AEON360 and Google Cloud have announced a multi-year collaboration to build an AI-driven commerce platform across AEON’s retail, finance, and lifestyle ecosystem in Southeast Asia, beginning in Malaysia.

The partnership centers on what the companies are calling a continuous commerce experience — a system designed to carry a shopper’s context, preferences, and history seamlessly across every AEON interaction, from product discovery through checkout, financing, and post-purchase support, without requiring them to re-establish their identity or preferences at each step.

At the core of the initiative is a contextual intelligence engine built on Google Cloud’s BigQuery data platform. AEON360 is using it to construct an enterprise knowledge graph that reconciles customer data across its various entities and touchpoints. In practice, a shopper searching for groceries in Kuala Lumpur would receive curated product recommendations, member pricing, and real-time stock availability drawn from their purchasing history — alongside dynamically integrated financing options such as installment plans, cashback, and loyalty points.

Also Read: Three Myths That Are Keeping Brands Away From AI

“Our ambition is to create an ecosystem that serves consumers as a cohesive, intelligent entity, so every engagement feels crafted just for them, no matter where they begin their shopping journey,” said Daisuke Maeda, chairman of AEON360. “We’re shifting from simple digital interactions to AI agents that surface the most relevant offerings and perform complex tasks on our customers’ behalf.”

To support the rollout, Google Cloud will help establish the AEON360 Innovation Foundry in Kuala Lumpur — a dedicated center designed to train AEON employees in AI fluency and equip teams across its lines of business with Google skills and certifications. The Foundry will also serve as an environment for developing and scaling agentic AI solutions across AEON’s merchant and supplier ecosystem.

The platform will deploy two AI agents built on Google’s Gemini Enterprise for Customer Experience. The first is a shopping agent that acts as a digital concierge — processing text, voice, and image inputs to autonomously build carts and execute transactions on a customer’s behalf. The second is a customer experience agent providing round-the-clock query resolution while offering real-time guidance to human representatives.

AEON360 also plans to extend its agentic commerce capabilities beyond its own digital properties by adopting the Universal Commerce Protocol, an open standard pioneered by Google that allows AI agents and retail systems to operate across different consumer surfaces, businesses, and payment providers. The company is additionally exploring integrations with Google Pay that would allow shoppers to authorize agent-led transactions across touchpoints, with payment and shipping information stored securely in Google Wallet.

Also Read: The Organizations That Survive Disruption Never Had to Recover From It

“For too long, traditional retail technologies have created fragmented experiences where shoppers are forced to restart their journeys at every turn,” said Hana Raja, country manager for Malaysia at Google Cloud. “Our collaboration will deliver agentic commerce solutions grounded in a continuous thread of context — ensuring that information flows with the shopper to help AEON360 anticipate and act on what they need next.”

The collaboration will initially focus on Malaysia, with a roadmap to expand across other Southeast Asian markets where AEON operates.

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Birch Coffee, one of New York City’s most beloved independent coffee brands, has opened its twelfth location, powered by Square’s unified commerce platform. Built on a hospitality-first ethos and a commitment to neighborhood connection, the brand is steadily scaling by using Square to simplify operations, support frontline teams, and deliver a consistent customer experience across every location.

Birch Coffee’s origin story is one of humility and hustle. Founded in 2009 by two bartenders new to the industry, the brand has grown from a neighborhood newcomer into a name recognized city-wide for its warmth, craft, and community. Believing that every cup of coffee has its own story and soul, the founders early on established relationships with South American farmers behind their beans, with a commitment to discovering the best processing methods and flavor profiles. As they embarked on ambitious expansion, the team needed technology that would evolve alongside them, upholding their high product standards without adding operational complexity.

“We really immersed ourselves in the industry, learning as much as we could and becoming more confident in our point of view,” said Jeremy Lyman, co-founder of Birch Coffee. “What started as a small, uncertain venture has grown into a more established and intentional brand. We’ve become much clearer about who we are and how we operate, and we need systems that support that as we grow.”

That intentionality has paid off. In 2025, Birch Coffee posted 16 percent year-over-year growth — a testament to the brand’s local reputation and the commerce infrastructure supporting it. Before Square, Birch Coffee ran on a legacy point-of-sale system that was struggling to keep pace with rapidly advancing customer expectations. When word spread that other local coffee operators were making the switch to Square, the founders took notice.

