Stop Wasting Money On AI Agents: 5 Rules For Choosing The Right Use

Every executive I speak with wants to deploy AI agents in their business. Yet most are making the same costly mistake: choosing the wrong tasks to automate.

In my previous article, A Beginner’s Guide To Building AI Agents, I explained how to get started with agentic AI. Now it’s time to tackle the most critical step: finding the right jobs to use them for. Get this wrong, and you’ll waste time and money. Get it right, and you’ll transform how your business operates.

Agents are best thought of as autonomous AI assistants capable of carrying out far more complex and multi-step tasks than simpler ChatGPT-style chatbots.

Currently, the hottest topic in business technology is sometimes referred to as “virtual workers”. However, I’m cautious of this line of thinking for two reasons. Firstly, it implies that they can in some way replace humans. Secondly, I believe understanding how they’re different from human workers is critical to using them effectively and responsibly.

So, they’re still tools, albeit very autonomous ones, and picking the right situations to use them is essential.

Here are five rules to help anyone find use cases that generate valuable results rather than simply waste time and money.

1. Pick Simple, Repetitive, High-Frequency Tasks

Agents excel at automating tasks where the workflow is predictable, and that need to be done so frequently that they become a time-sink for human workers. This usually means jobs that follow simple, clear rules, drawing on structured data and without the need for nuanced, contextual understanding or judgement. This is why use cases such as monitoring and automatically replenishing stock inventory, or classifying, reconciling and chasing up invoice payments are ideal. So, while creative tasks such as defining and developing a long-term branding strategy are probably not a good choice, AI agents are great at crafting messaging and assets that align with your strategy, once a human marketing team has conceived it and put it in place.

2. Choose Tasks That Involve Clear, Structured Decision-Making

If the decision-making process can be defined by a straightforward flowchart, then it’s a good indicator that it’s at the right level for agents to handle. Some good examples include triaging customer support tickets, where their natural language processing capabilities let them classify and prioritize queries, automatically retrieve solutions from knowledge bases when possible, and route them to human agents when human insight is needed. Avoid situations involving overriding standard operating procedures. Agents lack the 360-degree oversight of situations needed to accurately navigate exceptional circumstances and edge cases, which only comes with human experience and judgment.

3. Choose Tasks You Understand Deeply

This one might seem obvious, but it’s important that your business understands a task intimately before you trust an agent to do it for you. Attempting to automate something not owned by someone who fully understands the data, processes and success criteria will only lead to confusion and unreliable results. Agents should never be a replacement for skills you don’t have in-house. Tempting as it may seem, don’t delegate legal or compliance tasks simply because you don’t have a legal department. Instead, pick straightforward tasks that are simply too time-consuming, rather than too difficult, specialist or unknown.

4. Measurable Outcomes

To prove your agents deliver value, benchmark their performance against human workers doing the same tasks. This means logistics tasks could be good options, because it’s easy to benchmark agentic performance against the time it takes human teams to schedule deliveries and consolidate shipments. Likewise, sales tasks involve scheduling outreach calls and updating CRM records. When outputs are more subjective, for example, evaluating performance against qualitative rather than quantitative metrics, their output is likely to be significantly less valuable. Good use cases should clearly demonstrate solid results, such as time and money saved, increased customer satisfaction, or reduced waste. If it doesn’t, it’s probably not an ideal initial use case.

5. Start Small, Then Scale

Zooming in on a single, manageable, and measurable task is a far better idea than jumping straight to automating an entire end-to-end process. This way, it’s far easier to review the available tools, get to grips with the process of creating automated workflows, and develop an understanding of measuring and iteratively building on success. If you’re looking at introducing agentic AI to your financial operations, this might mean starting out by simply categorizing expense items, rather than automating the full month-end close. If you’re more interested in marketing, it could involve drafting, scheduling and measuring the impact of social posts to a single channel, rather than optimizing an entire campaign. Focusing on a single “quick win” lets you build confidence and competence, rather than jumping straight into use cases spanning multiple business processes or departments.

These principles should help you identify your first agentic AI use cases. If you're ready to begin, I've outlined some practical examples here that any business can benefit from. Whatever you choose, the fundamentals remain the same: look for quick, repetitive, structured tasks that are easy to understand and measure. Most importantly, the goal isn't replacing human workers. It's freeing their time for more strategic and rewarding activities. That's where the true value of agentic AI is found.