One Amazon Assistant to Rule Them All: Welcome to the “Alexa for Shopping” Era:

Amazon announced today they are combining Rufus and Alexa+ - their two separate shopping agents have now been combined into one named Alexa for Shopping.

All devices, the Amazon website, mobile apps and Echo devices will all be moving away from Rufus and/or Alexa to the new brand: Alexa for Shopping.

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Shared Context/Memory Across All Surfaces

Amazon examples of how your shopping preferences and contexts can be multi-modal/multi-device-al.

Use Case 1 (Web→Device Example)

One example is - let’s say you setup an alert for a Nintendo Switch 2 that is frequently out of stock using the Alexa for Shopping on your desktop website Amazon.com. That evening at 10pm you are home and your Echo device has a notification that the Switch 2 is now available and you can order it from the device.

Use Case 2 (Device→App Example)

In this example, you could be talking with Alexa on your Echo Show with your 16yr old to think through different science fair projects and choose the project. Then when you are on the Amazon app on your phone ordering some dog treats, Alexa for Shopping prompts you with a list of products you’ll need for that science experiment - you remember you forgot to order them earlier in the day and you throw them in the cart and the arrive over-night with the dog treats. Alexa for shopping remembered this context that started on an Echo device and surfaced it on the mobile phone app during a standard shopping session.

When Will Alexa for Shopping Role Out and What Does it Look Like?

Amazon says that over the coming week this will role out and the Rufus icon will be replaced by an Alexa icon across all surfaces. We know that Prime Day is coming in June this year, so this will all be rolled out before Prime Day I imagine.

Right now in the US as of 8am ET for me on the website it’s switched over (see above) in the website, but not on the mobile app.

On the help docs they do show the mobile app will look like a stylized ‘Alexa a’→

All Amazon customers can use Alexa for Shopping for free when signed into their account, no Echo device, Alexa app, or Prime membership required.

On the PDP and under the hero image there’s a sparkly treatment.

Here’s the hero page on the help docs for the new Alexa for shopping:

🔮This Move Was Predicted Over a Year Ago!?!🤔

If this all sounds oddly familiar to you, Amazon 1P “Rufus Whisperer”, Andrew Bell, found an Amazon patent over a year ago and from that patent predicted that this exact move would happen - as one prediction maker to another - that’s a 💯 prediction! (details here, here and here). Wow - mad respect to Andrew on 🎯 this one! 🫡

Other Rufus Alexa for Shopping Updates.

Note: Some of this was covered already in previous posts through leaks and early discoveries here and here.

• Unified Search Bar (semi-covered already) - You can now ask Alexa for Shopping questions right in the Amazon search bar and it recognizes when you’re asking a question and Alexa for Shopping and routes it to Alexa for Shopping to help answer it — from general questions like “What’s a good skincare routine for men?” or “How to plan a unicorn-themed birthday party,” to product comparisons like “Breville Barista Express vs Pro” or “Compare Kindles,” to order inquiries like “When did I last order AA batteries?” or “Where is my order?”

• New Compare Products Functionality - Compare products from search results: Select multiple products directly from your search results and Alexa for Shopping will compare them side by side, helping you quickly evaluate features, prices, and reviews to find the best option.

• AI Overviews (covered) - Get AI overviews in search and on product detail pages: Alexa for Shopping surfaces AI-generated overviews at the top of search results in the Amazon Shopping app, giving you a quick summary of a product category and what to look for before you start browsing. You’ll also find AI overviews on product detail pages to help you make more informed purchase decisions. The feature is already available to millions of customers and rolling out to all U.S. shoppers.

• Price History Updated to 1 Year - Check product price history for up to a full year: Tap “Price History” on any product detail page or ask Alexa for Shopping for the price history to see how the price has changed over the past year on hundreds of millions of products in Amazon’s store.

• Schedule Routine Purchases: When chatting with Alexa for Shopping, tap the “+” icon next to the message bar to create a Scheduled Action, such as adding healthy kids’ snacks to your cart each month, restocking regular household items like pet food, paper towels, and detergent, alerting you when your favorite author releases a new book, or getting gift ideas ahead of family and friends’ birthdays and holidays. You can also get more specific with prompts like “Add this sunscreen to my cart if the price drops to $10 and I haven’t purchased it in the last 2 months.” Alexa for Shopping handles the product research and will either notify you or add relevant items directly to your cart — as a one-time action or on a recurring schedule — so all you have to do is review and check out.

• Easy Add to Cart: Effortlessly add items to your cart: Alexa for Shopping can search past orders and add them to your cart or quickly build new carts with conversational directions. Say things like “add my regular dog treats” or “add my frequently ordered cleaning products” or “add my favorite protein bars to my cart,” and then check out with a tap.

• Personalize Your Experience/Memory: View and update details like family members, pets, interests, dietary needs, and more by simply asking Alexa for Shopping what it knows about you. This is live today: prompt this: “Review my shopping preferences”.

• Custom Shopping Guides - Learn about new product categories and items: Whether you’re making a big purchase like a TV or exploring a new product category for the first time, Alexa for Shopping can create a custom shopping guide that compares features, prices, and reviews across Amazon and the web based on what matters most to you.

Analysis: Why Now and What’s the Strategic Move Here?

Why now? This makes logical sense- two shopping assistants is one too many and the use cases make a ton of sense.

Amazon, for the first time in 20+ years faces a competitor, Agentic Commerce, facing them on two fronts:

1. A frontal ‘selection assault’ from a group of 5+ powerful and large attackers.

2. A flanking ‘horizontal assault’ via a super-assistant attack also the group of 5+ attackers.

That’s a lot of ‘incoming’ attack vectors for a company that has run over all the big retailers with barely a speed bump.

What’s the strategic move?

While on the surface this may feel like a re-brand, I think it foreshadows something much bigger. Amazon’s just announced ‘embrace and extend’ UCP strategy is designed to counter the ‘selection attack’.

The horizontal assault is tougher to counter and is illustrated here:

In his Q2 remarks, Jassy was very specific to say that the Amazon vertical shopping experience will always be superior at shopping vs. horizontal, but this doesn’t make a ton of sense, because yes it will always be a better shopping experience, but it will increasingly lose the battle of “overall context”. This shows us they are thinking a lot about this.

Here’s why it’s a threat.

People are telling ChatGPT about their trips, their car shopping, their kids, their meal plans, their finances, their business plans, their health care issues and fitness regiment. That’s a list of 8 shopping-adjacent contexts that Amazon is totally blind t, but ChatGPT can use to help you find products. They are sneaking up on Amazon from the side. Oh let’s talk about your skin-care - 5 turns in and you’re buying a beauty product not even sold on Amazon - this is Amazon’s worst nightmare.

Before ChatGPT none of this was a concern as none of this shopping-adjacent was collected in one place that even threatened shopping Expedia, WebMD, etc were, if anything, Amazon associates. Now here’s the first time that’s not the case, consumers love it, they are innovating at an insane pace and to make matters worse, you have 6 mega cap companies going after this opportunity. This is not a typical a 2-dimensional flanking diagram, it’s a 6-headed hydra coming at you on a 3-D chess board.

How Do You Defend Against a Horizontal Super Agent?

The best way to de-risk the horizontal agent threat is to…Expand Alexa to be YOUR horizontal super agent. Use your shopping wedge to go wide before the attackers can create a wedge into your high gound.

My prediction is over the next year we’re going to see Amazon take Alexa wide/horizontal by adding health, travel and other features to have an answer to both the product selection attack and horizontal personal assistant attack they now face from Agentic Commerce and the Answer Engines.

What’s Next?

Will these new attack vectors work? Too early to call, Amazon’s in a great position and it’s strategy reaction here feels appropriate and it’s rapid compared to the emergence of the threat. This Holiday will be an interesting battle in the war and we’ll be reporting on it from the front lines. Stay tuned.

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Note: Brands pictured here were listed on the DSS website as speakers, but representative of the types and size of brands in the audience.

I was invited by the awesome folks at Salsify to attend the Digital Shelf Summit 2026 and to deliver a keynote at the opening day to ‘set the stage’ for the theme of the event.. you guessed it: Agentic Commerce. We had permission to record that and will be publishing it as a podcast next week, stay tuned. I also gave an interactive Agentic Commerce Optimization Workshop:

I started the session by going deep on one SKU, a Silk vanilla almond creamer, in what we call an Agentic Commerce Audit. This process illustrates the patterns that you start to notice patterns about how the engines talk about your products. You can take the positives and negatives patterns and apply what you learn in one SKU to every product in your catalog. The deep-dive stuff was fun, but the best part of these sessions is always the Q&A. Brands asked the questions that are actually on their minds → not strategy or theorticals but , the real tactical “what do I do Monday morning” after this conference questions.

Q1: How Fast Are LLM Training Cycles Moving?

The background for this question was part of my setup for the workshop. Part of ACO strategy for brands that don’t sell direct to consumer (commonly called 1P/Wholesale or B2B Brands) is to make sure their purely informational websites have all the correct metadata outlined in our ACO strategy (introduced here). While this doesn’t drive direct sales, the agents pull in the data that improves the product-level results from retailers selling their items. Because the 1P agents (Rufus/Sparky/TSA) are build on models like OpenAi/Claude/Gemini, they will pick up this data in their training data and it will go into ‘long term memory’.

With that, background, here’s the answer:

The training window is closing in faster than you’d think, and they’re accelerating. Training cycles used to be 18 months → then a year → now we’re at about six months for a foundation model refresh (Claude is here, I think everyone will catch up this next cycle).

And that’s just the base model. The RAG / web-index layer that lives at your 1P partner is the part that actually pulls in your fresh product content and that updates constantly. Anthropic, OpenAI, Google, Perplexity, CoPilot, META → they all have crawlers hitting your site in something close to real time.

Want proof of where any engine stands? Ask it directly: “What’s your training cutoff?” You’ll get a real date back. As of this writing:

ChatGPT 5.5:

Claude Opus 4.7:

Gemini 3.1 Pro:

Takeaway: Belt and Suspenders: Publish to your content to your website for training data longer-term AND make sure you optimize what you send your retailers. This puts your product data in the ‘short-term’ memory and the ‘long-term memory’.

Q2: There are Two Schools of Thought: Flood the Zone With Brand Content (AEO/AIO) or Go Deep at the Product Level. Which is Right?

I’m going to sound like a broken record and we’re already on Question 2, but, you guessed it - you should do both.

AEO/GEO is the awareness/sentiment piece - usually managed by your PR/marketing/brand team and not product-level content.

ACO is the product-level where you make sure your product-level content is beefed up for the LLM era and you’ve optimized it over several iterations across your 1P agents and the big 6 answer engines. This optimizes the part of the funnel involving products, and thus your product catalog: Research→Find→Buy.

AEO gets your brand mentioned in the conversation. Product-level optimization gets your SKU onto the offer card. Two different pieces of real estate, and you want to win both.

The AEO-leader, Profound, style of “pump out a thousand pieces of brand content” approach is directionally fine. Brand-story level content builds the brand-mention (sentiment) layer. But if your Attributes, supplemental attributes, reviews and FAQs, distributed viadata feeds aren’t in shape, no amount of brand-level content saves you when a shopper says “I’m remodeling my kitchen and need a built-in espresso machine.”

The best news is ACO is both more actionable and faster to see action, and thus more measurable too.

Q3: What about Grok? Does it have a merchant center yet?

Not yet. Here’s the scorecard as of now →

ChatGPT → has one, it’s still gated, but should be open for online signup soon.

Gemini → The existing Google Merchant Center

Copilot → has one tied to the Copilot merchant center.

Perplexity → has one-ish, routed through PayPal, a little squishy on how to get access - there’s some chaos over at PayPal these days and merchants are getting mixed messages and delays.

Anthropic → rumor is working on it, nothing announced.

Grok → nothing.

But I’d bet on Grok showing up to this party. The infrastructure is too obvious to skip, and X is too motivated to monetize. Give it two quarters to get through the API.

Q4: Are Retailer Walled Garden Agents like Rufus and Sparky Bleeding Signals Back into the Open Agents (ChatGPT, Gemini)? Can Optimizing for one Hurt the Other?

Yes, there’s cross-pollination - but it doesn’t go this way, it goes the other way. Because these are walled-off retailes

Rufus is most likely a Claude derivative sitting on Anthropic’s model. Sparky sits on OpenAI’s model. So when you optimize for ChatGPT, ~90% of that work is automatically lifting Sparky too. Same foundation model, same eating habits. When you optimize for Claude, you’re long-term (training data) getting benefit in Rufus.

A large % that is additive is the retailer PDP and behavioral signals. Signals like what shoppers actually click, search, and buy inside Amazon or Walmart. That part you can’t reach from outside.

So yes you can feed the foundational models, the foundational models feed into the models underneath the retailer agents into their long-term memory. Then your PDPs come into their short-term memory.

Q5: How do you Optimize for a Retailer that Doesn’t Have an on-site Agent yet - A Grocery Retailer, for example?

Manually, retailer by retailer. For now.

You have to partner with your retail partners and ask them: “Can I add an FAQ block to my PDP on your site? Can I push metadata to the PDP? Can I expand my product description fields? Every retailer’s PDP is, at the end of the day, an HTML page. There’s always a way to inject the content the agents will want.

The good news: I’m calling it: every major retailer will ship an agent within ~12 months. Albertsons is out. The Krogers, Targets, and Lowe’s (grocery) of the world are all heads-down on this. → This is a problem that’s actively solving itself.

Do what you can today. The surface area expands automatically.

Q6: Do we really have to supplement each retailer’s “agent diet” by hand?

Short answer - for the next few quarters, yes. But here’s the framing that changes that math:

Your website is doing 80% of this work for you. The engines crawl your DTC site, ingest your metadata, pull in your FAQs, digest your reviews, and forklift all that data into their vctor databases. That index then powers every agent downstream, retailer-owned or not.

When those agents do launch, it’s largely going to inherit the product intelligence you already published on your own site. Also, everything you do today, will pay further dividends down the road because everything your organization learns about ACO will apply to LLMs now and into the future. The underlying love of natural language won’t change.

For a non DTC brand your website went from a 1 to a 9.5 on a scale of 10. It’s not a vestigial brochure anymore. It’s the source feed for the entire agentic ecosystem.

Q7: 🤯 How do you Handle AI-Generated Fake Reviews? How do you Even Know What’s Real Anymore?

This is the gnarly one, and it’s getting worse fast. Three angles →

(1) Verified-purchase signals → Tie every review to a real transaction, the Amazon model. By far the strongest verification of a review, and the hardest to fake. If you’re not running DTC, lean on retailers who do this well.

(2) AI-detection tooling → There are services that score text for AI-generation probability. They’re imperfect but improving. Your review platform (e.g. Bazaarvoice, PowerReviews, Yotpo) should be building this into their stack right now. Talk to your rep and ask what they’re shipping.

(3) Owned content as anchor → When the review pool gets polluted, your owned, brand authoratative content (FAQs, structured product data, schema.org metadata) becomes a bigger share of the engine’s truth. The brands that invest heavily here are building insulatation.

