Building Your Company’s First AI Agent? Here’s How to Choose A Smart Use Case

According to PwC, 79% of companies have adopted AI agents, and two-thirds of them are seeing better productivity from those solutions. Yet, nearly half still worry they’re falling behind competitors in their approach.

It’s not hard to see why, then, many boards and executive teams are asking IT and operations leaders what they’re doing about AI agents. At the same time, launching into org-wide deployment, just for the sake of keeping up, doesn’t automatically lead to good results. 

If you’re responsible for AI implementation, it’s important to evaluate your options and come up with a defensible AI agent strategy—one that will actually support everyday work and have a marked improvement on business efficiency. 

Choosing your first AI agent comes down to five things:

1. Identifying repeatable, data-backed processes,
2. Narrowing those down to a low-risk, high-friction task,
3. Confirming that your data can support it,
4. Deciding how much autonomy the agent should have, and 
5. Making sure you’re on the right track with a few key metrics.

Here, we’ll walk through each step and give you a framework to find the right AI agent pilot.

Where to Start With AI Agents (And Where Not To)

With so much attention on what AI agents can do, it’s often easy to focus on the most ambitious possibilities first. Operations that span many user groups or automate multiple tasks make for compelling demonstrations. The challenge is that complexity doesn’t always translate into value, especially when teams are new to AI agents. 

Rather than thinking about the technology itself (what the agent can do), consider the problem you’re trying to resolve. Your first AI agent for business users should address real bottlenecks, but it doesn’t need to solve the hardest ones. The goal should be to earn trust in the organization. This almost always means starting with a simple workflow that’s likely to work well immediately and win user buy-in. 

If you’re thinking about an agent that touches four or five different departments and systems, that’s a red flag that you’re doing too much, too soon. The most successful rollouts typically start with a narrow scope and expand over time as governance and user confidence progress.

How to Choose an AI Agent: 5 Practical Steps

While early AI agent initiatives are best kept small and focused, how do you actually choose the right use case and ensure success?

Here are five strategies you can use to evaluate your options and make a confident first move. These aren’t intended to necessarily help you choose a vendor, but to vet workflows before committing resources to an AI agent.

1. Choose Repeatable Processes Backed by Data and People

An AI agent will have the highest chance of success when applied to a process that’s:

• Repeatable, with clear, consistent steps that happen frequently.
• Supported by data that’s organized and accurate. 
• Owned by users who can validate the process’s value and existing friction points.

Look for structured operations that rely on data to produce a consistent output, like a report or invoice. In these workflows, employees generally follow the same routines each time rather than making judgments or assumptions. Accounts payable and onboarding processes often fall into this category and can make for good first AI agents. 

For example, consider a capital lending organization that Convverge partnered with to build an AI agent. Every morning, the company’s underwriters would manually review submission forms and third-party sources to summarize lending opportunities and their risk level in a report.

The task is a textbook candidate for an AI agent: a clearly defined workflow that follows the same steps every day, built on centralized data from reliable sources. It’s also owned by a consistent user group that could vouch for the benefits of automating some of the manual work. In this scenario, an agent allowed the team to work more efficiently by analyzing data sources, authenticating customer information, and generating a summary.   

2. Target High-Friction, Low-Risk Opportunities

While it’s important to choose a use case that supports real users, eliminate those that could have big consequences if the agent makes a mistake. The best first use cases are both high-friction and low-risk: the agent reduces tedious work, but errors are easy to fix and unlikely to have major legal, financial, or security impacts.

Avoid agents that will touch multiple systems and stakeholders, or influence high-risk decisions. It’s also worth avoiding low-friction, low-risk tasks. While these might seem like safe starting points, they can often be handled with simpler rules-based automation, making the cost of an AI agent hard to justify.

