• Part I: The Next Phase of Agentic Commerce Optimization: Context Capture is here.

• Part IIA: Product-Level Context Capture: Where, How and When? <YOU ARE HERE>

• Part IIB: RCCRL Examples. (coming July 8th!)

• Part III: <conclusion> Coming July 14th!!

In Part I, we introduced the concept of context capture and how it is now necessary to beef up the product-level context to match the sharp increase in shopper context.

Product Catalogs in the Agentic Commerce Era

Before 2022, Product Catalogs were largely static. There were new products added every quarter and some products ‘deleted’. Seasonal businesses like fashion had more movement like that. But once created, each individual SKU didn’t really change except for price and inventory which have been increasingly dynamic over the decades.

In the post Agentic Commerce Era, due to the new importance of integrating context into product catalog as covered in Part I.

Where Should You Capture Context?

There are 6 major areas where you can capture extremely valuable consumer shopping product-level context. They are:

1. Answer engines - With Google releasing AI Performance insights and rumors that ChatGPT’s ad engine is going to be sharing a similar level of information. We are entering an era where Search Engines shared little to nothing, but Answer engines are going to have much more transparency. This information is valuable context capture.

2. Retail agents - Like Answer Engines, I suspect that Amazon/Rufus, Walmart/Sparky and other retailer shopper agents like Target’s TSA are going to be providing more infromation about what prompts shoppers are using, the topics being discussed, product features compared, comparative products, etc.

3. Physical stores - If you are omni-channel, your retail stores are a wealth of context that you previously haven’t thought of leveraging elsewhere, but now in the AI era, there’s very rich information you should vacuum up for your on-line efforts. Sales rep training manuals, product guides and anything that a store associate would be trained on can be leveraged by an LLM for product-level context.

4. Brands/Manufacturers - As a retailer, the deepest level of product information is typically available from the manufacturer, and then frequently this is either ignored and recreated or condensed down to 2% of the original content. If you are the brand, frequently the online group actually does the same thing as a retailer - takes the design briefs, hundreds of pages of product information, spec sheets, technical details and boils it down to 1% for the human readable PDP, which is normal, but now we live in a world where all of that information is gold dust and we need to go figure out how to keep it at 100% from offline→online.

5. Social media channels - For hundreds of years, centralized merchants were the taste makers in retails, but we are now in the Kardashian/TikTok era and the tastemakers are independent influencers, celebrities, athletes and content creators. When influencers recommend, are seen with or even have a similar product to something you offer (either paid, organic or collab’ed) , that is important context that needs to be captured.

6. Website - Your online website is one of the largest surface areas of lost context. Each shopper is browsing, searching, receiving personalizations, leaving and consuming reviews and increasingly prompting your on-site shopping agent. Those contextual artifacts on both the positive (high converting) and negative side (low converting) are significantly valuable contextual clues to product-level shopper behaviour that needs to be captured, gathered and actioned.

Across these six opportunities to increases context capture, every retailer will have different amounts of context and priorities for their business. In fact it can seem overwhelming when you really start thinking about how much context could be captured. We recommend this best practice to solve that problem:

Building a Compounding Context Capture Recursive Loop

Before AI, workflows across programming/coding and optimizing digital sales be it ecommerce or ads or what-not was a mostly linear process with a bit of a maybe quarterly, semi-annual or annual closing of the loop. Since November 25 when the agentic capabilities of the ChatGPT and Claude models took a huge leap forward, developers have realized that with AI automating a lot of the process that these ‘loops’ can be significantly tighter. Developers are particularly obsessed with these loops now.

Why are AI Developers Obsessed With Loops?

There are two types of Loops - ones with AI in the middle as the ‘judge’ or ‘tagger’ and then those without a human.

If a human is in the middle it’s called Reinforcement Learning with Human Feedback - RLHF. If AI is in the middle, it’s called Reinforcement Learning with AI Feedback. As you can imagine, relative to AI, humans are slower and less scalable and more expensive so if RLAIF is » RLHF. (Want to learn more? Start here.)

This started with some basics and then one of the most famous AI developers, Andrej Kartpathy who has a mini llm he calls nanoGPT, created a RALAIF recursive loop so this llm model codes itself and if the new version improves it’s benchmarks, it is good, otherwise, it deletes that and tries again. In this post, he ran it in this mode for 2 days on this loop→

In this chart down and to the right is better. You can see that after 2 days with zero human interaction the sytem got better.

Today, ChatGPT and Claude are built this way - they build a new version of themselves. If that version is better. v2 then builds a better version to v3 and so on. This is why model releases are bending the exponential curve. (Anthropic hired Andrej btw).

While that’s a little scary 🤯, let’s stay in our World where YOUR job is to drive more sales and profit for your org (while crushing the competition) and answer the most important question of the day:

What Do Recursive Programming Loops Have to Do With YOU?

This concept of loops can be applied to any system that has a definitive zero sum or measurable outcome. That’s not only coding, it’s digital retail and marketing.

• Example from coding: Did the code compile? Did the code pass the tests? Was the desired outcome generated by the code?

• Example from Digital Retail: Did the SKU map correctly? Make a change to optimize the product catalog - did that that increase conversions? Did the ad hit the ROAS target for SKUX?

• These are perfect for the digital vs. analog world because in the digital world no humans are in the loop so the loop is faster and tighter. For example, if you wanted to use RLAIF for store inventory, the loops are too slow, over 3yrs it will have an impact, but not 6 months.

I believe that in the next 18 months we’re going to see significant digital retail disruption due to these loops. Some forward-leaning retailers and brands, are going to lean into these and use them for digital advertising.

Answer engines (Claude, ChatGPT, META, SpaceXAI, Gemini) are going to be using these to hyper-improve the customer-experience and conversion rates of Agentic Shopping and the performance of AI Ads.

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Quick Recap to Tie it All Together (so far)

If you’re with me, here’s the logic path from Part I to where we are here at the end of Part II:

• Product-level Context is the next frontier - After doing the basics (steps 1-5 of our agentic commerce optimization framework), and the advanced step 6. To do steps 7,8,9 you need a loop (that’s why we put those little circles there back in Sept of 25 ;-) )

• UCP/ACP have given us plenty of ‘room’ for context - In addition to much wider basic attributes and conversational attributes, we have an essentially unlimited product-level FAQ data feed we can send the answer engines→

Good news! We now have all the pieces in place to build such a system….

Leveraging the AI Loop Concept and Context Capture: Compounding Context Capture via a Recursive Loop

I believe what all retail and DTC brand CDOs need to be thinking about maybe by Holiday 26, but definitely by the end of 2027 is how to create a recursive compounding context recursive loop or a RCCRL. Here’s what it looks like:

Step 1: Optimize Product Catalog SKUs

In this step we publish the latest version of the product catalog and measure the impact. While that measurement is happening we simultaneously collect context.

Step 2: Gather Online Context (Answer Engines, Website, Socials)

I grouped the online sources together because being digital they are always going to be faster than offline. You can API/MCP into your datasources or larger orgs will have a real-time-ish data lake they can pull from. Because of this maybe you pick up digital every time through the loop and offline ever 4th time or maybe once a month type thing for what fits your org.

Step 3: Gather Offline Context (Store, Manufacturer)

Offline is always slower and won’t update as much as online, so maybe for your org once is enough through here, but if you have stores, you should prioritize a more real-time way to pull store associate feedback- specifically any interesting consumer behaviors changes/nuances into the system. That can be made pretty quick, especially if you are on a retailing type in-store system.

Step 4: Evaluate Context and Prioritize

In Step 4, the evaluation from the current version is done and opportunities for improvements have been discovered and made. At the same time, new context from steps 2 and 3 is pulled in and evaluated. A new version of the catalog is ready for the next loop.

Step 5: Loop with new context in the Product Catalog

Catalog new version is ready to go and published and the loop starts again. Faster and faster.

I realize this is significantly different than the way we have operated in the past and it maybe seem like overkill, and we’ll get to more of why this is a priority in the next post.

What’s Next?

I was going to keep going right into examples here, but that’s a lot to digest and sit with, therefore I’m going to split part II into 2 parts IIA and IIB. Part B will be out July 8th and Part III will be out on July 15th. Until then we’ll continue to have awesome pods for you and also don’t forget to subscribe to our new channel! - www.retailplaybook.ai

Have a great Independence Day everyone! Here’s to 250 years of America! 🇺🇸

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Here at ReFiBuy/Retailgentic HQ, we talk to hundreds of brands and retailers every week. Amongst brands, both Hybrid (DTC+Wholesale) and Wholesale-only Brands, as well as large 3PM sellers, one theme has gone from a whisper to a roar:

We’ve talked to top brands who have been top 1/2/3 in their category for years that are now seeing disruptors leverage Rufus/Alexa to upsurp them. We’re seeing dynamic and nimble 3P sellers who understand the importance of this on Amazon and Walmart surge in sales while incumbents scratch their heads as they look everywhere to understand what’s going on.

These Retailer agents are very new and all built on LLMS so we’ve seen some pretty crazy hallucinations like saying something is Kosher when it isn’t or telling a shopper that some chicken nuggets are vegan (they are not).

The Hunger for more “Retailer” Answer Engine Content

As these conversations have surged, so has the popularity of our content on Retailgentic on this topic. Our coverage of the Rufus data from SensorTower here and here was extremely popular and viral. Our Sparky coverage has been equally well received and the messages from readers like you has been - we want more!

Enter: Andrew Bell

After looking at the data at how popular our Rufus→Alexa and Sparky articles are, we had a wacky idea, what if we had someone that focused 100% on this. We researched everything we could about optimizing systems like Alexa and Sparky, every piece of content pointed back to one person who was the authoritative source of information, largely in the form of playbooks.

Close observers took note when we announced that Andrew Bell had joined as VP of Research and we got a LOT of questions about what was going on.

Well fellow Agentic Commerce Maximalists, the time has come to reveal a bit more about what’s going on, but not everything 😀

Introducing RetailPlaybook

Today, we are launching RetailPlaybook, a new ‘channel’. It’s a substack newsletter and companion podcast authored and hosted by Andrew.