The bigger picture? Review trust is going to get harder before it gets easier. Brands with verified-purchase ecosystems and strong owned-content programs are going to dramatically outperform. Plan accordingly. Start today.

These seven questions map directly to where every brand should be focused right now: training cycles, AEO vs. product-level, engine coverage, cross-pollination, prompt engineering, retailer agents, your DTC site as foundation, and review integrity.

Stay tuned for incoming news on this broader topic later this week 🤫 🚀

Let’s start with the big news, then go over the ‘why’ plus the ‘what does it mean’ and then wrap with the Amazon Frenzy 😵💫.

Introducing Product Feed Campaigns

Long-time Retailgentic friend Joe was poking and prodding at the ChatGPT Ad management user interface and was able to surface this feature. Note in the past this has indicated, this is a feature either coming soon for a test or already being tested by an audience using what’s called a feature flag that allows a vendor to ‘opt in’ one set of customers in a feature and the others don’t see it.

Background: Retailers and brands are able to upload datafeeds to ChatGPT now through their Merchant Center. Many brands/retailers may have multiple feeds and it’s good data hygiene for feeds to have a unique identifier (feed ID)

The first tell is here in the Ad Campaign creation flow, there’s a new ad type:

Here we have a pretty typical ‘campaign builder user interface with a 3 step wizard:

1. Create Campaign

2. Create Ad Group + Ads

3. Review

4. (Publish!)

Once ‘Product feed’ is selected as the ad type, the ad builder moves to step 2

Product Feed ID and Product Filters?!

Here’s what step 2 looks like:

Now we see the ability to create ad groups inside of the campaign, but with the exciting new twist, they are built on a Product Feed foundation.

Here’s how it works:

• First, you have to provide the product feed id (see backgrounder above for why).

• When the feed is found the UI pings Merchant Center and tells you there are X products loaded from that feed (in this example, 1,284).

• Then you can either run those ads against the whole feed (highly unlikely - see Analysis section for why), or you can carve off a set of products and run ads against that subset. ChatGPT is calling this ‘Product filters’ and the help text says: “Choose Product attributes to narrow which feed items can serve in this ad group.”

Available Product Filters

When you click on the Product filters selector you get 6 filter choices (today - I’m sure this will expand rapidly):

• Product Category - Here you can choose to advertise shoes, accessories, men’s, women’s whatever. and it’s a multi-picker.

• Brand - You can choose to runs ads against specific brands.

• Seller - This is a third-party marketplace setting. Etsy is an early launch partner with all the Agentic Commerce companies, so my bet is they are a design partner here and this was added at their request. They have some featured sellers they want to promote or they have a list of sellers that are paying to be promoted (down in the Etsy merchant center) which would allow Etsy to ‘pass them up’ to ChatGPT for ads.

• Rating - You may want to run ads against highly rated products (this is a weird one, I bet nobody uses it).

• Color - Also a weird one, nobody runs ads against ‘red’ only.

• Condition - A huge growth area in ecommerce is ‘circular fashion’ or recommerce. This feels like a setting where you could run ads against your new and recommerce categories or exclude refurbs if you were an electronics retailer, etc.

Long-time ecommerce marketers will immediately see that there are some key atributes obvious missing! We’ll cover that in the Analysis section.

‘Stacked’ Product Filters

In this example, we’re only going to run ads for products that meet three criteria: Brand=Adidas, Condition=New, Category=Trail shoes. That’s a pretty narrow ad group, but maybe it’s hiking season or we’re running a special promotion in partnership with Adidas. Regardless of why, it shows the power of ‘stacking’ filters like this.

Final Step: Ad Creative

Now you need to give ChatGPT a ‘recipe’ for how to merge in your product data with the ad creative. Do you want the product image? if so, which one? Do you want the brand mentioned, the color, the condition, the price, etc?

This simple ‘builder' allows you to do that create a mail-merge like setup where your product data feed data is married with the ad creative template to generate a pragmatically individualized ad for each SKU.

Now that you’ve seen what it looks like in teh UI, let’s analyze what this means for ChatGPT, the Digital Ad market and Agentic Commerce.

Analysis of ChatGPT’s Product Ads Feature

If you’re not familiar with this concept, the first question you may have is:

Why would you want to run ads against a datafeed?

The challenge marketers in ecommerce have is four-dimensional:

• Scale: Your average brand has 2-20k SKUs and the average retailer has 20k-100k+ SKUs

• Price Variation: Your price in that assortment most like varies wildly from sub $10 to over $300 or higher. (30x delta)

• Margin variation: Your margin (either $ or %) for these products is usually not 100% correlated to price or color, or category.

• Ad Copy: Creating bespoke Ad copy for 2-100k SKUs is tedious if not impossible.

Therefore ONE CPC ad for all your products doesn’t work. You’d be significantly overpaying for your $5 pair of socks and under investing in your $300 hand held vacuum cleaner.

The Solution:

The solution is you chunk up the datafeed into pieces that have similar margin/revenue/ROAS goals (and conversion rate) into a bucket and bid the same across that bucket. BOOM! Product Ads are born.

Complexity Awaits

The hidden complexity challenge of this approach is advertisers start to request near infinite ways to slice the data into buckets - what about by price range, what about by margin, what about X/Y/Z? May of these interfaces then either go into that being in the datafeed, or totally algorithmic (e.g. Google PMAX, etc.) where the advertiser just has to trust that the algorithm will hit their blended goal.

One last point:

Why is ChatGPT Moving so fast on ads?

I can boil this down to one picture:

According to Axios and The Information, OpenAI raised their last round on projections of $100B ad revenues by 2030. To put that number in perspective here it is against where the Big Ad Three are today:

ChatGPT is starting at 0. It took Amazon, with their giant distribution 20yrs to get to $70b (started in 2006). It took META 12yrs and Google 17-18. Can they do it? We’ll see. Life on the exponential only guarantees one thing: predictions are very very hard for humans to wrap their heads around.

Amazon’s Job Posting Frenzy

Earlier in the week, Joe found this job posting on Amazon’s website→

Retailgentic friend, Jason Del Rey covered this in the Aisle here (usually paywalled but his one is free - try it out and sub!)

Both Joe and JDR are assuming this means that Amazon is about to embrace one of the Agentic Shopping Engines like Gemini, ChatGPT, Perplexity, Meta, Claude, etc. The most eye popping part of the job listing is this part, that I think is causing the frenzy: “leading and mentoring technical programs for an engineering org of 40 people" within one of three specialized teams. More on that in a sec.

I’m going to diverge on this one and take the ‘under’. Let’s parse through the job listing closely with a bit of background first:

Background

Key datapoints for my thesis from the last 60 days:

• 3/11: Amazon Expands the Shop Direct program - adds a merchant center and we’ve heard though the grapevine they are out selling this program to brands not on Amazon (1P or 3P) aggressively. In fact Tuesday I’ll talk about Amazon being at Digital Shelf Summit which has to be a first.

• 4/28: Amazon announced they have joined UCP - Way back then (haha) I predicted a UCP checkout will be what replaces the browser-based Buy-for-me implementation that is a sub-set of Shop Direct. (I suggest they brand it UCP-for-me ;-) )

• 4/29: In the EPIC Quad Earnings Day Jassy was 100% NOT complimentary on ‘horizontal search engines’ and was giddy over Rufus. They also mentioned Shop Direct in prepared remarks/PR.

One more background item. Amazon has a famously different management setup than traditional companies - they believe in a flat org with lots of two-pizza teams tackling opportunities with loose coordination. The role that sits off independently and attempts to herd these cats is called the TPM role. It’s quite unusual role and I’ve never seen anything like it. It’s also insanely hard because you are held accountable for shipping things and hitting KPIs, but none of the people report to you. It’s best described as a ‘influence and persuasion’ job with a mix of project management. Want to learn more? Amazon’s job site has a complete guide the the TPM role here.

Amazon is playing Defense, not Offense here.

To summarize the Retailgentic 4/28 article into one picture:

I believe Amazon sees UCP/Agentic Commerce as a threat - a viable alternative to Amazon 3P with cheaper economics, but most importantly, a set of great inventory that is not available on Amazon. (The left side of the diagram).

By really leaning into Shop Direct and UCP they ‘eat’ the threat by making the selection effectively available to the Amazon shopper via Amazon.com and taking away any reason what-so-ever to leave the Amazon ecosystem. In business strategy this is called ‘Embrace and Extend (and sometimes…Extinguish).

My Big Amazon TPM Agentic Commerce Prediction….

My prediction sits on the fact that Amazon could easily have 40 devs working on both Shop Direct (SD) and Buy-For-Me/UCP programs. You could sketch-out people working on: Merchant Center, SD sales support, SD account management, SD feed processing, SD→ Rufus integration, SD→ Search integration, SD→Mobile, SD/BFM front-end, SD/BFM back-end, etc. Heck maybe you even want to integrate it with Alexa+. That’s like $10m to protect the $743B (TTM as of Q1) cash cow? Jassy can find that in the couch cushions up in Jeff’s old office.

Therefore, I believe this TPM person is coordinating the Shop Direct teams rapid growth in functionality and merchants as well as the move from the browser-based checkout in Buy-For-Me to the UCP which has the side benefit of allowing them to slurp up all the delicious selection that currently sits outside the Amazon Empire.

What confuses me the most is the stated fact in the ad that it’s 40 people across 2-3 teams. That’s a range of 13-20 people per team. Those are either massive pizza’s or they now have a 3-4 pizza team rule. I have heard from insiders that after Covid and RTO the previously religious 2-pizza team rule has flexed a lot.

Only time will tell, but the good news is I can guarantee you we’ll know before November 26th (🍗)! Stay tuned.

Microsoft - Welcome to Retailgentic!

In this episode, we sit down with Michele Fisher (Global Director, Business Strategy for Retail & CPG) and Atanu Ghosh (Director of Agentic Commerce & AI Innovation), who sit at the intersection of Azure AI, Copilot, and real-world retail and brand Agentic Commerce implementations.

They’re in constant conversations with global retailers and brands trying to figure out what actually changes when AI agents become the customer.

Listen/Watch Our Interview:

This conversation dives into how Microsoft is thinking about agentic commerce as a full operating model shift, not just a feature or channel.

From Copilot shopping to data infrastructure to ads inside LLMs, the discussion connects the dots between:

Highlights

• Conversational discovery is fundamentally different from search: The way people talk about brands inside AI systems looks nothing like traditional keyword behavior, and most companies have zero visibility into it.

• Agentic commerce is not one thing, it’s an operating model shift: Microsoft defines it broadly: agents participating in discovery, decision-making, and transactions, not just checkout.

• Copilot + catalog + data = the new commerce stack

• Ads are changing from persuasion to evaluation: Agents don’t care about creative. They evaluate structured data, trust signals, and relevance.

• Loyalty becomes infrastructure, not a program

• The “frontier firm” is the North Star: A future where humans and agents work together across workflows, not just AI acting as an assistant.

• Protocols like UCP matter more than people realize: Open standards enable discoverability across surfaces and prevent fragmentation in agent-driven ecosystems.

• The funnel is collapsing

If your brand is still thinking about traffic as the starting point, you’re already behind.
In an agentic world, discovery happens before you ever see the user, and increasingly, without them.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

Check out the materials we discuss:

👉 2025: The year the Frontier Firm is born

Want More Awesome-sauce Agentic Commerce Content?

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We're here to make sense of what’s next in retail + AI and bring you along for the ride. Subscribe and stay tuned!

Up first, Google as been absolutely COOKING, as we say in the AI World, and we have two new feature Leaks that we’ve discovered that we wanted to share.

Editorial note: As with past leaks we’ve surfaced, these could very well be tests that don’t become perfect or change substantially. We surface them as more ‘directional’ information than ‘this is coming to production soon’. Only when the companies make formal announcements do we know we’ve crossed that threshold.

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Leak 1: We Spotted Google’s New Ad Unit Live in the Wild

On Feb 11th, we covered the Google blog post that focused on UCP being live in AI Mode, but in that same blog post, Google announced a new Agentic ad format that I coined Agentic PLAs (still don’t know the official name). As a refresher here it is.

Here it is:

In this example, we did a search for a popular beauty product - a makeup palette and clicked on one of the items. The offer card is now clearly segmented into two sections:

• Sponsored stores - In this example (super-sized in the middle) there are 4 stores (Belk, Macy’s, Ulta, Poshmark) that are paying to be sponsored and featured above the..

• All stores - here we have BOTH the sponsored stores listed again and then non-sponsored. In this example (bolding indicates sponsored): Macy’s, Sephora, Ulta, Belk, Nordstrom, Dillard’s. (Poshmark is in there, cut for brevity).

My guess from the screen shot in the blog post was that there would be only one sponsored store, this is quite a surprise first that there are more than one and second that they are duplicated in the ‘all stores’ - they get both top-billing and ‘re-billing’ again. This is quite a valuable ad unit, it’s not clear how much the merchant’s are paying for it and in what format (CPM/CPC/?)

Leak 2: Google moves UCP-powered Agentic Commerce checkout to ‘main Google’! 🤯

This one is wild! Previously, the UCP product search experience live in ‘AI Mode’, not Gemini mode or what I call core or traditional Google mode. In core Google mode, there was the traditional Google shopping experience.

Now somewhat reliably across devices, logged in, not logged in, we are seeing the AI mode UCP-powered shopping experience show up for core google.

In the example above, I’m looking for a Star Wars rug and here we see the Wayfair listing has a fully functional UCP Buy button activated. Example:

Leak 3: Rufus: RAIOs→ Rufus AI Overviews

This one is thanks to our super-sleuth Joe. For certain ‘vague’ searches in the core Amazon search engine, they are now showing what looks relatively similar to a Google AI Overview - or what we’re calling Rufus AI Overviews. Here’s a screen shot and a video of how it appears→

Note that it is clearly labeled: “AI results’ and it has a SKU that is surfaced. Up in the top left there’s also what we call an ‘escape hatch’ back to the OG experience. My guess is they are now using an AI to example inputs into the search box and starting to route them to different tools - ‘trad search results’ is tool 1 and now we have tool 2: “AI results” that seems to receive search terms that are ‘largely information or vague’ requiring more context.

Here’s a video for how they show up:

Leak 4: Rufus: Skip search go directly to Rufus

We discovered this one with a happy accident. I was entering a prompt in rufus and the focus had changed to the search when I wasn’t watching. The result - a long prompt resulted in the search box ‘auto routing’ (the third tool?) the search term (which was an accidental prompt) to Rufus→

The short video is the best way to see this one in action.

The Theme of These Leaks

Leaks 2, 3 and 4 share a common theme I’ve been wondering about. Here’s the logic path:

• Both Google and Amazon on their quarterly earnings calls in Q4 and Q1 have talked about the high conversion rates of Gemini and Rufus, later in this report we’ll have some third-party data that supports this.

• Therefore, if you have a better experience than the old one, wouldn’t you want to at least test or start moving more significant percentages of your traffic to that new upgraded experience?

