AI in E-Commerce: How Agentic Commerce Is Rewriting the Future of Retail

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From chat-to-checkout experiences to AI-powered pricing and supply chains, 2025 marks the year commerce itself became intelligent.

Table of Contents

1. Redefining the Customer Experience (CX) with Generative AI

   • Multimodal AI and Hyper-Personalization

   • The Rise of Conversational Commerce (Chat-to-Checkout)

2. Dynamic Pricing and Margin Optimization with AI

3. AI-Powered Operational Resilience and Supply Chain Optimization

4. The Strategic Mandate: Opportunities and Risks for Leadership

5. Conclusion: Mastering the Algorithmic Future

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How AI is Evolving: Agentic Commerce

The last wave of retail technology digitized transactions. The next one thinks for them.

In 2025, artificial intelligence is no longer just a marketing add-on, it has become the infrastructure of commerce. Every stage of the buyer journey (discovery, evaluation, purchase, and fulfillment) is now shaped by autonomous systems that analyze intent, predict behavior, and even execute transactions.

This evolution has a name: Agentic Commerce- where AI agents represent both shoppers and brands in a new, conversational economy.

It’s a shift from “selling on a platform” to “selling through intelligence.” And for eCommerce operators and brand owners, it’s changing how discovery, pricing, and logistics work at a fundamental level.

Redefining the Customer Experience with Agentic AI

The rise of agentic commerce began quietly with generative AI chat interfaces. But the major turning point came when OpenAI’s Assistant Commerce Protocol (ACP) went live — allowing customers to search, compare, and even buy directly inside ChatGPT.

Multimodal AI and Hyper-Personalization

AI now processes vast quantities of shopper data (browsing, purchase history, sentiment, and real-time activity) to create unique, highly detailed customer profiles. This enables:

• Multimodal AI and Visual Search: AI systems can process and combine text, images, and even audio simultaneously. This capability powers visual search—where customers snap a photo of a shoe or a piece of furniture and the system instantly finds matching products in your catalog. This powerful capability accelerates the path from inspiration to purchase, removing the difficulty of describing complex items with words alone.

• Personalized Product Recommendations and Contextual Search: Algorithms dynamically adjust product suggestions based on what the user is currently looking for (intent). Instead of just matching keywords, advanced systems use contextual search to understand the underlying concept of a query. For instance, a customer searching for "a casual outfit for a weekend hiking trip" gets results that match the feeling of a hiking trip, not just the word "outfit." This dramatically increases conversion rates.

• Generative Content at Scale: GenAI allows marketing teams to create thousands of personalized variations of ad copy and visual assets in minutes. This ensures that every message and image is perfectly tailored to small, specific groups of shoppers, driving higher engagement and relevance.

The Rise of Conversational Commerce (Chat-to-Checkout)

The key shift is the transition from simple automated messaging (assistive AI chatbots) to intelligent assistants that execute multi-step transactions on the customer's behalf. This new era is called Agentic Commerce.

• Instant Checkout via the Agent Protocol: This is the most critical emerging feature. Instead of directing a user to a website, AI agents, utilizing protocols like the Agentic Commerce Protocol (ACP) codeveloped by OpenAI and Stripe, are authorized to act as a personal shopper, assemble a cart, and complete the purchase directly within the conversational chat. The ACP essentially provides a secure, audited set of "rules" for the AI to handle a real transaction.

How Agentic Commerce Works (End‑to‑End Flow)

How ACP works (simplified)

Step	What happens
1 – Provide a product feed	The merchant supplies a structured list of products that includes the title, description, price, inventory level, images, and any variations. This feed is similar to the data used for Google Shopping and must be kept up to date. Imagine it as an automated catalogue that the assistant reads.
2 – Discovery in the AI assistant	When the user describes what they want, the AI ranks relevant products based on factors such as relevance, price, availability, and quality. If the product supports Instant Checkout, the user sees a Buy button; otherwise, they see a link to the merchant’s website.
3 – Create a checkout session	If the user clicks Buy, ChatGPT sends a request to the merchant’s “create session” endpoint. The merchant reserves inventory and calculates tax and shipping; the assistant updates the cart in real time. This is analogous to adding items to a shopping cart on a website.
4 – Delegated payment	The assistant collects the user’s shipping address and payment method (such as a credit card, Apple Pay, or Google Pay) and uses a one-time delegated payment token that the merchant can charge once within specified limits. This protects the user’s payment information while still allowing the merchant to process the charge.
5 – Order confirmation	The merchant completes the order through their payment provider, returns confirmation to ChatGPT, and triggers fulfillment. The assistant displays the receipt and keeps the user informed about shipping updates. From the shopper’s perspective, everything happens inside the chat interface.