RetailPlaybook’s Focus

Built for 1P Brands and 3P sellers, RetailPlaybook provides cutting-edge research, original heavily researched playbooks, and future-proof strategies to optimize for agentic commerce on Amazon’s Alexa for Shopping, Walmart’s Sparky, Target Shopping Assistant and beyond. As the name implies, Andrew is going to be publishing regular Playbooks that address the current and top concerns from 1P and 3P brands and sellers. The initial focus will be Amazon, but there will be plenty to cover on Sparky and Target as well.

Here at Retailgentic, we’ll continue our focus on the Answer Engines and the occasional big news items over at Amazon and Walmart and the overall Agentic Commerce industry.

Check out RetailPlaybook Today!

Today the inaugural substack article is available here and podcast is live.

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

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

Next Steps…

If you want to optimize your product catalog and drive increased sales on Amazon, Walmart or Target, make sure to:

1. Subscribe to the RetailPlaybook.ai substack!

2. Subscribe to the RetailPlaybook audio pod here.

3. Subscribe to our RetailPlaybook YouTube Channel here.

We hope you enjoy this new offering from Andrew Bell!

I’m going to go through this fast, buckle up, this is our first every Retailgentic DOUBLE FLASH - two, yes two breaking Agentic Commerce news updates in one:

1. Target is live with the Google Gemini UCP-powered cart

2. (while exploring 1, we discovered this am..) Google has turned on Universal Cart in the wild.

Note: All the new Google/Gemini goodies start in AI Mode - so assume I’m talking about that always. (AI Mode: go to google.com - click on AI mode - right side of search bar, make sure your Google now says “AI Mode” in the upper left.)

Quick history here:

• Jan 11 - Google announces UCP at NRF

• Feb 11 - UCP went live

• March 19th - Google announces multi-item cart and Loyalty

• April 22nd - Microsoft DUMPS ACP, Launches USP with Target and first Loyalty integration.

• May 19th - Google announces Universal Cart (their implementation of UCP multi-cart) is done and coming in “Summer”.

• June 19th - The first Retailgentic Double Flash🚨, we have both Target now launching on Gemini PLUS the first Universal Cart live sighting with live demo!

Live Demo

The best way to see this is via this ~10m video. I was doing the Target Cart demo and had tried it on another account to get a feel for it. That account did not have Universal Cart, so mid-demo here it turned into a double demo. A walkthrough follows.

The Trigger: blog post update from Target

What got me here is several Target watchers saw that on socials they mentioned that they had updated this blog post and when you look at the update, it’s saying they are live on Google checkout.

I tried last night and couldn’t get it to pop so checked first thing this am (rollouts take overnight sometime to propogate to all servers/accnts.

Here’s what I discovered this am→

Detailed Walk Through of Target and Etsy in Google’s Universal Cart

Step 1: Research→Find

I skipped research and went right to a sku I knew would be at Target and trigger a product card, but it also showed the new add to cart! (We’ve seen visuals of this from Google I/O and GML).

Step 2: Add to Cart 🎉

When I added the falcon to the cart I got this→

And in the upper right of my google account is now a persistent cart icon! Feels very, umm, Amazony! 🤔

Here’s the little cart that could forever change Agentic Commerce and retail→

Step 3: Load that CART UP!

Since I was full on ‘hallucinating’ (because a universal cart isn’t possible according to the naysayers), I figured heck may as well make it a good one and I loaded the cart with an Etsy item and a Target bag:

Step 4: The Holy Grail 🏆: True Universal Cart Nirvana!

You can see in the above screenshot I have two options: “Buy Now” or “Checkout on Target”

If I hit Buy Now → 2, yes 2 TARGET items in one checkout, the holy grail has been achieved!

OR, If I hit Checkout on Target, it does cart injection and takes me there→

What’s Next?

We’re going to spend the weekend decomposing this and learning more, stay tuned. As I was writing this, Walmart seems to have flipped to ‘on’ as well→

Live on the exponential growth curve, buckle up everyone this holiday is going to be BONKERS!

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Today, Shopify announced their Spring Edition and it’s chock full of Agentic Commerce features. If you want the total details of the release, they are provided here.

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I was able to get an early look at the features for Retailgentic readers and then ask a Shopify representative for some clarifications on the features. We’ve identified four of the biggest features that focus on Agentic Commerce. For each of the four, we first cover it’s high-level capabilities (as briefly as possible, more detail is in the Spring Edition detailed post linked above) and then cover the Q+A provided by a Shopify spokesperson.

The Four Features (out of many more) that caught our eye are:

• Agentic Admin

• Shop Campaigns

• Sidekick App Extensions

• Shop App Personalized Shopping Assistant

Up first, Agentic Admin:

Agentic Admin

Spring Edition Feature: Managing Agentic Storefronts and Insights in Shopify Admin

For the first time, merchants have one place to manage Agentic Storefront channels in their admin, track performance across channels, and see actionable insights. What it unlocks:

• Merchants can now see orders, sales and conversions from ChatGPT, Google AI Mode and Gemini, and Shop at a glance. Products are automatically accessible to AI channels through Shopify Catalog

• Search intelligence shows the top AI queries in a merchant’s category, including which ones they’re ranking for, and where they’re not showing up paired with actionable insights

• Knowledge Base shows merchants which questions AI agents are asking about their brand that haven’t been answered yet (like retail locations, bulk orders, and customer service) and lets them fill those answers in directly from the Admin.

Agentic Admin Q+A

Q: Search intelligence - Google announced AI Performance Insights at GML. This seems similar but across all answer engines. What can a merchant learn here?

There’s a natural comparison but the scope is different. Google’s AI Performance Insights shows how a brand performs across Google’s AI surfaces specifically, including share of voice against competitors. Shopify’s Agentic section in the admin looks across all the AI channels Shopify connects to: ChatGPT, Copilot, Google, and Shop.

Merchants can see the top AI queries in their category, whether their products rank for those queries, and where they’re not showing up. They can search any query against the Catalog API and see the exact same results AI agents see, including top-ranking products across all merchants, not just their own. They can also preview their actual ranking live in ChatGPT and Shop with one click.

Where Google’s tool focuses on visibility and share of voice within Google’s ecosystem, Shopify’s gives merchants cross-channel intelligence plus actionable next steps. If a product isn’t ranking, listing quality signals tell the merchant specifically what to improve: descriptions, images, reviews, pricing, options, policies, or titles. Sidekick can then recommend fixes. So it’s not just reporting, it’s a path from insight to action across every AI channel where their products could appear.

Q: Can you say more about where the data comes from - is this panel data or shared via Google/ChatGPT/etc.?

The search query data comes from Shopify Catalog. When a merchant searches a query in the Agentic section, they’re hitting the actual Catalog API, so results reflect what AI agents see when they search on behalf of buyers. Performance data (sessions, orders, conversion, sales) comes from Shopify’s own analytics, attributed by channel. AI channels don’t currently share impression data with Shopify, so the metrics are sessions, orders, conversion, and sales, not impressions or views.

Q: Knowledge Base recommends questions that aren’t answered - where do these answers go? (e.g. meta-data on the site, data feeds to the engines, on the human readable PDP?)

The Knowledge Base surfaces questions that AI agents are asking about the merchant’s brand that haven’t been answered yet, things like return policies, shipping details, retail locations, or bulk order info. Merchants answer them directly in the Admin through the Knowledge Base feature.

Those answers become the authoritative source that agents reference when buyers ask about the brand. Instead of an agent guessing or generating an answer from scraped web content, it pulls the merchant’s answer from the Knowledge Base. The answers live in Shopify and are available to agents through the same infrastructure that powers Catalog, so agents get facts, not guesses. Think of Knowledge Base is for shops as Catalog is for products. It’s a better alternative to scraping for this information.

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Shop Campaigns - Growing your business with AI-driven campaigns

Shop Campaigns, Shopify’s performance marketing product, expands to three new surfaces: Pinterest, ChatGPT, and the open web via Microsoft Monetize. Merchants only pay when customers convert.

Shop Campaigns Q+A

Q: ChatGPT has rolled out a wide variety of ad formats, bid options, etc. What’s the matrix supported?

We have been experimenting with all formats of product catalog based advertising that OpenAI offers to determine what works best for our merchants. We don’t share specifics of the exact settings or formats that Shop Campaigns operates on, at a given point in time, on any platform.

Q: Is support there for Gemini’s various ad formats?

We advertise on Google across a variety of ad formats and campaign optimization capabilities. We don’t specifically target Gemini ad formats, but some of the ad formats that we leverage may lead to ads in Gemini.

Sidekick App Extensions

Sidekick App Extensions connect third-party apps to Sidekick, so merchants can get AI-powered answers and take action across their full tool stack from a single conversation.

Sidekick App Extensions Q+A

Q: Any conceptual examples of the types of things Shopify would like to see innovated as an extension to sidekick? Any launch partners there?

App Extensions launched with 15+ partners including Klaviyo, Loop, Smile, Judge.me, Matrixify, Avia, Seguno and Checkout Links. Any developer in Shopify’s ecosystem can build one going forward.

Examples include:

• Merchants using Klaviyo can ask “find my best-performing subject lines” and get real campaigns back, with open rates and click rates pulled from live data.

• A merchant using Judge.me can ask Sidekick to “help me respond to recent low-star reviews,” and Sidekick opens the right page in the app with the review context and a draft reply already filled in. The merchant reviews and approves before anything posts.

Q: Is this the same framework and business model as the app store (15%) or is it more like an open MCP protocol type thing?

There is no additional cost for developers or merchants who use Sidekick app extensions. Developers configure their extension and merchants benefit from Sidekick’s ability to access data and actions from their apps.

Shop App Personalized Shopping Assistant

Shop App, Shopify's consumer shopping app, now has a shopper-facing personalized shopping assistant built into search. Buyers describe what they are looking for in plain language and get recommendations shaped by their real purchase history, browsing, and stated preferences. Each interaction sharpens the recommendations over time.