Also Read: The Organizations That Survive Disruption Never Had to Recover From It

“It’s easy to use, easy to train on, and easy to scale with,” said Lyman. “Being able to run so much of the business through one ecosystem while still integrating with best-in-class partners is a big advantage. The overall simplicity and flexibility would be hard to replace.”

Birch Coffee’s portfolio runs entirely on Square, combining hardware, software, and integrations that support both frontline operations and back-office management. The business uses Square Register and Square for Restaurants to power its service operations, while Square’s email marketing tools and gift cards help drive community connection and repeat visits from loyal regulars. Square also powers the brand’s coffee subscription service. Birch Coffee additionally uses a custom-built API alongside an integration with workforce scheduling platform 7shifts — an example of how Square’s open platform can be tailored to a brand’s specific needs.

Running a coffee brand in New York City poses distinct challenges. The pace is fast, real estate is tight, and with local competition fierce, customers expect standout experiences every time they walk through the door. Square is designed to meet those demands, offering the reliability to handle dense urban footprints, tools to manage large and rotating teams, and the mobility to take commerce anywhere.

“Cafés have some of the most demanding commerce needs, with high transaction volumes, complex shift work, tip income, and customers who expect excellence with every visit,” said Nick Molnar, global head of sales and marketing at Block. “Square is purpose-built to meet those demands, and what Birch Coffee has achieved across its locations in New York City is a testament to what’s possible when capability scales. The same platform that powers a single neighborhood café can run a multi-location enterprise without adding complexity. That’s the promise we deliver for operators like Birch Coffee.”

Also Read: Protecting Loyal Customers From Your Own Return Policies

With its twelfth location now open and further expansion on the horizon, Birch Coffee shows no signs of slowing down. The company remains focused on deepening its presence in New York City’s coffee community, and with Square as its unified commerce provider, it is positioned to keep growing without losing the hospitality-driven character that defined it from the start.

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An e-commerce merchandiser’s day is a constant negotiation between forces that rarely agree: what customers are clicking and buying, what inventory exists and at what depth, what marketing is pushing this week, what the brand wants front and center, and what revenue and margin targets demand. Aesthetics. Creativity. Inspiration. Each pulls in a different direction. Merchandisers reconcile all of them — every day, across a vast catalog of products and collections.

Experienced merchandisers often have sharp instincts. But the forces shaping 24/7 ecommerce are dynamic, complex, and unrelenting. Under that pressure, there is rarely any confirmation that a merchandising decision outperformed what it replaced — or what else was possible. Teams won’t know for days or weeks, and by then, shoppers’ preferences, brand preferences, and market signals will have already shifted.

Every merchandising decision is a trade-off. Most are made without ever knowing what the alternative would have cost. That gap between decision and consequence is where revenue quietly slips away.

New Arrivals, Best Sellers, and Hidden Winners

Ask any senior merchandiser how they decide what goes where, and they’ll walk you through the logic: new arrivals get prominent placement to build velocity, best sellers stay visible because they convert, slow movers get deprioritized to protect real estate. It sounds rational. For many years, it worked well enough.

But in the fast-moving reality of modern e-commerce, “rational given my experience” and “optimized” are two very different things. Both matter. The gap between them, however, costs real revenue.

Consider new arrivals. Most teams give them a fixed window at the top — a week, two weeks, sometimes a month — regardless of how they are actually performing. The logic is understandable: new products need exposure to get a fair shot. But this approach treats every new arrival the same. A dud sits in a premium position just as long as a breakout hit. That is not a strategy. That is a schedule — and it burns valuable catalog real estate on products that have already signaled they won’t earn it.

Now consider best sellers. A product that ranks in your top ten overall is not necessarily a top performer in every collection. A bestseller in “Evening Wear” might underperform in “Casual,” where it occupies a prime position while stronger contextual fits remain buried. What works across a full catalog does not always work within a collection — and promoting it heavily in the wrong context wastes resources that could be driving real conversion elsewhere.

Then there are the hidden winners: products with strong conversion signals that never get the traffic to prove themselves because they’re sitting on page four, crowded out by slower-moving items consuming the placement and promotion they deserve. Without a system to surface them, they stay buried. Teams never know what they missed — or realize it only in hindsight, too late to act. And customers never find what they would have loved.

