The Home Depot and Google Cloud made it clear that the future of home improvement retail isn’t just digital—it’s agentic.

The companies announced an expanded strategic partnership that introduces a new wave of AI-powered tools designed to deliver real-time, expert-level assistance to both DIY homeowners and professional customers such as contractors, remodelers, and renovators. Built on Google Cloud’s Gemini models and Gemini Enterprise for Customer Experience (CX), the initiative extends Home Depot’s iconic “Orange Apron” expertise far beyond the store floor—into phones, job sites, and even Google Search.

This isn’t about smarter recommendations. It’s about AI that understands intent, context, and complexity—and acts on it instantly.

From Store Associate to Always-On Digital Expert

Home Depot’s vision is an AI-first retail experience that feels native, personalized, and available wherever customers are. By deploying agentic AI across its ecosystem, the retailer is effectively turning decades of home improvement knowledge into a network of digital experts that can reason, guide, and solve problems in real time.

Jordan Broggi, EVP of Customer Experience and President of Online at The Home Depot, summed it up succinctly: the goal is to put Orange Apron expertise in every customer’s pocket—whether they’re standing in a kitchen, on a jobsite, or in an aisle.

Looking ahead, these capabilities won’t be limited to Home Depot-owned channels. The company plans to participate in agentic shopping experiences across Google Search’s AI Mode and the Gemini app, signaling a broader shift toward AI-mediated commerce.

Magic Apron Evolves Into a Conversational Project Partner

The most visible upgrade is the expansion of Magic Apron, Home Depot’s AI assistant. Once limited to product-page support, Magic Apron is now becoming a conversational, project-aware digital companion for both DIYers and pros.

Customers can describe projects in plain language—anything from fixing a leaky faucet to planning a full kitchen remodel—and receive expert guidance, personalized recommendations, and step-by-step advice. Powered by Gemini Enterprise for CX, Magic Apron is also gaining multimodal capabilities, including image uploads and visual guidance to help customers navigate complex projects.

One standout innovation: a localized, in-store Magic Apron experience. Currently piloting in select locations, the AI integrates real-time store inventory and aisle-level product locations. Ask which grout works best for glass tiles, and Magic Apron won’t just explain the difference between unsanded and epoxy—it will direct customers to the exact bay where those products live and suggest complementary items. A national rollout is planned in the coming months.

Faster Quotes and Planning for Pros

For professional customers, Home Depot is rolling out AI-powered materials list builders on its pro digital platform. Contractors can describe a project via voice or text—or upload an existing list—and the agent interprets intent to generate a comprehensive, grouped materials list.

The system even flags commonly missed essentials, helping pros create more accurate estimates in minutes instead of hours. Launched in beta in November 2025, the feature is now scaling nationally, aiming to compress planning cycles and free up time for revenue-generating work.

Smarter Last-Mile Delivery With AI Route Intelligence

The partnership also tackles one of retail’s hardest problems: last-mile delivery. Home Depot is deploying AI-powered route intelligence using Gemini and Google Maps Platform to predict and prevent delivery failures.

By combining customer-specific details—like delivery windows and site constraints—with external factors such as weather, road conditions, and access limitations, the system can identify risks before trucks roll. Multimodal AI analyzes map data to flag narrow roads, gated entrances, or unloading challenges, and will soon recommend the right equipment and crew size for complex deliveries.

The result is a more reliable, consistent experience—especially for large or professional-grade orders.

Conversational AI Replaces Rigid Customer Service

Home Depot is also overhauling customer support across chat, SMS, and voice, replacing menu-driven automation with conversational AI that understands intent and resolves issues in real time. Live today, the platform allows customers to speak naturally instead of navigating endless prompts.

Early results show higher engagement and resolution rates, pushing conversational AI beyond simple issue deflection. The company is now testing next-generation AI voice agents in select stores, freeing associates to focus on high-complexity interactions.

Empowering Associates With Gemini Enterprise

Behind the scenes, Home Depot is equipping thousands of Store Support Center associates with Gemini Enterprise, Google Cloud’s agentic platform for automating end-to-end workflows.

From predicting project bottlenecks to drafting marketing copy and auditing digital designs, specialized agents handle routine execution in seconds. The intent isn’t replacement—it’s leverage. By offloading repetitive tasks, teams can focus on strategy, creativity, and long-term growth.