Shop App Personalized Shopping Assistant Q+A

Q: You’re announcing a personalized shopping assistant in the SHOP app - will this PSA be available for merchants to put on their store so they can have their very own ‘rufus or sparky?’

Agentic search on Shop is specific to the app and web that buyers can use throughout their shopping journey. Buyers can tap the search icon on Shop and ask it any question in natural language. Buyers can use search to discover and purchase products, ask about a merchant’s return policy and shipping updates, and more – all directly on Shop. Search learns a buyer’s taste over time and asks smart follow-up questions to provide the most personalized recommendations.

Shop search is built on Shopify’s commerce infrastructure — the Shopify Catalog, Shop Pay identity, buyer profiles, and MCP tools for cart, checkout, order lookup, and more.

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Roger Dunn has watched retail media grow up from the agency side (Mediacom, GroupM, WPP), the tech side (he launched Criteo's retail media business in Australia), and the brand side (a global retail media role at Diageo, home of Johnnie Walker, Guinness, and Baileys). Now he's CCO at Thrad, a Silicon Valley startup building the ad infrastructure for LLMs, "ChatGPT ads, but for everyone else."

Listen/Watch Our Interview:

In this episode, Roger and Scot get into where advertising actually lives inside answer engines, what's working today, and how brands should think about showing up when shopping stops looking like a search box.

Highlights

• What Thrad is building: both the supply side (helping retailers and chatbots monetize conversational surfaces) and a self-serve buy side where brands can run ads across existing AI inventory globally, not just in the four countries where ChatGPT ads have launched.

• The new ad formats: sponsored messages, sponsored prompts, polling ads, and product carousels, plus the steady stream of formats Google unveiled at Google Marketing Live.

• AI-native campaigns: how Thrad turns a URL, a brief, or a prompt into keyword, prompt, and dynamically generated persona targeting, and why creative (human faces beat a bare logo) is where the human edge still lives.

• What ChatGPT ads look like right now: CPCs roughly in the $1–$5 range, and why the early “hot mess vs. holy grail” takes are both missing the point.

• Roger’s definition of agentic commerce: not a robot doing the final click, but AI shaping every stage of the journey: discovery, comparison, price, review synthesis.

• The state of the field: Gemini’s full-court press, Perplexity’s Comet browser, Meta routing UCP checkout into Instagram, the Shopify question Wall Street keeps asking, and where Anthropic/Claude might (or might not) play.

• Down Under as a test lab: Woolworths’ Olive, Bunnings’ Buddy, a duopoly retail market, and why Australia keeps getting the pilots.

• The advice brands actually need: start small (think $10–20K in a market) but start; get out of “keyword jail”; and the analogy that lands it: keywords are ice cubes, prompts are snowflakes. A few uniform ice cubes everyone fights over, versus infinite unique prompts where a challenger brand can finally own its wedge.

This is a genuinely useful tour of the side of agentic commerce most of us squint at, ads, from someone who has built the playbook three times over. Enjoy the conversation.

🎧 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!

👀 First Look at Claude’s New Agentic Commerce Product Cards 🎉

Our ReFiBuy monitoring agents are constantly running product-related prompts across all the Answer Engines. Yesterday, very unexpectedly they started reporting that Claude is now showing product cards. Here’s an example for a hairdryer:

It’s important to note that these Claude Product Cards are pervasive across all categories. Here’s a collage of a variety of product cards across several categories from electronics to pet to fashion and beauty:

While these are rudimentary, the intent is clear here. If you recall back on Feb 9th, we pointed out that Claude had mentioned they are “particularly interested in the potential of agentic commerce, where Claude acts on a user’s behalf to handle a purchase.”

In that same piece we pointed to reports from NRF in January that Anthropic was meeting with Merchants and we’re aware of representatives that were at Shoptalk also holding highly confidential meetings.

Historically, over the 27yrs I’ve been working in retail, this time of year is when new features are frequently introduced with a ramp into the Holiday selling season. Could we see Claude rapidly go from ‘Research→Find→Buy’ in time for Holiday 26?

Updating The Five Levels of Agentic Commerce Tracker: Claude ‘up’ and Perplexity ‘down’.

Here’s our updated Agentic Commerce Levels of Autonomy tracker:

As the arrows indicate, we are downgrading Perplexity to Level-1: Research. Not only has Perplexity removed the buy capability, but according to our Monitoring agents, they have eliminated all Product Cards. Optically Perplexity seems to be focusing on the financial vertical and their Computer agent (kind of an OpenClaw type user agent with more rails that OpenClaw).

At the same time Perplexity is moving down, we are now upgrading Claude from Research to Find due to their first iteration into Product Cards.

That yields this end-state for where we are today on the tracker (minus the arrows in case you want a clean version).

Note you are free to use this in your presentations, we appreciate attribution for Retailgentic and a link back if you do publish it online.

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That’s not the only big Agentic Commerce news today→

Adobe Releases Q3 AI Traffic Report: AI-Driven Retail Traffic is up a Startling 138% Y/Y!

The first datapoint Adobe dropped is that Retail sales accelerated to 138% y/y growth (the green line above). That’s impressive, but check out the yellow line! AI driven travel spiked 194% y/y. Financial services spiked to 105% y/y as well. My educated guess is as Gemini and ChatGPT - the big 2 from a traffic-perspective are ramping up their ad programs, they are working on the user experience, perhaps adding touches like product cards and other elements that are accelerating this move.

Also note that going back to October 2024, AI Driven Retail Sales are up 1,324%! obviously it’s a small base, but at 138% it’s not slowing down, it’s accelerating!

On top of that, this is not ‘garbage firehose traffic’→

Look at this trend, as Answer Engines are building out their training set, building up memory capabilities and understanding Shopper Intent, and getting better at Product-level information - the ‘match rate’ is growing rapidly. Today swirl all that together and visits initiated by Answer Engines are worth 53% more.

The Adobe Q3 Report offers two insights into why:

On top of consumer sentiment improvement (this is from a n=3,000 survey performed in March 2026), Adobe has some pretty crazy data that is the first statistic I’ve see on the topic of Agentic Commerce and returns:

There’s a lot of scary aspects of Agentic Commerce:

• Rapidly changing traffic patterns

• Increasing evidence that Google is accelerating the path to zero clicks

• Lot’s of hard work to upgrade your catalog that can seem insurmountable

• An alphabet’s soup of protocols

• New players entering and leaving the arena at a frenetic pace (see above)

But what if Answer Engines are a bit of a Holy Grail? Evidence is mounting to support this:

• The traffic converts 54% higher (the real-world data I can see points to significantly higher than this believe-it-or-not)

• The AI Ads are significantly more performant than pre-AI Ads

• And now…Agentic Commerce Traffic could reduce returns 69% 🤯

Finally, while Google is acceleration the decrease in SEO/SEM traffic, Answer Engines growth is accelerating. I believe the ‘traditional search’ and 'Answer Engine Search’ lines are going to cross faster than anyone ever predicted.

Adobe’s Updated Demographic Information: Zillennials Are Driving the Agentic Commerce Trends

These types of adoption curves are common - in the early days of E-commerce it was the same thing. What’s new, is this curve is more pronounced. That delta between 53% at Gen Z and 34% at Baby Boomers is a 19% difference - huge.

Where to go for More Information?

If these trends are interesting to you and helpful, the complete Adobe report is available for download here→

Q3 2026 Ai Sourced Traffic Insights

7.8MB ∙ PDF file

Download

Download

Also this weekend we surfaced 8 news items that are on-topic for these trends that you should read if you find these topics of interest:

What’s Next?

We’ll continue to keep a very close eye on Claude’s implementation of Agentic Commerce, stay tuned for more details as we find them!

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It was a chaotic week with all the Mythos and the SpaceX IPO news dominating all the headlines. In addition to the big Walmart Sparky and ShopAgentic news, we have curated the Top 8 Agentic Commerce stories that both delivered new strategic data-points and also flew under the radar this week.

Amazon Reveals Interesting Agentic Ad Details and new Datapoints.

Our new VP of Research, Andrew Bell noticed in this Amazon News article about Agentic Ads, they quietly dropped 2 new stats we’ve never seen before:

• 20% of shoppers who interact with a prompt continue the conversation about that brand

• Adding prompts to a ‘sponsored brands’ ad drives a 6% increase in conversions.

The conclusion is that even though they are very new, we’re starting to see very promising ROAS data from Sponsored Prompts. This dovetails nicely with this one as we move to OpenAI’s ad system where this week we learned even more new stats→

Pacvue x OpenAI: 20% of ChatGPT Conversations Have Shopping Intent

Pacvue ran a webinar with OpenAI on ads, and it surfaced some first-party stats we hadn’t seen before:

• 20% of all ChatGPT conversations carry shopping intent

• OpenAI frames the journey as Discover → Eval → Buy, with a “single product evaluation” step baked right into the funnel

• OpenAI’s own comparisons show how much better ChatGPT performs vs. traditional search

My open question → if customers are discovering, evaluating AND buying inside ChatGPT, then why ditch the checkout? Something I question every day.

You can watch the webinar in it’s entirety here without a login→

Similarweb: Google/Gemini Keeps Chewing Away at ChatGPT’s Lead (and 2 more datapoints!)

Similarweb just dropped their May GenAI traffic report, and the trend line we’ve been tracking held → Google’s AI surfaces keep eating into ChatGPT’s lead, month over month. As a reminder, this is observed traffic data, not a survey — it’s what people are actually doing, not what they say they’ll do.

In their X post, Similarweb framed the trend this way:

12 months ago:

• ChatGPT: 76.4%

• Gemini: 8.9%

• DeepSeek: 5.3%

• Grok: 2.8%

• Copilot: 1.9%

• Perplexity: 1.8%

• Claude: 1.6%

1 month ago:

• ChatGPT: 52.7%

• Gemini: 27.3%

• Claude: 8.9%

• DeepSeek: 4.0

• Grok: 2.8%

• Copilot: 2.0

• Perplexity: 1.3%

Google started from behind, but this shows the power of distribution. Google is leveraging search, gmail, maps and every consumer touchpoint they have to not only stay relevant, but to definitely put a damper on ChatGPT’s aspirations of owning this market.