• EVERYTHING in our world rotates around the holiday selling season. Therefore the best time to test the theory is in the relatively slower May/June/July and even August time before we ramp into Q4.

I believe these leaks, are testing this theme. If you’re Google and Amazon you want to start learning about what happens when you pour hundreds of millions instead of single/double-digit millions and even into the billions of queries into these newer systems. Questions they would want to answer:

• Do they higher conversions and economics continue with scale

• Do they systems scale well, or do they get more expensive and slow with massive scale?

• How do you run some tests that prove without a show of a doubt the causality of the lift. For example, was it self-selecting higher intent shoppers that caused the higher conversion or via random sampling - where they don’t have the choice - A and B have the same dynamics?

We’ll know if these are going well if/when we see a big move toward Rufus and AI Mode/UCP taking increasing percentages of traffic heading into Holiday 26.

Speaking of Rufus: Fresh Sensor Tower Data

In Feb we had Retailgentic friend Ian Simpson from Sensor Tower on the pod and he introduced their fresh Holiday 25 data here:

Late last week Sensor Tower dropped more data here. Two charts tell the story:

Here we now can see two quarters worth of data. And what’s super interesting is, as you’d expect with extreme seasonality the volume of sessions has dropped from peak (25%), but counter-intuitively you are seeing the conversion rate LIFT from that period from 26^ to 35%. Historically conversion rates peak during peak holiday season and then back off significantly more than traffic declines because there is less urgency outside of holiday.

Over that 6 month period of time we’ve been cataloging here on Retailgentic the massive improvements and features Amazon has been rolling out relentlessly in Rufus. There’s obvious ones (like new user interface features) and this indicates less obvious changes- the data quality, speed and algorithm of Rufus has to be improving at the same time to generate these counter-cyclical results.

Finally, Sensor Tower revealed this data:

Rufus shoppers are 2.74X more likely to purchase vs. a shopper without Rufus interactions. As mentioned in the leak section, if this stat is true and you’re amazon and GMV is the core driver of your economic engine - you want more more more traffic going through Rufus and if it holds, all of it. We just don’t see conversion rate bumps like this in ecommerce. Big companies invest millions for .5% in their search engines. This equates to 247% increase in conversion rates. At Amazon’s scale, if this holds and if the causality is the Rufus experience, this feature alone could double Amazon’s GMV. That’s two big “IFs” which I think is what they are likely testing with Leaks 3 and 4.

SimilarWeb AI Ads Report

Keeping on this theme of interesting new ‘observed data’ reports, SimilarWeb came out with detailed analysis of ad performance across ChatGPT, Gemini and Google AI Overviews. It’s a great report and pretty dense. My TL;DR, these new ads are nascent and already showing signs they are more performant than traditional search. Once they are optimized on the RLAIF exponential feedback loop, by Holiday 2026, we could also see Agentic Ads scale up as well as search changes.

Attachment Ai Advertising Insights From Real User Behavior (1)

2.78MB ∙ PDF file

Download

Download

Anthropic Releases Detailed Claude Observed Usage Data

If you recall, OpenAI released some usage data back in October of 25 and we had OpenAI Chief Economist, Ronnie Chatterji on the pod to discuss:

At that moment in time, it was disclosed that ChatGPT has 2% of prompts related to products/shopping.

In the Anthropic report here. they show that 6% of the conversations fall into ‘consumer’ which anthropic describes as “Product comparison + purchase decisons. Rental decisions. Vehicle purchase and repair.

Like the ChatGPT data, when you dig into how this classification, it reveals to me that this is understating the shopping queries. The way it works is they look at the START of the multi-turn conversation. Fo an example, if someone starts talking about say their weight, this classifies as a ‘health/wellness’. But let’s say on turn 3 the answer engine suggests they try some protein bars, a heart/sleep monitor, etc. Products are surfaced and purchased. You guessed it, still Health/Wellness.

The engines take privacy so seriously they are reluctant to point a categorizer at anything but the first convo, but I believe the unintended impact of this is an understating of the Consumer bucket.

They don’t really dig into the data other than this chart. We have a theory at Retailgentic (one of my 2026 predictions) that Anthropic will enter Agentic Commerce. If you look at their jobs area, you’ll see they have three vertical teams.

You’ll note that the number 1 category (Healthcare) now has a team and the number 4 (Financial) has a team. How long before they get to number 6? Could they already be there and just not transparent about it? If they are going to do something, we’ll see it before Holiday 26.

It’s a Wrap

I realize that’s a LOT to digest in one update, but this is how we’re going in Agentic Commerce - these are the top seven items we wanted to surface because on the surface they may seem tactical, but they point to the bigger strategic direction we’re going to see Agentic Commerce go in the next 2 quarters.

Good morning Retailgentic readers! Today the big news in Agentic Commerce comes from our own backyard. Before we get into the details, a little backstory for everyone…

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A long time ago in a galaxy not too far away (in North Carolina actually)….

Editorial note: Yes it’s not May 4th anymore, but shouldn’t EVERY day be Star Wars day?

James, Cameron, Derek and I (all ChannelAdvisor alumni) started ReFiBuy way way (this story takes place in AI time) back in January of 2025 with the idea that Agent-based systems (agentic we call it now) could solve some of ecommerce’s hardest problems. We had an inkling that the best application of these solutions could be what we now all call Agentic Commerce.

We were fortunate to raise a pre-seed round and announced that in mid-March. Fun fact, it was led by ChannelAdvisor’s B investors, G20. We then quickly launched our first product Commerce Intelligence Engine at the first RetailClub in September of 2025

As we entered 2026 and after Google’s big splashy UCP news at NRF, we knew the time had come to not only double down, but super-de-duper quadruple down on Agentic Commerce Optimization.

In early March we started the process of raising a Seed round.

ReFiBuy’s Seed Round Details

Today we are announcing that we’ve raised a Seed round. What we’re most excited about, that I’m pretty sure for the first time ever, all four of the early stage RetailTech/EcommTech investors have invested in one company! Let’s start with the new RetailTech/EcommTech investors:

• New Road Capital Partners - New Road led the round and we’ve really enjoyed getting to know everyone at the Bentonville based firm and already learned a ton from them. Everyone at New Road eats, sleeps and breathes retail from the back office supply chain to the frond-end user experience and all the pieces in the middle. New Road has a big investment theme around Agentic Commerce and found us through their research

• Ridge Ventures - Based out of Silicon Valley, Ridge has had an Agentic Commerce Optimization investment thesis since July 2025 (maybe earlier, that’s when they first reached out to us). They are avid Retailgentic readers and it’s been fun to debate the nuanced details of ACO, strategy, how the market is moving with the team. We’re really excited to work with Alex, Eli and the rest of the crew at Ridge Ventures as we work towards building ReFiBuy to the next phase.

• Silicon Road - Also new this round, and based out of Atlanta, Silicon Road is self-described as focused on CommerceTech. Their DNA goes back to founder Sid’s company SPI that was a full-service tech solution partner focused on retail from the go-go years of 1994-2016. From there it’s about as Atlanta as you can get with the ATDC, lots of Georgia Tech, Coke and orange aprons around. We were introduced shortly after our pre-seed by a mutual connection and have enjoyed getting to know this team more deeply.

• Commerce Ventures - Another Silicon Valley firm, the name Matt Nichols may be familiar to the Retailgentic podcast audience as he’s been the guest for not only one but TWO of our top rated/viewed/downloaded pods (here and here if you want to catch up). As their name implies, they are also 100% focused on CommerceTech. They also publish a ton of great strategic thought pieces on the future of retail/digital like their Commerce Continuum framework. Independently, but very similar to New Road, Ridge, Commerce Ventures has had a long-time thesis around ACO. Like G20, Commerce Ventures has been with us since the earliest days and we’re thankful for their support!

Outside of the CommerceTech focused investors, our good friends in Boston, G20, continued their early support by contributing materially to this round in in both capital as well as advice and wisdom through the process. Also out of Atlanta, Knoll Ventures continue to be very supportive and participated in both our pre-seed and the seed. Finally we were able to add the Silicon Valley ex-Googler run firms: VELA Partners (who uses and an AI to find and score companies!) and Incubate Fund that has deep relationships with brands, retailers and tech companies in Japan. We also had the support from many angel investors in the industry which is great to be working with so many OGs - thank you!

Thank YOU!

Many of our customers have come either directly from the Retailgentic community or via word-of-mouth from Retailgentic subscribers. Thank you for your support. Every time you like, forward, comment or engage with our content, it gives us important feedback for where the industry is going that helps keep us out there on the bleeding edge making sure we are researching the latest and greatest so we can pass on what we find to you. The content engine then feeds directly into the product roadmap.

What Does This Mean for Retailgentic!?

Great question, I’ll wrap up and hand this one over to huge Retailgentic fan, Kylo Ren to explain our strategy in very detailed terms for Retailgentic going forward.

May the Force be with you…ALWAYS!

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Stephan Ritter is Director of Commerce Transformation at Deloitte, where he leads agentic commerce initiatives across Europe. With a background spanning digital agencies, global eCommerce rollouts, and customer experience strategy, Stephan now sits at the intersection of AI, commerce, identity, and data. He has become a leading voice on how brands and retailers should adapt as AI agents reshape how products are discovered, evaluated, and purchased.

Listen/Watch Our Interview:

In this episode of Retailgentic, Stephan breaks down how agentic commerce is evolving globally, with a particular focus on Europe’s rapid adoption.

We explore why AI-driven shopping is accelerating faster than previous technology shifts, how brands are already losing visibility without realizing it, and what practical steps companies can take today to stay competitive.

From product data strategy to protocol wars to the future of loyalty, this conversation offers a clear view of where commerce is heading next.

Highlights

• Discovery is the new battleground: If your product is not readable, trustworthy, and rich in context for AI agents, it will not be recommended.

• Agentic commerce starts before checkout: The most important shift is happening in discovery, comparison, and decision-making, not the final transaction.

• Europe is moving differently, not slower: Adoption among younger consumers in markets like Germany is already very high, with many using AI in shopping workflows.

• Product data is now infrastructure: Years of optimizing for search engines are being replaced by the need to structure data for AI systems.

• Loyalty and identity are becoming core systems: Future commerce will depend heavily on how well brands manage identity, value, and customer relationships across AI ecosystems.

• Protocols are evolving fast: Systems like UCP and MCP are shaping how agents interact with product data, pricing, and checkout layers.

• Small experiments already create visibility gains: Even limited API exposure or structured data improvements can increase inclusion in AI-driven recommendations.

• Agentic commerce is accelerating existing problems: Many of today’s challenges, like poor data quality and disconnected systems, have existed for years but are now becoming urgent.

Agentic commerce is already reshaping how products are discovered today, and the brands that adapt fastest will define the next era of retail.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

Want to Learn More?

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We're here to make sense of what’s next in retail + AI and bring you along for the ride. Subscribe and stay tuned!

Welcome to the Retailgentic MAG4 Mega Earnings Agentic Commerce Report! (…said in my best monster truck announcer voice)

In a first ever in the history of internet stocks (confirmed with my Wall St buddies!), we had the four public companies we watch the closest here at Retailgentic report earnings and host their calls in the 120min window from 4-6pm today. It was insane! Look CNBC had to go to the rare 4 box!

Even good friend of Retailgentic, Wall St. analyst Gene Munster, who is usually the calm in the earnings storm, had his mind blown live on CNBC!

(Seriously though, watch this segment, it’s A+ and talks about, well you’ll see).

Who Was the Winner with Wall Street?

The table above shows the after hours moves in the stock at around 8pm ET which tells you how Wall St. reacted. Google was the only one of the three that had what is called a clean beat and raise and Wall St. loved it sending shares up 7%. Remember this is a $4T market cap stock so UP 7% is about 5 Targets (by market cap math) worth of market cap added.

Meta was the loser on this short-term sentiment measure dropping a similar amount to the downside (7%). More on that later, let’s look at…

Who Was the Agentic Commerce Winner?!

CNBC and Gene are going with:

This Retailgentic ‘Agentic Commerce mentions’ scoreboard tells the same story. We went to work when the last call was done on tallying the mentions of ‘agentic commerce’ and surprisingly Amazon came out on top. Perhaps it was because they joined the UCP alliance and released 8 big Rufus features right before earnings? In any case they won on that super-un-scientific dimension:

Let’s go from least mentions to most and dig into what was said. If you add it up that’s 20, yes 20 Agentic Commerce mentions so this is going to be long, but for such a historic moment in time, we think it’s important to really capture this moment in all it’s grandeur.

For each section, we’ll highlight three different areas that are all part of the earnings mosaic: Press release mentions, prepared remarks mentions and Q+A questions that mentioned Agentic Commerce. One editorial note, we are favoring speed over perfect, these are preliminary transcripts - the gist is right, but every word may not be perfect.

At the end we’ll roll it all together into some conclusion.

Google’s Q1 Earnings Agentic Commerce Mentions

Google’s press release is very financially oriented, but in prepared remarks they added this from Philipp Schindler, senior vice president and chief business officer

“AI Mode already surfaces organic product recommendations based on a user's query, and we're now testing a new ad format that displays retailers who sell those recommended products. In addition, the retail industry is rapidly coalescing around the open source Universal Commerce Protocol, or UCP, we launched in January in partnership with the ecosystem. Last week, we welcomed Amazon, Meta, Microsoft, Salesforce, and Stripe as new members to the UCP Tech Council. They join founding members Shopify, Etsy, Target, Wayfair, and Google to further accelerate the transition towards an agentic future. Partners like Sephora and Macy's have joined companies like Ulta Beauty, who are already rolling out UCP and can now redefine consumer journeys from discovery to checkout. Ulta Beauty just last week launched agentic commerce within AI Mode and Search and the Gemini app.”

Question (Eric Sheridan, Goldman Sachs):

“Philipp, to bring you into the conversation, you referenced UCP and there’s been a lot of industry inertia around UCP very quickly. Talk to us a little bit about what UCP means. For the services business as agent to commerce scales in the years ahead. Thanks so much.”

Answer: (bolding is my own)

“Look, I mean, we’re in the early stages of the agentic era. Agentic is more than just completing transactions. We all know this. We see agentic experiences as additive. And it will really transform how we shop from discovery to decisions while helping, obviously, brands differentiate themselves. Our goal is really to remove the grunt work of shopping. So consumers can focus on the enjoyable parts. For decades, you could either shop fast or smart.

And I think with agentic commerce, you no longer have to actually choose between speed and certainty here. The universal commerce protocol, a new open standard for agent e-commerce that works actually across the entire shopping journey from the discovery to the buying and the post purchase support that we just talked about.

It was really co developed with the industry leaders, including I mentioned Shopify, Etsy, Walmart and so on. And we’ve received tremendous feedback so far from hundreds of top tech companies, payments partners, retailers really interested in integrating. It will help power a new checkout experience in AI mode and search and the Gemini app and allowing shoppers to actually check out from select merchants right as they’re researching on Google.”

