Retailers embrace agentic commerce but warn AI shopping still faces major execution gaps

Retail's future is tied up in agentic commerce, yes? Well, up to a point, says BestBuy, Wayfair, and Electrolux with welcome notes of caution struck

In retail circles, it's all about agentic commerce, right? Certainly the data keeps coming in that agentic commerce is top priority. This week Logicbroker, which pitches itself as provider of an Agentic Commerce Orchestration Engine for enterprise retailers, released "The State of Agentic Commerce Adoption" report, based on a survey of more than 600 enterprise e-commerce leaders. This finds that more than 90% of enterprise leaders polled expect AI agents to influence at least 20% of online orders by 2027, and more than a third believe AI could in fact shape more than half of all transactions. More than half of organizations say they plan to roll out AI-shopping agents within the next six months. Some 95% of enterprises reckon to have already deployed at least one AI-driven commerce capability, while 47% say they plan to invest $1 million or more in AI-driven commerce initiatives over the next 12 months. And hopes are high for what this will deliver in terms of ROI. Nearly half expect returns within the first year, rising to three-quarters of respondents predicting the same within two years. So, the bandwagon is rolling and there are no signs of the wheels coming off just yet. Just this week DIY and home improvement group Kingfisher announced plans to roll out agentic AI shopping and commerce capabilities based on Google Cloud's Vertex AI across its B&Q, Castorama, and Brico Depot France brands in Europe following what the retailer calls "meaningful results" from pilot trials at B&Q. Benefits expected include: With e-commerce already accounting for more than 20% of Kingfisher's Group sales, CEO Thierry Garnier is looking to this expanded Google relationship to deliver the next phase: Through this partnership with Google Cloud, we are enabling our customers to search for and buy home improvement products with AI, delivering a fully personalised and easy shopping experience. These investments put Kingfisher at the forefront of AI-powered shopping, delivering meaningful innovation as part of our expanding digital ecosystem, and helping us to meet rapidly evolving customer needs." Safe to assume that Garnier is an agentic fan. Others are perhaps being a little more hesitant about their own strategies here At BestBuy, CEO Corie Barry says the firm has "taken our time working out way into agentic commerce". Why? He explains: You do need to go in with a plan around what ubiquitously needs to be available in agentic commerce versus what is very unique to your brand, your experience, your data. I think it's going to be an 'and' world. You're going to need to be present in agentic commerce. In our case, in working with chat, we have really good data that could help [agents]. But the tech is still nascent, he cautions: Especially in consumer electronics, you would ask [AI] questions and there was a lot of wrong answers coming back because there are so many models in the world. They can be dated before you know it. There's so much data. So we're trying to go in with a plan around [that fact that] we need to be present, but we also need to say, 'Look, if you want this installed, if you want us to make for an easy upgrade, then you got to come back to the site'. A "real solid plan" is needed around what is a retailer's data, he advises: You also need to think about your site and your content, because not only are you out in an agentic shopping engine, but their bots are coming and scraping your site for information. So your site needs to now be geared not just toward people, but you may actually have hidden pages of data, not for customers, simply for bots that they can scrape so they're more knowledgeable, which, again, you might feed a little bit of that in the Agentic answer and then you say, come back to the site if you want more expertise on it. So does all that suggest that BestBuy will continue to proceed at a slow pace down this route? Barry argues: We'll see. It's early....No-one's figured it out and everyone's testing a bit. Over at home decor firm e-commerce specialist Wayfair, founder and CEO Niraj Shah is also quick to set agentic expectations appropriately: I think this notion that LLMs and AI agents can do everything is not the case, but they can do a lot. The companies that can optimize themselves in that world, the same way in the days of search and social media that what we did with Meta, what we did with Google, we do with Pinterest, advantaged us, I think we can do the same thing. There have been lessons learned among providers, he suggests, pointing to Google: Their efforts like Google Shopping, which tried to have transactions finished there, ultimately found that consumers want to go lower funnel. But basically what OpenAI the other day said, 'Well, that's probably the bulk of what people want to do'. Google already knows this lesson from years of balancing both sides of it. And it depends what the operating model and target market of retailers are, he notes: I think the reality is this - the LLMs, they're very helpful for customers to kind of get the landscape of something they don't understand, but it can only take it so far. If it's something the customer can be very articulate about, like, 'I use Dove soap bars, I use Mrs. Meyer's hand soap, I use Seventh Generation dishwasher tabs etc and I want to replenish that