Want ROI From AI Agents? Choose the Right Use Case!

The future enterprise is going to look nothing like the present, and at the center of this generational shift are GenAI Agents.  

As leadership teams explore what it means to become “agent-powered” or even “agent-first,” the conversation is shifting from whether to use AI agents to where and how to use them to create value. If you pick the wrong use cases, you won’t scale beyond Proof of Concepts (POCs).  

Every function wants a piece of the agentic advantage whether it’s for driving productivity, handling more nuanced customer conversations, or unlocking opportunities that weren't feasible before. But here’s the truth: not every process needs an agent.  

And in some cases, using one could actually set you back—cost-wise or operationally.  

So the real question is: When should organizations use GenAI agents? 

Not Every Process Needs an Agent 

Just like every employee in your organization doesn’t need to be a PhD or a strategist, not every task in an organization requires the sophistication of a GenAI agent.  

Some workflows follow well-defined rules with no ambiguity or judgement involved. They just need to be executed. Others are messier: they require human-like reasoning, context awareness, and decision-making. GenAI agents are best suited for the latter. 

The ROI of Intelligence: Complexity and Scale 

To evaluate whether GenAI agents are right for a process, plot it across two axes: complexity and scale. 

• Complexity refers to ambiguity involved in making a decision, variability in context, or the need for judgment. 

• Scale refers to the frequency or volume at which the task occurs. 

When both are high, agents become a strategic advantage. They don't just automate, they converse, think, decide and act contextually and in personalized fashion. That's where their ROI becomes visible and obvious. 

 Use this as your north star when evaluating agent use cases: 

Agentic Decision Framework 

Agentic Decision Framework for Banks & NBFCs 

Agentic Decision Framework for Banks & CPG's 

High Complexity + High Scale = Prime Agent Terrain

This is the sweet spot for GenAI agents.

Take, for example, loan underwriting in a large NBFC or bank. You might be processing 10,000 applications per month, each with different financials, health metrics, and behavioral patterns. You’re looking at fraud risk, identity checks, creditworthiness and a combination of these and it is not all black-and-white.

Or imagine a contact center fielding thousands of unique calls a day. No script can cover that sort of diversity. Here, GenAI agents can understand the context, personalize responses, and resolve issues with autonomy and consistency.

These are classic high-complexity, high-scale workflows. They demand a level of judgment and scale that traditional automation simply can’t handle.

High Scale + Low Complexity = Stick with Automation

Now consider a finance team that processes 5,000 invoices monthly. The steps are clear: extract vendor name and amount, match with a contract, approve or flag exceptions. There’s no ambiguity. No judgment. Just structure and rules.

This is where Robotic Process Automation (RPA) excels. A simple bot or scripted system can do this faster and cheaper than an agent. Introducing GenAI agents here adds unnecessary cost and complexity without proportional value.

Bottom line: If it’s rule-based and high volume, automate it. Don’t agent it.

High Complexity + Low Scale = Invest in Human Expertise

Some processes are nuanced, but don’t occur frequently. Think of R&D, annual workforce planning, or board-level decision-making. These involve subjective judgment, external variables, and business context that changes frequently.

Yes, they’re complex but they don’t scale.

In these cases, your best ROI comes from developing skilled mid-level managers and leaders, not training or deploying GenAI agents. You might use AI as a copilot or assistant here for certain processes but not as a replacement for the overall workflow.

Low Complexity + Low Scale = Low-cost Automation or Nothing at All

And finally, some tasks are just simple and occasional like one-off data pulls, or operational hygiene checks. These can be handled by junior staff, simple scripts, or even left as manual.

There’s no justification here for agents or heavy automation.

Agentic Readiness is a Strategic Decision

It’s easy to be swept up in the GenAI wave. But real transformation requires thorough business understanding before rushing into action.

Although some tasks might be performed well by agents, you also need to be able to justify the cost.

Before you roll out agents across your org, ask:

• Does the task involve context, ambiguity, or judgment?

• Does the value of agentic automation justify the token and engineering costs?

• Will the agent materially outperform existing automation or human teams?

If the answer is “yes” across the board, you’re looking at a high-value opportunity.

GenAI agents are powerful tools when matched to the right jobs. This is not about blanket adoption. It’s about deploying agents where they deliver maximum value—either in terms of cost, customer experience or by opening up newer possibilities.

That’s how you move from experimentation to transformation.