Also don’t sleep on number 3 - Claude is quietly growing with consumers at 9% I see increasing evidence of this every day. On weekends I wear a Claude hat and have been for 6mos - At first, nobody knew what Claude was now a large % of people recognize it and want to chat Claude.

Similarweb dropped two more nuggets in our world this week→

ChatGPT Advertisers grew 46% w/w! (2,585→3,770)!

Finally, probably the biggest datapoints that slide right under the radar that impacts Retailers the most: Google’s

On the surface this is pretty scary, we went from Google delivering 55% of their traffic in, right back ‘out’. Today that’s 32% and dropping daily. But it gets worse, when you look at the 32%→

Of that 32%, 27.4% of the traffic goes right back into Google -kind of an ‘in house ad’. 6% goes to paid ads (SEM) and 66% goes ‘out’ unpaid (organic/seo).

If you’re a retailer or brand with SEO+SEM, that’s a 72.6% shot on 32% of the traffic or a paltry 23.2% of Google traffic now coming ‘out’ that you have to share with everyone else in the mix.

Forbes + S&P Global: 55% of Merchants Say AI Agents Will Be a Major New Channel

Forbes ran a piece built on an S&P Global survey of 500 merchants, and they take a payments angle that’s worth the read. The underlying report is super-paywalled, but here’s what’s revealed in the summary:

• 65% of merchants strongly agree they’re considering adding a new payment partner to support agentic commerce

• 55% believe AI agents will become a major new transaction channel — my favorite stat in the whole piece

• Two-thirds believe agents could initiate at least 10% of their e-commerce transactions within three years

• 94% cite pace of innovation as an important criterion when picking a payments partner

• Top required capabilities → data privacy / regulatory compliance and trusted identity & authentication for AI agents

Here’s the question I keep coming back to: if agentic commerce is a hallucination, why are 55% of merchants leaning in? Are they hallucinating too? Or do they see something in the data that isn’t a hallucination? Naysayers often use AR/VR as an example of past ‘industry-wide mass hallucinations’, but I can guarantee you there was never a survey tht said

Link Here

DCG Benchmark: The Gap Is About Execution, Not Technology

DCG — an independent digital commerce benchmarking firm with clients like Diageo, Mars, Henkel and L’Oréal — shared findings from their AI-in-digital-commerce benchmark across 13 product categories.

• ~30% are building genuine AI capability; only ~10% have generated real actionability; under 5% are deriving measurable value

• 90% have AI governance in place, but only ~35% have named a clear internal AI owner

• 60% believe AI can lower costs; only 38% can prove it today

• 15% can measure any incremental sales benefit from AI so far

• No manufacturer scored above 50% on any agentic-readiness question

The pattern we keep seeing → the gap between AI leaders and everyone else is about execution and ways-of-working, not budget or tech. Fix the data layer (product data!) first, then layer AI on top.

Link → https://salsify.ondemand.goldcast.io/on-demand/cd56a56e-8630-4d6b-9741-367b071c3815

Sopra Steria: Agentic Commerce is A €310B European Opportunity

Sopra Steria put out the first European study dedicated to agentic commerce, based on a survey of 8,400 consumers across eight countries. They peg the potential European market north of €310 billion within ten years — but the headline is that the race won’t be won on technology alone:

• 41% of Europeans don’t yet trust any single actor to provide a shopping agent. Banks emerge as the most trusted (27%)

• 55% have already heard of agentic commerce; awareness hits 76% in Norway vs. just 38% in France

• 45% would delegate electronics & tech purchases to an agent, but only 16% for healthcare and groceries

• 74% still hit friction shopping online today

My takeaway from this is we have a “take of two Europes” with the North leading and the south lagging. The data indicates that trust is the gating factor. Right now nobody owns it, which is a wide-open field for whoever gets their first.

Link → https://www.prnewswire.com/news-releases/agentic-ai-could-assist-310-billion-worth-of-european-e-commerce-transactions-within-ten-years-sopra-steria-study-finds-302796673.html

P&G’s CIO Lays Out the AI Playbook (35 Petabytes and Counting)

At the Evercore Consumer & Retail Conference, P&G CIO Seth Cohen spent nearly an hour detailing what may be the most sophisticated AI deployment in all of CPG. TIKR’s writeup is a great recap:

• A proprietary ~35-petabyte consumer behavioral database, fed by 2M+ touch points a year and thousands of IoT-connected homes

• “Digital twins” of consumers that have cut some R&D timelines from 5+ years to under six months

• AI-driven, in-housed media: digital and social now need 10–200x the content volume — at higher impact and lower cost than agencies

• GEO deployed across every category — they found the #1 source for Pampers info wasn’t Pampers.com, it was a Forbes forum thread

• Supply Chain 3.0 running “unattended” portions of manufacturing shifts

What we have here is the CIO for the largest CPG telling us that one of their top priorites is GEO→

Link → https://www.tikr.com/blog/procter-gamble-cio-just-explained-how-ai-and-data-will-drive-the-companys-next-growth-cycle

Visa: Intelligent Commerce, Agent Scoring & an OpenAI Tie-Up

At the Visa Payments Forum 2026, Visa rolled out a stack of agentic-commerce infrastructure under “Visa Intelligent Commerce.” The payments players are clearly racing to build the rails:

• Agent Score → lets merchants test whether AI agents can navigate and complete tasks on their site (agentic-readiness, basically)

• Agentic Directory → a registry of agents and merchants Visa has verified as legitimate

• OpenAI partnership → secure Visa payments inside OpenAI’s agentic experiences

• Large Transaction Model → an AI model trained on billions of transactions to cut fraud and reduce false declines

• On the back end → stablecoin settlement now at a ~$7B annualized run rate, plus 160+ stablecoin-linked card programs live or in development

The top pull-quote is from the Visa Press Release and has caused me a lot of confusion.

Link → https://usa.visa.com/about-visa/newsroom/press-releases.releaseId.22491.html

Summary

That’s a lot to process, if we super up-level it, my take-away is across many dimensions - we’re seeing Agentic Commerce continue to grow and mature faster than anything we’ve seen from both the consumer and merchant side.

Retailgentic Editorial note: This week there are some news items coming and we don’t want to flood your inboxes, so there won’t be a Tuesday long-form, we’ll be back June 23 with Part II of 3-part series.

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This week is an exclusive first look as we down with Alexander Ringsdorff (CEO) and Kai-Thomas Krause (CPAO), the two co-founders of ShopAgentic, a brand-new European startup building an e-commerce platform from the agentic era up. It's a news-and-product deep dive: they're announcing a €1.9M pre-seed round, sharing two decades of e-commerce battle scars (Magento, CouchCommerce, NewStore, commercetools), and giving an early peek at the "squad of agents" they think replaces the old monolithic backend. Check out the official press release here.

Both founders join for the full conversation.

Listen/Watch Our Interview:

Welcome to our Retailgentic exclusive first look at ShopAgentic, where we cover a lot of ground with both co-founders. First we go over their big funding news, then rewind through two decades of e-commerce history that brought Alex and Kai to this moment, and why they think the ground is shifting under the entire industry and why we’re ready for a new platform, built from the ground up to be Agentic both from a structural standpoint, but also understanding that there will now be human customers…and agent customers. CloudFlare recently announced that Agent traffic » Human traffic - two years earlier than he predicted. Now he sees a world where traffic becomes 1,000:1 (Agent:Human!).

In that spirit, we discuss what changes when agents, not humans, become the buyers: how a commerce platform built for this era might actually work, how merchants would use it, and where payments, protocols, and front ends are all heading.

It’s part founder story, part state-of-the-union on agentic commerce, and part early peek at what they’re building. Hit play. 👇

Highlights

• The €1.9M pre-seed: Led by May Ventures and Greenfield Capital, with around nine entrepreneur and business-angel advisor/investors, a “US-feeling” community round raised in Europe.

• Who buys, not just how: Kai’s framing for the whole thesis, agentic commerce is the first shift that changes the buyer itself, not just the channel.

• Built for humans, not agents: Why the founders believe legacy platforms, even the ones they helped build, aren’t architected for what’s coming.

• The ACP moment: The OpenAI+Stripe protocol release that made the duo sit down and say “this is gonna change potentially everything.”

• The weekend prototype: Asked for screenshots for the pitch deck, Kai vibe-coded a working prototype instead, and it helped investors visualize and conceptualize what they were building and par of why investors leaned in.

• A squad of agents, not a traditional (monolothic or headless) platform: The product reimagined from the outcome backward, sold into innovation budgets rather than million-dollar replatforming.

• Ephemeral UIs: “The fast fashion of front ends” interfaces generated on the fly for whatever the moment needs.

• The protocol soup: Why UCP is “the one to go for” right now, and how ShopAgentic wants to absorb that complexity so merchants don’t have to.

• A wake-up call for brands: “You’re not even part of that conversation. That’s why you’re not seeing the traffic.”

• Debut at K5 Berlin (June 22–24): Their first time on the trade-show circuit, including a business-user AI hackathon. Go see them at K5 Berlin.

Two founders who've lived through every era of e-commerce software, now betting the next one gets rebuilt for agents. Whether you run a brand wondering where your traffic went or you're just curious what a platform "born agentic-native" looks like, this one's a glimpse into the future. Enjoy the conversation.

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

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

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Since inception, Walmart’s shopper agent, Sparky 🙂, has been limited to mobile/logged-in users only. Hard core Walmart shoppers (like me 👋) were able to use it, but that form factor can be challenging. Today, our new VP of Research, Andrew Bell, discovered that Walmart has rolled out to the broader web. In this post we’ll walk through some of the interesting user interface innovations we’ve discovered.

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First Look at Sparky on the Web (on Chrome)

When you first pull up Walmart and aren’t signed up, you get this bottom right float-over box using Sparky as an enticement to login.