stuff', [agents] could execute those orders for me. That is a very straightforward exercise. If it's something where there's a discovery, inspiration, emotion, aesthetic, whether you're talking about home or you're talking about fashion, or you're talking about beauty, I don't think the agent is going to help you going from the beginning to the end and just saying, 'OK, you want some more lipstick', unless you want to re-order the same exact one. But it won't choose a new sofa for you, he adds: What it will do -- and this is where we can optimize for this - is it will say, 'Wayfair is a great solution for you,' but there's a lot of choices you [as a human] get to make around the item. You're going to want to understand other items. We have information about what you purchased. We know what styles you like. We know what you have in your house. We can create inspirational imagery with AI that's very specific to you. We can also present you with a lot of options around delivery, assembly, taking away, packaging. He concludes: We play in a category that's very bespoke and unique. Basically, home, fashion and automobiles are the only three product categories that customers have so much curiosity and fanaticism about that they'll spend money to basically buy magazines, basically consume media, for the enjoyment of knowing what else is coming, what the trends are, what's available....There's an emotive content to it that is very hard to capture through Artificial Intelligence. So is agentic commerce and agentic shopping really the inevitable future for retailers that the likes of Google and OpenAI would have everyone believe as they sign up on brand after another? Yes, says Marienza Benedetti, D2C Ecom Personalization and Growth Manager, at consumer goods firm Electrolux: There's a future that is knocking at our door, a future that is already happening, and it's the future of agentic commerce. But there are questions that organizations like hers need to ask themselves first: The main question that we are asking ourselves at the moment is, 'For AI systems that will search, that will compare, and that will transact on behalf of the consumers, who will we be optimizing the experience for in this new landscape? Will it be the human or will it be the agent?'. While we are looking, we are still actually looking for the answers. One thing that came to us - and felt like a kind of a paradox - is that the more AI will drive the experience, the more human-centric it must become. Why? Because AI will [have to] somehow connect human needs to product features better than we do. It will help us directly optimize not just for clicks, but for lifetime value and for trust. It will surely be not as aggressive and promotional as we are. So it will be more relevant, more contextual, and more respectful. It will be basically empathy at scale. Or that's the theory, at any rate. Watch this space. The direction of travel is clearly set and there will be more and more agentic enthusiasts, like Kingfisher, racing to sign on to this latest e-commerce gambit. But the candor and cautionary notes struck by BestBuy, Wayfair, and Electrolux seem to me to be valuable counter-weights to a hype cycle that might otherwise be assumed to be out of control.

AI answer engines are changing how software gets discovered. Here's how G2 is adapting

The buying journey (and what is actually purchased) for enterprise software is undeniably changing. What does that mean for software review companies like G2? Tim Sanders, G2's Chief Innovation Officer, leads research at online review platform provider G2, studying market trends and the economics of AI, particularly AI agents. Part of Sanders' work involves thinking about how G2 can be more innovative and keep up with the move from business SaaS (software as a service) to business agents. Another part is tracking how gen AI is disrupting the B2B software buying journey. Both are becoming increasingly important for businesses to understand. Answer engines like ChatGPT, Google Gemini, and Perplexity are quickly becoming the go-to-choice for researching B2B software. What does that mean for review sites like G2? For the last nine years, G2 has published its Best Software Awards report to highlight success on the G2 platform. The report rewards companies for growing reviews on G2 and growing traffic or business, but buyers also use it to short-list software faster. Last year, G2 discovered that the Best Software Awards report and related content comprises around 60% of G2 citations on Large Language Models (LLMs). Sanders argues it's a powerful signal that LLMs are looking for sentiment at scale so they can confidently provide recommendations. Needless to say, the report has become very important to G2's AEO (Answer Engine Optimisation) strategy. So, when you ask Sanders if LLMs are taking traffic away from G2 and other review sites, his response is, "Yes and no." Traffic did decrease in 2023/24 for the company, like it did for many other companies (not only review sites), but then, in 2025, they saw traffic increase, with over 1 million human visitors coming from AI chatbots in the process of buying. G2 studies conducted in 2025 show that B2B buyers are spending half their buying journey on a chat, like ChatGPT, Gemini, or Perplexity. So, yes, answer engines are disrupting traffic - at least initially. However, another study found that review platforms compete for about 30% of citations; the other 70% go to vendors. So the real fight is between user generated content platforms (UGC), like Reddit, and publishers and review platforms, like G2 and TrustRadius. LLMs can give answers fast, but for most B2B buyers, it's not the final stop. Buying software is often a high-risk proposition, so B2B buyers "trust but verify." This explains why citations from answer engines are so important. Sanders cited a Prompt Watch report that found that citations from answer engines have a high click through rate, sometimes as much as 10x higher. What this means is that, yes, people use answer engines to kick-start the buying journey, but G2 is getting cited a lot, which is driving buyers to the firm's site. The closer a human gets to making a decision, the more dominant G2 becomes, says Sanders: G2 is focused first and foremost on influencing the answer. We educate the software marketers that it's not about citations, but about winning the answer. So if half the users start on ChatGPT and they're using it to build their shortlist, more than any other source, and many make their decision based on their shortlist, it doesn't really vary, then G2's influence in that large language moment is as important as coming to G2, if not more. Because we've always been in the business not only of helping buyers make better buying decisions, but we've also been in the business of influencing seller outcomes." If answer engines are becoming the first stop for buyers, the obvious question is how is G2 adapting its model to remain an authoritative source for those answers? How G2 is optimizing for AI citations and answer engines? The firm is facing a new way of influencing the buyer journey and the company is doing some things to improve the gross volume of citations they get from LLMs and from organic Google search. Some of these things include more freshness updates, and adding new types of content, including what Sanders called "frontier content," content that answers questions that haven't been answered before. This work, he says, has helped reverse G2's traffic slowdown significantly. G2 is also evolving the standard review model, leaning in to double verification to ensure the reviews are from real people who do use the software they review. The company also acquired Unsurvey in 2025 to leverage AI and voice to make the review process easier for people. Unsurvey uses AI to interview people, making it easier for them to respond and provide more information. Unsurvey had also been working on voice interaction, so now you can leave a review on G2 using voice. Sanders explains that the more specific people are in the review process, the better the signal to humans and the better opportunity for pattern matching the language models. He adds that G2 saw a huge jump in the number of reviews and the amount of information in each review after introducing voice capabilities. Another new review capability is the ability to add custom questions for categories like AI Agents that measure technical indicators such as hallucination rate and accuracy of actions, he explains: We know that with the rise of AI, technical buyers are stepping in, becoming economic buyers, and they ask different questions. They're not as concerned about Net Promoter Score as they are with errors and efficiency of the actual product. So we've been moving more and more to start capturing more technical signals in our review process to differentiate The best global software companies in the report (i.e., HubSpot, Salesforce, Google) all have one thing in common - they have embraced AI agents as a huge part of their Go-To-Market (GTM), evolving from SaaS to agentic platforms. There is a difference between the two, Sanders explains, with agentic having up to 10x the opportunity of improving an enterprise's results compared to SaaS. AI's great leap from machine learning classification to generative AI and now agentic has reset the playing field in a way that is toppling most of the mid-tier incumbents. Now the top-tier incumbents are very invested, and they've moved faster than history would have said they should have moved. If Clayton Christensen were alive, I think he would be pleasantly surprised at the agility of Microsoft, Salesforce, and even HubSpot, given their dominance over the last few years. What does the shift from SaaS to agentic mean really? Sanders frames the shift to agentic platforms not just as a technology change, but as a different economic model for software. He compares buying agentic platforms as buying wholesale, "buy one, make many," where the platform becomes an intermediary between the buyer and the seller. On the other hand, buying SaaS is more like buying retail. He said the growth of SaaS economically from an Annual Recurring Revenue (ARR) perspective is predicated on the retail model and companies will have to buy a lot of little SaaS solutions, resulting in a lot of different subscriptions. But the wholesale model is a different story and it's disrupting the retail model. Scott Galloway has talked a lot about the wholesale model, saying the problem with wholesale platforms is they will grow to become big, ugly, low multiple businesses, similar to airlines (see here and here for examples of his perspective). Sanders says: I think that the sexy AI wholesaler of today may be a very low-margin, low-multiple company tomorrow. This is why, as a person who covers the market, I've seen OpenAI make a bold decision to pursue ads and shopping commissions, to have more of a Meta meets Amazon multiple than accidentally pursuing a low margin business like API tokens for developers, and create