How to Use AI Agents for Sales and Marketing

Artificial intelligence (AI) agents for sales and marketing can recover abandoned carts, resolve customer service issues, and personalize marketing outreach to drive conversions. You set the parameters and an AI agent chains tasks together to work toward them, based on how you’ve configured it. 

Agents sit on the leading edge of a broader category of AI tools that small businesses are already using to grow revenue. According to Salesforce’s Small & Medium Business Trends report, 91% of small business leaders using AI say it’s helped them drive revenue growth. 

This guide covers how AI agents for sales and marketing work, how they differ from traditional automation, and practical ways you can start putting them to work today.

What are AI agents for sales and marketing?

AI agents for sales and marketing use artificial intelligence to complete sales and marketing tasks with minimal or no human oversight. They are designed to take over the repetitive, time-consuming parts of your sales and marketing processes so your team can move more quickly and focus on higher-value work. 

Agents can be semi-autonomous or fully autonomous. Semi-autonomous agents that handle repetitive tasks—such as drafting outreach and sorting customer data—are available today, with a human reviewing key decisions before the agents take action. Fully autonomous agents that run entire workflows are less commonly available as packaged products, though some companies are building custom versions today.

They pull from your company data—including your customer list, product catalog, customer order history, customer relationship management (CRM) data, and ad performance—to make decisions and take action, either autonomously or semi-autonomously, with a human in the loop.

Shopify developer and AI advocate Alex Pilon describes AI agents as “a purpose-specific configuration of AI.” As he explains, you take a general AI model and give it three things: instructions tuned for a specific workflow, software tools that let it interact with your business systems, and resources like your product knowledge base or brand guidelines. The result in a sales and marketing context, he says, is “a marketing expert that’s connected to [your] systems in [your] business.”

AI agents vs. traditional sales and marketing automation

AI agents work differently from rule-based ecommerce marketing automation and sales automation. Instead of running a single fixed sequence (e.g., “if cart abandoned, wait two hours, send Email A”), an agent works toward a goal—in this case, cart recovery—by choosing among the actions and channels you’ve made available to it. The decision logic is still structured, with guardrails you set in advance, but the agent picks the path rather than following one you’ve scripted.

Fully autonomous vs. semi-autonomous AI agents

All AI agents decide how to achieve a goal you give them—that’s what separates them from rule-based automation. The level of autonomy determines what happens after the agent makes a decision. 

Semi-autonomous agents handle analysis and decision-making up to a point (determined by a human) and queue recommended action for a human to approve. 

As an example, consider abandoned cart recovery. When deployed semi-autonomously, the agent could, for example, identify abandoned carts, draft recovery emails with discounts appropriate for each shopper, and send them automatically while requesting your review on unusual or high-stakes cases (for example, a larger-than-usual discount or an email to an especially high-value customer).

Fully autonomous agents act, then surface the activity for human review. The same cart recovery agent, deployed fully autonomously, would identify the abandoned cart, draft and send the email, update your CRM if the customer converted, then trigger a post-purchase follow-up email series—all without approval. 

How to use AI agents for marketing: 2 use cases

Here are two practical applications of AI agents for marketing.

1. Hyper-specific customer segmentation 

Rule-based customer segmentation has been used to target specific customer groups in ecommerce for years. Today’s marketing tools allow you to build a segment (like “customers who bought sunscreen in May and haven’t returned”) and target that customer’s unique needs by, for example, scheduling a win-back email and optimizing the send time based on past open behavior. 

An agentic workflow for customer segmentation takes a more sophisticated approach to the same goal by surfacing complex patterns—for example, first-time customers who only convert after three touchpoints—and using retail predictive analytics to recommend a campaign with messaging tailored to what’s worked for similar customers.

AI agents work from your data, so the quality of that data shapes the quality of the decisions an agent can make. Tag customers with the attributes an agent will need to act on—for example, first-time versus repeat customer, product categories purchased, acquisition source, and engagement level. Establish the segments you already know are valuable (e.g., cart abandoners and high-lifetime-value buyers), and document which campaigns have worked for each one. 

Whether you connect this data to a tool like Klaviyo or Rebuy, layer it into Shopify’s built-in segmentation, or build a storefront agent directly, well-structured customer data is essential for success. 

