As an extremely cool person, I've recently gotten really into Minecraft, the open-world sandbox game that's basically virtual LEGOs. But I've found that (as with any creative endeavor) the sheer possibility of building anything your heart desires means it can be tricky to actually come up with a good idea of something to create.
AI agents have a similar problem. The idea of software that can take a goal, make decisions, and do work on your behalf is genuinely compelling. It promises less busywork and more time spent on the parts of your job that actually require a human. But where do you actually start?
That jump from vague possibilities to practical, day-to-day use is where most teams get stuck. What do AI agent use cases look like on a Tuesday afternoon when your inbox is full, your CRM is a mess, and you're just trying to keep things moving?
To help close that gap and get you building (no diamond pickaxes needed in this case, unfortunately), I'll walk you through a few examples of how real teams are using AI agents at work. These use cases aren't futuristic hypotheticals or robots doing everything for you, but practical examples of agents taking on the kind of messy, multi-step work that slows teams down.
Table of contents:
What are AI agents?
An AI agent is a system that can take a goal, decide how to achieve it, then actually do the work—often across multiple tools and decisions—without you needing to micromanage every move.
Of course, that definition covers a lot of ground. There are many different types of AI agents, from simple rule-based agents to more autonomous, multi-step ones that plan, reason, and adapt as they go. If you want a deeper breakdown, check out our full guide on the different kinds of AI agents.
But for this article, suffice it to say that I'm not talking about Roombas, self-driving cars, or those not-creepy-at-all humanoid robots roaming human spaces. I'm focusing on the kind of AI agents that live inside your software stack and help you get actual work done.
In a workplace context, an AI agent might:
Monitor incoming data (like emails, leads, or support tickets)
Decide what matters and what doesn't
Take action across tools (send messages, update records, or trigger workflows)
Loop you in only when human judgment is actually needed
When set up correctly, you can tell AI agents what you want to happen, and they figure out the steps. That's what makes them different from traditional automation, which follows a fixed set of rules no matter what.
That shift is why AI agents are especially powerful for work that's messy or constantly changing. And it's also why they're starting to show up everywhere from marketing and sales to ops, support, and engineering.
AI agent use cases by role
Because I work at Zapier, where we're actively encouraged to experiment with AI tools (and AI agents are part of our product), I have plenty of experience trying to fit a robot-shaped solution into a decidedly human process—with disappointing but occasionally hilarious results. So I know that while everything looks like a nail when you're an AI-enthusiast hammer, not everything can be solved with an AI prompt and a good attitude.
So the real question is: where does agentic AI actually earn its keep?
The easiest way to answer that is by looking for work that's painful to manage manually, but brittle with traditional automation. Focus on tasks that are ongoing, multi-step, and dependent on context or judgment. Once you're able to spot the kind of problem an AI agent can own end-to-end, the use cases tend to reveal themselves pretty quickly.
Here are a few example use cases grouped by team, but you can also check out our template gallery for more inspiration across even more roles.
AI agent use cases for marketing
Instead of handling one task at a time, marketing-focused AI agents can own entire workflows: capturing leads, enriching them with context, drafting content, and routing work to the right place for review. That means less busywork and more leverage.
Slate, a digital publishing platform, wanted to increase lead volume without spending hours chasing down prospects or manually enriching data. So they built an AI agent that pulls in leads from multiple sources, enriches them automatically, and routes high-quality prospects straight to sales.
In one month, that agent helped generate more than 2,000 leads with no additional manual lift. The agent handled the repetitive work in the background, while the marketing team focused on nurturing and conversion.
Learn more: How Slate generated 2,000+ leads in one month with AI-powered agents
AI agents are also helping marketing teams scale content in ways that would be nearly impossible otherwise. At JBGoodwin REALTORS, a single marketing coordinator was responsible for supporting the online presence of more than 900 real estate agents. Instead of turning that into an endless game of catch-up, the team used an AI agent to manage the content pipeline.
The agent researches relevant local data and news, turns those insights into blog posts and social content, and distributes drafts to the marketing team for review. Each agent gets consistent, on-brand content tailored to their market without the central team becoming a bottleneck.
Learn more: How JBGoodwin REALTORS scaled operations with automation
AI agent use cases for sales
Sales teams lose a surprising amount of time to work that happens around selling: researching accounts, tracking follow-ups, logging notes, and keeping CRMs up to date. That overhead adds up quickly, and it's exactly where AI agents can make the biggest impact.
NisonCo ran into this problem while trying to scale lead generation. The team needed to identify new businesses entering their target industry, but doing it manually meant a rotating crew of part-time researchers scanning press releases, trade publications, and business directories every day.
So they handed the job to an AI agent. Now, their agent scans Google News and industry-specific sources daily, extracts key details like company names, websites, and leadership teams, and compiles potential leads into a shared Google Sheet. From there, another system kicks off targeted outreach automatically.
What used to require three to five part-time researchers now runs with a single part-time employee—and it generates more leads than before.
AI agents have also transformed how NisonCo handles follow-ups after sales calls. Previously, tracking action items, proposal requests, and next steps relied on manual notes and memory. Now, an AI agent reviews call transcripts, identifies key commitments, and takes action automatically. It logs prospect details in the CRM, notifies the team in Slack, and drafts personalized follow-up emails that land in Gmail, ready for review and sending.