Logged-In-User View

When you login, you now see a float-over sparky animation that looks all over the page, it’s pretty cool and a bit of a TBT to Clippy for you fellow Boomers out there:

Cleverly he winks when you hover over him - A+ on the animation Walmart, it’s smooth, engaging and whimsical.

Sparky on the Homepage

When you click on the Sparky face you get a tall vertical box on the right side (it’s still a ‘float over’, unlike Alexa, it doesn’t occupy what we call the ‘gutter’. Here’s the default for my account→

Notice that 4/5 of the suggested prompt pills are what Walmart calls ‘everyday essentials’, in other words Grocery (Shop BBQ essentials, Show me what’s for dinner tonight, Host a backyard BBQ, Give me recipe ideas for the week).

Why Grocery? Let’s ask Walmart’s CEO, John Furner!

In his quarterly comments, Furner has shared this about Sparky+Grocery (paraphrasing):

• When Sparky first launched, engagement “centered more heavily on general merchandise discovery missions.”

• As Walmart expanded Sparky’s capabilities (personalized replenishment, meal planning, smarter recs based on inventory, price, and speed), they observed customers increasingly using the agent for everyday essentials such as food and other consumables.

One take on this, Walmart’s edge on Amazon is Grocery and they are using Sparky as their Shopper Agent ‘wedge’ as well, which has proven at the store-level to ‘pull’ in the General Merchandise (aka not grocery).

What’s on the Menu this Week?

When I hit the “Give me recipe ideas for this week”, Sparky responds with

while this was the first take, you can see int he follow-up prompt pills (Make it vegetarian, Show quick 30-min meals only, Plan breakfasts too, Swap in more RTE meals).

Initial Conclusion:

I think what we can all agree on is: Sparky loves BBQ!

Continuing the tour: Sparky on the PDP Page

Here I went to a coffee machine and invokes Sparky:

Note only does it know the context of the PDP, it has a cool new UX element. Now the floating window adds:

• Handlebar - You can move the floating window, which defaults to bottom right, to anywhere you want while it hovers over.

• Expand - Sparky can expand and go ‘full vertical’

• Close - You can close it out.

Here are examples of these UX elements in action:

Above, we’ve moved the floating Sparky window over the product hero image.

Here, we’ve expanded the Sparky small window to be the taller ‘expanded’ version. Note the expand moves to ‘collapse’ mode and we still have the Handle Bar and Close gadgets.

Conclusion

From a user interface perspective, Sparky in the web is a very strong first showing. We’re still working on seeing if anything has changed ‘under the hood’ and early indications are that Walmart has significantly reduced the ‘off the rails’ things that Sparky has previously said about products. We’re still working through that, more on that later. Stay tuned and….

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Here at Retailgentic, we operate on the bleeding edge of Agentic Commerce and Agentic Commerce Optimization. Back in September 2025, we first introduced the concept of Agentic Commerce Optimization in this 4 part series:

Starting today we’ve going to expand upon the original 9-step ACO diagram and introduce a new framework. This new framework is from real-world experiments we ahve been performing with partners and customers to prove some theories we have had about where ACO is going. We now have enough evidence and quantifiable improvements we can confidently update the original ACO strategy with some new important changes.

After dozens of conversations with commerce leaders, one pattern kept emerging: nobody owns Agentic Commerce Optimization, and most teams believe they’re much further along than they actually are. The ReFiBuy Guide: “Building Your ACO Organization” breaks down how early ACO adopting merchants are organizing their teams, workflows and governance structures to build a great ACO Org. Access here (no signup needed)

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Context Capture is the Great Unlock - The Strategy

In this post we’re going to focus in on the foundation of these new recommendations which is what we’re calling Product-Level Context Capture. In this series we’re going to reveal the next-level Agentic Commerce Strategy and the tactics that you should start implementing to support the strategy. Regardless of if you are a retailer, a DTC brand or a wholesale brand or a hybrid of those three, you will be able to start implementing this strategy now and have impact by Holiday 2026.

Context Capture Strategy in a Nutshell

At a super high level, because of the power of LLMs - they are very good at mapping shopper intent with products, but due to 20yrs of keyword jail, we are very far behind on the side of giving the LLMs the product-level data they need to satisfy the dramatic growth in shopper intent we are seeing.

Before we dig deep into this in more detail, to give it…context… for you briefly let’s recap the 9-step Agentic Commerce framework and some other additions to the Agentic Commerce world that lay the groundwork for where this is going.

ACO Recap

Back in September 2025, before ChatGPT announced their checkout plans that they have now shelved, we proposed a 9-step Agentic Commerce Framework:

To summarize:

• Steps 1-2 - Get your Catalog to the Agents - It’s increasingly rare that we see merchants in the top 7 Agentic Commerce categories blocking bots, but when merchants from categories outside of those big 7 reach out, it’s still happening. For example, we just had this come up in the automotive category. This framing has been resonating with both brands and retailers I’ve talked to: Think of your product catalog as the ‘payload’ - the valuable information we are trying to get into the agent unfiltered and with maximum integrity. Your brand.com/retailer.com website is the primary delivery vehicle. We need to figure out the multi-layer strategy so humans get a great PDP, GoogleBot/SEO doesn’t break and for ACO the agent has unencumbered access to that payload. We can also deliver that payload directly via datafeed, but we have a ton of real-world evidence that you 100% need to do both - datafeed and optimized PDP to belt-and-suspenders the delivery of this super important ACO optimized product catalog payload.

• Steps 3-5 - Do the Basics right - ACO is a linear progression, and after making sure you are getting the product catalog into the LLM, the highest impact thing you can do is to expand the content inside each existing attribute. 20yrs of Google Shopping where brevity was rewarded is reversed, now verbosity is rewarded. Authentic+appropriate verbosity - not AI slop verbosity.

  • Note: Do not make the JV mistake of using an off the shelf LLM for this, 80% of their source materials (Reddit/Youtube transcripts) are unhinged and this will work into your product catalog and cause more problems than doing nothing. This is 100% not vibe-codable.

• Steps 6-9 - Expand, widen and continuously optimize- Once you are through Steps 1-5, the real optimization begins. Here you’re elevating and looking at the entire process: how are your product showing up on product cards, are they mapped right, if yes, where are you on the offer card? who is competing with you, what are they doing, why are they ahead of you. What are the product-level negatives and competitors the engines are surfacing about your products, optimize into all of that. It will be overwhelming at first, but when you start to see results, it will ring the register faster than you would think.

4 Super Lanes of Product Catalog Information Available in ACP/UCP

In steps 6-9, Merchants that are well versed in the legacy datafeed world sometimes have a hard time with the tactics of ‘what do I do’. If some Redditer in 2007 left a 100% false review, what do you do? The good news is you now have tools to at least tell your side of the story and as a merchant, you have a lot of authority with the engines and can headway in the war of ‘brand vs. Reddit crazy’ for the first time.

At Google Marketing Live, Ashish Gupta in his fireside chat with Agustina Sartori, Head of Agentic Commerce at Ulta. In that talk, one summary item I wrote down came from when the host asked the panel: “What’s your number one recommendation for merchants looking to optimize for Agentic Commerce. Ashish’s simple answer packed a big punch: “You need to supercharge your product catalog.” That’s it. For Agentic Commerce, it always, ALWAYS comes back to the first-principle that your catalog is the core asset, it’s the new Agentic Commerce gold-dust. The lowly old catalog -we’ve been hacking at it for decades using excel, pushing it around antiquated CSVs and now here it is - the driver of our future.

What’s starting to emerge over the last year from the initial release and updates to the two core protocols is four lanes of product catalog information that the engines want:

• Lane 1: Your pre-agentic basic product attributes -but beefed up and expaned for ACO

• Lane 2: Conversational Attributes - Google/UCP has added a set of, for now, 6 expansion attributes that add critical context for the agent to pick up on: pairings, sales-rank indicators, occasion, the ‘mysterious’ document link,

• Lane 3: Product-level reviews

• Lane 4: Product-level FAQs

ACP has many of these same features, but uses different nomenclature and structure.

What we’ve learned after doing some real-world experiments is you canuse the product-level FAQ and document links tell your authoritative side of the story and thus continuously optimize your product cards (steps 7/8/9), and counter the negativity out there and tell your side of why ProductX should be picked over a competitor or even in your product line who the perfect customer is.

On top of THAT, you can also further super-charge your catalog through a process we are calling product-level context capture.

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Why Do You Mean Shopper Intent is Expanding?

Earlier I mentioned ‘shopper intent is expanding’. Long-time Retailgentic have seen evidence of this as we’ve gone from LLMs added memory and upgrading it 2-3x, as well as past prompts, browser connections for page history and much more. The best and most current examples and datapoints came out of Google I/O // Google Marketing Live a couple of weeks ago - I’m particularly tuned into this and here’s what I captured:

• The average AI Mode search is triple the length of a traditional Google search query — people are writing in full sentences rather than keywords.

• Follow-up queries in AI Mode have grown 40%+ per month on average in the U.S., showing real back-and-forth conversational behavior rather than one-shot searches.

• Top first words have shifted to “What,” “How,” “I,” “Is,” “Can”, and top embedded keywords are “find,” “information,” “identify,” “explain,” “summarize” — conversational, not keyword-stuffed.

• More than 1 in 6 AI Mode searches in the U.S. are now voice or image (non-text).

• Image-based searches are growing 40%+ month-over-month.

• Planning queries (itineraries, fitness routines, dinner parties, budgets) have grown 80% faster than AI Mode queries overall in the past 6 months.

• Brainstorming queries are growing 30% faster than overall AI Mode usage since launch.

• Decision-making queries — searches starting with “which,” “which of,” “which one” — are growing 40% faster than overall AI Mode queries; this is the language of someone narrowing a purchase.

As a merchant, we’re all merchandisers, and you can see these examples (e.g. “which is a bottom of funnel product comparison”).

Summary:

We’re at a moment in time where:

• We know definitively the unlock for the future is Supercharging your Product Catalog (Ashish@Google is building the future, he has explicitly told you what to do, couldn’t be clearer) He sees all the data we can’t see.