2. Retention marketing

Automated win-back emails and replenishment reminders are rule-based. They fire on a schedule (e.g., 30 days after purchase, 60 days after purchase) regardless of what the customer actually bought or how they’ve behaved since.

An AI agent-driven version of post-purchase customer engagement can reason about each customer individually and follow conditional paths based on how they respond. 

For example, say a customer buys a yoga mat. Three days later, the AI agent sends an email suggesting a mat carrying case. A week later, it follows up with a loyalty offer: Spend $50 and get free shipping on your next order. A month later, if they haven’t returned, it sends a discount code. If the customer clicks one of those emails but doesn’t buy, the agent uses customer data to adjust—trying free shipping instead of a discount, or offering a personalized product bundle.

Like agentic segmentation, agentic retention depends on clean customer data. Tag customers consistently across your store and track the behaviors that drive repeat buying—including product categories purchased, time between orders, and engagement with past campaigns. That data is what lets an agent decide who to re-engage, when, and how.

How to use AI agents for sales: 2 use cases

AI sales agents can cover a lot of ground, from answering shoppers’ questions before they buy to following up weeks or months later. 

1. Selling through AI shopping channels

Consumers are turning to AI assistants to help them shop, whether that’s asking ChatGPT for gift recommendations or using Google’s AI Mode to compare options. Shopify’s 2025 Global Holiday Retail Report found that 64% of all shoppers (and 84% of those ages 18 to 24) said they were likely to use AI when making purchases. 

Increasingly, those AI assistants are acting as agents on shoppers’ behalf. This shift toward agentic commerce involves the AI agent evaluating options, comparing products, and, in some cases, completing the purchase. 

You reach these shoppers by showing up in AI conversations. Shopify Catalog helps customers discover your products by allowing AI platforms and agents to search and display your product information, including pricing, options, and real-time availability. 

When a shopper asks an AI assistant to “find me the best running shorts under $50,” for example, Shopify’s Agentic Storefronts can surface your products, then narrow the options in response to shopper feedback, and answer follow-up questions. 

On some platforms, the agent can complete the sale within the conversation. The Universal Commerce Protocol, which Shopify co-developed with Google, lets AI agents transact with businesses through the full commerce journey. It powers checkout in Microsoft Copilot and AI Mode in Google Search and the Gemini, and is available for developers to build their own agentic shopping experiences.

To show up accurately in these AI conversations, your brand and product information needs to be accessible to AI agents through your ecommerce platforms. Shopify’s Knowledge Base is one example. You can upload your brand FAQs, sizing guides, shipping and return policies, and care instructions, and Shopify syndicates them across agentic shopping channels alongside your product descriptions and metadata.

2. Customer support

AI sales agents go beyond what an AI chatbot can do—using natural language processing to follow entire customer conversations, pulling in data about customer behavior, and taking action based on what they find. This is how the agent is able to identify upsell and cross-sell moments mid-conversation.

Say a customer asks about sizing for a rain jacket they’re considering purchasing. The agent checks a wide range of previous customer interactions. In their order history, the agent sees the customer bought a base layer in size medium last month, and cross-references the sizing for that item against the sizing for the jacket. Upon confirming that the sizing runs the same for the two items, the agent can propose the appropriately sized jacket.

Brands are already handing off customer support to AI agents. “I will get emails from friends that are buying on Naadam, and they say, ‘Oh my God, I love so-and-so, they were so helpful.’ And I write back and say, ‘It’s not a person. That’s an AI agent,’” says Matt Scanlan, founder of the cashmere apparel brand Naadam, on an episode of Shopify Masters. The shift has let Matt’s teams focus on product and marketing instead of routine customer support inquiries.

Accessories brand Ridge uses AI agents for support in a similar way. CEO Sean Frank describes the impact: “At this point, 60% of our tickets are being answered by AI,” he says on Shopify Masters. “It’s quicker; it’s more accurate.”

You don’t need to replace your entire support team to benefit from AI sales agents. Start with one or two ticket types—perhaps order status questions, return instructions, or product availability. Those interactions are predictable enough for an agent to handle reliably, so your team can focus on the cases that require human judgment. As you build confidence in how the agent performs on these repetitive tasks, you can expand to more complex ticket types.