Question: (Ross Sandler, Barclays):

“Just following up on the last question on agentic shopping. So it seems like we’re at the point in time where this is actually going to start happening finally. So, Philipp, just to elaborate a little bit, as you try, as you look at carrying the AdWords business from kind of the old way of doing things to this new agentic friction with shopping way, how do you see the price and volume kind of growth trends for core AdWords evolving as, as you start implementing more agentic workflows in search?”

Answer: (Philipp again)

“Look, our number one focus is obviously on the user experience here. I think, the most important part in this is, what I mentioned before. We are carefully designing the space in the agentic workflows for the users to actually see the valuable components, within that shopping journey. The second you have the space, you obviously have the ability, for interesting app advertising models. I think it’s also worthwhile noting that, beyond just the traditional agents, there is a lot of additional ways we can actually use AI to improve the shopping experience. You can think about it like our apparel try-on tools, that is now available in the U.S. You can think about Google Lens.”

Meta’s Q1 Earnings Agentic Commerce Mentions

In her prepared remarks, META’s CFO, Susan Li, said the following:

Last, I want to touch on our commerce efforts. People discover products on our platforms through ads and organic posts, with brands increasingly turning to creators to promote their products. This is contributing to rapid growth in our partnerships ads product, with its revenue run rate more than doubling year-over-year in Q1 to $10 billion. To support the product discovery and purchasing happening through creators, we're expanding our solutions beyond ads. Last month, we rolled out our affiliate partnerships offering on Facebook to more test partners, so creators can tag products from participating retailers on their posts and earn a commission when someone makes a purchase. We have also started testing similar experiences on Instagram. We see a real opportunity to help people more easily discover and buy products within our services, particularly as we incorporate AI deeply across our platforms.

Then in Q+A, the first question, and this is going to sound like deja vu…

Q: Eric Sheridan, Goldman

Thanks so much for taking the question. Maybe if I can build out on one of the topics that was discussed in the prepared remarks, but just the opportunity set that sits in front of the company with respect to putting agentic compute in front of both consumers and enterprises. You’ve long been associated with sort of the consumer landscape, and I am curious about how you’re thinking about extensions of the media engagement parts of your business model and the commerce parts of the business model to become more agentic over time. What do you see also as the opportunity set that sits in front of you across SMBs and enterprises where historically you maybe haven’t had as much product velocity? Thanks so much.

Zuck answers:

Of course, as we are able to build out more agentic capabilities, you know, enabling agents to help people be more productive, but also agents for businesses and enabling, frankly, those agents to interact with each other, and, you know, build what we hope will be a thriving commerce ecosystem on our platform. You know, I would say some of these are a little bit further out, you know, especially in that latter category of things. Again, the focus is on building personal super intelligence. You know, building a consumer agent that can work for you and help you get things done. That right now is a consumer experience that we're focused on, but we think there will be clear monetization opportunities over time. You can imagine commission structures or a premium offering.

Question: Ken Gawrelski with Wells Fargo

First, you talked on the Muse Spark launch, you talked about 2 categories or 2 verticals. You talked about health and wellness and shopping. Could I dive a little bit and ask you to dive a little deeper into the latter on the shopping and commerce side? Were there any learnings, you know, in the 2021-2022 phase where you pushed deeper into commerce on Instagram and on Facebook? Any learnings from that period that you might apply? Is there an opportunity for a next-gen marketplace-type business in commerce?

Ok, this one was music to my years - he tied together their plans for the future, but also some of the mis-steps in the past as well as pulling in marketplaces 👨‍🍳😘 question Ken!

This one is a really great answer, and I think you’ll get more out of listening to it here:

If you really want to read it: (it’s a 4min answer!)

I might give you a somewhat loftier answer to the question. You're asking about shopping. I think it's sort of an interesting example of the way in which the work that we're doing is different than what I think others are doing out there. You know, these products, AI agents get better when you fully optimize the stack. That's why we believe that we need to be a company that builds frontier models in addition to building the agents. You of course, need to build your infrastructure in order to be able to do that well. We're undertaking this large investment to be able to do that top to bottom.

I think a lot of the way to think about the investment that we’re making is a bet that the individual things that people care about and that people are going to be more important in the future. That’s sort of like I think it should be a pretty obvious thing to say, but I think so much of the rhetoric around AI in the industry is around, like, a company trying to build some kind of centralized thing that, like, does all the productive work in society in some way or something like that. That just is very different from how we see the world. Like, our vision for the future is one where society makes progress by individuals pursuing their own aspirations.

Some people care about big, grand things like curing diseases, and a lot of people care about personal things like finding the right shirt for my daughter. I just think that we’re gonna build things that help deliver this vision for personal agents for people. I think that part of the lane and what is interesting and differentiated about what we’re doing is that that’s just so different from how I hear everyone else talking about the work that we’re doing. Even though I think some of these ideas, they seem like they should be so obvious, I actually think that our approach of trying to empower individuals and building consumer things is just in the details extremely different from what others are doing.

Shopping might be one kind of specific example that I think is gonna have interesting commercial implications, and I think people, consumers are going to like it. You know, I don’t hear any other labs out there talking about how they’re building an AI that’s really good at shopping. I think that the reason for that is not because shopping is the most important thing by itself, but because empowering people to do the things that matter in their lives, whether that’s local or understanding social context or shopping or personal health things or understanding what’s going on around them visually, which is gonna be really important on the glasses. These are all elements of the personal super intelligence vision.

I think, like, a lot of this and when you're thinking about kind of the investment in Meta over time, I think you should think about it as coming down to these set of values. Around what do we want AI to do in society? If what you want it to do is empower individuals and build a world where the AI is in service to individuals' goals, then that is what we are going to build, and I think it's going to be incredibly valuable.

What’s funny about this one is it was a two-parter with part being a tactical question for Susan and she’s clearly a bit gobsmacked by Mark’s off-the-cuff monolog and to answer her question starts off with this lead in: “Gosh, I almost wish we could end on that answer.”

Amazon’s Q1 Earnings Agentic Commerce Mentions

Amazon had a hat trick: press release mention, prepared remark and TON of mentions in Q+A.

Let’s start with Q+A:

From Jassy’s prepared remarks:

Rufus, our agentic AI shopping assistant, continues to resonate with customers. Rufus can research products, track prices, and auto-buy products in our store when they reach a set price. Monthly active users are up over 115%. Engagement is up nearly 400% year-over-year.

He also echo’d the press release statement, we won’t duplicate that for brevity.

In Q+A the first question came from Brian Nowak (Where was Eric!?)from Morgan Stanley:

I have two questions. <cut for brevity> the second one, as you sort of think about milestones for Rufus and agentic commerce for you in 2026, what are you most focused on making sure you accomplish on the agentic side this year, just to make sure you stay at the nice edge of the agentic commerce offerings? Thanks.

Jassy answers: ( a long one!)

On the agentic commerce milestone question, you know, we are very bullish on what agentic commerce will look like. I think it's gonna be very good for customers in the long term. I think it'll be good for us too. You can see some of that focus from us in what we're building with Rufus. If you haven't checked out Rufus in a while, it really substantially improved over the last year, and we have a lot of customers using it.

As I mentioned earlier, you know, you see the monthly active users up over 115% in Rufus and the engagement up over 400% year-over-year. You know, I think, while I think there will be, we'll do a lot of work with third-party horizontal agents to try and make that customer experience better. By the way, I do think today it reminds me in some ways the stage we're in of what we saw in the early days of search engines, and they're trying to refer business to e-commerce. You know, it's never been a giant part of the referrals to our e-commerce business. Over the years, the experience got better.

What you see with agentic commerce is it's a small fraction of what we see with the search engine referrals, but the experience just hasn't gotten great with these third-party horizontal agents yet. They're not often able to get the pricing right or the product information right. They don't have any personalization data or any shopping history. We do wanna see that get better with third-party horizontal agents. We're having conversations with all those folks to try and make that better and find something that works for customers and all the companies. It'll be interesting over time which agents customers choose to use.

I happen to think that if you're going to a particular retailer that you like to do business with and you like to shop from, if they have a great agentic shopping assistant, you're gonna often start there because it's where you're doing your shopping. They have better product information. They have better information about what other customers like you are buying. You can make all sorts of changes to how your account and your shipping information is working there. You know, that's what we're aiming to make Rufus be, is we're aiming to have it be the best shopping assistant anywhere, and I think we're on that path.

Question: Shweta Khajuria with Wolfe Research

I have no doubt that Rufus could be the best shopping assistant available over time. For advertising opportunity, how do you view that if agents would be the ones taking action to shop? Thanks a lot.

Jassy Answers:

I think on the agentic commerce and how that impacts advertising, you know, I actually believe that we’re gonna like this for advertising. I think it’s gonna be good for customers, and it’s gonna be good for our business. I think first of all, the first thing to remember is the way that our Ads team has built tools and agents themselves is making it so much easier to do advertising.

You know, if you look at small, medium-sized businesses that had to take, you know, weeks and months to do creative and to pick the right audience, all that is just, it’s so much faster and so much easier because of our advertising agentic tools. You no longer have to take as much time or spend as much money building the creative. I think they’re gonna be a lot more advertisers with the rise of what’s happening in AI. If you look at the agentic commerce experiences, or you look at any of these agentic experiences, they tend to be multi-turn conversations, where you’re not interacting with one search and getting an answer. You tend to find that you’re asking questions, you’re narrowing questions, it’s asking you questions on what you want.

You know, in that process of having multi-turns, there are multiple opportunities to surface relevant products to customers, you know, many of which will be organic and some of which will be sponsored. You know, it also gives rise to opportunities like sponsored prompts. One of the interesting things that has been very successful for customers in our store has been when they ask certain questions, we give them a number of suggestions that are, that are, all created through AI. You know, we’ve gotten pretty good at also having sponsored prompts and that mix of questions and prompts that make it easy for people to keep digging deeper into what they’re interested in. I actually believe that, advertising will do well in a world of agentic commerce.

Headlines from Q1 Earnings

To summarize:

• Microsoft had other priorities this Q 🤷‍♂️

• Google is very bullish on the UCP momentum and took a minute to ‘spike the ball’ celebrating their big wins (at the expense of OpenAI’s ACP) and celebrating the big win with Ulta launching both on Gemini, but also using Google as their on-site agent partner.

• Zuck at META threw the gauntlet down that he thinks they are thinking about shopping in a different way than any other company and it’s going to be “really good at shopping”

• Amazon gave us three new Rufus datapoints this Q!→

  • 20% - The % of shoppers who interact with a Brand Prompt continue to conversion with that brand.

  • 115% - The growth in active users (probably a y/y measure).

  • 400% - Year-over-year growth in engagement

• Jassy frames Answer Engine Agentic Commerce as ‘horizontal agents’ (which is what we call it - 🤔. But he frustratingly still acts as if the experience is terrible (he cites wrong pricing and bad information) . I’m sure he’s pretty busy, but I think someone at Amazon needs to write him a 6-pager on why this is just 100% wrong.

• Jassy is beyond bullish on Rufus, he’s downright giddy by my read. To me that says, they are blown away by the conversion data coming out of Rufus (more on this later with some fresh hot data that dropped earlier today, so much Agentic Commerce news, not enough time!).

• On top of that, he’s just as jazzed about Ads in Agentic Commerce (Rufus)

Conclusion: Amazon, Google and META continue to be all-in on Agentic Commerce, continue to invest at a crazy pace in AI infrastructure, products, teams, etc. We are in a very strong setup for the rest-of-the-year heading into not only Holiday 2026, but an IPO season that could see SpaceX, OpenAI and Anthropic pushing to get public before EOY. 🚀🚀🚀🚀🚀🚀

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For More Information…

If you want more analysis on the Q1 results from Amazon, Meta, Microsoft and Google such as broader AI topics, backlogs, their insane AI Capex and all the broader topics, Jason and I are recording an episode this week, and we’ll drop that on the Jason and Scot Show soon.

Bonus charts for staying this long:

Meta capex: 🤯🤯 (spoiler alert - this is why the stock went down)

(this is microsoft)

And Google→🤯

This Wednesday (April 29th), we have a big first ever situation (in my memory at least). Four of the Magnificent 7 (aka Mag7) are reporting on the same day: Alphabet, Amazon, Meta and Microsoft. That means $14T of AI-powered market cap reports their first calendar year earnings in a < 12 hour window - it’s going to be a crazy news week to say the least.

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On the Amazon front, I predict in Wednesday’s report we are going to hear a LOT about Rufus because last week not only did they drop a ton of cool new features we will detail, but they also shocked the Agentic Commerce world by joining the UCP alliance!

Let’s start there and then jump into the new features.

UCP Flexes It’s Muscles

On Friday, April 24th at 11:05am (weird right?) Google issued this press release that Amazon, Meta, Microsoft, Salesforce, and Stripe have joined the UCP Tech Council. Stripe co-created ACP with ChatGPT, so that one hurts and we knew Microsoft already from their announcement and ‘first to launch’ a loyalty (Identity) implementation as covered here at Retailgentic.

Meta is also a bit of a surprise, because we’ve seen evidence they are engaged with ACP (the only company checking in code over there in a long time). Perhaps they are hedging or they tried both and voted with their feet - I suspect we’ll hear something on this one Wed.

The really shocker was Amazon being added to the list. My phone lit up like a Christmas tree - with people asking:

Ok, But What Does Rufus+UCP Even Mean?

All we know is Amazon was on a list, but if you marry this announcement with the features we’re about to go through it shows you a general direction that Amazon is going called.

Embrace, Extend and …..Extinguish?!

When the internet started soaring in popularity, led by the Netscape browser, Microsoft famously developed a strategy to take the internet protocols, embrace them, extend them and then extinguish the challenger companies by ‘out disrupting’ them. I believe Amazon is employing a strategy like this.

Here’s how I see them utilizing this here. First, let’s look at the classic Amazon Third Party marketplace flywheel:

At the heart of the flywheel is the magical world ‘Selection’. Amazonians are well aware that this little diagram is at the heart of Amazon’s growth over the last 20 years and it’s drilled into the training so deeply that anyone on the retail team can replicate this diagram blindfolded with one arm tied behind their back. Selection, selection selection.

Agentic Commerce’s Threat to Amazon: Selection

Up until today, if you wanted to get to the selection that isn’t in Amazon, you would have to visit hundreds to thousands of websites or use the non-transactional Google Shopping which isn’t really a ‘transactional’ threat. ACP and UCP threatens to challenge that. What if you can now find all of the stuff ‘not on Amazon’ in one location. What if the economics are so good, Amazon starts to bleed 3P sellers? That’s a threat worth keeping an eye on and looking the opportunity to embrace and extend to neuter the threat. Here’s a Venn diagram visualization:

If you’re Amazon, this is bad. As pictured, I’ve made it very small. but what if it grew, what is all the selection not in Amazon was in one place that at least checkout-wise was as awesome as Amazon. What if the discovery was a bit better too and didn’t have ads? Hmm, that’s a potential dagger to the heart of the flywheel - not good.

What Embrace and Extend looks like:

Boom! Not only have you addressed the threat (embrace) you’ve actually INCREASED Amazon’s key flywheel component: Selection. Amazon 1P+3P+UCP is « Amazon 1P+3P. How would it work? Here’s how.