Here are some common AI agent use cases for enterprises that fit into each risk and friction category:

3. Make Sure Your Data Is AI Agent-Ready

If you’ve picked some low-risk AI agent capabilities that have a clear use value, the next variable to consider is the state of your data. An AI agent will only perform as well as the data behind it. Before moving forward, first make sure that the agent’s underlying data is: 

• Accessible, clean, and structured. Content should be centralized in a unified platform like a data warehouse for easy access. It should also be accurate and up-to-date, with duplicates and inconsistencies removed. 
• Relevant. The quality of your data matters as much as its volume. Make sure the agent is supported by relevant content, and use metadata to help it interpret context more effectively. 
• Verifiable. Rather than relying on tribal knowledge, data should come from a trusted, documented source of truth that administrators can easily access and verify.
• Properly permissioned. Users—and the agent itself—shouldn’t have access to unauthorized information that could compromise security. Limiting data access also ensures that the agent’s responses stay relevant for specific tasks.
• Governed by a designated owner. Establish clear data ownership so someone is accountable for maintaining its accuracy, relevance, and access controls over time.

4. Decide How Much Autonomy Is Appropriate

One of the most important decisions you’ll make when building an AI agent is how much autonomy it has. This depends on the task, the impact of any errors, and your organization’s risk tolerance. When you’re just starting with AI agents, it’s wise to restrict functionality to surfacing insights or recommending actions rather than executing (even with human approval). 

A conservative approach allows teams to build confidence in the agent’s performance before giving it more responsibility. If you get to a point where more autonomy is justified, make sure these tasks fall within the high-friction, low-risk category we covered earlier. Set clear boundaries that keep the agent’s abilities within your risk tolerance, and ensure a human can easily review or reverse actions.

Here are some real-world examples of what that might look like: 

Examples of Low-Risk AI Agent Autonomy
Task	Internal IT ticket triage	Knowledge base maintenance	CRM data management
What the AI agent does autonomously	• Reads and categorizes help desk tickets • Assigns priority levels • Routes tickets to the right queue	• Flags outdated information and duplicate content • Creates draft revisions	• Merges duplicate contacts • Fills missing fields from approved sources • Flags stale opportunities
What the agent can’t do	• Close tickets • Change priority levels above a predefined threshold • Make system changes	• Publish changes without content-owner approval	• Delete records • Execute major record merges
Why it’s low-risk	Misclassifying a ticket usually has minimal business impact and is reversible.	Drafts can be reviewed and validated before becoming a source of truth.	Most actions can be audited or rolled back.

5. Build on What’s Performing Well

Start tracking performance as soon as an AI agent is deployed. Within the first few weeks, you should be able to confirm whether it’s working as planned by measuring adoption:

• Usage rate: How many people are using the agent?
• Repeat usage: How many users return after their initial interaction?
• Task resolution: Is the agent helping users resolve the intended task? How many interactions are required before they reach a successful outcome?

If adoption is steady and the agent reliably solves a target problem, the next step is to evaluate business impact. Specific metrics depend on the use case and your goals, but the main objective for most organizations is to drive productivity. 

Measures like customer response times, overtime hours, project completion rates, and time spent on manual tasks can help quantify the value being created. In the first few months of implementation, these will indicate whether the agent is a success and can help build a business case for expanding the technology to other processes.

A Note on Handling Pushback

When introducing new solutions like AI agents, some level of scepticism is inevitable. Often, the employees closest to the processes being targeted are the most hesitant. It makes sense, given that they have the deepest understanding of how work gets done and are usually the first to be impacted by potential issues.

The best way to address that scepticism is through involvement and transparency, not persuasion. Bring those employees into the process early, giving them visibility into testing and encouraging feedback. This gives them a chance to build trust in the technology while helping shape a solution that reflects the reality of their day-to-day work.

The First Agent Is Just the Beginning

Choosing your organization’s first AI agent should be a process of reinforcing user confidence and building momentum for what’s next. In fact, the earliest agents are often the most important ones you’ll create, even though they should be the simplest: When employees see real results and trust the technology, they get better at recognizing strategic opportunities for AI and automation. Over time, that mindset shift can evolve into a broader strategy backed by proven business value and ROI. 

If you’re evaluating potential AI agent use cases or looking for a second opinion before moving forward, Convverge can help. We work with organizations to sanity-check and pilot AI agents that solve real business problems. Get in touch with our team.

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