This is part 2 in our Agentic AI in Ecommerce series. 

Read Part 1: Transforming Customer Service with Agentic AI: The Next Step in Autonomous CX

Read Part 3: Agentic AI in Marketing: Leveraging Autonomous AI Agents for Ecommerce Growth

Generative AI in ecommerce isn’t new anymore. It's almost impossible to navigate to any website or SaaS tool without encountering some sort of AI-powered feature built to save time and money by doing everything but your laundry. 

More recently, however, we’ve been seeing a rise in sales-based AI agents in ecommerce, built to work more autonomously than your typical LLM-based chatbot, capable of interacting with your customers and store to offer more personalized shopping experiences and help your customers find what they’re looking for (as well as a few things they may not even know they need). 

In this article, we’ll be looking at agentic AI for ecommerce sales. We’ll break down everything you need to know about these agents–how they work, what they can be used for, and how you can start building your own fleet of intelligent AI sales associates to help increase cart sizes and conversions. 

What is Agentic AI in Ecommerce Sales?

Agentic AI refers to AI systems or “AI agents” that can operate with a high degree of autonomy to achieve goals set by the user. In practical terms, an ecommerce AI agent is like a virtual sales representative that perceives its environment, learns from data, and takes actions independently to assist with sales-related tasks. 

Unlike other AI tools, agentic AI isn’t just reactive in the sense that all actions and responses need to be prompted by the user. Instead, AI agents proactively make decisions and act without constant human guidance. 

What are AI Sales Agents?

AI sales agents can handle multi-step processes. For example, searching a product catalogue, comparing options, and adding an item to the cart, etc., all on their own.

These agents are programmed with specific objectives, such as maximizing conversions or helping customers find the right product. They utilize techniques from advanced AI to plan and execute steps toward a goal, rather than simply answering one question at a time. 

Autonomous AI agents for sales don’t wait for shoppers to ask questions. Instead, they can anticipate needs and offer help, such as popping up with a personalized offer if a customer lingers on a product page for a certain amount of time, for example. They can also continuously learn and adapt from interactions and changing data, like shifting customer preferences or inventory levels. The longer you use them, the better they get at their “job.”

In the first part of our agentic AI series for customer service and CX, we broke down a dual agent strategy: one consumer-facing agent and one associate-facing agent, each with its own skills and role. This should be the same when discussing agentic AI for sales. 

The consumer-facing agent interacts directly with the customer, while the associate agent is capable of pulling data from internal resources, such as Standard Operating Procedure (SOP) documents, and making changes to elements like product pages, pricing, and more. Both agents should be able to work together, but they must be separated enough that they do not share internal information with the customer. 

Why Agentic AI Matters for Ecommerce Sales

Implementing AI agents in ecommerce can seriously increase customer engagement, satisfaction, and sales, enabling merchants to personalize experiences with minimal human intervention. 

When consumers have more choices than ever, and expect to be able to interact with businesses on a 24/7 basis, it’s important for ecommerce CEOs to look into solutions like agentic customer service or AI sales automation to ensure that they can sell to–and support–customers quickly and effectively, or else risk losing them to a competitor.  

Use Cases: How do AI Sales Agents Enhance Customer Interactions?

Here are some real-world use cases for AI assistants and AI-powered sales automation that can enhance the customer journey and drive long-term sales growth. 

1. Personalized Shopping Experiences

Consumer-facing AI agents can be implemented as personal shopping assistants to help customers find exactly what they need on your website, just like a human shopper could help someone find their way around a department store. 

A customer could say something like “I need a gift for my wife’s birthday, she loves hiking, and my budget is $150.” An AI agent can then interpret this request, search your catalogue, compare options across categories, and present a curated selection of hiking-related gift ideas, complete with images and reviews, for example. 

They can even execute the purchase. OpenAI’s new “Operator” agent, for example, can navigate websites on the user’s behalf to browse products and place orders autonomously. In practice, this means an AI agent could take a customer’s request, such as ordering next week’s groceries, and handle every step – from adding items to the cart to checkout - with the customer simply reviewing and confirming at the end.

2. Conversational Commerce and Context-Aware Cross-Selling

Building on the offering of personalized shopping experiences, these AI agents should be able to fully converse with customers to narrow down choices and explain products via text or voice chat, in at least a few languages. As a Canada-based ecommerce agency, our clients often need to serve customers in both English and French, so any solutions we build must be available in both languages. 