an airline 20 years from now. So it's a fascinating dynamic. SaaS isn't dead. It's not even close to it. However, I would say that SaaS that lacks agentic capabilities in their roadmap will have to go down market to small business and to heavily industrial verticals to survive. I do know that. A couple of other interesting facts is that almost half of the 2026 top 100 best software and 86 of the 100 fastest growing software products weren't on the list last year. So, there's a lot of new software hitting the market that companies are looking for. The question is will companies continue to keep buying smaller AI applications/agents or will they shift to agentic platforms? Sander said G2 did a study of B2B software owners centered on AI agents and they found that ready-made agents were delivering more cost-savings than agent builder platforms or in-house agent building. Sander's theory is that many agent builder platforms have been around a long time and carry a lot of UI legacy debt. This debt has made it hard for them to pivot from their existing user experience. But AI startups don't have that problem. They can quickly adapt to what users need in the moment and for this reason small vertical agents will, in the short term, win out from a customer satisfaction point of view. However, he also believes that five years from now, mid-sized companies with technical leaders will start to think more wholesale than retail and look for general purpose tools to write a majority of the solutions they need, instead of buying individual software applications. Again, this is more long-term; the short term reality is not the same, despite what Wall Street keeps saying with its 'SaaSpocalypse' paranoia. There's also the trust factor to overcome. Companies need to fully trust agents before they get rid of the software they have grown to know and trust (even if they hate it), and that isn't going to happen overnight. Plus, agentic platforms require people to have that builder mindset and even those who are frustrated with their current solutions may not have the time, knowledge, or desire to build something on their own (especially if security is a top concern). The question of whether review platforms are still needed in the answer-engine era isn't the right question. The right question is how is the B2B software buyer journey changing as a result of AI and how does any company - vendor, review platform, or other, remain relevant? LLMs need trusted sources to cite in buyer searches. Without those trusted sources, buyers would have no confidence in the answers they get and stop using LLMs. G2 is evolving to ensure it continues to be one of those trusted sources. And the changes it is making are smart, including the AI interviews, voice review option and the custom questions for AI agents. Sanders perspectives on SaaS versus agentic also make a lot of sense. Enterprise software is attempting to shift to agentic platforms. You just have to read LinkedIn to see the shift happening (or my inbox). But despite what many vendors (or Wall Street) might say, it's not going to happen overnight and for a while we are going to see both SaaS and agentic in a single platform until we get to the point that agentic is trusted enough and works well enough to take over.

Why AI Shopping Is Still Just a Smarter Search Bar | PYMNTS.com

By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. But that wasn't the most interesting part of my experiment. Even if the AI had identified the right toaster, it still couldn't confirm the actual price, verify that the item was in stock or tell me whether it could be delivered within any reasonable timeframe. It could not place the order. To do that, I was referred to a merchant site I had never heard of to navigate the checkout. So, I ended up doing what most people still do instead. I went to Amazon and bought the toaster there. I have repeated my Toaster Test periodically ever since. The LLMs are smarter, faster and more conversational than they were a year ago. They have added a few better brands to the list they produce. They still cannot produce the brand I wanted to buy, confirmed in stock, at a real price, available for purchase right now. The research loop is impressive. The transaction loop is anything but. My test is not evidence of a technology failure. In fact, the AI part worked amazingly well. It is a marketplace failure. And it illustrates the core challenge of agentic commerce in 2026. The models are breathtakingly amazing at helping consumers do the research and the shortlisting of options. The infrastructure required to actually curate all of the available options, and then execute the purchase, is a big gap yet to be filled. What is billed as a revolution in commerce is, for now, mostly a highly intelligent search bar. A better one than Google. A more conversational one. But still, at its core, a tool that finds the answer and then hands the consumer off to someone else to close the deal. The behavioral shift behind this problem is no longer theoretical. It has gone mainstream quickly. PYMNTS Intelligence research from January 2026 found that 41% of consumers have already used dedicated AI platforms for product discovery. More striking is that a third say they have fully replaced their prior methods. They are not layering AI on top of old habits. That's just the tip of the behavioral iceberg. In December 2025, 34% of AI power users relied on native AI interfaces as their primary method for shopping discovery. One month earlier that share was 