Short on Time, Not Signals

Most e-commerce teams measure what they did or conduct retrospectives on what happened days, weeks, or months ago — clicks, conversions, revenue per session, return rate. These are reasonable metrics. But very few teams systematically measure what they didn’t do: the product that could have been promoted but wasn’t, the placement that could have driven conversion but went to something weaker, the new arrival that earned its spot on day three but stayed there until day fourteen because the calendar said so.

Opportunity cost — the revenue left on the table by not surfacing the right product in the right place at the right time — is the most consequential metric almost no one is tracking. The reason is straightforward: you cannot measure what you cannot see. And most merchandising systems are not built to make the alternative visible.

This is not a data problem. Most brands have plenty of data, and AI is producing more of it every day. It is a decision intelligence problem. The data needed to make better calls already exists — it is simply not being assembled in a way that supports the actual decisions merchandisers need to make, in the moment they need to make them.

What AI Merchandising Support Needs to Do

As AI becomes embedded across e-commerce, the goal should not be piling on more tools and dashboards. It should be deploying a genuinely intelligent merchandising approach — not “here are your top ten products” reports, not weekly performance summaries, but real-time decision support that reconciles competing constraints, surfaces what even the most experienced merchandiser cannot possibly see, and quickly tells you whether the arrangement you just published is performing better or worse than the one it replaced.

In practice, this means an AI model that knows when a new arrival has accumulated enough signal to be called a winner or a loser — and alerts a merchandiser, or automatically adjusts placement, rather than waiting on a fixed schedule. It means understanding that a product’s rank in one collection says almost nothing about how it should be positioned in another. It means flagging overexposed products that crowd out items with stronger contextual fit and surfacing quiet performers before the window to act on them closes.

The goal is not to replace the merchandiser. It is to handle what the merchandiser cannot: processing more variables simultaneously than any human can track, across more collections than any team can actively monitor.

The teams that will win in the next few years are not the ones with the most data or the most sophisticated tech stack. They are the ones that close the loop between decision and outcome fastest. E-commerce waits for no one. The catalog changes. The customer moves on. The question is whether your merchandising intelligence moves with it — or whether you’re still finding out what happened last week.

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There was a time when brands could simply list a product online and expect it to sell. Today, success requires a coordinated strategy across retail media, the digital shelf, pricing, and inventory. That strategy should be informed by data, but with thousands of SKUs across numerous marketplaces, brands not only have to interpret endless insights but also act on them. At scale, consistent execution becomes nearly impossible to manage manually.

In a recent survey, nearly half of business leaders said their data isn’t actionable, and more than 40% cited a lack of data accessibility or decision-making time as a core operational challenge. 

Teams don’t need more visibility — they need the ability to keep up with and act on the data they already have. It’s impossible for e-commerce teams to adjust every bid, fix every product description, and catch every stock issue themselves. To close that gap, brands need to rethink how the work gets done.

Why Today’s Execution Model Can’t Keep Up

Teams aren’t lacking data; they have too much of it. Dashboards have become more sophisticated, but seeing more doesn’t necessarily help teams do more.

Every optimization still requires someone to pull the report, identify the issue, decide what to do, and then execute. That delay between spotting a problem and fixing it is where performance starts to degrade. By the time a team corrects a content issue, the marketplace algorithms have already shifted.

What retailers are experiencing is a gap between insight and execution. The old approach was to do more: more reports, more meetings, more manual tweaks, more tools. But today, teams can spend most of their time looking for what’s broken and patching the same issues over and over.

The way the work used to get done won’t keep up with how fast modern e-commerce moves. With AI-powered discovery through Amazon’s Rufus and Walmart’s Sparky shaping how shoppers see and buy, the pace will only continue to accelerate.

Many brands have tried to address this by turning to agencies for support. In a recent survey, 67% of commerce teams said they rely on agencies, and about half said they spend 15% to 30% of their budget on agency fees alone. Yet 55% said those costs are too high for the results, and 40% said response times can’t keep up with the speed of algorithms.

While agencies can provide expertise, they don’t operate 24/7 — and the marketplaces retailers sell on never pause. Retailers can’t match that speed by adding more headcount or additional agency hours. 

Brands today need an algorithm to keep pace with the algorithm. They need a new execution model that can handle the volume and velocity of decisions in modern ecommerce.

