Latest / Retail Media Breakfast Club / Why OpenAI Ads Are a Wake-Up Call for Retail Media Networks (And What Happens Next)
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- Why OpenAI's Ad Announcement Should Concern Retail Media Networks - And What Comes Next === [00:00:00] Kiri Masters: A couple of weeks ago, OpenAI announced that it would begin testing ads within chat GBT responses for free and go tier users. The example they showed was a sponsored product recommendation for hot sauce appearing [00:00:15] after a query about Mexican dinner party recipes. [00:00:18] The ad was clearly labeled and separated from the organic answer. I've been presenting this exact scenario to retail media executives in recent months explaining how [00:00:30] AI enabled shopping would capture the high margin onsite advertising revenue. That's fueled retail media's explosive growth over the past few years. [00:00:40] The announcement from Open AI validates that the ground is [00:00:45] already shifting under retail media's foundation. Whether the industry wants to acknowledge it or not. [00:00:51] Open AI capturing sponsored product placements is just the opening move. The real threat extends beyond onsite advertising [00:01:00] revenue, and I predict the bigger disruption is still ahead. [00:01:04] But this is not a funeral for retail media. The industry has a chance to reinvent itself. Here's what I predict comes next. [00:01:13] This episode is based on [00:01:15] a article that I wrote for my column at the Drum on January 20 called Why Open AI's Ad Announcement Should Worry Retail Media Networks. Let's jump in. [00:01:24] [00:01:26] Kiri Masters: Not every hyped technology reshapes [00:01:30] consumer behavior, the metaverse flopped, voice commerce stalled. So why will AI enabled shopping be different? I've developed a framework for identifying lasting change in retail, and and it comes down to five [00:01:45] conditions that must be all present pain technology, frictionlessness culture and economics. [00:01:52] AI enabled shopping while not yet in its final form as a fully agent shopping assistant is [00:02:00] still rapidly checking off those boxes. Let's start with pain. The current e-commerce experience objectively kind of sucks even if we've normalized it. Consumers wade through fake [00:02:15] reviews, millions of identical products, 12 open browser tabs, comparing prices and opaque pricing that makes trust impossible. [00:02:24] A proposed class action lawsuit filed in late 2025 accuses Amazon of [00:02:30] fake. Discounts during Prime Day alleging inflated list prices to make deals appear better than they actually were. When shoppers can't trust whether a deal is actually a deal, they're primed for an alternative that [00:02:45] provides genuine transparency. [00:02:47] The technology piece is straightforward. AI can already compare options, explain features, filter choices, and it's starting to build shopping baskets. While fully agentic shopping pathways are very [00:03:00] limited right now, we don't need full autonomy for disruption to begin. We just need consumers to trust AI assistance more than they trust traditional search and browse behavior. [00:03:10] Frictionlessness matters because shifts stick when they [00:03:15] slide seamlessly into daily life. AI assistant sit right in our pockets. No new hardware required. No behavioral learning curves steeper than typing a question. [00:03:24] As this capability disappears into operating systems, it becomes invisible [00:03:30] infrastructure rather than novelty. According to Salesforce's holiday predictions 2025 report, 30% of global consumers already use AI chat assistance while shopping in brick and mortar stores With that number, jumping to over [00:03:45] 40% for Gen Z and millennials, culture is where the real transformation lives. [00:03:51] Consumers don't adopt new behaviors because technology exists. They adopt new behaviors because they trust it. And our relationship with [00:04:00] AI has evolved rapidly through three distinct stages from the intern that we offloaded tasks to and supervised to a companion or a bestie who knows our preferences, and the frontier is a [00:04:15] coach who guides us towards our private dreams and ambitions. [00:04:19] When you trust something with your health goals, your budget constraints, your taste preferences, your aspirations. You trust it with your purchase decisions. And that brings [00:04:30] us to economics. The final and most powerful force, when consumer behavior shifts, money follows, [00:04:36] and right now the economic incentives for AI platforms to capture commerce are overwhelming. [00:04:43] So those are the five forces that [00:04:45] shape major retail and behavioral shifts. Now let's talk about how it affects retail media. Retail media today operates on three distinct revenue models, each with dramatically different margin profiles. First we have [00:05:00] onsite advertising. These are the sponsored product listings and display banners on retailer websites and apps. [00:05:06] They generate profit margins of 70 to 80% in the US market. [00:05:11] These are industry estimates, but they reflect the reality that [00:05:15] serving ads on surfaces that you already control costs very little while commanding premium pricing from brands desperate to capture high intent shoppers. [00:05:25] Open AI's sponsored product