How to Use AI Agents in Sales and Marketing

Artificial intelligence today is going beyond simple chatbots. AI-powered agents are capable of fundamentally transforming traditional workflows: from recovering abandoned carts to deep personalization of offers. By configuring the necessary parameters, you get a “smart assistant” that independently connects chains of tasks to achieve your business goals.

What are AI agents in sales and marketing?

AI agents are tools capable of performing marketing and sales operations with minimal or no human involvement. They take over routine tasks, allowing the team to focus on strategy and activities that require creativity.

It is important to distinguish levels of autonomy:

• Semi-autonomous agents prepare draft emails or organize data, but require final approval from a specialist.

• Fully autonomous agents execute chains of actions without human involvement, based on predefined algorithms.

Essentially, such an agent is an AI configuration tailored to a specific task. You provide it with “instructions” for working with your business systems, access to a knowledge base and brand guidelines, resulting in an expert that is constantly connected to your internal processes.

AI agents vs traditional automation

Standard automation works on rigid rules: “If A happens, do B.” AI agents operate differently. They do not just follow a script but choose the best path to achieve a specific goal (for example, revenue recovery). Although their logic is limited by the “safety boundaries” you define, the agent independently determines the sequence of actions and communication channels.

Using AI in marketing

1. Hyper-precise customer segmentation

Customer segmentation based on rules has long been used in e-commerce to work with specific audience groups. Modern marketing tools allow you to create segments (for example, “customers who bought rubber boots in September and did not return”) and work with their needs in a targeted way — for instance, launching win-back email campaigns with optimized send timing based on previous behavior.

The agentic approach to segmentation solves the same task but at a deeper level. It identifies complex behavioral patterns — for example, users who make their first purchase only after three touchpoints — and, using predictive analytics, suggests campaigns with messaging based on successful scenarios from similar customers.

AI agents work based on your data, so its quality directly affects decision accuracy. It is important to structure data in advance and define key attributes: new or returning customer, purchase categories, acquisition source, engagement level. It is also important to define key segments (for example, cart abandoners) and document which campaigns performed best for each group.

Regardless of whether you use tools like Klaviyo or Rebuy, Shopify’s built-in segmentation, or develop a custom storefront agent, properly organized customer data is a critical success factor.

2. Retention marketing

Classic retention scenarios — such as win-back emails or repeat purchase reminders — are based on rigid rules. They are triggered at fixed intervals (for example, 30 or 60 days after purchase) and do not account for either the customer’s specific purchases or their subsequent behavior.

AI agents enable a more flexible post-purchase communication model. Instead of linear flows, they analyze user actions and adapt communication depending on response.

For example, a customer buys a water filter. A few days later, the system sends an email recommending replacement cartridges. A week later, an additional incentive is offered — such as free shipping above a certain threshold. If the customer does not return within a month, a discount coupon may be sent. At the same time, if the user interacts with emails but does not purchase, the AI adjusts strategy — testing alternative offers such as free shipping instead of discounts or a personalized bundle of consumables.

As with segmentation, the effectiveness of retention strategies directly depends on data quality. It is important to consistently structure customer information and track key metrics: purchase categories, order intervals, repeat purchase frequency, and campaign response. These data points allow the system to accurately determine when and how to bring a customer back.

Using AI in sales

AI agents in sales can cover different stages of the funnel — from answering pre-purchase questions to re-engaging customers weeks or months later.

1. Sales through AI channels

Customers are increasingly using AI assistants when choosing products — for example, asking for recommendations in ChatGPT or comparing options via Google AI tools. According to the Shopify Global Holiday Retail Report 2025, 64% of users (and 84% among the 18–24 audience) are willing to use AI during shopping.

Gradually, these assistants are becoming full-fledged agents acting on behalf of the user. Within agentic commerce, AI can analyze offers, compare products, and in some cases complete the entire purchase.

To appear in this scenario, brands need to be present in AI-driven conversations. Shopify Catalog allows AI platforms and agents to find and display your products, including prices, variants, and real-time availability.

For example, if a user searches for “best wireless headphones under $100,” Shopify Agentic Storefronts can show relevant products, refine results based on feedback, and answer additional questions.

On some platforms, purchases can happen directly within the conversation. The Universal Commerce Protocol, developed by Shopify in collaboration with Google, allows AI agents to complete the entire purchase journey — from selection to payment. It is used in products like Microsoft Copilot, Google Search AI Mode, and Gemini, and is also available for developers building custom solutions.

To be correctly represented in such AI scenarios, your brand and product data must be accessible to AI through your e-commerce platform. For example, Shopify Knowledge Base allows you to upload FAQs, sizing guides, shipping and return policies, care instructions — and distributes this data together with product descriptions and metadata across AI channels.

2. Customer support

AI agents in sales go beyond traditional chatbots. They are capable of conducting full conversations with customers, analyzing context, considering user behavior, and making decisions based on that data. This allows the system to identify upsell and cross-sell opportunities directly during interaction.

Unlike basic scripts, AI agents use data from previous purchases and interactions, match it with the current request, and generate personalized recommendations in real time. This makes communication more accurate and relevant for each customer.

Today, more and more brands are delegating part of customer support to AI agents. This helps automate routine inquiries, increase response speed, and reduce team workload.

However, there is no need to fully replace support. The optimal approach is to start with the most predictable types of requests: order status, returns, or product availability. These scenarios are easy to scale and highly suitable for automation.

As data and experience with AI agents accumulate, their role can gradually expand, covering more complex cases and increasing impact on sales through personalized interactions.

Conclusion: the future of your sales

Implementing AI agents is not just a trend — it is a necessary step for businesses aiming to scale without proportionally increasing headcount. By delegating routine processes to intelligent algorithms, you free up resources for strategic thinking and creativity.

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