What an Agentic Retail Strategy Looks Like

The shift happening now is toward agentic AI execution, where human teams are no longer tasked with doing the heavy lifting of weeding through data, reports, and manual workflows. Instead, they drive strategy and make final decisions while their agentic AI counterparts analyze large volumes of data and identify growth opportunities. 

This approach lets brands continuously extend execution across their entire catalog around the clock, rather than focusing only on top-performing SKUs. In practice, this looks like specialized agents that operate across the digital shelf:

• A content agent identifies and resolves product page issues across thousands of SKUs simultaneously, reducing the time required to update product detail pages from 35 minutes to 35 seconds.
• A media agent optimizes campaigns using dozens of signals at a scale no human team could match. 
• A sales agent monitors pricing and promo performance to flag gaps or opportunities.

To keep pace with the speed of modern ecommerce, retailers don’t have to add more tools on top of the same manual process; instead, they need to fundamentally change how the work is done. With agents handling the execution layer, the brand manager’s role changes. They spend less time pulling reports and making fixes and more time setting guardrails and deciding priorities.

The Real Shift Starts in Execution

Teams already know the old model isn’t working. The speed required to compete today has outgrown what humans can manage manually. There’s too much data to interpret and too many actions required to keep up with how quickly the digital shelf changes. 

Agent‑driven execution removes that bottleneck. According to a recent report, 82% of commerce leaders expect AI investment to increase in the next 12 to 18 months, and 71% are already familiar with or actively using AI agents. 

With the industry moving in this direction, retailers can’t afford to be left behind. The next era of e-commerce will belong to the companies that can execute at the same speed as the market.

The post Dashboards Created Visibility, but They Didn’t Solve Commerce Execution appeared first on MartechView.

Macy’s has launched an AI-powered shopping chatbot called “Ask Macy’s,” built on Google’s Gemini model, and the early numbers are hard to ignore: shoppers who use it spend approximately 4.75 times more than those who do not.

The chatbot rolled out across all of Macy’s digital platforms this week after several weeks of testing with roughly half of the retailer’s website visitors. For a company that has spent a decade navigating declining sales, the results represent one of the more concrete early validations of AI’s commercial potential in retail.

Also Read: Your Rebrand Is Failing. Have You Tried Listening?

Why the Numbers Make Sense

Max Magni, Macy’s Chief Customer and Digital Officer, offered a straightforward explanation for the spending gap. Shoppers who engage with the chatbot tend to arrive with a specific purpose — an outfit for an upcoming event, a gift for someone particular — rather than browsing without intent. That specificity translates into higher conversion and larger basket sizes.

Magni also suspects the chatbot is drawing in a younger customer base, a demographic Macy’s has struggled to capture as its core shoppers have aged and its store footprint has contracted.

The two most popular features reinforce that hypothesis. The “complete the look” option suggests accessories to pair with a chosen outfit — a function that mirrors how a knowledgeable sales associate might engage a customer in store. A virtual try-on feature allows shoppers to see how an item looks on them before purchasing, and is available in physical Macy’s locations as well for customers who want to evaluate fit without using a dressing room, according to Chief Stores Officer Barbie Cameron.

Getting the Tone Right

The chatbot did not arrive fully formed. Thousands of Macy’s employees contributed feedback during development, and early versions had notable shortcomings. The original build failed to account for regional climate differences, surfacing the same product selections to shoppers regardless of where they lived. There were also tone problems.

Magni recalled asking the bot for T-shirt suggestions for his son and receiving a response that read: “Here’s a T-shirt for a 10-year-old.” Clinical, transactional, and precisely the kind of interaction that drives customers away rather than toward a purchase.

The revised version handles the same question differently. The bot now responds: “Ten-year-olds can have so much fun with colour — do you want a brighter or more muted colour selection?” The shift is small in technical terms and significant in commercial ones. “The machine continues to learn,” Magni said.

Also Read: Marketing That Predicts, Not Reacts

The Broader Race

Macy’s early results arrive as the competition to define AI-assisted shopping intensifies across the retail industry. Phoebe Gates — Bill Gates’s daughter — founded Phia, a browser extension that compares prices across retailers in real time. Shopping agent Wizard, founded by Marc Lore and Melissa Bridgeford, publicly launched in February after more than four years in development.

The field is crowded, the approaches are varied, and no clear winner has emerged. Macy’s, for its part, is not claiming to have solved the problem.