ads will compete for this [00:05:30] revenue specifically when a shopper asks chat GBT for dinner party menu ideas, instead of searching a grocery retailer's website, the retailer loses the opportunity to serve that sponsored product ad [00:05:42] this isn't capturing new [00:05:45] budget pools. It's capturing the same high intent moments that used to generate those 70 to 80% profit margins for retailers. [00:05:52] So that's one danger. The other danger is that there's going to be compression of onsite inventory as [00:06:00] discovery increasingly happens in AI interfaces is even well before an AI agent checks out for me and completes a transaction, we are doing more research upfront in these LLMs and we are [00:06:15] arriving later@aretailer.com. [00:06:18] After already conducting our research, I already know exactly what the skew is that I wanna buy. As a result, there are less signals for that retailer to [00:06:30] create audiences for advertisers to activate against on site Retailers know that a marketplace model can dramatically boost product assortment, [00:06:45] shopper engagement, and total revenue. But to get the most out of your marketplace, you need an ad tech solution that can really engage sellers. Miracle Ads is powering the future of retail media for leading [00:07:00] retailers to activate both three P Sellers and one P brands. [00:07:04] Learn more@miracle.com. That's M-I-R-A-K l.com. [00:07:11] [00:07:12] Kiri Masters: the second leg of the [00:07:15] three-legged stool of retail media is offsite retail media, and this is where retailers sell their first party transaction and behavioral data to help brands target shoppers across third party publishers like Connected TV or social media. And [00:07:30] this doesn't generate as high of a profit margin, maybe around 40% in the us. [00:07:36] But it is still often a very significant component of retail media revenue for retailers. Now, this whole value proposition rests [00:07:45] on data exclusivity. A retailer will say, I know your audience. I know everything about them. You can activate against their behavioral signals using my data set, and it's a, a very effective way to [00:08:00] reach in market shoppers or brand switches or different audiences that you reach across different platforms. [00:08:07] But what happens if that exclusivity erodes. Now here we're talking about truly [00:08:15] AG agentic shopping, where a LLM is actually processing a transaction for you. And this is how it works. In instant checkout, both open AI and the retailer will see that purchase signal. [00:08:30] The retailer still owns the actual transaction data, the um, customer identity. [00:08:37] But OpenAI now holds something that is also very valuable, which is those cross retailer [00:08:45] comparisons. What consumers compared, what recommendations did they end up converting on? And they also see when a transaction actually went through. [00:08:55] So no LLM has announced yet an audience [00:09:00] extension or offsite advertising capability, but to me this is a no-brainer path to monetization for LLMs. They get all the upside of advertising revenue without eroding the user experience inside the app. So I predict we'll see [00:09:15] this move from at least one LLM this year. [00:09:18] And again, just like the threat to onsite revenue, as more transactions flow, as as more upper funnel activity gets done in these AI intermediaries. [00:09:28] The retailers [00:09:30] get less behavioral and click data upstream, which makes their overall data proposition just a little less valuable. [00:09:39] Now the third leg of the three legged stool is in-store retail media, and this is the [00:09:45] unexpected survivor. Things like shelf displays, digital screens, audio advertising. Right now, it is a very small percentage of the overall retail media revenue in the us, but this is the format that might be the most defensible [00:10:00] part. [00:10:00] Of all precisely, because it cannot be intermediated by ai. No LLM can replicate physical presence, sensory context, real time proximity to products. When everything else becomes [00:10:15] abstract mediated through screens and algorithms, the physical store becomes more valuable, not less. And people still like to go to stores. [00:10:24] They love to have that third place, a place to go, a place to be inspired, [00:10:30] and the act of shopping in stores will remain something that many consumers genuinely enjoy for specific shopping missions. [00:10:38] Now there are other safe harbors and paths to collaboration that I think [00:10:45] could make this next era of retail media the most trusted and collaborative one that we've had so far. I know I sound like a domer, but I really do think that this is a chance for a reset for the retail media [00:11:00] industry and a way to develop new ways of working with brands and across the commerce media ecosystem. [00:11:08] It might actually force a healthier model. [00:11:13] But here's the thing. The retailers [00:11:15] who recognize this shift early have the runway to adapt and build these new ways of working. Those that dismiss it as futuristic speculation will find themselves reacting to market forces rather than shaping them. [00:11:29] [00:11:30] The technology exists. Consumer trust is building, and the economics makes sense for AI platforms to capture commerce revenue. [00:11:37]