Just recently (March 11) , we covered in detail here Amazon’s moves to expand the Shop Direct program to add datafeeds to the crawled solution. At the time it was interesting that there was not real mention of the Buy-For-Me solution. The first save of Agentic Commerce checkouts used ‘browser-based checkouts’ - Perplexity Buy with Pro, Google Buy for Me and Amazon Buy-For-Me. With the exception of Amazon Buy-For-Me have all been replaced by UCP or other non-browser solutions. My prediction is the final step here is Amazon will deprecate or slowly move to the background the Buy-For-Me and we’ll see UCP used to enable transactions in addition to or instead of Buy-For-Me on Shop Direct inventory.

Now you have no reason to shop on Google, for example here’s the “Gemini selection” Venn diagram:

Google Gemini’s inventory is just UCP, no Amazon 1P+3P. By being ‘read only’ with UCP meaning they won’t publish UCP datafeeds but will ‘consume’ the end points they starve competitors of a large percentage. Embrace, Extend, Extinguish - game over. Check and mate.

Amazon’s Flood of New Rufus Features

As a reminder, on Amazon’s Q4 2025 earnings call, Andy Jassy announced that Rufus is now available to 300 million active customers and is already driving roughly $12 billion in down-stream transactions. Days before the UCP announcement, Amazon via a post on the About Amazon / Amazon News blog site, they announced a raft of new features in Rufus. Let’s go through them in detail.

Rufus New Feature (RFN) 1: Scheduled Actions

The best way to see this one is to show you some examples. To get to Scheduled Actions, you press the new little Plus box (popularized by ChatGPT now used by everyone as a UI metaphor) and it only has ‘Scheduled Actions’ (today!).

If you just choose this you get these ‘prompt pill’ pre-canned suggestions:

My first line of thought - this is going to be great to get a jump on in-stock alerts! Well, turns out Amazon will only run a scheduled action once a day:

One nit-pick, if you’re only checking once a day at, say 6am or midnight or whatever, then you aren’t going to be letting me know ‘immediately’.

I then thought, what about price lowerings, there’s already a price tracker feature, but you can now use Scheduled Actions to set price alerts:

Finally, I had it set an alert for my Veggiedent’s to be on sale at their lowest price and an overall weekly deal tracker. Now when I say “show me my current actions” Rufus will help me manage my list:

When the alert fires, I get an email like this:

RNF2: Rufus Persistent Account Memory

If you’ve watched my Rufus videos, i’ve always find it kind of mind boggling that Rufus didn’t have access to my oder history. Amazon has fixed that, now Rufus is faster at finding an order than your order history. Let’s say I can’t remember exactly what I ordered, Rufus finds it instantly with natural language, no need to remember the specifics:

RNF3: Personalized Deals

Rufus also now has access to your wish list and Amazon browsing history in addition to your past orders. These feed into what they are calling Personalized Deals. You can ask Rufus some prompts like: What are the best deals for me? or even more specifically, “Are any items on my wishlist on sale?”

RNF4: Custom Shopping Guides

You can use Rufus to deep dive into topics you’re interested in. Ask Amazon any high level question about doing something and it will now piece together the products you need, punch out things you already have from your history and give you a detailed plan for how to use that plus anything new. Here’s an example where I asked Rufus hypothetically what I would need for a video blog: (edited for brevity and to save space).

RNF5: Easy Reorder

You can be specific or general such as: reorder dog treats or “reorder everything I used to make pecan pie” and Rufus will load your cart up for you.

RNF6: Shop Direct is Now Available In Rufus 🤔

I haven’t been able to recreate this one, so going to include Amazon’s stock screen shot here→

RNF7: Compare with Similar

On many PDPs under the hero image where there have historically been Rufus Prompt Pills, you will now see a ‘compare similar’ prompt pill. You can also manually ask this at any time. Here it is for some On shoes:

Here the Rufus UX is really restricted by the teenie tiny little left hand gutter space it has been allocated. Inside that one inch gutter is a comparison matrix that isn’t super useful because when I scroll i can’t see everything. They clearly need to rethink this UX. If I screen shot it and glue it together for you, here’s what it should look like→

When you see it this way, it’s a great feature, what an awesome table - but when you have to scroll to see 1/2 of the column you can’t tell what’s going on. The feature is significantly diminished by the UI limitation of the Rufus gutter.

RNF8: Personalized Product Summary

The final feature is another “PDP Prompt Pill” feature. In fact if you go back up to my shoe example above- there it is!

The response is kind of funny, can you tell I literally ran this search after the video blogging shopping guide example?

I’ve really got to hit those trails before my next big vlog shoot, phew. 🤪

Pulling it all together…

That’s 8 new awesome Rufus features that are Agentic (scheduled actions) and Personalized (7 out of 8).

The UCP announcement and the Rufus personalization family of features both have one thing in common: Single-directional Embrace and Extend.

• UCP - Allows Amazon to super-set all the UCP selection, but in a ‘pull not push’ model - You can shop all the inventory on Amazon, but only UCP everywhere else.

• Personalization Features - Your Amazon order history, your wishlists, past Rufus convos and also product browse history are Amazon exclusives. Gemini won’t be able to use these as input (well…if you connect your gmail account that receives your Amazon emails, maybe there’s a back-door to your order history, but it’s not as ‘clean’ as what Amazon has).

Will Amazon’s Embrace and Extend strategy work? Will they be able to push off the competitors at the gates, like Microsoft did with Netscape? We will see. Next up in Amazon-land we have the big Q1 news, we’ll be reporting on what we see there, buckle up it’s going to be a busy week in Agentic Commerce!

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Krystina Gustafson s the Co-Founder and Co-CEO of RetailClub, a next-generation event platform focused on the intersection of retail and AI. Prior to RetailClub, she spent seven years at Shoptalk, where she led content across the global event portfolio during a period of rapid growth.

She started her career in journalism at CNBC before moving into events, where she spent seven years helping build Shoptalk into one of the most influential gatherings in retail. Along the way, she’s worked closely with executives, operators, and innovators across the industry.

Today, she’s focused on one thing: helping the retail ecosystem navigate the shift to rise AI and what comes next.

Listen/Watch Our Interview:

Krystina talks to hundreds of retailers across the globe and is able to synthesize what she’s hearing into amazing conference content. In this episode she shares what she’s hearing from operators, board members, and data leaders on what is top of mind in their organizations. What are the biggest opportunities and what are the biggest fears?

At the same time, we explore how agentic commerce is reshaping traffic, discovery, and brand relationships and what that means for the future of ecommerce.

Highlights

• AI is forcing a full end-to-end transformation of retail

• Many companies are prioritizing the wrong AI initiatives, focusing on hype instead of P&L impact

• A growing concern: “dark” or untracked AI-driven traffic is already impacting performance

• Data strategy is becoming a core battleground: What do you give AI platforms? What do you get back? Do CRMs need to evolve entirely?

• The real unlock for AI shopping is complex, multi-step purchases (travel, events, planning)

• The industry is early, but pressure from boards and declining traffic is accelerating adoption

The future of retail will be defined by who applies it to the right problems.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

Want to Learn More?

Stay on top of what’s happening in agentic commerce, from the tools powering it to the founders building with it by following us across platforms:

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We're here to make sense of what’s next in retail + AI and bring you along for the ride. Subscribe and stay tuned!

In what appears to be a stunning reversal in our corner of the Agentic Commerce World, Microsoft casually dropped a blog post for Microsoft Merchant Center (MMC) that talks about their UCP support and announces Target.

This leaves ACP hanging by a thread with only ChatGPT supporting it (the datafeed piece) and META appears to be leaning ACP.

Let’s dig in!

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Background

Here’s a brief history of Microsoft CoPilot’s short-lift relationship with ACP:

• January 8, 2026 - CoPilot Checkout was announced and based on ACP.

• January 22, 2026 - In a Retailgentic exclusive, we discovered CoPilot Checkout live in the wild and demonstrated a live purchase from Anthropologie.

The Breakup Details

Here we are today a short 104 days and the breakup came in a blog post! Note: for comparison to Hollywood drama, this is longer than Kim Kardashian was married to Kris Mumphries which came in at 72 days.

The post is here and some of the details:

The top of the post has a good hook (note we’re not going to get to UCP for a while)

This is pretty good framing of the three eras of the web. About 40% down the page you see this (insert record scratch sound): (highlighting is our editorial addition)

What? Back in January, CoPilot was 100% aligned with ACP and here we see UCP. But the language isn’t exclusive “introducing support” could mean they support both?

New CoPilot Loyalty Experience

A bit further down is when we start to get an inkling on the ‘why’:

New CoPilot Agentic Commerce Ad Format: “Offer Highlights”

In this ad format, the merchant, based on deep context can surface/highlight something about the product in a sponsored listing in context. In this example they provide, the user is looking for a laptop in NYC and BestBuy has an “Offer Highlight” ad promoting in-store local availability and free Geek Squad Protection (red square and green arrow our addition):

Best Buy was announced as an early customer for this new ad unit.

Later in the article we’ll explore where ACP went, but let’s jump into the cool new thing here - Target’s loyalty link. As a reminder, this UCP feature (along with multi-cartwas just announced by Google March 19th (details here) and Google/Gemini is not live with the feature ye

CoPilot/Target’s UCP Loyalty Connection

Here’s how it works:

1. I Research→Find a Target product→

2. When you hit Buy, the usual checkout pops up but with an important twist!

3. When you click that→

This is a great customer experience, very clean and clear to the user. Plus as an avid Target Circle member, I like to see I’ll get ‘credit’ for these purchases.

4. Once you accept that, you are back at the checkout and your account with email (which is handy for those of us with a lot of emails).

That’s a really good implementation and it will be interesting to see what Google does.

Where’s Multi-cart?

The other big UCP innovation announced was multi-cart and it’s somewhat a bummer we also didn’t get our first UCP multi-cart ‘live in the wild’. We’ll now have a race between CoPilot and Gemini to cross the multi-cart winner’s line.

Where’s ACP?

To verify if this is CoPilot supporting ACP AND UCP or they have made a swap we checked out the Agentic Commerce landing page here.

On this page, they have some updated datapoints that are interesting and good to see Agentic Commerce so front and center. About half way down in the “Improve product visibility in Copilot”

You will see here in the higher-level docs, there’s no mention of UCP. Then I checked the MMC help area, here’s the results of the search for ACP:

Summary

What started out as a kind of boring blog post revealed:

• Microsoft CoPilot has abandoned ACP for UCP

• CoPilot’s new UCP checkout supports loyalty linking (identity)

• There’s an interesting new Agentic Commerce Ad Unit we’ll be watching: “Offer Highlights”

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Last week I was on a Wall St analyst ‘expert channel check’ call. The analyst has been a long-time reader of Retailgentic and one of his questions a the top of the call was:

“I’ve been following you on the J+S show and now Retailgentic and your predictions have gotten really good, like north of 80%, how do you do that?”

My answer was, there are three things that I work on to help improve my ability to predict where things are going:

• History/Incentives - I’ve been fortunate to have a front-row seat to the birth of paid-search, eBay, ecommerce, omni-channel, mobile, Amazon marketplace, social commerce, retail media networks and so-on. This history helps understand how big companies act, how the stakeholders in our industry react to change and what all the underlying incentives are. “Show me the incentives and I’ll show you the outcome” - Charlie Munger, Berkshire Hathaway

• Shots on goal - Doing the Jason and Scot show for 11yrs gave me a decade of getting use to making very public predictions and having to be graded on them. This gave me tough skin, humility and a good understanding of how to frame a prediction along with the time window.

• First Principle thinking - I would say the first 2 are 30% but the 70% comes from first principle thinking. History, incentives and practice matter, but first principle thinking I learned as being an entrepreneur for 30yrs. For many businesses, first party thinking goes back to physics or economics (incentives again). But for our industry, I realized about 10yrs into ChannelAdvisor that the ‘real’ first principle predictor for our industry is consumer behavior. Want to learn more about first principle thinking? Here are two good resources to get you going: (James Clear and Farnam Street).

Two Types of Consumer Behavior

In our industry there are two types of consumer behavior data:

• Observed Consumer Behavior Data - In our industry, observed data comes from a variety of areas:

  • Platform layer - Ecommerce platforms like Adobe, Salesforce, BigCommerce and Shopify ‘see’ every transaction through the union of their customer’s sites.

  • Infrastructure layer - Above and below the ecommerce layer there are opportunities to ‘see’ what consumers are doing from search engines, marketplaces, analytics platforms, etc. (examples: IBM, Adobe, Salesforce also have elements of this data)

  • Panel data - There are companies that work with consumers to get permission to monitor their behavior, they then aggregate that data to understand consumer behavior (Examples: Similarweb and Sonar Tower)

• Stated-preference Consumer Behavior Data - Stated preference is where a polling agency asks X consumers questions and then uses known statistical methods to ‘scale up’ the data to represent an audience. In our world, I look for:

  • Scale - n=1500 at a minimum (2k is preferred)

  • Audience - US online consumers

  • Recency - Agentic Commerce is changing so fast, you really need to look for not only when the survey results were published, but when the survey itself was conducted.

As my podcast partner Jason Goldberg would say, stated preference data is notoriously ‘tricky’ to the point of many times being unreliable. The classic example in our industry is if you ask consumers ‘would you like advertising’ they will say “no, I do not like advertising, I would never click on an ad”. How do you square that with Google, Amazon Ads and META.

Two ways to filter stated preference consumer behavior

Because stated preference, I apply two filters to the data:

1. Triangulation Filter - I like to accumulate 2-3 surveys that have similar topics, look at the ‘meta results’ and use that to triangulate to reality. This approach takes out the drift that comes from the underlying wording and ‘way’ the questions are asked.

2. Past/Present/Future Filter - Stated preference works best when you ask people what they have done and are doing. Where it gets especially fuzzy and unreliable is when you ask them about the future. Where it becomes totally unreliable is when you ask them about future economic decisions.

If we can ‘double triangulate that’s the best. What I mean by that is if we can find where observed behavior and stated behavior overlap, AND in the observed we’ve triangulated, and in the observed we’ve triangulated that’s the gold standard. Usually during the holiday period is the only time of year we can do that in ecommerce because the amount of data output cranks up.

For example in 2025, you may have noticed we would frequently in one post talk about a poll or two (stated preference) along with data from Adobe/Salesforce/Sensor Tower/Similarweb (observed behavior) - double triangulation!

Last week, Thursday, we had a big drop by Adobe for Q1 and in this post we’re going to double triangulate that data with 4 polls I’ve been accumulating from Q1 to get enough data to triangulate some stated preference data.

Four New Q1 2026 Consumer Behavior Polls

In this article, I’ll be referencing four Consumer Behavior Polls:

1. Adobe - In addition to the observed preference data we covered last week, Adobe also ran a stated preference poll of n=5,000 , US Online Consumers, Conducted March 2026

2. Harris/Quad AI Shopping Poll - Harris is one of the most well known polling companies in the US. Quad seems to have been the one to contract with them and sponsor the survey and publish the analysis. US consumers, Feb 2026, n=2180.

