A shopper used to search “best carry-on suitcase,” open five tabs, compare prices, skim reviews, check delivery dates, and (maybe) come back later to buy.
Now, that same shopper can ask ChatGPT, Gemini, Claude, Perplexity, Amazon Rufus, or an on-site AI assistant for “a lightweight carry-on for a three-day work trip under $250 that fits overhead bins and ships by Friday.”
That’s the new reality behind agentic commerce. Shopping is becoming more conversational, more personalized, and much less tied to the old search, click, filter, compare, and checkout pattern.
Of course, that doesn’t mean search is gone; it’s just changing.
AI shopping agents are beginning to influence what shoppers see, which products make the shortlist, and how quickly a customer moves from question to order.
This guide takes an in-depth look at what agentic commerce means, how it works, and what brands need to prepare for next.
What is agentic commerce?
Agentic commerce is ecommerce powered by AI agents. Through a conversation, shoppers research, compare, decide, and eventually complete purchases with less manual effort.
Traditional ecommerce still relies heavily on shoppers doing the work. The customer searches, filters, scrolls, reads, compares products, and eventually begins the checkout process. Agentic commerce shifts more of that work to AI.
At its most basic, an AI shopping assistant helps answer product questions. In a more advanced form, an agent can compare products, apply customer preferences, build carts, route shoppers to checkout, or place an order within approved limits.
The key difference is action.
A chatbot answers a question. A recommendation engine suggests a product. An agent can interpret intent, compare options, and move a shopper closer to purchase.
Types of agentic commerce
Agentic commerce is not a single channel or platform. Think of it more as a set of shopping experiences that can show up in several places.
On-site AI shopping assistants
Certain AI shopping assistants live on a retailer or marketplace site. Amazon Rufus is one example; Walmart’s Sparky is another. A shopper can ask questions about products, compare options, or get help narrowing down choices without leaving the shopping environment.
General AI assistants
Tools like ChatGPT, Claude, Gemini, and Perplexity are becoming research layers for shopping. Customers use them to ask broad questions, compare categories, identify brands, and understand which product fits a specific need.
In other words, people can uncover the best products for their needs in one place. A shopper might open ChatGPT and ask, “I have sensitive skin and need a lightweight tinted moisturizer with SPF for daily sun coverage. I want something fragrance-free, non-greasy, and under $40. What are my best options?” Instead of searching for products one by one, the shopper asks the AI agent to compare options based on skin needs, ingredients, budget, and use cases.
The answers come from the product information, reviews, third-party content, retailer data, and web sources that the AI tool can access and interpret. AI helps facilitate the best fit by matching those details against the shopper’s stated needs, such as sensitive skin, SPF coverage, budget, ingredients, and preferred product feel.
Platform-integrated commerce
Not long ago, Shopify and ChatGPT announced an integration, meaning product data, catalog access, and checkout paths are now connecting with AI interfaces. The details will keep changing, but it’s clear that ecommerce platforms want their merchants to be discoverable in AI-led shopping moments.
Marketplace and retail media agents
Marketplaces already have huge product catalogs and shopper intent data. AI assistants make that experience more conversational by helping shoppers find the right item faster.
Autonomous shopping agents
This is the longer-term version. A shopper may eventually ask an agent to reorder household products, find a better subscription price, buy a birthday gift, or replenish a routine item within set rules.
Each version changes how brands compete. Product visibility will depend less on ranking for one keyword and more on whether an AI tool can understand the product, trust the information, and match it to the shopper’s request.
Autonomous shopping agents take the next step by acting on a shopper’s preferences with less manual input. For example, a customer could tell an AI agent to reorder their preferred protein powder when the container is almost empty. Then it will compare available prices, confirm the flavor and dietary requirements, and complete the purchase through an approved retailer or brand site within a preset budget.
Why agentic commerce is gaining momentum
The shift in shopping behavior is happening right now. Adobe reported that traffic from AI sources to U.S. retail sites grew 393% year over year during the first three months of 2026. Not only this, but during the 2025 holiday season, AI traffic rose 693% year over year.
Consumer use is also growing. Adobe found that 38% of U.S. consumers had used generative AI for online shopping, while 52% planned to do so. The most common uses included research, product recommendations, deal hunting, shopping lists, gift ideas, and finding unique products.
Additionally, Capital One Shopping reported that 63% of consumers want help shopping from generative AI, and 58% use GenAI instead of traditional search to find recommendations.
The long-term projections are even larger. McKinsey estimates that agentic commerce could influence up to $1 trillion in U.S. B2C retail revenue by 2030, with global projections reaching $3 trillion to $5 trillion.
