Leader Spotlight: Preparing for agentic commerce before it's too late, with Jeff Douglas
Jeff Douglas is a digital commerce executive and active voice on agentic commerce. During more than 20 years at Nebraska Furniture Mart, he launched its ecommerce business and scaled it from zero into a Digital Commerce 360 Top 500 operation. He brings that operator’s perspective to executive ecommerce leadership, industry speaking, research, and writing on how AI assistants are reshaping product discovery, merchandising, and digital commerce operations.
In our conversation, Jeff explains why agentic commerce is an executive leadership challenge — not a marketing or IT initiative — and why retailers should treat Labor Day as the deadline to prepare for AI-driven product discovery ahead of the holiday shopping season. He also expands on ideas from his recent guide, Agentic Commerce for Retail Leaders: July 2026 Quick-Start Guide, outlining the operational, technical, and organizational changes leaders should prioritize today.
How agentic commerce starts with leadership
Why is being visible to AI assistants a leadership problem for retailers rather than just a marketing or IT project?
It’s a leadership problem because retailers can’t navigate this correctly unless marketing, content, IT, security, site merchandising, customer service, and other teams all work together. Each area owns part of the solution, but none of them can solve it in a silo.
Without a single leader, you’ll naturally get departmental gridlock. Your security team is trying to block scrapers and reduce server load, while your marketing team is trying to drive organic discovery. Left on their own, IT may lock everything down and unintentionally make your site invisible to AI agents. This requires a leader who understands how to balance competing priorities, align teams, remove roadblocks, and stay accountable for the outcome.
Being accessible to AI assistants is only the first step. Products also have to be understood, trusted, recommended, and ultimately fulfilled correctly. That’s why this has to be owned by someone with end-to-end accountability.
For retailers who are just beginning to consider agentic commerce, what’s the first operational change you would recommend?
I would start more technically — with an agent access and catalog readiness audit before improving content or data structure. Retailers first need to understand whether AI assistants can even access their information and which paths they’re using. That means reviewing robots.txt files, CDN and WAF settings, bot management rules, crawler permissions, APIs, and product feeds.
That doesn’t mean opening everything up. Legitimate crawlers and approved partners provide ways to identify and manage access, but retailers still need rules around authentication, spoofing, rate limits, and who is allowed to consume what.
I would also identify the 50 products that generate the most revenue and use them as the first test set. That gives the team a manageable place to find problems and build a roadmap before scaling across the rest of the catalog.
Building AI-ready product data
What else has to be true about a retailer’s catalog for AI agents to find and correctly recognize a product?
First, the catalog has to be accessible. Then the product has to be identifiable. The table-stakes information needs to be complete and accurate: product name, brand, description, model number, GTIN, dimensions, images, specifications, price, and more.
Products also need to be mapped correctly. Variants should be connected as variants instead of appearing as unrelated products. Price, inventory, reviews, images, and offers all need to attach to the correct canonical product.
Think of the canonical product record as the recognized product family, with each size, color, or configuration correctly connected as a variant, and each retailer offer attached to the right item. A tent available in five colors may have five individual SKUs, but an AI assistant should understand that they belong to the same product family instead of treating them as unrelated products.
Information also needs to stay consistent across product feeds, structured data, and product pages. Ultimately, retailers want to own the product card. That means the AI assistant correctly recognizes the product, understands its variants, associates the retailer’s offer with the right item, and can trust the price, availability, reviews, delivery promise, and policies associated with it. The goal is not only to earn a click, but to earn the recommendation and create an accurate path to the transaction, wherever that transaction ultimately happens. Eventually, transactions may happen directly inside AI assistants, but discovery is the priority today.
Most product pages were written for human shoppers. What has to change to optimize for agentic commerce?
For the last two decades, we optimized for keywords and human clicks. Once someone landed on a product page, marketing copy did the heavy lifting. Today’s AI shopping systems can draw from merchant feeds, platform catalogs, structured data, product pages, and retrieval systems to answer questions directly, often before the shopper begins a traditional browsing journey.
To succeed, retailers need to provide context. Why does someone want this product? Who is it best for? What problem does it solve? What questions are customers trying to answer? What limitations should they be aware of before buying? The answer isn’t writing longer product descriptions. That’s a trap.
Information needs to be direct, structured, and verifiable. Product type, use case, and compatibility shouldn’t be buried in lifestyle copy and left for the AI assistant to infer. Marketing should explain why those facts matter. That is where Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and Agentic Commerce Optimization (ACO) serve different purposes. AEO and GEO help influence the answer. ACO prepares the product for selection, verification, and action.
In your guide, you argue that retailers need to capture that natural-language shopper intent, not just product specs. Where should retailers find the language and detail that’s missing from their product content?
Most retailers already have this information. One of the best places to start is the site’s search engine. Retailers can see exactly what customers typed, where they landed, and whether they found what they were looking for. That language often differs from a retailer’s official taxonomy.
Then look at zero-result searches — queries that produced no results. Customer service chats, reviews, returns, warranty claims, and other support channels all contain valuable language customers naturally use.
Work with those teams to map that language back into product records, structured data, FAQs, buying guides, and platform feeds used by Google, ChatGPT, Gemini, and other AI assistants. Very few retailers operationalize that information today, which creates a significant opportunity.