3. Visa - n=2,000 General Population US Adults, between 1/29/26-2/6/26

4. Morgan Stanley AlphaWise - I’ve been following this one since 2010 and I don’t know if it’s the longevity, the fact it’s a top-tier Wall St. firm building the survey and doing the analysis or what, but I’ve found it to be head and shoulders above all other polls - it’s my ‘go to’ stated preference data. For this dataset, n=2,000 US consumers aged 16-75, conducted 1/22/26-1/26/26

Four datasets, all within Q1 2026, all > n=2000 - this gives us a great foundation to ‘quadrilate’ on some data.

US Agentic Commerce Usage Trends

Let’s start by peeling the onion, starting on the outside. What do our four polls tell us about how consumers are using Agentic Commerce.

MS AlphaWise data above has time trend data from 10/25-2/26 and shows continued activity by consumers in the 20-40% range that ‘made a purchase’ based on an AI recommendation. Think of this as a Discovery signal. Usually in ecommerce we see a Holiday bump, then a Q1 slump, the a recovery with a new high in Q4 for the next Holiday period. Here we’re not seeing that. Note that Meta jumped 4% the most - remember that Product Cards rolled out in Q1 (we noticed them in early March).

That being said, the ‘Retailer-Specific AI tools’ section did see a bit of a Q4→Q1 decline for Sparky and TSA fell off a cliff with usage going from 43% down to 24% - a 19% drop, but interestingly Rufus is hanging in there at ~40%

The Visa poll lines-up stating:

It’s interesting Visa asked the ‘would not have considered’ question, I’ve long theorized that Agentic Commerce would accelerate overall ecommerce. This datapoint supports there is some incrementality here. It’s hard to tell if they were shopping for something already and found a new brand, or if it was totally organic. We’ll be watching the commerce data for information on that. If it’s going to happen, I think we’ll see it Holiday 26.

The Harris poll goes into some demographics and reasoning behind ‘why’ Agentic Commerce:

Finally, Adobe has AI Regular usage at 30%, 61% familiarity and ‘positive vibes’ at 73%:

How/Why Are Consumers Using Agentic Commerce?

What do consumers like/dislike about Agentic Commerce? This information helps us get to the underlying reason Agentic Commerce is popular.

The AlphaWise report goes through 10 different reasons, I pulled this one as it’s the largest - 40-55% of consumers are research products and/or price comparing.

The Harris poll has some more color on this:

These are some new reasons I haven’t seen before: decision fatigue reduction, staying on budget, spotting price inconsistencies and the top we’ve seen before: simpler interface. This last one speaks to the ‘broken-ness’ of the existing ecommerce search experience both at the individual retailer level and the google level. As retailers implement LLM/Agentic commerce type technologies on their websites, it will be interesting to see how this changes.

Trust and Agentic Commerce

Harris also had some trust questions that are interesting. This one across demographics caught my eye. 45% of all american’s trust AI over store associates with Millennials over-indexing at 60%.

Agentic Commerce Demographic Trends

This batch of polls produced a wealth of new demographic trend data for Agentic Commerce, here are the highlights:

Adobe has a both adoption data from GenZ to Boomers:

And then breaks the demos down across three ‘why agentic commerce’ questions:

This shows us that younger consumers, especially Gen Z and Millennials, are more likely to use AI assistants across the full shopping journey, from discovery to post-purchase. Confidence in AI-driven purchase decisions is consistently high across generations, but drops slightly among older cohorts, indicating trust gaps.

One other datapoint I noted is younger shoppers (Zillennials as I’ve heard many marketers say for GenZ+Millennials as a cohort) report Agentic Commerce is reducing returns (make sense, better research and decision making results in down-stream return improvement). This trend is strongest among younger shoppers, showing they are trusting the agents more and putting in the work.

Agentic Commerce and Ads

I always try and understand if a poll has a motive of some kind (back to incentives). I thought it was very interesting that the Harris poll really drilled in on that. I’ll also point out that these questions are definitely into the ‘future’ section I warned about and I’m sure the Agentic Commerce Naysayers are going to pounce on this. With those caveats here we go:

Yep, you can hear the soundbite now: 75% of consumers would lose trust in AI if there were ads. Who do you think would benefit the most from that datapoint being out there? I’ll give you a hint - think about some controversial superbowl ads from February….

On top of that the consumer survey around the AI companies showing the ads, the poll results are firing warning shot at brands saying: “If you advertise on this, you will destroy your trust too!”.

Personally, I’m going to take a wait and see approach on this. Brands advertise on Amazon, Meta, Instacart and Google and consumers keep coming back for more, so something doesn’t pencil here.

This is a shining example of the flaws with stated preference. I think ChatGPT/OpenAI totally know this is nuanced and they are going to delicately approach advertising and figure out ways to do it that are additive to the discovery and checkout experiences.

Agentic Commerce’s Impact on Social Media and Retail Stores

The Harris poll had some interesting data on the knock-on effects of Agentic Commerce.

This one caught my eye:

Perhaps this helps explain the urgency we’re seeing from Meta to enter the race. Consumers now have something to compare social media commerce to and are increasingly preferring Agentic Commerce.

The Harris poll picked up on three signals around physical stores:

First, they found that:

Putting on my first principle thinking hat here, ‘agree’ is a tricky word in polling, we’d have to see the wording of the question, (e.g. It’s probably something like: “shopping stores feels like a social activity, even if I’m alone: strongly agree/agree/neutral/disagree/strongly disagree and I bet they added the agree/strongly). The math works, but who would say: disagree or strongly disagree?

In any case, I’ve made a mental note to track this in future polls.

This next datapoint suggests that there are latent opportunities for retailers to leverage Agentic Commerce to acquire customers and harmonize/omni-channel them into the store through email sign-up or catalogs:

Finally, Harris found that 52-55% of Zillennials use Agentic Commerce while they are in-store to shop as a personal shopping assistant:

I recently witnessed my 19yr old daughter do this in an Ulta store - she pointed ChatGPT at a wall of lip-liner products with ChatGPT in voice mode and asked it which product met her three criteria and it said: brand x on the right three up from the bottom and four from the right. She referenced decision fatigue as the use case. Anecdotal, but another n=1 datapoint!

Summary

Highlights from this analysis:

• We talked about the two types of data and the importance of first-principle thinking and triangulating on data across the observed and stated preference datapoints. We looked at the intersection/highlights of four polls: Adobe, Harris/Quad, Visa, MS Alphawise

• 20-40% of consumers using AI are making a purchase through them.

• We did not see a ‘Q1 slump’ after a huge Agentic Commerce across the Agentic Commerce Engines, but we did see a pretty big drop in usage at Target Shopping Assistant and less of a decline at Sparky, but Rufus stayed robust at 36% (AlphaWise)

• In Q1, 40% of americans made a purchase they normally would not have considered using Agentic Commerce. (Visa)

• Generationally, 62% of GenZ’ers prefer AI powered shopping tools (Harris)

• The top reasons why consumers use Agentic Shopping: To spot pricing inconsistencies, reduce decision fatigue and they prefer the simpler user experience.

• 50-60% of Zillennials now trust Agentic Commerce more than a store associate

• As with previous surveys, Zillennials lead the pack in Agentic Adoption, but Boomers are up to 30-40% adoption where they have been below 20% historically.

• 70-76% of consumers rejected the idea of ads in Agentic Commerce saying they would lose trust if that rolled out and they’d also throw the brand under the bus too - this datapoint is suspicious at best.

• 39% (much higher for Zillennials) use Agentic Commerce as an in-store shopping assistant and 49% of all shoppers agree that if an AI shopping assistant recommended a newsletter or catalog, they would sign up.

When an What is the Next Big Data Drop?

Starting next week April 27-May 8, the companies we watch closely: Microsoft, META, Alphabet/Google and Amazon. Meta, Alphabet and Amazon all had comprehensive mentions on their last quarterly earnings referring to various aspects of Agentic Commerce. Here’s what we’ll be monitoring for and sharing here on Retailgentic to give you a bit of preview:

• META - Zuck said back 1/29/26 they would be releasing Agentic Commerce soon and they did - will they go into more for their Q1 earnings release?

• Google - Sundar highlighted their NRF release of UCP - will we get a progress report or a hint at things to come?

• Amazon - Jassy dropped that Rufus was used by 300m consumers, it increased the probability of purchase 60% and drove 10-12B of incremental revenue. We’ll be looking for an update and also Wall St. is eager to hear about the sponsored prompts testing.

• Microsoft - While not part of earnings, right after they announced their checkout and big push in a blog post, will we get an update/mention in earnings this time?

• Walmart - Retailers are on a different schedule, they will report more mid-May. Last Q the CEO John Furner touched on Sparky saying that half the mobile users had tried it. It’s time for Sparky to come to the web, we’re all waiting on that and looking for any indications about their ad test.

Google I/O

After earnings season we head right into Spring conference season with Google I/O set for May 20+21 in Mountain View. Last year this is when the now already obsoleted ‘Buy for Me’ was announced and they officially ‘Entered the Agentic Commerce arena’ - our coverage was here.

Details on Sources for this article

If you want to go deeper, here are the sources for the four polls mentioned in this article:

Morgan Stanley AlphaWise

This data is only available to MS clients, If you work for a public company, you probably have a relationship (ask the CFO or investor relations) and they can get the report for you. AlphaWise cuts across all their research, what you specifically want is this report by Brian Nowak, their internet analyst and friend of Retailgentic→ (don’t let the title misdirect you, the great AlphaWise data is in the back if you’re not excited about SMB advertising).

Visa Poll

Press release is here and the detailed report is here.

Harris Poll

Press release is here.

Coverage with anti ad messaging here and here.

Full PDF is here→

Quad The Harris Poll The New Rules Of Retail Trust In The Age Of Ai (1)

1.43MB ∙ PDF file

Download

Download

Adobe Poll

Announcement is here and the PDF is attached for your convenience:

Q2 2026 Ai Sourced Traffic Insights

2.3MB ∙ PDF file

Download

Download

Up Next on Retailgentic…

We’re working on an Agentic Commerce news update that will be out either Wed or Fri depending on how the week develops, stay tuned, lots to update you on in the rapidly evolving world of Agentic Commerce!

We also have our standard Thursday pod drop for your listening/viewing enjoyment as well!

Adobe is out this am with an update to their AI Traffic data and we have a complete analysis. then, for the first time they have released AI Visibility data that is an urgent reminder of the importance of ACO. Let’s dig into the AI traffic/retail traffic trends first…

Adobe AI Traffic Data Update

In Q1 2026, Adobe reports traffic from AI sources to U.S. retail sites jumped 393% year over year. In March alone, it was up 269%. As a reminder, during the November-December 2025 holiday period, AI traffic surged 693%.

That’s the top-line number, when you dig deeper, it gets better.

The Great AI Conversion Flip

As of March 2026, AI traffic is converting 42% better than non-AI traffic (paid search, email marketing, affiliates, etc.). That’s a new all-time record in the Adobe data!

To understand the historical implications of this datapoint, if we look back at March 2025, AI graffic converted 38% worse than traditional channels. That’s an 80-percentage-point swing in conversion in 12 months 🤯.

What’s driving this surge in AI conversion?

Another trend we’ve been tracking for a year is the trust relationship between Answer Engines and shoppers.

Adobe’s companion shopper survey (5,000+ U.S. respondents) found that 66% of consumers believe AI tools provide accurate results. And 39% say they’ve already used AI for online shopping, with 85% saying it improved their experience. When shoppers trust the AI recommendation, they buy.

Only one thing can explain this 80% swing in conversion rate - an Agentic Commerce flywheel -

• Answer engines are getting better at recommending products

• Because they have better shopper data (memory) and product data (ACO)

• Better shopper/product data is increasing trust

• More trust increases conversion rates, that closed ‘re-enforcement learning with AI Feedback (RLAIF)’ loop gets smarter and smarter

The result: an unprecedented 80% swing in conversion rate in one year.

Agentic Coding (Codex/Claude Code) use a similar RLAIF closed-loop flywheel system, but they’ve been at it longer and we’re in the phase where each ‘spin’ of the flywheel is creating huge gains in capabilities. New models (Mythos+SPUD) will even further elevate this. In 6 months we could see another 40-60% improvement in conversion.

Sidebar: Life on the Exponential Growth Curve

Like many of you, I’ve been in ecommerce for over 20yrs. We’ve experiences periods of growth like this in the early days of ecommerce and then it slowed dramatically - the standard ‘rule of large numbers’. Even Amazon has succumbed to this rule. That’s what our brain tells us is going to happen.

But it’s different this time. What’s happening now is we have just seen clear evidence two key factors of ecommerce are simultaneously growing (traffic and conversion) and one of them, conversion-rate is accelerating. We’ve never seen a RLAIF flywheel before. This is new territory.

The improvement we are seeing across AI happens when not only does one factor improve rapidly, but 2,3 even 4 factors all improve at the same time. When you multiple these you get an exponential curve.

Exponentials are hard to predict because they accelerate when we’re used to the ‘law of large numbers’ kicking in, they create surprising, even shocking results. Failure to realize you are on an exponential and you don’t invest in front of it can have faster and more severe ramifications than we’ve ever seen.

I know, I know, that experience we all have makes it hard to imagine something like this. Here’s a real-world example happening right now - using the exact same RLAIF flywheel→

PLUS: AI Shoppers Are Better Shoppers

Not only are traffic and conversion rates growing at a frenetic pace, but the Adobe data reveals 3 more factors that are more evidence we’re on the exponential":

It’s not just conversion. Adobe’s March 2026 data shows AI-referred visitors outperform across the board:

• +42% higher conversion rate vs. non-AI channels

• +48% more time spent on site

• +13% more pages viewed per visit

• +12% higher engagement rate

Now let’s look at the new data in this report - US Retail AI Visibility:

Adobe US Retail AI Visibility Data

The scorecard above shows the average score across the entire retail sector using Adobe’s proprietary new AI Visibility data. We assume they are looking for the basics on each of the pages of a website: bot blocking, page readability, metadata for javascript-hidden content, etc and then rolling them up, by category, into this report-card style scoring.

Here you can see the top performing pages are Returns/Exchanges, Contact Us and FAQ all above 80% - these are typically text-heavy pages so this makes sense.

In the 70-80% zone we have: customer service, loyalty/membership, homepage and cateogry pages.

Finally, the most plentiful pagesof any ecommerce site, the PDP, scored a terrible 66%!

Adobe Data Reveals: The Invisible Shelf

In the visibility data, Adobe doesn’t reveal the range of outcomes for the PDPs, but for homepages at 75%, they do show the lowest performing retailers are as low as 54.2% and the highest at 82.5% - that’s a ~28% swing. Homepages are pretty basic and other than product carousels they tend not to be javascript heavy.

I’m sure in future reports we’ll see it, but my educated guess here is the range of outcomes is at least 2X from homapages - that’s a 56.6% swing with a midpoint of 29.3% - that would mean PDPs are as low as 37.7% with the midpoint at 66%.