“Every retailer is trying to figure it out one step at a time,” Magni said. “This is anybody’s game. Nobody has cracked the code.”

What Macy’s does have is evidence — however early — that when an AI shopping assistant is built with enough care, it can change how customers behave in ways that show up directly in revenue. In a retail environment where comparable sales growth of 1.5 percent qualifies as a recovery, that is not a small thing.

The post Macy’s AI Chatbot Is Making Shoppers Spend Five Times More appeared first on MartechView.

Fast Simon, an AI-powered e-commerce optimization platform, has launched a capability that gives merchandisers real-time visibility into the true performance of every product in their catalog — and quantifies the opportunity cost of each merchandising decision across the full portfolio.

The update addresses a problem that has persisted even as AI tools have proliferated in e-commerce: that merchandisers overseeing thousands of products across fast-moving catalogs are still relying on delayed reports to make decisions that compound in real time. Generic AI tools have offered limited relief, producing recommendations that are often too vague to act on or based on signals that do not reflect current conditions.

Fast Simon’s updated platform is designed to answer three questions that most merchandising tools leave unanswered: how quickly a new product is becoming successful or failing, which products are receiving disproportionate visibility at the expense of better-performing alternatives, and how today’s merchandising decisions will affect the portfolio’s performance over time.

“The sooner merchandisers know the true performance of a product, the faster they can adapt and make more money,” said Zohar Gilad, co-founder and chief executive of Fast Simon.

Also Read: Your Rebrand Is Failing. Have You Tried Listening?

The platform now also measures the short- and long-term opportunity costs of merchandising decisions across the entire catalog — a capability the company describes as the first of its kind in e-commerce. Rather than evaluating products in isolation, the system surfaces the revenue and profit a merchant forgoes when choosing one merchandising approach over another.

“Day-to-day merchandising decisions not only have consequences in today’s cart, but also in the future performance of the portfolio,” Gilad said. “Fast Simon unravels the complexity of opportunity costs, showing merchants the potential revenue and profit they are missing when making one merchandising decision over another.”

Fast Simon’s platform is used by brands including Steve Madden, Hillberg and Berk and White Fox Boutique, and integrates with Shopify Plus, BigCommerce and Magento.

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Canto, a digital asset management platform, announced Tuesday an expansion of its DAM for Products offering, adding new integrations with Shopify and Amazon as brands struggle to keep product content accurate and consistent across a growing number of e-commerce and retail channels.

The new integrations allow brands to synchronize product images, metadata and attributes from Canto directly into their Shopify and Amazon storefronts, eliminating manual uploading and ensuring that every product page reflects current, brand-approved content. The company said it is also expanding its partner ecosystem with additional syndication partnerships to help customers distribute product assets across further channels.

The announcement comes as the operational complexity of managing product content across e-commerce platforms, social marketplaces and retail channels has grown significantly. Marketing, e-commerce and creative teams routinely lose time chasing assets, correcting channel inconsistencies and manually pushing updates across multiple storefronts — a problem that compounds as the number of channels increases.

“Getting product content right across every channel is one of the hardest operational challenges brands face, and the cost of inconsistency compounds the more channels you add,” said Alan Beiagi, Canto’s chief product and technology officer. “DAM for Products gives brands the infrastructure to move faster without sacrificing accuracy or consistency.”

Also Read: The Real Retailer Readiness Gap Isn’t Price. It’s Content.

Christine Baker, senior graphic designer and production lead at Marini SkinSolutions, said the platform had resolved a fragmentation problem the company had previously managed through spreadsheets, emails and document files. “Before Canto DAM for Products, our product data was scattered across Word documents, spreadsheets and team conversations, which created a real risk of using outdated or incorrect information,” she said. “Now we can keep our product imagery and data in one place and ensure updates are reflected in Shopify.”

DAM for Products is built on Canto’s existing digital asset management platform and connects product images, stock-keeping units and attributes in a single system, activating them across channels automatically. Canto Media Publisher, a component of the platform, delivers approved assets via content delivery network at the speed and scale that commerce operations require.

The post Canto Expands Product Content Platform With Shopify and Amazon Links appeared first on MartechView.

Klaviyo and Shopify have deepened their product integration to better serve growing global brands. The expanded interoperability helps enterprises unify customer data across regions and deliver consistent, localized experiences worldwide.