A customer visiting your store should be able to head to your website, open a chatbox and ask: “I’m looking for a pair of straight leg blue jeans in a size 7 for under $100,” but then be able to reply to whatever options the bot offers to narrow down the selection further or pull a fresh list based on new feedback–like realizing they don’t want any jeans with a distressed look, or would prefer a darker wash. 

Once your customer has made their choice from the AI model’s list, it can then work on some context-aware cross-selling to increase the cart size. Agentic AI can offer personalized product recommendations based on what’s in someone’s cart or which page the customer is on. 

For example, a customer can add their chosen dark wash jeans to the cart, and the AI can then pop up to offer some shirts that will complement them. Shoppers don’t even need to interact with the product to receive suggestions. 

Consider someone buying a new sink for their home. From the sink’s product page, they could ask the AI: “Find me a faucet to match this sink.” The AI could ask some clarifying questions, such as, “What kind of styles do you prefer? Centerset faucets, single handle, or widespread?” and then pull a few relevant options that match the colour and finish of the sink. 

Behind the scenes, the AI agent queries your store’s inventory, product specifications, availability information, product descriptions, and more, to confidently suggest items to customers much faster than any single staff member or customer using the store’s search bar and filters. 

3. Automated Profit Maximizing (Strategic Price Response) 

While many of our use cases are customer-facing, we can’t forget the value of associate-facing agents when it comes to increasing sales and revenue with agentic AI. AI agents can monitor market trends, competitor prices, and real-time demand to adjust your pricing or create targeted promotions. For example, if an item is underselling, an agent can automatically devise a promotion or discount campaign to boost its sales. 

Consider this situation: your merchandising manager notices that one of your haircare brand’s competitors has significantly lowered the price of one of their “hair care basics” bundles. Your brand also has a comparable bundle, but it is priced much higher. With agentic AI, you could request that it simulate the margin impact of lowering the price of your bundle to compete with the other brand’s option. 

With the data provided by the AI, you realize that lowering the cost of your bundle to match will result in an unsustainable reduction in profit. Still, thankfully, the agent can also list a series of high-margin hair accessories commonly sold with the bundle that you could combine instead, to offer a “complete hair system” that is both appealing to customers and that won’t hurt your profits. 

4. Inventory and Demand Management

Agents can also help ensure you have the right products in stock to meet demand. They might predict which products will be popular next month (using AI forecasting) and alert you to stock up, or conversely, identify slow-movers and initiate tactics to clear them, like bundling or discounting. By autonomously handling these merchandising tweaks, AI agents help maintain sales momentum and prevent lost sales due to stockouts or mispricing.

You can also leverage associate-facing bots to help make sales that might otherwise be lost. Consider an omnichannel store. A customer could approach an associate to inquire about a specific bathtub they saw in an interior design magazine, which they liked. The salesperson can take out their tablet to get the SKU and then ask the AI agent to pull real-time data on whether the tub is available in the store or at their warehouse. 

If the item happens to be unavailable, agentic AI can ensure that the sale isn’t lost by recommending similar products available in the store. This way, the customer can leave with something that is just as good, rather than walking out empty-handed because of a stock issue. 

How to Build an AI Sales Agent: Implementing Agentic AI in Your Ecommerce Business

Step 1: Start with a Single, High-Value Problem

Start by figuring out where an AI agent could make the biggest difference. Is cart abandonment a major issue? Then, a checkout assistant agent might be able to help. Do you frequently miss upsell opportunities? Maybe a recommendation agent is the answer. 

Focus on a specific sales challenge or goal, like improving conversion rate on mobile, or fixing a consistently low AOV, as the pilot project. These objectives will then guide the design of your agent.

Step 2: The "Build vs. Buy" Conversation for Small & Mid-Sized Businesses

While large enterprises with infinite resources may opt to build custom agentic systems in-house, this path is often impractical for most SMBs. The process is capital-intensive, requires a team of specialized AI and machine learning talent, and involves a long time-to-market.