• Real-World Use Case: Retail giants like Walmart, along with platforms such as Etsy and Shopify, are integrating their product catalogs with platforms like ChatGPT. This allows a user to chat with the AI about their needs ("What are the best gifts for a ceramics lover?") and, upon receiving a relevant product suggestion, complete the purchase (with payment processed securely by Stripe) without ever leaving the ChatGPT conversation. This shortens the sales process, radically lowering cart abandonment and opening a vast new sales channel.

*Image from Amalytix

Dynamic Pricing and Margin Optimization with AI

The retail industry’s profit margins are under constant siege. AI introduces optimization tools that treat every transaction as a strategic, data-driven decision, ensuring competitive positioning without sacrificing profitability.

• Real-Time Price Adjustment (Pricing Optimization): AI systems continuously ingest competitor pricing, current inventory levels, market demand, and external factors like weather. This sophisticated dynamic pricing strategy ensures the price is always optimal for maximizing revenue and maintaining healthy profit margins simultaneously.

• Targeted Promotions and Profit Protection: The era of mass discounting is over. AI-driven predictive models determine a customer's discount need—their likelihood of needing a discount to complete a purchase. By using tailored incentives only where necessary, retailers secure a sale and protect margin on customers who would have bought at full price. This precision is essential for maximizing Customer Lifetime Value (CLV).

AI-Powered Operational Resilience and Supply Chain Optimization

The true operational leverage of AI for industry leaders is found in the complex, costly operations of the supply chain, transforming logistics from a cost center into a source of competitive advantage.

• Predictive Inventory Management and Demand Forecasting: Advanced ML systems look beyond historical sales. They integrate internal data with external factors like market spikes and social media trends to forecast demand with incredible accuracy. This proactive approach minimizes both costly stockouts (lost sales) and expensive overstocking, leading to streamlined warehousing and faster inventory turnover.

  For example, Amazon's upgraded Seller Assistant uses agentic AI to proactively flag slow-moving items before they incur long-term storage fees and automatically suggest shipment plans based on demand patterns.

• Logistics and Route Optimization: AI-driven platforms manage complex delivery networks in real-time. They analyze traffic patterns and delivery windows to continuously optimize routes for delivery fleets. This capability allows retailers to eliminate millions of unnecessary driving miles, drastically cutting fuel costs and achieving material improvements in supply chain sustainability.

• Post-Purchase Experience (PPDE) as a Loyalty Tool: AI tools are essential for managing the delivery phase. They predict delivery windows with accuracy and automate proactive updates, significantly reducing the number of "Where Is My Order?" (WISMO) calls. This transparency manages customer expectations and enhances satisfaction during the most uncertain part of the transaction.

The Strategic Mandate: Opportunities and Risks for Leadership

The integration of AI is a holistic, enterprise-wide transformation. To lead this shift, executives must understand both the unprecedented opportunities and the severe risks that come with adopting Agentic Commerce.

AI Commerce: Opportunities & Risks for Brands

Category	Opportunities for Brands	Key Risks & Challenges
Growth & Visibility	Reach New Customers: Being surfaced within platforms like ChatGPT, where consumers begin their discovery phase, opens a vast new, low-friction channel.	Platform Dependency & Loss of Control: Products are surfaced by the AI's algorithm. Brands risk becoming overly reliant on a single AI platform and losing direct control over their brand narrative and visibility.
Friction & Conversion	Reduce Friction: Completing a purchase in a chat reduces the number of steps required, leading to higher conversion rates and reduced cart abandonment.	Displacement Risk: If transactions are completed entirely within AI agent platforms, brand websites may see reduced direct traffic, threatening the valuable relationship with the customer.
Customer Trust & Data	Deliver Personalized Experiences: Agents provide tailored recommendations and real-time support, helping customers feel valued.	Fraud & Liability Laps: AI-driven purchases blur accountability. Merchants face rising risks from automated fraud and complex “friendly fraud” disputes when a customer claims their AI agent made an error.
Operational Efficiency	Improve Operational Efficiency: AI automates repetitive tasks such as content creation, demand forecasting, and customer service, allowing human employees to focus on higher-value work.	Data Control and Compliance: Sharing product feeds and customer data with AI platforms raises privacy concerns. New regulations demand transparency, requiring merchants to ensure compliance with data-protection laws.

Mastering the Algorithmic Future

AI in e-commerce is creating a new competitive chasm between organizations that treat it as a tactical tool and those that embrace it as a strategic operating system. The winners of tomorrow's online retail landscape will be those who view AI as the primary mechanism for delivering unique customer value, maximizing operational leverage, and driving sustainable margin expansion. The time to transition from experimenting with AI to scaling its adoption across the enterprise is now. Leaders must prioritize a unified, data-driven AI strategy that marries innovation with rigorous governance to master this algorithmic future.