AI Agents in Retail and Ecommerce: Transforming the Shopping Experience

In the early 2010s, AI development for retail was still in its early stages. Most of the tools were simple and rule-based. For example, there were automated emails and simple chatbots that could answer a few questions that were already set up. They didn’t allow for much interaction and weren’t able to do anything more complicated than straightforward tasks.

In the mid-2010s, as machine learning techniques advanced, AI personalization truly took off. Ecommerce companies started using it to make sense of browsing and purchase behavior.

Amazon’s recommendation engine quickly became a key part of sales strategy, boosting online sales and customer engagement through suggesting relevant products.

Around the same time, retailers also started experimenting with AI chatbots for customer service. By 2016, companies like Sephora were using chatbots on Facebook Messenger to offer makeup advice and product suggestions, giving shoppers round-the-clock assistance and interactive engagement.

AI technologies matured in the late 2010s, and their applications in retail expanded and diversified. Visual search came along, letting customers upload a picture of an item and find similar products.

Walmart and other big stores implemented predictive analytics to manage their inventory and supply chain. They used algorithms to predict demand and find the best stock levels. This helped cut down on problems with out-of-stock items and too much inventory, which made things run more smoothly and saved money.

In the early 2020s, due to the COVID-19 pandemic, retailers were pushed to adopt AI faster than ever before. As more and more people shopped online, businesses used artificial intelligence to handle more customer service requests.

Virtual assistants got a lot smarter and more useful thanks to improvements in natural language processing. H&M, for example, launched an AI-powered chatbot that gave personalized fashion advice and answered customer questions. This was a useful tool when in-store help wasn’t possible.

Dynamic pricing tools became more popular at the same time. These tools change the prices of products in real time based on demand, inventory levels, and market trends (a strategy that was borrowed from the airline industry and quickly became standard in ecommerce).

By 2023 and beyond, AI in retail entered a new phase with generative AI and hyper-personalization. Advanced AI models (like large language models and image generators) opened up possibilities for creating content, crafting marketing copy, and delivering an even more tailored customer experience.

Retailers such as Ralph Lauren began using generative AI for personalized marketing campaigns and virtual try-ons. The influence of AI at the strategic level is evident: 93% of retail organizations report that generative AI is now a boardroom topic of discussion, and over 60% have dedicated teams and budgets to integrate artificial intelligence into future products and services.

Modern retailers are deploying conversational AI agents (typically chatbots on websites, messaging apps, or in-store kiosks) to handle customer inquiries and assist shoppers in real time. These chatbots can:

• answer FAQs about products
• check order statuses
• guide users through returns 24/7

Hands-free shopping becomes possible through spoken commands. Integrated into smart speakers (Amazon’s Alexa, Google Assistant, Apple’s Siri) and smartphones, such agents let consumers search and shop by voice, for example, saying “Order my usual coffee pods” or “Find me a size M black jacket.” The appeal is convenience and speed: busy customers can add items to a cart while cooking or driving and don’t have to navigate a screen.

Voice assistants leverage AI to recognize natural language and even learn user preferences, so they can present personalized suggestions (e.g. reordering a frequent purchase or recommending a complementary item).

A particularly creative example was Nike’s collaboration with Google Assistant: during an NBA game halftime, fans could simply speak to buy limited-edition sneakers, and the shoes impressively sold out in just 6 minutes via voice orders.

Shoppers can now search using images instead of keywords. With visual search, a customer can snap a photo of an item (or upload a picture of a look they love) and the agent will identify similar products available for purchase.

Luxury retailer Neiman Marcus, for instance, introduced a mobile visual search feature called “Snap. Find. Shop.” allowing customers to take a picture of, say, a handbag or shoe, and find similar items in Neiman’s inventory. The result was increased customer engagement and higher app usage, as shoppers found it fun and convenient. Their CMO likened it to a “Shazam for shopping.”