22%. Among light users, reliance on AI models jumped from 5% to 16% in the same period. These are not gradual shifts. They are new habits forming at a pace the industry did not anticipate. Read More: Gen AI: The Technology That Broke the Adoption Curve The experience driving this shift is genuinely different from traditional search. Instead of scrolling through pages of links and sponsored listings, consumers receive a structured answer that explains the tradeoffs between competing products and can be refined through conversation until it matches the actual buying decision. It is something keyword searches could never deliver with any sort of precision. But then the consumer leaves the conversation and goes somewhere else to complete the purchase. The question that matters now is not whether agentic commerce will eventually close that gap. It is who becomes a casualty on the agentic highway, and who benefits. And how. Let's start with Google, because the damage there is real and already underway, even if their headline numbers still look healthy. In Q4 2025, Google Search revenue grew 17% year over year. Gemini has 750 million monthly active users. Alphabet crossed $400 billion in annual revenue for the first time. This is not a wounded player. It is also not the dominant one it used to be. Google has been trying to become a commerce destination since it launched Froogle (a play on "frugal") in 2002. It rebranded that effort multiple times, built Shopping tabs, launched Google Express, acquired Pointy and embedded Gemini. Despite all of it, Google Shopping remains a listing service that shows products and sends consumers somewhere else to buy. The transaction, the customer relationship and the post-purchase experience all happen in someone else's ecosystem. What has changed is the top of the funnel. Consumers who once opened Google to research a product are now opening ChatGPT, Claude or Perplexity instead. The PYMNTS data makes that shift quantifiable. A third of consumers who have tried AI for shopping discovery have fully replaced their prior methods. Read More: What Happens to Stores When AI Agents Do the Shopping? That is not a marginal shift in behavior. It is a structural fracture of the search functionthat Google has monetized for two decades. Defending a position is not the same as expanding it. The highest-value transactional queries, consumers who already know what they want and are ready to buy, may still run through Google's standard channels. But the middle of the funnel, the research and comparison phase where consumer intent is shaped, is moving to AI platforms that Google does not own and cannot easily monetize, despite Gemini's headline user numbers. Consumers who switch to AI for research are already bypassing Google at the top of the funnel. If Google cannot capture them at the bottom with a transaction, it loses the journey, and that customer, entirely. Google's answer is the Universal Commerce Protocol, an open standard built with Walmart, Shopify, Target, and two dozen other partners, designed to let AI agents complete full shopping journeys inside Google's own products. The logic is that if Google can plug trusted commerce players into its AI surfaces, consumers can transact through Gemini while the actual commerce relationship belongs to the retailer. Google becomes the front door without building the back end. Read More: The Protocol Power Struggle Reshaping AI-Driven Commerce This is more or less what it does today with Google Shopping, except with agents. It is also an acknowledgment that Google cannot build what a commerce network requires. It is borrowing the trust it does not have from partners who do. Then there's Shopify. Their situation is more complicated and more consequential for the merchants who depend on it. Twelve months ago, Shopify had the most credible claim to being the open-web alternative to Amazon. Millions of merchants, a high-profile AI commerce partnership with OpenAI that sent competitors scrambling and a narrative about the future of direct-to-consumer commerce that the industry largely accepted. The OpenAI native checkout partnership is gone. OpenAI pulled it after fewer than 30 merchants went live with a product that had not built the systems to collect state sales taxes. The Shopify landing page built specifically for ChatGPT now redirects to its homepage. Catalog syndication still exists, so Shopify merchants can be discovered inside ChatGPT. But discovery without a transaction is a referral, not a commerce relationship. OpenAI is now a discovery layer that sends consumers somewhere else to buy. That is exactly what Google has always been. It's not where Shopify needs to play. Read More: From Assistive to Agentic AI: Consumers Wade Into Autonomous Commerce The second front is Amazon. Shop Direct, launched in February 2025 and expanded to more than 100 million products from over 400,000 merchants, allows Amazon Prime customers to buy from brand websites using their stored credentials through the Buy for Me capability. Amazon is now offering independent brands something Shopify cannot match: Prime subscribers, Amazon payment rails, Amazon logistics and an AI agent already in the consumer's pocket. Shopify's strategic response is to position itself as open-protocol infrastructure for agentic commerce, building integrations that make merchants on its platform discoverable across any AI surface. That is a reasonable long-term play. Read More: Shopify Bets Big on Agentic AI It's also coming from a fundamentally weaker