Global e-commerce sales are forecast to reach $6.4 trillion in 2026 as international expansion becomes a critical growth lever for modern retail. To compete globally, businesses need more than localized storefronts — they need infrastructure that keeps commerce and customer data connected in every market.

Until now, localized product data often stopped at the storefront, forcing marketing teams to manage separate catalogs or build manual workarounds to avoid regional errors. Klaviyo’s customer relationship management platform now offers a fully synchronized, multi-market data foundation that natively integrates Shopify Markets’ localized catalog data.

That foundation includes a new feature called Locale Aware Catalogs, which automatically syncs translated content, regional pricing, currency and market-specific URLs into Klaviyo. The tool powers personalized experiences across Klaviyo’s marketing and customer service products without requiring multiple catalogs or complex manual workarounds.

Also Read: The Real Retailer Readiness Gap Isn’t Price. It’s Content.

One Global Store, Hyperlocal Experiences

Locale Aware Catalogs feeds the right product information to Klaviyo’s AI-powered tools — including Smart Translations and Personalized Send Time — so brands can reach each customer in the language and currency of the market where they are shopping.

According to a recent IDC Business Value Executive Summary, brands using Klaviyo and Shopify together saw 73% revenue growth over three years, underscoring the impact of a tightly connected platform.

Andrew Bialecki, co-founder and chief executive of Klaviyo, said: “Our partnership with Shopify is built on a shared vision to make brands more successful as they scale globally. Shopify enables merchants to sell anywhere, and Klaviyo helps make every customer relationship more valuable. Innovations like Locale Aware Catalogs allow merchants to access Shopify Markets in Klaviyo, helping businesses run one global strategy while delivering experiences that feel truly local in every market — reducing operational overhead for global teams in the process.”

With Shopify Markets natively integrated in Klaviyo, global brands gain several capabilities: automated, localized content showing the correct language, currency and pricing for every product in an email or text; smart regional filtering that ensures shoppers see only recommendations available in their specific market; and automatic product links that direct customers to the correct localized storefront. Brands also benefit from unified global workflows using a single marketing template that dynamically adapts to a customer’s location and language, as well as a fully localized Customer Hub that inherits each shopper’s Shopify Markets settings — from recently viewed items to order history and support content.

Also Read: The Next Retail Advantage is Smarter Inventory

Expanding International Growth Through Partnership

“Maintaining a seamless and localized customer experience is critical,” said Marc Le Roux, chief executive of Reebok Europe. “Shopify Markets gives us the infrastructure to localize our storefront, and with Klaviyo’s Locale Aware Catalogs, that same accuracy carries through to our marketing and customer engagement.”

Atlee Clark, vice president of partnerships at Shopify, added: “Extending Shopify Markets’ infrastructure into Klaviyo makes it easier for merchants operating across multiple regions and channels to scale internationally. This is our ecosystem at its best: native integrations that help merchants reach more customers globally, without added complexity.”

Klaviyo said it is committed to expanding its Shopify integration as the platform evolves its Markets capabilities, ensuring brands can immediately activate new commerce data within their CRM.

The post Klaviyo and Shopify Deepen Ties to Power Global Commerce appeared first on MartechView.

Before a big sales event goes live, you put your focus on setting competitive pricing and maintaining healthy inventory to protect the buy box. But midway through, the lift you expected isn’t there. Traffic is strong, and the buy box is intact, but shoppers aren’t converting. The issue isn’t with price or availability; it’s with content.

Retailer readiness in 2026 goes beyond defending the buy box before a big launch. It requires keeping content aligned across every marketplace and SKU at all times. Because even small gaps can erode sales and weaken retail media performance. 

Google reports that 85% of shoppers say product information and pictures are important when deciding which brand or retailer to buy from. High-quality, detailed visual content builds trust, influences purchasing decisions, and reduces return rates.

This makes one thing clear: Brands can’t afford to have content gaps. Product detail pages that aren’t aligned with the PIM or retailer guidelines force teams to make copy changes based on intuition rather than hard data, which hurts SEO, AEO, and conversion rates. 

In 2026, the retailers that outperform their competition won’t be the ones searching for answers in a postmortem. They’ll be the ones leveraging content agents to keep every PDP compliant, optimized, and up to date at the speed at which the marketplaces they sell on shift. 