• We know the steps and the sequence to do the basics (9 step ACO Framework)

• We have the supercharged product catalog delivery mechanism now (the 4 lanes). We can now deliver a LOT more product catalog information into the Answer Engines.

• Consumer Behavior is racing ahead of us - consumer queries are getting more wide and deeper. Shopper intent is ahead of product-context. We need to level up.

What’s Next in ACO? Context Capture

That brings us to our newest discovery. What happens when you’ve looked under every nook and cranny for information to enhance your product catalog? You’ve thrown open your virtual doors to the bots and you’ve made sure that across ChatGPT, Gemini, Meta and CoPilot all of your product cards are properly canonicalized and your offers are optimized. Now what? Now we need to move on to the next Phase that begins with Context Capture.

Give Me Examples of Product-Level Context?

At it’s most basic level, Product-level context is anything and everything you can learn about how humans are shopping for, discovering, researching, describing, comparing and evaluating your products. Because each human shopper is as unique as a snowflake, the surfacer area of this is immense and you’ll never capture all of it. But today most merchants are capturing 0% of it. If you can get that up to just 10-20% you are going to be adding infinitely more product-level context to your product catalog.

Context Capture: For the next level of ACO, merchants need to programmatically and continuously pull in context from all available locations. Think of it as the most valuable asset that’s currently falling on the floor.

Examples of product-level context we’re going to want to capture and how it ties to shopper intent.

• Projects/usage signals - What projects are being done your products are being used for?

• Search terms - What are the search terms people are using to find each products.

• Prompt intent - When people are talking to agents, what are they saying, what products are being surfaced, are they the right products, if not why - what was missed in the prompt that could have been in the product data to make the perfect match?

• Unsuccessful (zero results, zero convert) search terms- what are search terms or prompts users are entering that aren’t finding products?

• Cart abandonment - If someone abandons a cart, why Was it the product, was it the price, was it?

• Social proof - When people are reviewing the products, there is a ton of context in the social proof. What are they using it for, what did they like, what did they dis-like and why?

• Trend signals - Is this product popular on wishlists, is it getting a ton of social media love, what search terms/topics is it trending for?

• Influencer information- has an influencer endorsed the product? Have they been seen in it at a big event?

Up Next

In the next issue we will dig deeper into the ‘how’ of Context Capture. How and where can you get this, and what do you do with it all? In part 3, we’ll reveal a new strategy we’ve pioneered that builds on this concept of Context Capture, what we like to call: ACO REDACTED UNTIL NEXT WEE (redacted until next week, stay tuned!).

SpaceXAI Enters the Agentic Commerce Arena?

One of my most ‘unlikely to come true’ 2026 predictions was “X/Grok get into the game” (number 8 if you are keeping track).

With the SpaceX IPO roadshow starting this week, this item flew under the radar of all the activity going on around SpaceX. Gopuff released a pretty unique and powerful AI assistant powered by Grok. It’s not really a general-purpose shopping piece inside their Answer Engine, but to pull this off, Grok has to gain a deep understanding of their product catalog and the shopper behavior.

That means that all the pieces are there to up-level the Grok shopping experience. After the IPO, the pressure will be on for Grok/SpaceXAI to add a bunch of features and what better feature to add that’s totally in Elon’s wheelhouse (remember who founded PayPal?) than..Agentic Commerce for the holidays! let’s see - 174 days until Thanksgiving 🦃! Here’s the hype video→

Juniper Research’s Agentic Commerce Forecast

Juniper Research’s $15T global forecast for agentic commerce puts them in the same strike zone as Morgan Stanley (20%), PayPal(25%) and Bain (15-25%). Another datapoint that re-enforces the ‘consensus’ around Agentic Commrce being a material share of e-commerce by 2030.

Link here.

Nielsen IQ: 42% of Consumers Now Use AI to Shop

Nielsen IQ is the gold-standard of data companies and their panel is second-to-none. They released a set of updated consumer AI behavior data. The highlights:

• 42% of consumers have used at least one AI tool to shop within the past month

• 17% have used AI for product recommendations

• 10% have used a voice assistant to purchase and/or reorder items

• 19% use a subscribe or auto-replenish feature for repeat purchases

• 10% have engaged with an AI-powered shopping assistant

• 5% have used fully autonomous AI agents to place orders on their behalf

Survey data is making it clear that the Research→Find shopper journey in Agentic Commerce has officially gone mainstream. Now the question is how fast does Buy follow? I think we’re going to see some really interesting data come out of Holiday 26 - but we’ll have to wait until Q1 2027 to see it.

Link to details here.

Similarweb: AI Ads Webinar- A Treasure Trove of Data

Another company with great panel data is Similarweb. They recently had a webinar that went really deep into Agentic Ads and there was a truckload of new data I haven’t seen before. Here’s a sample of two slides I found particularly intriguing:

6X deeper AND 17 turns - wow! Also the intersection of AI and ads is going to generate SO MUCH ad inventory it’s going to be insane.

Ads in AI are actually generating commercial intent in addition to reacting to it!

If this is a topic that you’re interested in, I’d flag this one as ‘must watch’.

Accompanying presentation is here:

Similarweb Webinar Ai Ads 5 25 26

24.3MB ∙ PDF file

Download

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Bessemer Venture Partners: The Rise of the Delegated Buyer

Bessemer is a large top-tier venture capital fund that publishes some great content. They dropped a big Agentic Commerce thesis article that’s worth a read if you’re newer to the space and want a pretty tight recap of everything. It’s got a good hook: “The rise of the delegated buyer.”

Link here:

Walmart’s Sparky Makes Big Moves

Our friends at Digital Commerce 360 had a good summary of Walmart’s quarterly conference call where they spent a lot of time talking about some new Sparky data.

• 100% increase in weekly active users in the last quarter

• 40% improvement in answer quality

• 35% higher AOV vs. non-Sparky users

• Added in-store and reorder capabilities

• Expanded from GM into everyday essentials

Details here on Sparky’s Great quarter!

McKinsey: Agentic Commerce Autonomy is a Curve, Not a Switch

File this under: “things I’ve been saying for 18 months” 😄, but seriously it’s good to see McKinsey come around to the same conclusions. In short:

• We’ll get to autonomy soon

• But even then consumers will use Agentic Commerce in various ways (zero/research/find/buy/autonomous/etc.) depending on the tool they want for the shopping mission vs. the false binary of ‘all autonomous or it’s a hallucination’ mindset.

Link here.

Horizon: 82% of Consumers Use AI for Product Research

Here’s a bonus early-June survey from Horizon:

• 82% of consumers have used AI for product research or comparison

• 64% have used AI for price tracking

• 62% have used AI for research

• 90% report satisfaction with AI-assisted shopping experiences

This slide packs a big punch 🤜:

Detailed PDF here:

Agentic Commerce Building Trust In The New Era Of Ai Shopping Futures Report (1)

820KB ∙ PDF file

Download

Download

Link here.

News Summary

Stepping back, two big themes jump out from May’s data and news items:

1. Consensus is forming on the 2030 number. Morgan Stanley, PayPal, Bain, and now Juniper all in the 15-25% of e-commerce zone. Different ways they all get there, same triangulated result.

2. Consumer behavior continues to show Agentic commerce Adotion . NIQ at 42%, Horizon at 82% for research, Similarweb at 460M users and $1K+ brands buying ads, Walmart’s 35% AOV lift on Sparky. We are well past ‘is this real?’ and into ‘will it stay on exponential’ territory…

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This week we have a rare doubleheader. We sat down with two Salesforce leaders back to back to help us unpack the company’s big June release, headlined by the launch of its newest platform, Storefront Next.

Nitin Mangtani is the Executive Vice President and General Manager at Salesforce, a few more things in his title than the last time he was on the show (check it out here). Nitin ran Google Shopping for years before founding PredictSpring, a point-of-sale company that Salesforce acquired in 2024. Two years in, he now oversees the full commerce stack: storefront, point of sale, and order management.

Lennart Stevens is the Vice President of Product Management for Agentforce Commerce at Salesforce, making his first appearance on the show. Lennart has spent nearly two decades in commerce, almost all of it on the partner side. He was one of the early implementation partners for Demandware (the platform Salesforce acquired and rebranded as Commerce Cloud), so he’s seen the customer’s side of every platform migration in the book. Salesforce brought him in to carry that customer perspective into the product, and his purview now covers Storefront Next, checkout, and the new commerce applications framework.

Listen/Watch Our Interview:

First up is Nitin, who walks through the full sweep of the June release and makes the case for why, taken together, it's Salesforce's biggest commerce release in five years. Then Lennart takes us deeper into the agentic guts, one of our favorite parts.

The two interviews rhyme in one important way: both leaders insist that agentic commerce on your owned-and-operated channels matters just as much as showing up in ChatGPT and Gemini. As Nitin puts it, skipping your own shopper agent is like the old days of saying "I participate on Google Shopping, but I don't have search on my website." Highlights below.

Highlights

• Biggest release in five years: Nitin frames the June release as Salesforce Commerce’s biggest swing since he arrived.

• “It’s 2026, let’s combine the two together”: The forced choice between enterprise-grade and easy-to-launch is a false distinction that can be engineered away.

• The acquisition cheat code: Rather than spend five years building modern AI search in-house, Salesforce bought Cimulate. It now powers every shopper agent prompt, and it’s available even to non-Commerce Cloud customers.

• “This code should be written by Claude prompting and not by you”: Storefront Next ships you to the 90% point out of the box; the last 10% is vibe-coded. Migration utilities plus Claude Code shrink what used to be a capital-T front-end project.

• Live in under 30 minutes: Lennart’s demo path: name the storefront, connect a code repo, click go, and you’re previewing a high-performance site in under half an hour.

• You need your own Rufus AND to play off-site: Both guests hammer the distinction between agentic commerce on owned-and-operated channels and syndicated commerce via UCP on Gemini, OpenAI, and beyond. One without the other is like being on Google Shopping with no search box on your own site.