position than the one Shopify held a year ago. With an uncertain timeframe for when commerce, at scale on AI models will happen. Walmart has made a deliberate and smart decision. It has opened its full product catalog online to Google's Gemini through its Sparky assistant, which surfaces a Walmart-branded experience inside external AI platforms. When a Gemini user searches for a product, Gemini calls Sparky, which opens what Walmart's head of AI describes as a window inside Gemini where the Walmart shopping experience takes over. This has been packaged as a bold bet on open agentic commerce. But is it? What Walmart is doing with Gemini is not meaningfully different from what it has always done with search advertising. It's funneling traffic to Walmart.com through a different front-end interface, hoping to snag net new customers who might not have found Walmart through a traditional search query. The transaction still happens in Walmart's commerce system. The consumer relationship still belongs to Walmart. The agent is the channel, not the commerce infrastructure. Calling it agentic commerce is a generous description of what is, in practice, a search query that renders inside a chatbot window owned by Google that funnels queries to Walmart. Read More: Why the 'Person' of the Year in 2025 Should Be the Chatbot The more interesting and strategically significant bet Walmart is making is Sparky and the opportunity it represents to convert its 100 million per week physical store shoppers into online customers it can monetize through agents. This is where Walmart's agentic story becomes genuinely compelling. Walmart has a physical footprint and, with One Pay, an online wallet and payment method that's akin to Amazon's in the physical world. If Sparky can move even a meaningful fraction of those in-store shoppers into a digital commerce relationship where Walmart can apply personalization, subscription economics and agentic purchasing, well let's just say that the opportunity is substantial. Read More: Walmart Rolls Out Agentic Advertiser Assistant The risk in the Gemini relationship is also real. Openness means the agent can send consumers to a competitor when the competitor's offer is better. Walmart is trusting that it wins those comparisons often enough and that Google surfaces its products without letting its own commercial interests shape the ranking. That trust has not been tested at scale. And it runs directly into the structural problem that has undermined every search-based commerce experiment for two decades. How intent and eyeballs get monetized. The reality of agentic commerce in March 2026 produces a short list of those in the pole position right now. Amazon is at the top. The status quo of how most people actually shop online is a close second. Amazon didn't wait for the industry to agree on rules. It spent three decades building out a marketplace and AI-enabled it inside its own ecosystem. It's now extending that solution outward on its own terms. Rufus, Amazon's AI shopping assistant, handled 250 million shoppers in 2025, with monthly active users growing 140% year over year. Amazon says that its Rufus users are 60% more likely to complete a purchase than non-Rufus shoppers. When the conversation ends, the transaction completes immediately because the marketplace already exists behind it. Shop Direct, however, is the more significant development. By expanding to more than 100 million products from over 400,000 merchants and enabling Prime subscribers to buy from brand websites using stored Amazon credentials, Amazon is doing something strategically important. It's not just defending its marketplace. It's making its marketplace bigger, more open and more valuable, while ensuring that every transaction, wherever it originates, runs through Amazon's infrastructure on Amazon's terms. The Prime subscriber base is the strategic asset that makes this possible. Prime members spend significantly more than non-Prime customers. Their purchasing intent is high. Their payment credentials are stored. Their trust in Amazon's fulfillment is established. When Amazon brings consumers to a brand's website through Buy for Me, it is not sending a casual browser. It is delivering a buyer with an intent to complete a purchase. That is a value proposition that competing platforms cannot replicate without the same combination of payment infrastructure, logistics capability and consumer trust that Amazon has spent 30 years building. That's despite the recent Perplexity lawsuit, which, within the space of a few days, clarified the legal landscape in Amazon's favor, as another judge just yesterday (March 17) reversed it pending an appeal, according to Bloomberg. For now, the bots are there, free-riding on an ecosystem that they didn't build, and according to Court filings, they allegedly disguised their bots in order to gain access. Read More: Amazon Injunction Could Change the Future of Agentic Commerce The second winner is the status quo of how online shopping actually works. Most consumers, most of the time, in 2026, still shop the way they did before AI agents arrived. The search and purchase journey is largely disaggregated. They search Chat or Google or Amazon, then buy from wherever they can find the right price and the fastest delivery. Read More: Legacy Business Models Break The gap between what AI can figure out and what it can actually do about it is not a small one. Closing that gap requires things that do not yet exist in combination. It requires a real business model