Deloitte also points to a fast-moving shift, noting that analysts project 25% of global ecommerce sales could be enabled by AI agents by 2030, while 55% of digital consumers could begin product research using large language model platforms.
Retail has been moving toward this for years. NRF has noted that digitally influenced sales already exceed 60%, and AI agents are expected to increase that share as they support recommendations, decision-making, and replenishment.
All in all, the numbers show that shoppers are getting comfortable asking AI for help before they buy.
What categories are showing up first?
AI shopping will not hit every category at the same speed.
Adobe’s research found that AI traffic share in consumer electronics was 4 times that of apparel and footwear, while home goods had a 3x share.
That makes sense. Some categories naturally lend themselves to AI-assisted comparisons. Electronics, appliances, home goods, beauty, sporting goods, travel accessories, and specialty products often involve specs, reviews, use cases, compatibility, or personal preferences.
Shoppers may ask:
- “What’s the best air purifier for pet allergies in a small apartment?”
- “Which running shoes work best for flat feet and road running?”
- “What skincare routine is best for dry skin under $100?”
- “Compare these three carry-on bags by weight, warranty, and storage.”
- “Find a gift for a coffee lover who already has a grinder.”
These are not short keyword searches. They are context-heavy questions, which change how product content needs to be written.
How people query AI shopping tools
Traditional search is usually clipped and keyword driven. AI shopping queries are more specific and personal.
A shopper may include budget, lifestyle, timing, product constraints, material preferences, sizing issues, delivery needs, values, or the intended audience. They may also ask follow-up questions.
Instead of searching “best wireless earbuds,” someone might ask:
“What are the best noise-canceling earbuds under $150 for commuting, taking work calls, and staying comfortable for several hours?”
Queries like these give the AI tool several jobs. It needs to understand product type, use case, size, style, material, and buyer intent. Brands that only provide thin product descriptions may struggle to show up in those moments.
AI tools are also good at comparison. Shoppers may ask for the best, cheapest, most durable, best reviewed, or best fit for a specific situation. With that level of desired research, the product content cannot stop at features; it must provide far more information.
How to optimize products for agentic commerce
Optimizing for agentic commerce does not replace SEO, it takes building on it.
Search engines, AI assistants, marketplaces, and shopping platforms all need clean product data. The difference is that AI tools rely heavily on structured, trustworthy, and current information to generate useful answers.
Brands should prioritize:
| Area to optimize | Why it matters for agentic commerce |
| Clean product data | Product titles, descriptions, specs, pricing, variants, availability, images, and reviews need to be consistent. If a product has different names or conflicting details on different channels, AI tools may struggle to interpret it correctly. |
| Conversational product content | Product pages should answer the kinds of questions shoppers ask, including size, fit, compatibility, materials, use cases, shipping timing, returns, and what makes one product different from another. |
| Structured data and schema | Schema markup helps search engines and AI systems understand product attributes such as price, rating, availability, brand, color, size, and reviews. |
| Fresh reviews and FAQs | AI tools often summarize third-party information, review sentiment, and product Q&As. Strong review volume and detailed FAQ content can help clarify who a product is best for. |
| Accurate feeds and catalog connections | OpenAI now allows merchants to apply to share product data for ChatGPT shopping experiences. Shopify and Etsy catalogs are already integrated, which signals how important product feeds will be as AI shopping matures. |
| Crawlable, accessible site content | Product pages should be easy for search and AI crawlers to access. Hiding important product information in images, scripts, or gated experiences can limit visibility. |
| Operational accuracy | Product visibility is only useful if inventory, pricing, shipping promises, and delivery windows are accurate. If an AI tool recommends a product that is out of stock or unable to ship on time, the customer experience breaks quickly. |
The takeaway? AI needs clarity. Brands should make product information easy for both people and machines to understand.
What agentic commerce means for fulfillment
Discovery is only one side of agentic commerce. Fulfillment is the other.
AI shopping compresses the path from question to decision, so a shopper can move from “What should I buy?” to “This is the one” in minutes. That speed puts more pressure on everything that happens after the recommendation.
Fulfillment teams need to support:
- Accurate inventory availability
- Fast order routing
- Clear delivery promises
- Real-time order visibility
- Flexible returns workflows
- Marketplace and retail channel requirements
- Reliable parcel performance
This matters because AI shopping tools are likely to compare more than product features. Shipping speed, return policies, reviews, availability, and price can all influence recommendations.
A product that looks great but ships too slowly may lose to a competitor. A product with unclear sizing or messy return policies may be passed over. A brand with disconnected inventory may struggle if AI-driven traffic creates sudden demand for a specific SKU.
“Agentic commerce may change how shoppers discover products, but it doesn’t change the operational requirements behind the order. In many ways, it raises the stakes. If inventory isn’t accurate, orders aren’t routed correctly, or delivery promises aren’t met, the customer experience breaks immediately.”