Why Content Is a Critical Retailer Readiness Gap

For most brands, every content change means bouncing between the PIM, retailer product detail pages, retailer guidelines, analytics tools, and even possibly an LLM for copy suggestions. That’s far too much application switching for any one person to keep in working memory, so most optimizations end up being educated guesses rather than decisions grounded in a complete, up‑to‑date view of the data.

When a hero SKU has copy that isn’t synced with the PIM, ignores retailer guidelines, or misses critical keywords, it quietly falls down the page while a competitor with richer, better‑optimized content takes the top spots. 

These content gaps are expensive. Almost 53% of U.S. shoppers abandon their carts when met with conflicting, missing, or confusing details. By the time teams notice, the damage is already done. Media budgets have continued to push shoppers to weak product pages. Shoppers who were ready to buy shifted toward competitors with better content. 

As retailer platforms, performance tools, and SKU catalogs expand, retailer readiness becomes far more complex. Even the most nimble teams can’t keep every title, image, bullet, and description up to date and compliant across all marketplaces. When relying on manual checks and traditional SaaS dashboards, gaps quickly become major problems, such as lost sales, wasted media, and weaker category positions.

Retailer Readiness Improves When AI Content Agents Lead

The gap between retailers that use AI and those that don’t is only going to continue to widen. Retailer readiness today requires technology that optimizes the full SKU catalog for content across every marketplace. 

Instead of juggling PIM data, PDPs, analytics tools, and ad platforms, teams need to see what’s working and what’s not all in one place. Content agents are built for exactly this. They connect to back‑end systems, check against retailer content guidelines, and consult search and consumer behavior data to understand which words, structures, and assets actually drive visibility and conversion. Then they present the content lead with specific recommendations and the reasoning behind them, so they can accept it as is or make quick modifications. 

This allows teams to maintain content compliance with the PIM, deliver retailer‑optimized PDPs, and continuously improve SEO and AEO at scale, which is now a core factor in retailer readiness. Working with some of our largest customers, our content agent has reduced the time it takes to update a PDP with conversion-ready content from 35 minutes to 35 seconds. 

These AI agents go beyond simply reporting what’s happening. They diagnose why a product detail page is underperforming, highlight the changes most likely to move search rank and conversion, and can even implement approved updates across marketplaces. The result is execution speed and consistency that no manual process can match. 

This is what happens when AI leads retailer readiness. Brands don’t just get ready once and hope for the best; they stay ready until the last customer checks out.

What the Next Era of Retailer Readiness Looks Like

Retailer readiness today means moving from postgame analysis to real-time adjustment. With content agents, you can make updates as conditions shift on the field, instead of reviewing the tape after the final whistle to figure out how weak content cost you the win. 

In 2026, it will be the brands that redesign retailer readiness to leverage AI content agents who will own the digital shelf in the years ahead.

The post The Real Retailer Readiness Gap Isn’t Price. It’s Content. appeared first on MartechView.

Raj De Datta has spent over a decade building Bloomreach into one of the most quietly formidable companies in commerce technology. With $260 million in ARR and customers like American Eagle and SPANX, the platform’s AI engine — Loomi — isn’t a bolt-on feature. It’s the whole architecture. But as generative AI rewrites the rules of how people shop, discover products, and interact with brands, De Datta is less interested in celebrating how far things have come and more focused on what comes next. 

In this conversation, he makes a case for why the traditional e-commerce site is becoming infrastructure rather than interface, why personalization and automation are converging faster than brands are ready for, and why the winners of the next era of commerce won’t be those who automate the most — but those who automate responsibly.

Excerpts from the interview; 

Bloomreach just crossed $260 million in ARR, powered by Loomi AI and brands like American Eagle and SPANX. What did you get right in scaling an AI-native platform — and what nearly broke along the way?

We got a few core decisions right early. First, we built Loomi AI as the foundation, not as a feature. It is the intelligence layer that powers our applications and agents across email, search, personalization, and conversational shopping. That architecture meant that as AI advanced rapidly over the past few years, we remained ahead of the curve.

Second, we focused on real-time data and first-party context. Loomi AI combines customer and product data with real-time infrastructure, AI decisioning, and orchestration across channels. That allowed us to deliver personalization that compounds — every interaction feeds the system and improves the next one.