Predictive systems analyze vast amounts of retail data (customer behavior, transactions, market trends, etc.) and predict future patterns or outcomes. These AI agents are like strategists working behind the scenes to answer questions such as:

• What products is this customer most likely to buy next? 
• How much of item X should we stock for the holiday season? 
• Which shoppers are at risk of not returning, and how can we re-engage them?

By leveraging machine learning on historical data, predictive analytics tools can generate insights that help retailers make proactive decisions. The major applications are personalized recommendations, targeted marketing, and promotions.

Inventory management is a critical retail operation now being revolutionized by autonomous AI agents (both software and robotic solutions) that manage stock with minimal human presence. These agents ensure that the right products are in the right place at the right time.

On the physical front, retailers are deploying autonomous robots and drones to handle inventory tracking in stores and warehouses. A great example is Tally by Simbe Robotics — a tall, slim robot that roams store aisles on its own, scanning shelves with cameras and sensors. Tally can instantly detect out-of-stock items, mispriced products, or disorganized shelves and report that data back to the inventory system.

The biggest initial mistake is diving into AI without defining the problem it should solve. So you should start with a clear business-driven objective for your AI agent.

Involve stakeholders across departments (marketing, operations, IT, etc.) to discover pain points and refine objectives. An AI project must have quantifiable KPIs for success (e.g., “increase online sales by 15% via personalized recommendations” or “cut response time to customer inquiries in half”).

Note! Avoid vague goals like “improve customer experience” without metrics.

With objectives in hand, evaluate the best technological approach to meet them. The AI landscape is crowded with options, but not every solution is right for your business.

Large language models (LLMs) come in three flavors:

• Open-source tools offer flexibility but require in-house expertise for maintenance
• Third-party models are ready-made but can be costly
• Custom solutions can be tailored to your needs but demand significant expertise

Note! Avoid chasing hype. Not all LLMs are designed for the same purpose. Some are optimized for writing code or processing images, while others specialize in specific programming languages like Python. Certain models don’t support external tools at all and are best suited for basic conversational tasks. To get the most value, you should choose an LLM that aligns with your existing tech stack and can solve your specific business problem.

SaM Solutions’ AI team, experienced with ChatGPT, Claude, LLaMA 2, LLaMA 3, Phi-3, Phi-4, Gemma 2, Gemma 3, and some other models, can guide you to the right choice. 

The next step is deciding which tools the selected model should use. This is managed through a Model Context Protocol (MCP) — a standard that defines how the AI agent interacts with different data sources. You can either build a custom MCP or use a ready-made one. For example, HubSpot offers a proprietary MCP that gives clients access to all of its platform features.

Weigh different options in light of your budget, timeline, and talent. If you lack internal skills, consider bringing in a specialist partner to help select and implement AI technologies in your business.

Data is the fuel for any AI agent, so invest time in gathering and preparing the right data. Identify what data sources are relevant to your chosen use case (customer purchase histories, website interactions, CRM records, inventory levels, etc.) and consolidate them for analysis. Data quality is critical: poor or incoherent data will lead to flawed outcomes, rendering even a promising AI use case unworkable.

Don’t treat the AI agent as a standalone silo. Plan from the outset how your agent will integrate with your current systems and workflows. In retail and ecommerce, an AI agent must connect with a website, CRM, ERP, or inventory management system to be effective.

Use APIs and modular architecture so the AI agent can plug in to existing workflows without overhauling everything. It’s also crucial to address scalability: ensure the integration can handle large transaction volumes and peak shopping periods without performance issues.

Launching your AI agent is the beginning of continuous improvement. After deployment, closely monitor the AI agent’s performance and impact on your key metrics. Track business KPIs related to the agent’s function (e.g., cart abandonment rate, customer satisfaction scores, fulfillment speed).

Establish a feedback loop where data from the AI agent’s interactions is fed back into retraining or fine-tuning the model. If the agent’s performance falls short, be ready to update algorithms, retrain on new data, or adjust parameters. Post-launch maintenance and support also means watching for technical issues and fixing them promptly.