• Focus is magic: Nitin consolidated 20 initiatives down to 4–5, runs two-week iteration cycles instead of two-year PRDs, and credits that discipline (plus AI-native development) for the release’s scope.

• Advisor and confidant: Lennart’s favorite anecdote… a jewelry brand found customers were more comfortable asking an agent “how big should the ring be, how much should it cost?” than asking a friend.

• Checkout wisdom from Nitin: Stop offering eight payment options. “Just put Apple Pay on your PDP” four options max, on a good day.

The era of choosing between enterprise and easy looks like it's ending and the agents are coming either way. Enjoy the conversation.

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May 20th at Google Marketing Live, before the high-level strategic chat with Nick Fox, SVP of Knowledge and Information at Google (you can read/listen to that one here), I was also able to to sit down with friend of Retailgentic: Ashish Gupta, VP & GM of Merchant Shopping at Google.

Ashish was on the pod back on March 19th which was a very popular episode. He’s a Google Fellow, which is an extremely rare and hard earned engineering distinction inside of Google, and he runs the merchant side of Google’s Commerce focused team.

The org has two tightly-coupled sides: a consumer group and a merchant group. Ashish runs the merchant side. That’s the side that owns the feed, UCP, Merchant Center, and now the new AI performance insights. I got to also meet many of the folks on the consumer side - we’ll work to get one of them on the pod next!

This interview was recorded right after his fireside chat with Augustina Sartori, Head of Agentic Commerce @ Ulta Beauty) at GTL. In that chat , he laid out the 4 things merchants should do right now:

1. Product feeds

2. Data strength

3. AI campaigns,

4. Agentic infrastructure (i.e., UCP).

In this interview we discuss we dig deep into all 4 for you as they are super-important and should be foundational to your Google Holiday 2026 strategy:

• The 6 new conversational attributes — just launched, live now in the supplemental feed (FAQs, related items, popularity rank, document links, additional variants, item group). Why they exist, how to use them, and the “best sales associate in your store” analogy for how AI surfaces are now expected to behave

• UCP’s expansion into new verticals — food delivery and travel. Why now, and why the “U” in Universal Commerce Protocol is doing real work

• Loyalty in UCP via Google account linking — your Ulta $10 reward shows up at checkout. Critical for merchants who don’t allow guest checkout

• Data strength — bringing first-party loyalty data into Gemini / AI Mode surfaces so loyal customers see more of the brands they love

• AI Max & P-Max and the new ad formats showing up across AI surfaces (and YouTube)

• AI Performance Insights coming to Merchant Center — the new Share of Voice metric, why the funnel doesn’t behave like a normal funnel (it can actually widen deeper in), and how Google is moving from a “click/keyword” company to an “answer engine” company

• The keyword vs. long-tail question — in an answer engine world, is there even such a thing as a “popular search term” anymore? (Ashish’s answer is interesting to this one!)

What’s neat about doing these back-to-back is you get both layers in one day. Nick’s interview is about framing: how Google thinks about AI Mode vs. traditional Search, the Innovator’s Dilemma, the ~5 prior attempts at “transactions on Google” and why this one is different, and trust at the intersection of answer engines and AI ads. Ashish’s interview is the merchant-facing manifestation of all of that: here’s exactly what’s shipping, here’s what to put in your feed, here’s how we’ll measure you.

Read both together and you get a pretty complete picture of what Google is doing with agentic commerce in 2026.

Listen/Watch My Interview with Ashish:

If you want to watch, you can see it on YouTube here:

Or you can Listen here for wherever you listen to pods (Apple Podcast, Spotify, Overcast, etc..) OR you can listen directly from this post here:

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May 20th at Google Marketing Live, I was very fortunate to be able to sit down for ~20 minutes with Nick Fox, SVP of Knowledge and Information at Google. Earlier in the day he had a fireside chat with Ben Smith, Editor-in-Chief Semafor:

That was a great conversation and you can watch/listen to that talk here→

Later that afternoon after I had time to reflect on this conversation, I had a ~20min interview with Nick.

It’s not often you get to sit down with one of the top execs (Nick reports directly to CEO Sundar Pichai) at a ~$5T company so I worked a lot of strategic topics into a short time (~20mins).

As always, I approach these interviews with you, the audience, in mind. What would YOU ask? For context, before the interview, I had a super detailed and important, but tactical Agentic Commerce / UCP/Universal Checkout discussion with Ashish Gupta (coming soon!), so for this one flipped into high-level strategic mode.

In this interview we discuss:

• How does Nick ‘frame’ AIO/AI Mode/Gemini

• Will AI Mode/AIO and traditional search transition from traditional to AI Mode or will they merge, or ‘other’ down the road?

• Google’s had ~5 previous attempts from the Froogle era to today for ‘transactions on Google’ - why is this the moment they are getting super serious about it?

• Navigating the Innovator’s Dilemma

• Finally, managing trust at the intersection of ‘Answer engines’ and ‘AI Ads’

Listen/Watch My Interview with Nick:

If you want to watch, you can see it on YouTube here:

Or you can Listen here for wherever you listen to pods (Apple Podcast, Spotify, Overcast, etc..) OR you can listen directly from this post here:

Want to Learn More About Agentic Commerce?

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Sorry to pummel your inbox/notifications so much in the last 24hrs, one last Google I/O/GM! There’s just one more post hitting this time tomorrow then we’ll peace out until next week, promise! (Editor’s note: unless some really earth shattering news hits which can happen at any time these days!).

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Don’t miss our previous Google I/O coverage which this builds on:

• Google Announces Universal Cart here.

• Our awesome interview with Suresh (Senior Director of Product, Consumer Shopping) here.

2026 Google Marketing Live (5/20/26) Drops More Agentic Commerce News, Let’s Dig In!

Today Google is announcing four more Agentic Commerce news items. Let’s just jump right into these.

1: Conversational Attributes

If you remember our Agentic Commerce Optimization framework (above), we introduced a best practice of not only beefing up your current attributes (steps 3/4/5) but also expanding your existing attributes (step 6). Well today Google formalized that by announcing support for what they call Conversational Attributes. Think of this as a supplemental datafeed you can send to GMC in addition to your core attributes that enhances what the buyer may want to know about your products. Examples here could be: What occasion is it optimal for, what does it pair well with, what does

I’m excited by this one because Google is helping Merchants break out of keyword jail and laying down a very clear breadcrumb trail that product-level storytelling is where we’re going. I imagine this is just the start.

Want to learn more? Check out this Google Merchant Center help article here.

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2: Shoppable YouTube Ads Fueled by UCP and Datafeeds?!

Long-time readers know I love datafeeds! And this is a really cool marriage of UCP/Universal Cart/ads and datafeeds. Honestly it feels like with the right content and an expansion into pulling in influencers, this could give Tiktop shops a run for the money. Here’s a video showing an example of an Etsy video that has this turned on.

In the video, which is an ad, we see a bunch of gift ideas. Under the video is shoppable products that match the items that are recommended in the video. The user clicks on the “Monstera Tote Bag” and directly from the Youtube video ad enters the Universal Cart and buys the item with one click. Serendipitous commerce at it’s finest and now we know one example of Universal Cart on the YouTube surface!

3: AI Performance Insights

For the merchants out there frustrated at the lack of visibility into everything happening our soon-to-be zero-click World, Google has an answer. In Google Merchant Center, there’s a new AI Performance Insights section detailed here:

You can see lots of goodies in this short video, I’m looking forward to spending more time with this puppy today in one of the hand’s on sections and will report more on it as I’m sure every brand and retailer I know can’t wait to get their hands on this one:

• Share of voice

• Competitor’s average share

• Funnel performance across Discovery→Evaluation→Purchase (I prefer Research→Find→Buy but close enough!)

There’s even some sub-context like ‘did they search for product specs or user reviews, did they compare products, etc.’ and what are some terms (keyword jail!) people are using - this one I’m pretty skeptical about, but will reserve judgement until I see it IRL.

4: Expansion of UCP to Food and Travel Industries

Finally, less relevant to this audience, but highlights my theory about Amazon’s threat being the horizontal-ness of Agentic Commerce, Google is expanding the UCP tent to include Food and Travel companies and announced the addition of a whopping 14 new logos to the already busting-at-the-seams UCP partners logo slide(s)→

Open Questions from GIO and GML

Finally, as I spend time today at GML, I’m chasing down answers to the questions from yesterday, plus I’m sure there will be more added to the list from today’s additional four big items:

1. Googlers talk about Universal Cart being available on surfaces like Gmail and YouTube - what’s that actually look like IRL? What are the use cases that merchants can think about and start experimenting with?

2. Universal Cart is coming this Summer - that’s typically June/July/August - that’s a long window in AI-time, early/mid/late Summer?

3. I haven’t heard Google, at least the Agentic Commerce team, talk about AG2 in a while, and suddenly it popped back up in the context of the new consumer agent called Gemini Spark. What’s that all about that.

4. Loyalty (via identity linking) was part of the UCP features announced back on March 19th (details here) but kind of missing here? Even though if you read Vidhya’s blog post very carefully it says that Universal Cart uses “loyalty information”, but it’s not like the Copilot UCP-based loyalty linkage we saw with Target back on April 22 (here if you missed it).

5. (new) Will Conversational attributes grow over time, any other product-level storytelling you see coming to UCP/Gemini?

Sneak Preview of GML

We have two special interviews planned to record later today we’ll release tomorrow, stay tuned.

Also here’s the map of the pavilion/festival area→

If yesterday’s Google I/O was a theme park for nerds, this is a circus for digital marketers. I’ve seen 10 signs with ROAS, PMAX and Agentic Commerce on them. LETS GO! 🧐🎸

I’m particularly interested in the ‘Think’ section -notice 👀 the “Product Bar/Retail and “Retail in the AI Era” sections - I’ll be logging a good bit of time there ;-)

Today at Google I/O, in her part of the larger keynote, VP/GM of Ads and Commerce, Vidhya Srinivasan announced a raft of Agentic Commerce items with the anchor new feature being Universal Cart (covered by Retailgentic here and her blog post here and the keynote on YouTube here).