Third, we invested early in agentic capabilities. Nearly half of our customers now use at least one next-generation AI tool, and that adoption has more than doubled in the past year. We also saw strong engagement with our conversational shopping agent, including a significant uptick during the recent holiday season. That momentum reflects a clear product direction: move from static tools to systems that can take action.

The one ongoing challenge — though really more of an opportunity — was the pace of change in AI. Every day unlocked a new possibility. We constantly had to examine what we were building to ensure it was maximizing AI’s potential. That meant a lot of rapid iteration and required us to be willing to take products we thought were great and challenge ourselves to make them even better.

The past two years have rewritten the rules of AI almost quarterly. How do you build a durable product strategy when the underlying technology shifts this fast?

The technology will keep changing, and our strategy is to stay one layer above it. We built Loomi AI as the intelligence layer beneath all of our applications and agents. Because that foundation is consistent, we can adopt new AI capabilities as they emerge without rebuilding the core each time the model landscape shifts.

Durability comes from that architecture — every interaction feeds back into the system, so the platform continuously improves. The goal isn’t to predict the next breakthrough. It’s to build a system that seamlessly incorporates it. That’s what allows us to move quickly and keep innovating while maintaining stability for enterprise customers.

You’ve spoken about agentic commerce. What changes when AI stops assisting shoppers and starts transacting on their behalf?

When AI moves from assisting shoppers to acting more autonomously, commerce shifts from reactive experiences to outcome-driven ones. Instead of simply answering questions or suggesting products, AI can understand context, predict intent, and guide the entire journey in real time — from discovery to decision to purchase.

But this changes a great deal for brands. It means they risk losing control over customer journeys and relationships. That’s why this is such an inflection point for businesses right now. The brands actively investing in agentic commerce — preparing catalogs for agentic discoverability, building their own apps — are the ones that will be prepared for this new era of commerce.

If AI becomes the primary interface for product discovery, what happens to the traditional e-commerce site? Is the homepage becoming obsolete?

It’s more a case of “the website is dead… long live the website.” In reality, the website becomes infrastructure rather than interface, as we know it today. All of the data it houses — about your products, about your customers — remains as relevant as ever. But that data and infrastructure will transcend the website, too. It’ll extend into conversational experiences in AI apps, and into channels like email and mobile, as it already does today. The homepage isn’t obsolete. It just isn’t the front door anymore.

Every AI transformation has its blind spots. What assumptions about AI in commerce turned out to be wrong?

There were assumptions about speed and scale that were somehow both too big and not big enough. At the onset of generative AI and large language models, there was real fear that e-commerce would become irrelevant almost overnight. Obviously, that hasn’t been the case.

But at the same time — it will, and already is, wholly transforming what commerce looks like. The way people discover products, compare brands, build wardrobes — all of that is now done with AI in ways we couldn’t have imagined a few years ago. And I think the scale of that transformation hasn’t even peaked yet.

At what point does personalization cross into automation? And how do brands ensure agency remains with the consumer?

Personalization becomes automation the moment systems start acting without asking. That’s the real inflection point. For years, personalization meant better recommendations or more relevant messaging. Now, with agentic AI, systems can execute decisions across channels. The question isn’t whether that’s possible — it’s how it should be governed.

Brands need to be clear about intent. Automation should reflect what a customer has already signaled, not replace it. When AI is grounded in real customer context and real-time signals, it can reduce friction and help people complete their journey. When it drifts from that, it stops being helpful and starts becoming opaque.

Agency remains with the consumer when technology is designed to respond to their intent, stay transparent in its decisioning, and keep humans in control of outcomes. The more autonomous systems become, the more important those guardrails are.

In this next phase of AI, the differentiator won’t be who automates the most. It will be who automates responsibly — and keeps the customer at the center of that automation.

In 2030, what will feel outdated about how we shop today — and what will Bloomreach have built to stay ahead of that shift?

Today, much of e-commerce still centers on browsing: navigating pages, filtering options, and manually managing campaigns across channels. But the shift we’re already seeing is toward agentic experiences — systems that understand intent and take action in real time. As AI moves from insight to execution, customers will expect more conversational, personalized, and responsive interactions, not just better search results or product recommendations.

What will matter most is whether brands can operate across touchpoints as one connected system. Experiences won’t be confined to a single website or channel. They’ll need to work across email, messaging, mobile, search, and beyond — with intelligence that adapts instantly to customer context.

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