Critical Action Item for Q2/Q3: ACO!

Since 9/25 here in the virtual pages of Retailgentic, we’ve been advocating for something we coined: Agentic Commerce Optimization. We released our 9-step framework:

Back in September of 25, this was a ‘prepare for the future’ type scenario. Sitting here in April 2026 staring at Holiday 26 bearing down at us with AI traffic surging 393% y/y and conversion rates on that traffic swinging +80%. In 8 months, ACO has moved to a ‘must have’.

Note, we also have this updated and expanded ACO guide here.

Conclusion

In real time, we’re watching the internet get re-intermediated. Google built a $200B+ business as the intermediary between shoppers and brands. Now Answer Engines are becoming the new intermediary — and unlike Google, they don’t just give you a list of blue links. They make recommendations. They pre-qualify. They hyper-compress the funnel.

Adobe’s new data makes the economic case undeniable: AI traffic converts better, engages more, and is growing nearly 4x YoY. But here’s the twist — only the retailers who invest in their product catalog, optimize their product cards and start providing extra context to engines, and get on their own RLAIF curve will ride this wave vs. missing out on the biggest opportunity since the dawn of ecommerce. 🚀

Joseph Lee is the Co-Founder and CEO of Delectable AI, a vertical agentic commerce platform built specifically for grocery. He brings nearly three decades of experience in enterprise commerce, having held leadership roles at companies like ATG (acquired by Oracle), Hybris (acquired by SAP), VTEX (IPO), and Mirakl.

Joseph has spent his career at the center of e-commerce infrastructure, and is now applying that experience to one of the most complex and competitive categories in retail: grocery.

Listen/Watch Our Interview:

In this episode, we go deep into what agentic commerce actually looks like in a real-world vertical, and why grocery may be one of the most important battlegrounds.

We walk through the shift from “tools that help you shop” to systems that shop for you, and how that fundamentally changes everything from discovery to monetization.

We also unpack the tension between grocers and platforms like Instacart, the rise of retail media networks, and why owning your data, and your customer relationship, may matter more than ever in an AI-driven world.

Highlights

• Regional grocers are losing both data and margin to platforms like Instacart

• Agentic commerce shifts grocery from manual planning → “do it for me” automation

• Delectable combines food intelligence + shopper intelligence + real-time inventory

• AI closes the gap between inspiration (TikTok/recipes) → instant cart creation

• Vertical AI beats horizontal models with better accuracy, speed, and cost efficiency

• Grocery e-commerce hasn’t saved time, until now

• Retail media networks are massive, but broken by middlemen and inefficiency

• Agentic advertising creates unlimited, contextual ad inventory

• The future of grocery is about owning discovery, not renting it

If you want to understand where agentic commerce gets real, beyond theory and into infrastructure, this is one to pay attention to.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

Want to Learn More?

Stay on top of what’s happening in agentic commerce, from the tools powering it to the founders building with it by following us across platforms:

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Arjun Bhargava is the CEO and Co-founder of Rye, a company building the infrastructure layer that enables AI agents to actually complete purchases online.

With a background in software engineering at Goldman Sachs and Reddit, Arjun is now focused on solving one of the hardest problems in agentic commerce: turning intent into a real, fulfilled order.

Listen/Watch Our Interview:

Everyone is focused on how AI helps people discover products. But what happens after that?

In this episode of Retailgentic, we go deep on the missing layer of agentic commerce: checkout, payments, and fulfillment. Because it turns out, the hardest part isn’t finding the product. It’s buying it.

Rye is building what they describe as the “Stripe for commerce.” It connects AI agents, apps, and platforms to merchants handling:

• Checkout and order placement

• Payment orchestration

• Fulfillment across retailers like Amazon and Shopify

In short: if an AI agent decides what to buy, Rye helps it actually buy it.

Highlights

• Checkout is the real bottleneck: Discovery is improving fast, but actually placing an order across fragmented systems is still incredibly hard.

• Rye enables real AI purchasing: They connect agents to merchants, allowing products to be bought directly from platforms like Amazon.

• Amazon is a critical unlock: Most bots are blocked, but Rye’s API-based approach allows real transactions to go through.

• Protocols won’t solve everything: Even if UCP/ACP take off, long-tail merchants and major players won’t fully standardize.

• B2B is the sleeper opportunity: Complex procurement decisions are a perfect fit for agent-driven commerce.

• AI reduces returns: More context leads to better decisions, driving return rates extremely low.

• We’re still early, but close: One breakout use case could flip massive volume into agentic commerce quickly.

Agentic commerce isn’t just about what gets recommended, it’s about what actually gets bought.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

Want to Learn More?

Stay on top of what’s happening in agentic commerce, from the tools powering it to the founders building with it by following us across platforms:

• Podcast: https://retailgentic.transistor.fm

• YouTube: https://www.youtube.com/@Retailgentic

• Instagram: https://www.instagram.com/retailgentic/

• X: https://x.com/retailgentic

• TikTok: https://www.tiktok.com/@retailgentic

We're here to make sense of what’s next in retail + AI and bring you along for the ride. Subscribe and stay tuned!

Editorial note: To keep this article’s vibe flowing, we’ve detailed the eight referenced resources at the end of the article if you want to dig into this topic more. Also, we’ve done this enough times with GA4 that we are happy to help anyone that would like to do an analysis with their own data. Join our Retailgentic Slack community here and let us know in the #dark-agentic-commerce-traffic channel.

Retailer’s Unexplainable 2026 Traffic Trends

We had a scenario recently where we were working with a large fashion retailer and they were using ReFiBuy’s Commerce Intelligence Engine to implement our 9-step Agentic Commerce Optimization framework. Their product-card visibility and offer-card positions were all showing very strong improvements at the product-level. But they weren’t seeing an increase in AI referrals 🤔.

Simultaneously, organic/paid Google traffic across all merchants continues to decrease as we accelerate towards a zero-click future. It’s outside the scope of this article, but some good current reads on this are here and here (we believe publishers/b2b are a canary in the coal mine for merchants).

At the same time, traffic identified as ‘direct’ traffic is inexplicably growing faster than ever before. Engagement on ‘direct traffic’ is unusually high resulting in super-sized conversions

This is not a tagging error. That’s what we call dark agentic commerce traffic. Let’s dive deep into this one, given that we are are already 233 days away from Thanskgiving, we strongly recommend getting a handle on quantifying and tracking your DACT to create a baseline now enabling you to create a before/after for Holiday 2026.

What is Dark Agentic Commerce Traffic (DACT)?

When a user opens the ChatGPT mobile app, asks “what’s the best trail running shoe under $150 for wide feet,” gets a recommendation with a product link, and clicks through — that visit arrives at your PDP with no referrer. The referrer metadata and any UTM tags are stripped before the request is sent.

GA4 (Google Analytics 4) sees a new user landing on the /products/[shoe-name] PDP page with no referrer. It then puts them in Direct and you never know ChatGPT sent them.

🤯 The kicker

In controlled testing, when the Gemini iOS app sends traffic to a website, only 5 out of 56 visits (under 9%) are correctly identified as AI referrals in GA4. The other 91% land in Direct. (Source: Topify)

Which Traffic Sources Are The Culprit?

We’ve reviewed the research and done some of our own tests and the above matrix is what we believe to be the current state. Basically summarized - if someone is using an AI ‘app’ (both mobile and desktop versions) - that traffic is nearly 100% ‘dark’. For browser based traffic, that traffic is mostly trackable. The Answer engines want you to use their app experiences so they tend to put exclusive features in there. There’s no great reference that looks at which ‘surface’ shoppers are using for Gemini or ChatGPT, but I can guarantee your GA4 analytics is telling you that your AI referrals are 95%+ desktop. But we know that across e-commerce in most categories we’ve passed 50% mobile traffic and corresponding sales.

Is This Bot Traffic?

Note that this article is 100% relating to humans and not bots. 99% of bot traffic is easily identified and pre-filtered out by analytics programs, it’s identified by the ‘user-agent’ - bots/crawlers have to identify themselves as a specific user agent (we went into this back in this article if you want to learn more). These are most likely humans, unless someone is using an agent to manipulate a browser on their local machine which, outside of the Openclaw community, is pretty rare for your average shopper and would be in the browser side anyway, not on the app side. Because of this we’re certain these are real living breathing shoppers we’re talking about in this analysis.

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What’s the Scale of Traffic We Are Missing?

Loamly ran the most rigorous controlled analysis to date — 446,405 tracked visits — and found that only 29.4% of AI-influenced traffic carries any referrer at all. The other 70.6% lands as Direct. Apoorv Sharma at DerivateX validated this independently. Topify confirmed it with the Gemini iOS testing already mentioned.

Three independent datasets, same number. That’s not noise. That’s signal.

Now here’s the part that should make your head explode. That dark AI traffic — the stuff GA4 can’t see — converts at 10.21%. Average ecommerce traffic converts at 2.46%.

Therefore, What is essentially your best-performing traffic source is completely invisible and not attributed to anything. Note to our friends on Agentic Commerce teams - you should work on establishing credit for this ASAP.

What’s the Growth Trend of DACT?

Visibility Labs (via search engine land report) spent all of 2025 analyzing 94 ecommerce brands — 9.46 million non-branded organic sessions vs. 135,000 ChatGPT referral sessions. What they found:

• January 2025: 1,544 ChatGPT-attributed sessions/month across the dataset

• April 2025: ChatGPT launches shopping carousel → hockey stick begins

• December 2025: 18,202 ChatGPT-attributed sessions/month — +1,079% YoY

• CVR advantage: ChatGPT referral converts 31% higher than non-branded organic (1.81% vs. 1.39%)

• Revenue per session: $3.65 from ChatGPT vs. $3.30 from organic — despite lower AOV

Which Agentic Commerce Engine Causes the Most DACT?

Not all answer engines are created equal here. ChatGPT dominates referral share but is a mixed bag on stripping referrers.

ChatGPT is the dominant channel — Lantern’s data shows 87.4% of all AI referral traffic comes from ChatGPT.

Gemini on mobile is basically invisible in analytics. With 91% of its traffic landing as Direct, every Gemini AI Mode product recommendation that converts is almost certainly being credited to “Direct” or “Branded Organic” — robbing your agentic commerce optimization efforts of any signal.

Perplexity is actually the best citizen here. With only ~30% landing as DACT, Perplexity sends cleaner referrer data than its peers. This is one reason Perplexity’s referrer data shows up more clearly in GA4 reports, even though its absolute share is smaller with shoppers.

How to Track Your DACT In GA4

There’s no silver bullet here, we can’t make the various answer engines stop ‘not’ sending referrer data (perhaps when they have more ad revenue/focus this will change, but we can’t control that).

What we’ve helped customers do is:

• Understand and track the distinct ‘fingerprint’ for DACT

• Create a GA4 pre-Agentic Commerce Baseline and then measure from that baseline.

The DACT Fingerprint

The traffic that you can safely assume is DACT has these X characteristics:

• It is categorized as Direct or Brand (where traffic is categorized when referrers there are no referrers).

• It lands directly on a PDP page (not home/about/category pages - deep into the site).

• It has a low bounce rate and converts relatively high.

The Results

Based on this finger print, we created a baseline back to January of 2023 - days after the birth of ChatGPT, it didn’t have web search yet, Let’s call this X - and you will find it’s a very low number expressed either as a % of your traffic or as sessions.

Then we track this DACT traffic through March 2026. The results across many customers reveal that “Known AI sessions” (flagged by GA4 because they have referral tracking) are off by 100x of our estimates from the DACT traffic. For example, on a sessions % share basis if Known AI sessions are tiny at .12%, when you consider DACT fingerprinted traffic increased from baseline using GA4, that number grows to 12%.

I can also share that this traffic has an mind bogglingly high conversion rate and a very low (sub 20s) duration. Shoppers are hitting that PDP, adding to cart and moving on to checkout or to shop more of the assortment. As theorized, DACT is most likely your highest quality traffic and you’re not tracking it outside of Direct/Branded correctly.

As discussed from third party research, we can also confirm that the overwhelming majority (90%+) of traffic in the known AI referral bucket is attributed by GA4 to ChatGPT.

As mentioned at the top - we’ve done this enough times with GA4 that we are happy to help anyone that would like us (or help) to do an analysis with their own data. Join our Retailgentic Slack community here and let us know in the #dark-agentic-commerce-traffic channel.

One More Bucket of DACT: Influence Attribution 🤦‍♂️

Here’s where it gets even more mind boggling. There’s a second layer of dark influence that’s impossible to measure even with this fingerprint/baseline trick.

For example, what if a shopper asks ChatGPT for trail running shoe recommendations. ChatGPT names your brand. The user does all their research on ChatGPT, then opens Google. She searches “[your brand] + [product name]” — a branded query. They click your organic result and buy.

GA4 attributes that sale to Branded Organic Search. ChatGPT gets zero credit. Forever.

Interestingly, the Visibility Labs study found this pattern to be extremely common. They call it “intent compression” — Agentic Commerce Engines pre-qualify the shopper so thoroughly that by the time they hit the product page (via whatever path), they’re at the very bottom of the funnel. Your traditional channels are getting credit for closes that Agentic Commerce drove 90%+ of the influence on.

I do believe eventually, for this very reason, Agentic Commerce Engines will ‘pull up’ the transaction into the engine. In other words, I don’t think we’ve seen the last of ChatGPT Instant Checkout, especially if Google’s UCP checkout implementation has a strong Q4 in 2026.

Conclusions

DACT isn’t an analytics nuisance. It’s a signal that the ecommerce funnel has moved — and it’s moved to Agentic Commerce.

Shoppers are asking ChatGPT what to buy. They’re asking Gemini to compare products. They’re asking Perplexity to research specs. And then they’re landing on your PDPs — pre-qualified, high-intent, and ready to convert at 10x the rate of your average visitor.

The merchants winning this transition are making sure their products are showing up and giving the answer engines as much product-level content and context they can so that their products are recommended as that shopper works down the funnel.

Shopify’s Harley Finkelstein recently put a 7x traffic number on DACT.

Agentic Commerce isn’t coming. It’s here, hiding in plain site.

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Dark Agentic Commerce Traffic Resource Resources

This article references these nine recent and well researched sources:

1. ReFiBuy Internal Proprietary Anonymized Client-level Research, performed by Link Walls (inspiration and referenced here.

2. Armando Roggio — The Truth about ‘Direct’ Traffic , Practical Ecommerce — Feb 15, 2026

3. Apoorv Sharma (DerivateX) — AI Search Traffic: The Invisible 70.6%, LinkedIn — March 3, 2026

4. Loamly — State of AI Traffic 2026: Industry Benchmark Report, Loamly.ai — Dec 13, 2025

5. Dan Taylor — Why Direct Traffic in GA4 Isn’t What It Looks Like, MarTech — March 9, 2026

6. Yotpo — Track AI Referral Traffic: 9 Expert Tips (2026), Yotpo.com — March 25, 2026

7. Danny Goodwin — ChatGPT Ecommerce Traffic Converts 31% Higher Than Non-Branded Organic Search, Search Engine Land — Feb 26, 2026

8. Topify.ai — The Traffic Analytics Tools Every Marketer Relies On in 2026, Topify.ai — April 4, 2026

9. Laurence Nixon - Agentic Commerce is Here, fabric.com blog, Dec 2025.

Debra Aho Williamson is the Founder and Chief Analyst at Sonata Insights and one of the most respected voices in digital advertising.