for catalog access, not just technical protocols. Retailers need a commercial reason to make their live inventory available to AI agents, a compensation structure that makes that exposure worthwhile, and governance that protects them from being used as price comparison tools that send consumers to competitors. The open standards being developed are the pipes, not the economics. Read More: What Happens to Stores When AI Agents Do the Shopping? It requires careful thinking about those economics. Every commerce network built on a discovery foundation eventually faces the same crossroads. Merchants pay for visibility. Promoted products rise above better-matched ones. The consumer notices the results look like ads. Trust flies right out the window. Read More: Why Trust is Data's Only Real Currency The AI agent that tells a consumer it found the best product for their needs while receiving compensation from the merchant whose product it recommends is not working in the consumer's interest and looks like a more sophisticated ad disguised as advice. That requires brands to make hard choices and some big bets. Meanwhile, Amazon isn't waiting. It is using every month of industry delay to extend its position. Every new merchant added to Shop Direct, every consumer who uses Buy for Me, every improvement to Rufus deepens an ecosystem that is already the hardest thing in commerce to replicate. The open AI platforms are not competing against a static target. They are competing against a moving one. I have written about the promise of agentic commerce many times. The thesis has always been the same. Consumers want an agent that works for them. One that knows their preferences, understands their constraints, does the research, makes the comparison and closes the deal on their behalf. Not a better search bar. Not a more conversational listing service. An actual agent that shops. Read More: How Consumers Want to Live in a Conversational Voice Economy The consumer demand is not in question. It never was. A third of consumers have already abandoned their prior shopping methods entirely. More than 70% say they want to use AI agents to shop. These are not early adopters playing around with the shiny new object. They are mainstream consumers who've found something better than what they had and voted with their behavior. Those habits are sticking. What the past year revealed is that the gap between consumer readiness and commercial infrastructure is wider than most of the predictions of 2024 acknowledged. The technology moved faster than anyone anticipated. The commerce plumbing did not. Merchant agreements, live inventory access, payment rails connected to the conversation, governance frameworks, return policies, tax infrastructure, the whole invisible machinery that makes a purchase feel effortless rather than terrifying. None of that gets rebuilt in a product cycle. That doesn't mean it will take decades. The pace of AI development alone collapses the timeline. The models are improving every few months, the protocols are being written, the coalitions forming, the early experiments producing hard-won, even painful, knowledge about what the market actually requires. And all of it is laying the foundation faster than any prior generation of commerce infrastructure was built. Read More: AI Doers Drown Out AI Naysayers The question isn't whether the loop closes. It is who closes it, under what terms, and who is left holding the better position when it does. This is where time matters in a way the optimists tend to underestimate. Igniting commerce networks is more science than art, more strategic than wishful thinking. Every merchant recruited makes the next recruitment easier. And more consumers more likely to give it a try. Every consumer transaction deepens the trust that makes the next one more likely. Every improvement to fulfillment raises the bar that competitors must clear. Getting to that point is a tedious slog The players who are building real infrastructure now, actually solving the hard coordination problems, are accumulating advantages that will be very difficult to displace once the market tips. The opportunity on the other side of this infrastructure gap is unlike anything commerce has seen since Amazon proved that consumers would trust a website with their credit card if the experience was reliable enough and the selection was wide enough. What it produced is a marketplace that accounts for more than 60% of all online sales and a Prime membership that reshaped how an entire generation thinks about buying things. Not to mention the analog for how online transacting must behave. Agentic commerce, done right, with an agent that is genuinely aligned with the buyer, represents a comparable reset. Not an incremental improvement on search. A different model entirely. Read More: How Time Became the Next Great Asset Class My Toast Test still disappoints. The answers are better but still missing my go-to brand. Yes, I am picky. And the AI still can't buy it for me. But the day that changes is the day I'll buy, and then do it again, and build the trust that, next time and the time after that, an agent can do all of that for me. Until then, agentic commerce remains just a smarter search bar. A better one than what came before. And one that I will continue to use that way. Join the 21,000 subscribers who've already said yes to what's NEXT. PYMNTS CEO Karen Webster is one of the world's leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the Sezzle board, a publicly traded BNPL provider.