Google I/O Lots of Cool New Agentic Commerce Features…But We Have Big Questions!

After Vidhya’s keynote comments, I was left with these questions that I know merchants will have too:

• Googlers talk about Universal Cart being available on surfaces like Gmail and YouTube - what’s that actually look like IRL? What are the use cases that merchants can think about and start experimenting with?

• Universal Cart is coming this Summer - that’s typically June/July/August - that’s a long window in AI-time, early/mid/late Summer?

• I haven’t heard Google, at least the Agentic Commerce team, talk about AG2 in a while, and suddenly it popped back up in the context of the new consumer agent called Gemini Spark. What’s that all about that.

• Loyalty (via identity linking) was part of the UCP features announced back on March 19th (details here) but kind of missing here? Even though if you read Vidhya’s blog post very carefully it says that Universal Cart uses “loyalty information”, but it’s not like the Copilot UCP-based loyalty linkage we saw with Target back on April 22 (here if you missed it).

That’s a lot of questions - I only had 30mins with Suresh, so had to pick and choose, but we covered some good ground. The good news is I have two more interviews scheduled for Wed 20th at Google Marketing Live and will work them all in.

Listen/Watch My Interview with Suresh:

If you want to watch, you can see it on YouTube here:

Or you can Listen here for wherever you listen to pods (Apple Podcast, Spotify, Overcast, etc..) OR you can listen directly from this post here:

Do You 💕 Agentic Commerce Like We Do?!?

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Greetings LIVE From Google I/O!

Greetings from sunny Mountain View California where we’re all waiting here for the big keynotes and other festivities to kick off. Google I/O is foreign to us in the Retail/Brand world because it’s a developer conference for super hard core cracked devs. Well, guess what, AI is mashing up all our Worlds and here I am for the first time at one of the largest developer conferences out there. Later in the day I’ll be posting some pics and video to show you the scale of this thing. It’s a scale that I haven’t seen since the late 90’s Microsoft Visual C++ days and the engineers here are in a Agentic AI induced frenzy.

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This year Google is smooshing together their Ad conference, Google Marketing Live (GML) . Today, Tuesday May 19, one of the top topics at the keynote (I’m sure we’ll get new models and there’s an OpenClaw-ish agent called Spark coming that we’ll cover in another post later) and then Agentic Commerce takes the main stage. Then Tomorrow, Wed May 20th we go to GML

It will be two days chock full of announcements, live demos and we’ve secured some killer ✨EXCLUSIVE✨ interviews for Retailgentic readers that we will post as fast as we can. Because of this our posting frequency this week will be quite unusual, but we want to keep our reputation of getting you the fastest and deepest Agentic Commerce news so you have an edge.

Today we had advanced release of the biggest feature coming out today: Universal Cart.

Note this is not live today - they are saying: “Rolling out this Summer”. But we’re going to see live demos, so I’m thinking ‘early Summer.” - a question I’ll be definitely digging into for you.

Google Universal Cart: Intelligent, Proactive, Multi-dimensional, Multi-item, Multi-merchant Delivers “Agentic Superpowers for Shoppers”

There’s a lot to unpack here, let’s start with a list of all the features in the new Universal Cart and then I’ll show you examples. We’ve talked about the idea of Universal Carts back in January when we found evidence that ChatGPT was about to release theirs here. The TL;DR - before Agentic Commerce many tried, all failed. Google has 100% nailed it by building it on UCP.

Google’s Universal Cart (GUC) features:

• Multi-item - Agentic Commerce carts to-date have been single item single cart. GUC is multi-item - in fact it’s kind of a ‘lazy cart’ - you can throw stuff in there as you shop as almost a wait list and then checkout at your leisure. It’s persistent and follows you across devices and sessions. Almost like your shopping buddy - always there for you to sling it a product, hold it there and it will keep it safe for you and help you proactively.

• Multi-merchant - GUC is multi merchant. Working on a new outfit for a Summer trip across 5 retailers, throw it all in one cart and see it all together.

• Intelligent - Once you add an item to the cart, it can tell you about the price history of that product, if that product is frequently out of stock (‘hey you may want to check out now’), etc.

• Proactive - Let’s say you new Macbook Neo in your cart and you get a webcam too. GUC will check the compatibility, noticed that the webcam is USB2 and Neo doesn’t have a port for that and flag the incompatibility.

• Multi-dimensional -In addition to the above, it is aware of your wallet. I am a big Amex user and they have great offers and other discounts and cash back/points multipliers that are near impossible to use because they are locked at amex.com - they should be at the point of purchase, now they can be. GUC will surface these things when it detects them. Previously Google also announced partnership with our friends at Klarna and Affirm for BNPL, so that’s another dimension you can leverage here.

• Multi-surface - This is going to be live across not only AI mode and Gemini, but also Gmail and Youtube.

• Merchant friendly - Through all of this it maintains merchant-of-record and I imagine soon we’ll get loyalty program connectivity (this is part of UCP, Copilot has implemented it, Google hasn’t yet, seems like they wanted to nail cart first - this is going to be my top question I ask).

Before we get into some examples, we have an important Retailgentic ‘Agentic Commerce Players” update.

Agentic Commerce Levels of Autonomy Update

While it’s only Q2 and early at that, we’re going to make a mid-year update to our tracker:

Updates:

• ChatGPT: Down a level ☹️ - First, we have to move ChatGPT from Level-3: “Buy” back down to “Level-2: Find” - yes they have ChatGPT apps, but that experience is so terrible, we couldn’t live with ourselves having it in the same category as obviously superior offerings. ChatGPT - there’s still time to get back in this thing - wake up!

• Gemini: Up a level 😄 - We’re also calling it- with the announcement of GUC, we’re “3.0’ing” Google/Gemini and moving them as the first competitor to reach Level-4: “Advanced Buy”. We would have liked to see Loyalty in there, but we all know it’s on the docket, so we’re going ahead and moving them as the mountain of other features really fulfill our vision of what “Advanced Buy” could look like when we first published this in early 2025.

Now, let’s dig into some examples and bring this to life.

Note: these are static images provided by Google, I’ll be posting more when we get into the live demo/hands on parts of the days.

Example 1: Multi-Item and Multi-Merchant

Here we see a lot going on - we’ve got two merchants, Target and Ulta - in the Target part we have two items from Target a hat and a gardening kit - both are 5% off (presumably with a coupon or red circle?). Notice the new ‘checkout option’ where you can buy via UCP or xfer the cart over to Target.com

Then we have Ulta - we can only see one item, but it could be 100. All multi-item shipping discounts and what-not are detected and applied here exactly as they would be in the downstream cart.

Example 2: Intelligent

In this example, GUC has 2 products from Wayfair and tells us this desk is a pretty good deal and this would be an opportune time to buy this puppy before that price creeps up (it’s at a 30 day low).

Example 3: Proactive

Here we see two proactive vignettes:

• Vignette 1 - We’re building a gaming PC at Bestbuy throw some components in the cart and it detects an incompatibility.

• Vignette 2 - We have 2 items in our cart and there’s a discount with our Target Circle Credit card (ah-ha!)

Example 4: Multi-dimensional

By ‘multi-dimensional’ - what I mean is it cares more about price. Other dimensions important to me as a shopper are: speed of delivery, price of delivery, stock level, price trend, payment method discounts/special offers, price trend, etc. GUC is pulling all of this in and it’s already smart and will only get smarter.

What’s Next…

Later today I’m going to be posting some thoughts at the end of the day and then we’ll be back at it tomorrow at GML with some early interviews coming your way too as we produce them.

Happy Agentic Commercing! 🚀

Bonus - here’s the map of cool stuff I’ll have access to later today. It’s like a theme park for nerds LET’S GO! 🤓🤓🤓🤓🤓

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Elizabeth Lachhar, Executive Vice President of the Americas at Rezolve Ai, is a senior enterprise leader focused on bringing agentic commerce technology to retailers, brands, and large organizations across North and South America.

In her role, she works directly with major companies to adopt AI-driven commerce systems, spanning everything from search and personalization to checkout and loyalty.

Before joining Rezolve AI, she built her career at major technology companies including Microsoft, Google, and Oracle, where she led large-scale digital transformation projects for global enterprises, particularly in retail, consumer goods, and hospitality.

Today, she helps enterprise retailers and brands adopt agentic commerce, leads go-to-market and growth across the Americas, works across executive teams in commerce, marketing, IT, and finance to implement AI systems, and focuses heavily on driving real-world ROI with deployments that can often be realized in as little as 12 weeks.

Listen/Watch Our Interview:

In this episode, we unpack Rezolve AI’s end-to-end agentic commerce platform, including its closed retail-trained LLM, Brainpower, and modules across search, discovery, conversational commerce, checkout, and loyalty.

A lot of the agentic commerce conversation focuses on offsite experiences like ChatGPT, Perplexity, Gemini, and other AI shopping agents. But there’s another major shift happening onsite, where retailers are rethinking search, merchandising, conversational commerce, checkout, loyalty, and personalization for an AI-native world.

Elizabeth brings a deep enterprise background from Microsoft, Google, Oracle, and more, and now works with retailers, brands, food service, hospitality, and B2B companies navigating this transition.

Highlights

• Why onsite agentic commerce matters just as much as offsite AI shopping agents

• How Rezolve AI approaches search, merchandising, conversational commerce, checkout, loyalty, and geolocation

• Why closed, retail-trained LLMs may matter for hallucination, security, and enterprise trust

• How conversational commerce can help retailers move beyond flat, 2D ecommerce experiences

• Why B2B discovery is a major agentic commerce use case

• How loyalty, memory, and past purchases can create better personalization

• What retailers are asking about right now: security, compliance, fraud, ROI, and partner strategy

• Why Elizabeth believes agentic commerce is already here, not years away

• What Generation Alpha could mean for the future of shopping experiences by 2030

Agentic commerce is also about rebuilding the onsite experience so retailers can understand, personalize, transact, and convert in a world where consumers expect commerce to be faster, smarter, and more conversational.

🎧 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!

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 🤫 🚀