— John Servia, VP of Operations Excellence, Kase
Agentic commerce raises the bar for operational readiness. The front-end experience may feel conversational, but the back end still needs to be fast, accurate, and connected.
Why inventory management is critical
Inventory accuracy becomes a greater issue as AI tools begin guiding purchase decisions.
A shopper may ask for a product that can arrive by a specific date. An AI agent may only surface products that appear available, shippable, and likely to meet that promise. If a brand’s inventory data is delayed or fragmented by channel, it may miss the recommendation window.
This is where inventory management becomes central to AI-era ecommerce.
Brands need a connected view of inventory by SKU, location, and channel. They also need the ability to manage safety stock, route orders from the right node, and avoid overselling during demand spikes.
Strong inventory management helps brands think through the systems, processes, and visibility needed to maintain accurate availability as channels multiply.
At Kase, inventory snapshots update in real time across channels, giving brands a more accurate view of availability to help reduce the gap between what shoppers see and what can be fulfilled.
Why omnichannel fulfillment is part of the agentic commerce conversation
Agentic commerce does not live on one channel. A shopper may discover a product in ChatGPT, compare it against Amazon reviews, buy through a Shopify store, return it through a retailer, and contact support through a post-purchase platform.
Modern brands need omnichannel fulfillment that connect DTC, retail, marketplace, and wholesale workflows instead of treating each channel like a separate operation.
AI shopping makes that connection more important. When customers discover products through AI, they may not care which channel ultimately fulfills the order. However, they care whether the product is available, ships quickly, arrives correctly, and matches what the AI recommendation promised.
A disconnected fulfillment setup can create problems fast. For example, inventory may be reserved for the wrong channel, or orders may be routed inefficiently. Retail requirements may be missed. None of that fits the convenience AI shopping is trying to create.
How a 3PL supports agentic commerce
A 3PL cannot control whether an AI assistant recommends a product, but it can help make sure the brand is operationally ready when that recommendation turns into demand.
The right 3PL supports agentic commerce by connecting the systems and execution behind the order.
That includes:
- Real-time inventory visibility: Brands need to know what is available, where it is stored, and which channel can sell it.
- Platform and marketplace integrations: AI commerce will keep tying into ecommerce platforms, shopping carts, marketplaces, returns tools, and order management systems. Integrations reduce manual work and help orders flow accurately.
- Flexible order routing: As demand moves between DTC, marketplace, and retail channels, order routing needs to account for inventory location, delivery speed, cost, and service level.
- Ecommerce fulfillment execution: Fast, accurate ecommerce fulfillment still determines whether the customer experience lives up to the promise made during discovery.
- Returns and post-purchase support: AI-assisted shoppers may buy faster but returns still happen. Clear reverse logistics helps protect customer trust and keeps inventory moving back into sellable stock.
- Data visibility: Brands need order, inventory, shipping, and performance data they can actually use. AI-driven discovery will only make those feedback loops more important.
What brands should do now to prepare for agentic commerce
Agentic commerce is still developing, giving brands time to prepare before it becomes another crowded, expensive channel.
Start with the basics:
- Audit product data for accuracy and consistency.
- Add clear FAQs to high-priority product pages.
- Improve schema markup and structured product data.
- Keep pricing, inventory, and availability current.
- Watch for AI referral traffic in analytics.
- Evaluate Shopify, marketplace, and product feed readiness.
- Review fulfillment performance by channel and SKU.
- Strengthen inventory visibility before demand becomes harder to predict.
The biggest mistake brands make when thinking about agentic commerce is treating it solely as a visibility challenge. Visibility matters, but it’s also an operations challenge. AI may influence product discovery, but inventory accuracy, fulfillment speed, delivery reliability, and channel integration will determine whether brands convert that demand into revenue.
The brands that win in agentic commerce will have informative product content, clean data, connected systems, and fulfillment that can keep up.
The next phase of ecommerce is already forming
Agentic commerce is unlikely to be another trend. It’s a practical shift in how shoppers ask questions, compare products, and decide what to buy.
That said, AI shopping agents will not replace every ecommerce habit overnight. Customers will still search, scroll, browse, and visit stores. But the discovery layer is changing, and brands that ignore it risk being left out of high-intent shopping moments.
The smartest move is to prepare now.
Kase works to stay at the forefront of retail and ecommerce changes, helping brands keep pace with the technology, integrations, and fulfillment execution required by modern commerce. With fulfillment technology built around real-time visibility, order flow, and platform connectivity, Kase supports the operational foundation brands need as commerce becomes more conversational, connected, and AI-assisted.
