Pipeline

As your CoE evaluates new requests, asking better questions changes the trajectory of your automation program. Historically, developers scoped projects by isolating a specific, manageable fragment of a larger transaction to ensure the bot could execute reliably. Building reliable, contained bots was the exact right standard for protecting system stability. However, continuing that fragmented approach today inherently limits your overall business impact.

Agentic process automation allows you to broaden the scope of what is possible. When a new idea enters the intake pipeline, trace the transaction all the way back to its origin and follow it to its true conclusion.

You now have the capability to automate the entire end-to-end process rather than just a segmented piece of it. This shift in scoping separates programs that eventually plateau from those that consistently compound their value over time.
 

Take the Next Step

Sourcing your first agentic process automation use case requires diagnosing the operational friction already present inside your business. To evaluate your current pipeline effectively, access our Monday Morning Playbook and run your potential projects through the triage worksheet to determine exactly where an AI agent belongs.