For small and mid-sized businesses, the "buy" decision is generally the better strategic choice. This decision extends beyond simply subscribing to a software platform. The most successful implementations involve partnering with an expert implementation agency that can provide the strategic guidance, integration expertise, and customization services needed to ensure that off-the-shelf platforms and services deliver on their full potential. 

This reframes the decision from procuring a tool to engaging a strategic service partner who understands what’s available and how to set it up to work for you.

That said, dipping your toes into agentic AI for sales can be as simple as leveraging the tools already built into the platforms you already use. Adobe, for example, has its Agent Orchestrator tool, which enables users to create and manage AI agents across commerce systems. Similarly, Salesforce Commerce Cloud offers Agentforce, a solution with a similar functionality. 

Even looking at ChatGPT’s Operator mode to see how it might be able to fit into your business could be a good place to start.  

Step 3: Build Your Data Foundation

An AI agent is only as smart as the data and actions you allow it to access. For a sales-focused agent, integrate it with:

1. Product Information: Ensure the agent can retrieve up-to-date product details, pricing, stock levels, and other relevant information to make accurate recommendations or informed decisions.

2. Customer Data: Connect purchase history, browsing behaviour, loyalty status, and other CRM data. This powers the agent’s personalization (knowing what a particular customer might want).

3. Sales Channels: deploy the agent on the channels where it will interact (e.g., your website via a chatbot interface, a mobile app, or messaging platforms like WhatsApp). Also, link any fulfillment or payment systems if the agent will complete transactions.

Modern AI agents often operate as a layer on top of your existing systems, pulling information and triggering actions via APIs. Work with your developers or a solution partner to get these connections securely configured.

Step 4: Define Roles and Guardrails

Before turning an agent loose, clearly define what it should and shouldn’t do. You’ll want to set parameters or guardrails for its behaviour: 

1. Role and Scope: Document the job that your agent is responsible for (e.g., “assist customers in finding products and checkout” or “optimize promo codes for end-of-season sale”). This should be simple. You can always create multiple agents for different purposes rather than tuning one that tries to do everything. 

2. Allowed Actions: Decide what actions the agent is permitted to execute autonomously. Can it send discount codes to customers? Change prices within a limit? Place an order? For each action, ensure you have given explicit permission. Some platforms let you configure these in a workflow or via a low-code interface for transparency.

3. Escalation Rules: This part is extremely important. Determine when the agent should hand off to a human. For example, if the agent encounters an angry customer or an unusual request, you might require it to alert a human customer service or sales rep. Or, if an agent-generated discount exceeds 20%, maybe a manager's approval is needed.

Step 5: Launch a Pilot, Prove the ROI, and Scale

Once you’ve figured out your goals, roles and guardrails, it’s time to test and iterate. Start with a controlled rollout. Consider deploying the agent with a small subset of users or on a staging site. Monitor its decisions and interactions while collecting important metrics: Did conversion rate improve? Are customers engaging with the agent? Are transcripts/logs showing any confusion or frustration on the customer side? Use this data to fine-tune the agent’s rules and goals.

Because these agents learn, they can improve over time, but you might also need to tweak their prompts or data access if they aren’t performing as expected. A/B testing different approaches can quantify their impact on sales and help you decide whether you’re happy with what you have or if you need to go back to the drawing board. 

Finally, train your team to ensure that everyone understands how your ecommerce AI solution works and how to collaborate with it. For example, if the agent passes a complex query to a human team member, provide guidelines on how the human should follow up. 

This seamless integration between your human and AI staff members is how you can achieve the hyper-personalization and speed you need for increased sales conversion and overall better customer experience. 

Conclusion

There’s no question that agentic AI is about to change the way consumers browse and find the items they are looking for. Early adopters of this kind of agentic commerce are positioning themselves as industry leaders. By offering these kinds of hyper-personalized and efficient shopping experiences, you are seriously differentiating your brand from the rest. 

Shoppers are likely to gravitate toward retailers who make buying easier and more tailored, as well as smarter chatbots that close sales or autonomous systems that optimize pricing. This can greatly improve your ROI if done thoughtfully.

At Blue Badger, we’ve been experimenting with all kinds of agentic AI solutions for our clients across sales, customer service, and marketing. Get in touch with us today to learn more about how AI sales agents can fit into your ecommerce store. 

Next: Agentic AI in Marketing: Leveraging Autonomous AI Agents for Ecommerce Growth