In her 19+ years at eMarketer, Debra helped define how the industry understands social media’s impact on marketing. Today, she’s focused on the next wave: how consumer AI behavior is reshaping advertising, media, and commerce.

Her Substack, The AI Ad Economy, tracks the fast-moving intersection of AI platforms and advertising strategy and we recommend it here→

The AI Ad Economy

The AI Ad Economy follows the money as AI transforms how brands and retailers reach consumers.

By Debra Aho Williamson

Listen/Watch Our Interview:

In this episode, Scot sits down with Debra to unpack what might be the most important shift in digital advertising since social media.

This isn’t theoretical. It’s happening right now, and brands that don’t understand it risk getting left out of the new chain of commerce.

Highlights

• Why ChatGPT’s ad rollout is just the beginning, not an experiment

• The tension between trust vs. monetization in AI interfaces

• How retailers are both excited and terrified of AI shopping agents

• The emergence of “AI Media” as a new category (like social + retail media before it)

• Why current ad formats are “boring”… and what comes next

• How companies should actually prepare (hint: it’s not just media buying)

• The shift from advertising to agent influence, and what that means for the future

We’re still early, but not that early. The rules of digital advertising are being rewritten in real time, and for the first time in decades, the winners aren’t obvious.

The question isn’t whether AI will reshape commerce. It’s whether your products, and your data, are ready to compete when agents decide what gets bought.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

Want to Learn More?

Stay on top of what’s happening in agentic commerce, from the tools powering it to the founders building with it by following us across platforms:

• Podcast: https://retailgentic.transistor.fm

• YouTube: https://www.youtube.com/@Retailgentic

• Instagram: https://www.instagram.com/retailgentic/

• X: https://x.com/retailgentic

• TikTok: https://www.tiktok.com/@retailgentic

We're here to make sense of what’s next in retail + AI and bring you along for the ride. Subscribe and stay tuned!

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Adweek Covers First ShopTalk Debate

Adweek had a great piece summarizing the debate. Note if you missed it, we released the audio last week here. Here’s a good summary of Ekta’s take:

Gemini Updates

Google Gemini “Sponsored Store” Ad Unit Spotted In the Wild

Back on Feb 11, Google’s VP/GM of Ads and Commerce, Vidhya Srinivasan, posted this blog post and announced a new ad unit called “sponsored store”.

At the time, we questioned how this will work, is it basically going only be sponsored stores on this listing or will there now be ‘tiers of stores’? A sharp-eyed X user found a live example in the wild.

In this example, Gap is the only merchant with the offer, so they are effectively listed twice. If we pretend that StoreX also had this item, but wasn’t sponsored, they would be listed either above or under the ‘All stores’ designation. This is a pretty valuable ad unit, it’s way up top, very delineated and basically doubles your shelf space. It will be interesting to see how it’s priced and how retailers are going to be thinking about the ROAS on this one.

We tried in a variety of accounts to try to replicate this one, with no success, so it maybe a A/B test only exposed to a small flagged audience. If you want to try it:

• AI Mode

• “I am looking for gap men’s chinos”

GAP and Google Partner for UCP and Agentic Commerce

Tuesday, on-stage at Shoptalk, the GAP’s EVP and CTO, Sven Gerjets was on stage with CNBC’s Gabrielle Fonrouge and they announced that they are the first major fashion brand to jump in feet-first with Gemini/UCP.

I think this is spot-on and really echo’s what Ekta said in the debate on the same day - consumers have a new way for researching and eventually finding and buying products, the forward-looking retailers and brands should be leaning into these experiences, testing and trying everything and keep an open mindset. I think Sven’s mindset here is spot-on and bodes well for GAP’s future in Agentic Commerce.

The article does say the checkout isn’t live yet, but ‘imminent’ - we’ll be keeping an eye on this one and see if Google and the GAP use this big launch as an opportunity to show off the four new UCP features announced March 19th:

• Multi-item cart capability

• Enhanced catalog endpoint

• Loyalty program integration (via a capability called ‘Identity Linking’)

• The launch of a self-service merchant center

In true Retailgentic style, we’ll let you know when it launches!

They did release a screen shot which is here. Note there’s a partnership with a sizing company called bold metrics.

Here you see a typical variation picker (color/size) and then the size picker. The size thingy pops in there and tells the user that it’s a high confidence match which is a clever addition to the checkout flow and illustrates how flexible and customizable this can be with the right underlying investment in infrastructure.

META Updates

While on the surface, this group of announcements covered by TechCrunch are really AI additions to traditional ad formats, I thought this ‘one click checkout’ shoppable ad unit was interesting→

A couple of things stand out about this and I think we’re going to see this ‘treatment’ showing up in Meta’s Agentic Commerce Offering. Here’s some observations:

• It let’s you pick the variations (beige/medium) in the ad. Therefore, this is an ad unit tied into variation-level inventory - that is no easy feat.

• This is a merchant-of-record purchase - notice the clear messaging that the shopper is buying from “Wind & Wool” both on the button and under in the Ts+Cs

• This ad unit keeps the user in the instagram app. In the old format, where you may ‘click out’ from Instagram to a Shopify checkout and not come back into the app.

• Techcrunch credits this on: “built in partnership with payment providers Stripe and PayPal, which allows consumers to complete the purchase in just a tap”. Notice that Shopify isn’t mentioned, their “checkout register” did not ring on this one. 🤔

• This would be a great way to further compress the Research/Find/Buy cycle - imagine this in a product card, where you click and for certain items, you can checkout right in the product card.

More Evidence of Meta Joining the ACP Alliance?

Last week, we highlighted speculation that Meta would pick ACP over UCP. This week, Joe our Agentic Commerce Chief Sleuther, found that on github, this user ‘ihwa’ works a facebook and has some fresh pull requests (indicating they have resources working on the protocol and now actively making code contributions).

Will see an official announcement soon? Q2 ends tomorrow, therefore we’re < 45 days from earnings and META has a history of dropping this type of news on that cycle.

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Big ChatGPT Agentic Commerce Updates

To recap the big picture, early March saw a diversion of paths between ChatGPT/ACP and Gemini/UCP:

• March 4th - Information drops piece that ChatGPT is focusing on discovery over checkout and then there was a bit of a dramatic Walmart breakup around the checkout

• March 11th - Conversely, Gemini stays the course with in-line checkout via UCP and announces the 4 things that address the obvious shortcomings with everyone’s first attempt at a hosted checkout: loyalty, multi-cart, self-service merchant center and improved discovering capabilities.

I think this is a good thing for the industry because we now have one of the big players really focusing on this checkout to see if it can be solved and we have another one really experimenting a lot with discovery. In the Google updates above, you see them refining this with the ad unit and picking off one of the big fashion brands to be an early ‘anchor tenant’. In this section we’ll update you on all the things ChatGPT has been doing since that March 4th announcement. There’s so much going on here we’re going to lightening round this one with fast short updates

ChatGPT Apps Refresher

The ChatGPT Apps UI is, to be kind, a bit hard to navigate. We receive tons of messages from readers that get lost in the UX. Here’s how it works on desktop:

Steps to Add an App:

1. You may have to start by Selecting the ‘+’, then go to “… More” and then click on ‘Add Sources”

2. From there (as pictured) in step 1, you choose apps and then ‘Connect more’

3. that takes you to the apps section of your settings (you can also enter this through settings BTW) - in there you see your existing apps and click the ‘Add more’ button on the top.

4. From there now you are in the ChatGPT apps ‘discovery’ experience - there’s a browse experience and a search experience (box in upper right).

ChatGPT and the Sephora App

First up, at Shoptalk, Sephora took the stage and announced the Sephora App. It’s unfortunately not live yet, but here’s the features:

• The Sephora ‘Beauty insider’ loyalty program will be completely integrated

• You’ll do discovery on ChatGPT and then ‘finish’ in Sephora. You’ll see an example of this in a second.

• You’ll be able to ‘at or ‘@’ the Sephora app and ask beauty questions

While the press release and coverage states the app is live, there’s no indication of it anywhere I can find and no screenshots -we’ll keep an eye out for this one.

I thought these quotes were interesting→

ChatGPT and the Walmart App

Walmart is on the record as saying that conversions from ChatGPT were 1/3 their on-site conversion rate and the app will solve that. In this video I take it for a spin and illustrate how it works→ (~7mins)

To summarize the two use cases:

• Pecan pie ingredients - Not a great experience, unlike the integration with Instacart’s app, this is cumbersome and the walmart app doesn’t “listen” to the instructions I’m giving it through ChatGPT.

• Big Screen TV - Same feedback, the whole point of having Walmart is everyone (in my neck of the woods) is < 10mi from a Walmart so it’s perfect for the urgent in-store stuff. How come that basic use case isn’t supported? Gemini and ChatGPT checkout support/supported that so we’ve taken a step back IMHO.

ChatGPT’s Shopping Discovery Becomes More Opinionated

Joe the Agentic Commerce Sleuth put together this great ‘before and after shot→

Three Big ChatGPT Discovery Changes:

• New layout - First, a much cleaner, more visual, cleaner result peppered with product cards (3x3 carousel in the OG and now 8 in the new)

• Best Overall - At the top, ChatGPT has actually given a more firm recommendation vs. just showing you the pros and cons- we’re calling this the Best Overall Reco. This is, obviously, where you want your products to be (how? ACO people! Product-level Content and Context are the keys - search our archives for ACO if you are new).

• Top Four Grid - Finally towards the bottom, you will see a top four comparison matrix that’s very handy and completely personalized. If you told ChatGPT about your stride, and other things important to you as you look for shoes, those will be the rows in the matrix. Welcome to the Matrix!

ChatGPT’s Pinterest-ey Wall (or Browse All)

Joe doesn’t show it in the above screen shot, but now frequently at the bottom of either the conversation or after you click on a product card and see the offer card and scroll down, you’ll see either a 5 product card carousel with one marked “Browse all” or an “Explore more” section like this→

Once you click on this you are taken to a ‘Pinterest-esque’ view or an ‘Endless Aisle type display. What’s happening here is we now have an ability to look directly into ChatGPT’s core catalog and poke around. This is super helpful for, you guessed it, canonicalization! But let’s not go there today.

Sometimes you even get what we’re calling the “Browse All View’s Filtering System” at the top that lets you filter what shows up in the product wall based on various attributes provided (I’ve popped down the color menu in this Dyson Airwrap example

Here’s a short video that shows you just how ‘endless’ the endless aisle is.

ChatGPT’s New Product Offer Card Content and Context Expansion

Tons to go over here, some of it we knew was coming because we saw it being tested, but it’s all live now. First, there’s a ton more information on the product card, it’s so long now I had to chop it up into 4 pieces to show you:

The new sections of the Offer Card are:

• Offers - The Offers are sorted by ChatGPT’s proprietary algorithm (but easy to decode 😁)

• What to know - An ai-generated tight paragraph about the product and who it was built for (product-level storytelling - see, we predicted this!)

• What people are saying - Here you start with a summary of the reviews starting with positives and then any concerns. After that, somewhere between 2 and 10 reviews are presented - usually with a mix of positive/negatives. There seems to be some new filtering to take out the ‘reddit crazy’ - those reviews that are just written by someone, well, on reddit.

• Explore more - As mentioned above, this is a gateway into the new pinteresty wall - kind of an escape hatch - if you made it this far on this SKU and it’s not what you wan’t this allows you to go back up to ‘Find’ or even ‘Research’ from the Buy part of the funnel.

• Follow-up - This we saw in preview and it’s cool to play with now that it’s live. Here’s an example→

In this example, I’m looking for a Dyson Airwrap and got confused about which accessories come with this SKU. When you click on Follow-Up, it has this hovering pop up chat that has ‘selected’ in it (see red box) the SKU in question - a visual clue we are asking about this specific product’ and I ask my question. Then the answer, also in the hovering box is presented. This is pretty cool to see in practice and I now want to put 3 of them in there like Rufus and Gemini have and see how that works. It seems like the comparison matrix would be a great place to put some check boxes and when you check them it puts them into this type of setup.

ChatGPT Offer Card: “Best Price” Badge

This one is harder to produce but we’ve had enough people send it to us we feel confident it’s not just a test, but probably here to stay (at least for 2 weeks haha)

Here’s an example we found in the wild:

Here you’ll see are two offers Poshmark at $30 and Mercari at $35 and Poshmark has the new “Best price” call out. Note that it does seem to take into consideration pricing and only showing up if the price difference is well north of 10% savings. I’m sure this will be tweaked a lot over time.

BCG: Agentic Scenarios Every Marketer Must Prepare For

I thought this BCG article was pretty interesting and had some thought provoking scenarios and likely conclusions for how Agentic Commerce plays out. Here’s an example - they have this diagram detailing four possible agentic futures→

… in which you have Super-App in the upper quadrant where you have AI purchase decision and high market concentration.

You’re Caught Up!

Now you’re caught up and ready for more news, let’s see what the next week holds for us!

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Shoptalk Spring 2026

I was invited by Ben Miller, VP of Content and Strategy to participate in the first ever shoptalk debate format talk. The topic - Agentic Commerce Will AI agents transform retail?

For Agentic Commerce:

On the pro agentic side we had Ekta Chopin, CDO at ELF Beauty and myself. If you’re not familiar with Ekta, she’s a thought leading CDO and has a great substack for other CDOs about AI and various other topics I recommend here:

Anti Agentic Commerce

On the ‘anti agentic side’ we had Sarah Marzano, Principal Analyst, Retail Ecommerce and Retail Media at Emarketer and Andrew Lipsman - independent analyst and consultant for media, ads and commerce.

How we made this happen…

Before the show I asked and received permission from Shoptalk and all the participants to turn the raw audio into a podcast. In the AI era, speed matters so we are getting this in your hands only 2 days after it happened. This debate took place at 12pm PDT on Tuesday March 24th at Shoptalk’s AI Stage.

How to Listen:

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

How to “Watch”:

BONUS: Spicy Shoptalk Recap in Under 25 Minutes With Rick Watson

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The release is sparse and is here.

Here is a screenshot they showed (sorry the lighting is a challenge)

Here is a brief video they showed:

Joe the ChatGPT sleuth has found these live in the wild, here’s a great example of the compare function.

Build With ReFiBuy Announced!

Also this morning at ReFiBuy we announced the availability of our new “Build With ReFiBuy” offering. We now have APIs, MCP and CLI integrations with our product catalog Evaluate, Enrich/enhance, Distribute, Monitor and Measure core capabilities. This allows partners, Agent Builders and dev-forward customers to utilize our capabilities in many different contexts. Details here.

More news as it breaks here at Shoptalk!!