Learn more: How NisonCo fuels business growth with Zapier Agents
AI agent use cases for customer success
Churn risk is a challenge for any SaaS business. Healthie wanted a way to surface risks before it was too late, so their team created an Enterprise Health Insights Weekly agent to do exactly that.
Every Monday, the agent checks systems like Salesforce, HubSpot, Vitally, and Help Scout for signals of churn or expansion. It then posts a summary in Slack, where CS and Product leads can immediately review the insights and take action.
"Best case: it flags accounts the team already knew about. Worst case: it flags ones they didn't—which is actually the best outcome," said James Kase, Associate Director of RevOps at Healthie.
In the past, Healthie hosted monthly calls between product and customer support teams to review feedback from customer quarterly business reviews. But these conversations often happened after a customer had already churned—or not at all.
Now, the team uses the QBR Feedback Insights Aggregator, an agent that pulls data from Vitally and shares weekly summaries in Slack. It surfaces feedback and trends asynchronously, making it easier for teams to align and act on insights while customers are still engaged.
"We get all that information beforehand, while they're still a customer. It helps us act early and avoid churn," said James.
These use cases are just the tip of the iceberg. For even more AI agent use case ideas, check out our list of AI agents for business.
Best practices for using AI agents
Like my famous bourbon pecan sweet potato pie recipe, AI agents have a lot of potential, but they also have a lot of potential to go wrong. Here are the roadblocks teams run into most often—and how to think through them like someone who's built (and debugged) a few agents already.
Know what kind of work to hand over to an agent
If you're staring at a blank page, don't start by picking a tool to automate. Start by looking for a pattern in your day-to-day work:
Tasks you do manually, repeatedly
Work that involves analyzing, summarizing, categorizing, or organizing information
Processes where the "inputs" live across multiple places (email + CRM + Slack + docs)
That's the sweet spot for agents—especially when the work is mentally draining but doesn't require deep expertise every time. Think of your agent as a thought partner that can prep updates, reframe info, surface insights, and keep tabs on what's changing.
AI agents aren't right for every workflow, though. Sometimes traditional automation fits the bill better, especially when you need precision and predictability. But if you're comfortable letting the system improvise a bit (drafting copy, summarizing updates, triaging requests), an agent is often perfect.
If mistakes are costly (billing changes, strict data formatting, compliance-sensitive workflows), you'll want the guardrails and determinism of a Zap (our word for automated workflows). Or even better, you might go for the best of both worlds with a Zap that includes an agent step inside it.
Start with low-stakes workflows
It's normal to feel overwhelmed and reluctant to jump into the deep end. I, too, blanch at the thought of giving a new agent the power to post whatever it wants to the company Slack's #general channel in my name.
That's why the fastest way to build confidence is to start with low-stakes workflows where the worst-case scenario is "meh, that summary wasn't perfect." Here are a few beginner-friendly starting points:
A document summarizer that pulls from one trusted source (like a Google Doc)
A research agent that scans a specific set of webpages or internal notes
An "inbox triage" agent that drafts responses but doesn't send them
Once you trust the flow, expand step by step. Add tools and automations gradually instead of giving the agent access to everything all at once.
Deploy multiple agents for complex workflows
A single agent can often handle one job beautifully. But the more sprawling an agent's job becomes, the more variability you introduce.
As soon as your instructions get long, complex, or full of branching logic, you'll usually get better results by splitting the work up. Multi-agent setups are worth it when:
One trigger can lead to different paths (like routing requests to different outcomes)
You want to limit tool access (for example, only one agent is allowed to delete records)
You have multiple entry points that all need the same "finishing step" (like formatting and posting a final summary)
Narrow roles reduce the margin for error—and as an added bonus, they make your setup more modular. Build once, reuse often.
Use effective prompting
If your agent keeps almost doing what you want, it usually needs clearer instructions. Here are a few prompting habits that consistently help:
Assume zero context. Define acronyms, explain edge cases, and state constraints.
Specify the output. Clarify length, tone, format, and where to put the result.
Keep it crisp. Fewer words mean fewer ambiguities and fewer moving parts.
Give it a role. "Act like a RevOps lead" produces different thinking than "analyze this."
Structure the request. Format your prompt like role → task → steps → output. For long context, use clear boundaries (like <context>...</context>).
Rinse and repeat. Treat the first run like a draft, then refine with feedback.
And if you want a shortcut, Zapier Copilot (the building assistant inside the agent editor) can help you configure an agent quickly, troubleshoot when tests go sideways, and tighten up instructions without you having to start from scratch.
Learn more about how to create AI agents.
AI agent use cases, orchestrated
When AI agents work well, they don't feel flashy. They monitor, summarize, route, and follow up on work in the background. They catch things before they slip through the cracks, and they give teams back time and attention for the parts of work that need human judgment the most.
Zapier is the most connected AI orchestration platform, which means your agents aren't stuck inside a single tool or workflow. You can build agents that pull context from across your entire tech stack, take action where it matters, and work alongside your existing automations, not in place of them.
Whether you're just getting started with a single agent or coordinating multiple agents across teams, Zapier gives you the flexibility to design systems that match your real-world workflows.
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