Earning AI trust and recommendations
What makes an AI assistant trust one retailer’s claims over a competitor’s?
Trust is one of the biggest factors. AI assistants are more likely to trust information they can verify from multiple sources. They compare product pages, structured data, product feeds, reviews, pricing, and other signals. When those sources reinforce one another, confidence increases. When they conflict, uncertainty increases.
Each AI platform has its own ranking and confidence signals, but the operating principles are similar. Retailers with the clearest, most consistent, and most verifiable information become the safer recommendation. AI assistants depend on credibility. Before recommending a product, they need confidence that the information they’re presenting is accurate.
Why are small inconsistencies, like in pricing, inventory, or return policies, for example, more consequential when an AI assistant is involved in the buying journey?
A customer can usually work through a confusing message. They might click to another page, call customer service, or keep looking until they find the answer, but an AI assistant that compares several options doesn’t do that. If your price differs between the product listing page, product detail page, checkout, or inventory system, the assistant may favor a competitor whose information is easier to verify.
The other issue is scale. A small inconsistency that once affected a handful of shoppers can now influence thousands of recommendations and comparisons. It can also create promises your operation can’t deliver on, whether that’s the wrong variant, unavailable inventory, or an inaccurate delivery date. In an agentic environment, bad data doesn’t stay hidden inside your website. It travels much faster.
There’s a lot of excitement around AI-powered checkout. You recommend a more measured approach. How should leaders decide where to invest today vs. what can wait?
AI-powered checkout is getting a lot of attention, and for good reason. Technology companies are building rapidly in this space. AI-to-cart handoffs are already real, and human-approved embedded checkout is beginning to appear on participating platforms. Fully autonomous purchasing, where an agent completes a transaction under pre-authorized rules, is not mainstream yet.
Leaders need to separate what’s happening today from what’s technically possible tomorrow. Right now, the most important priority is preparing for AI assistant discovery. I would set a goal of being ready by Labor Day. A significant amount of holiday shopping, product research, and comparison will happen inside ChatGPT, Gemini, Copilot, and other AI assistants before customers ever reach a retailer’s website this year.
Discovery is the deadline that cannot move. Checkout should follow, or run in parallel, if you have the resources. Salesforce, Shopify, and other commerce platforms are building many of these capabilities. However, retailers still have to clean their data, implement the technology, establish governance, and ensure their operations can deliver on whatever an AI agent promises.
The takeaway is straightforward: Discovery first, checkout second, but don’t wait until October to begin either one.
Leading the organizational transformation
Agentic commerce touches merchandising, engineering, marketing, analytics, customer service, and payments. How do you keep it from becoming another siloed digital project?
This is where leadership comes in. You need a leader who already has relationships across those areas, knows how to speak their language, understands the technology, is trusted, and knows how to motivate teams to work together. That is the model I believe in, and what I have seen work.
But relationships alone are not enough. Give that leader ownership of the end-to-end outcome, put the work into the operating plan rather than an innovation lab, and create a shared scorecard tied to catalog quality, AI visibility, conversion, and returns. When the work is connected to the customer experience and the P&L, it is much less likely to become another siloed digital project.
You’re saying discovery has to be done by Labor Day. What has changed in the commerce landscape that makes this such an urgent leadership issue?
I’ve spent more than 20 years in ecommerce and have watched several major shifts reshape the industry. Each time, the same pattern emerged — customer behavior changed before most retailers believed it. Infrastructure was built before transaction volume became obvious. Then the convenience curve accelerated and adoption followed.
That’s happening again with agentic commerce. Google, Microsoft, Salesforce, Shopify, Visa, Mastercard, and others are building the infrastructure that will support the next major shift in how customers discover, compare, and purchase products.
The biggest risk isn’t missing a few AI-driven orders this year, but falling out of the consideration set before customers ever reach your website. It’s allowing competitors with cleaner, more trustworthy data to become easier recommendations. It’s missing the opportunity to learn while AI-driven traffic is still manageable.
If retailers wait until peak holiday volume to rebuild catalog feeds, ownership models, and operational processes, they’ll be doing it under the greatest possible pressure. That’s why I continue to emphasize Labor Day as the readiness deadline.
I’m actually working on another piece exploring what I call the six waves that have reshaped ecommerce. I have watched five waves reshape ecommerce, and the sixth wave is following the same script.
Do you recommend any resources for leaders on agentic commerce?
One resource I recommend regularly is The Jason & Scot Show. It’s a podcast that covers ecommerce broadly, but agentic commerce has become a major focus because it’s driving so much change across the industry.
I’d also recommend reading my Agentic Commerce for Retail Leaders: July 2026 Quick-Start Guide, which outlines practical steps retailers can begin taking immediately. Beyond that, stay closely connected with your commerce platform. Whether you’re using Salesforce, Shopify, Adobe Commerce, or another platform, follow their product announcements, work closely with your customer success team, and participate in pilot programs whenever possible.
The goal isn’t necessarily to be on the bleeding edge; it’s to stay on the cutting edge. You don’t want to fall behind, because in this environment, falling behind can quickly translate into lost visibility, lost traffic, and ultimately lost revenue.
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