Why This Expansion Matters

Retail has spent years experimenting with AI at the edges. Home Depot’s expanded partnership with Google Cloud shows what happens when AI becomes core infrastructure—connecting customer experience, operations, logistics, and employee productivity through a single intelligence layer.

As Jose Gomes, VP of Retail & Consumer Packaged Goods at Google Cloud, put it: this is retail moving from product suggestions to problem-solving.

For an industry under pressure to deliver speed, expertise, and personalization at scale, Home Depot’s approach may well set the bar for agentic commerce in the years ahead.

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CRM modernizations don’t usually make headlines—but when one delivers immediate revenue impact and reins in runaway costs, it’s worth a closer look. Optrua, a Microsoft Dynamics 365 and Power Platform consulting firm, has completed a CRM transformation for Advantage Design Group (ADG) that reshaped how the organization sells, forecasts, and scales.

The project centered on stabilizing and optimizing ADG’s Microsoft Dynamics 365 environment after an earlier migration from a legacy CRM exposed unexpected challenges. While the move promised a modern, web-based system, the lack of specialized Dynamics expertise quickly became a bottleneck.

Sales processes were inconsistent, pipeline visibility was limited, and database storage costs were climbing fast—an all-too-familiar story for organizations that modernize platforms before modernizing processes.

When Modern CRM Creates New Problems

ADG’s leadership saw the warning signs early. Sales teams weren’t aligned around a shared workflow, opportunities were slipping through the cracks, and executives had little clarity on deal status. At the same time, alerts around database growth raised concerns about long-term infrastructure costs.

According to Catherine Swingle, Chief Operations Officer at Advantage Design Group, the issues were operational as much as technical. Without clear process alignment, the CRM was acting more like a digital filing cabinet than a revenue engine.

That’s where Optrua stepped in—not with a sweeping reimplementation, but with a business-first diagnostic approach.

Fixing the System Without Breaking the Business

Rather than proposing a disruptive overhaul, Optrua began with a discovery-driven consultation focused on how ADG actually operates today—and where it wants to go next. That understanding shaped a phased improvement roadmap delivered through the Optrua Care Plan, the firm’s continuous improvement engagement model.

This incremental approach mattered. It allowed ADG to address critical issues—like bloated storage usage and fragmented sales workflows—without exceeding budget or overwhelming internal teams.

Swingle pointed to CEO Ryan Redmond’s hands-on involvement as a key differentiator. The engagement wasn’t about configuring features for their own sake; it was about aligning Dynamics 365 to real-world selling behavior.

From Cost Control to Revenue Growth

The results were tangible and fast.

On the infrastructure side, Optrua helped ADG reduce unnecessary database storage, easing cost pressures and stabilizing the Dynamics environment. On the sales side, automated workflows guided sellers through a consistent, repeatable process—bringing much-needed structure to lead management and opportunity tracking.

Sales leadership gained clearer visibility into pipeline activity, making forecasting more reliable and performance easier to manage. Perhaps most importantly, ADG’s internal applications administrator was equipped with the tools and knowledge needed to sustain improvements long after the initial engagement.

Then came the revenue impact.

ADG reports an 80% increase in lead capture, a dramatic lift that underscored how process clarity and system discipline can translate directly into growth. For Swingle, the outcome validated the decision to optimize rather than replace.

A Case Study in Smarter CRM Investment

The partnership also highlights a broader trend in CRM and MarTech strategy: organizations are increasingly favoring targeted expertise over permanent headcount. By outsourcing highly specialized Dynamics work to Optrua, ADG avoided overstaffing while still accessing deep platform knowledge when it mattered most.

That flexibility is becoming critical as CRM systems grow more complex and intertwined with revenue operations, analytics, and automation. Not every organization needs a large internal CRM team—but most need the right expertise at the right time.

An Ongoing, Not One-Time, Transformation

Both companies describe the engagement as an ongoing partnership rather than a closed project. For ADG, the confidence comes from knowing its CRM can evolve alongside the business without recurring disruption.

For Optrua, the project reflects a philosophy that’s gaining traction across B2B tech: CRM success isn’t about software alone. It’s about intentional implementation, continuous improvement, and alignment with how teams actually work.

In an era where CRM platforms promise intelligence and automation out of the box, this case serves as a reminder—real gains still depend on execution.

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Stensul, the campaign creation platform used by enterprise marketing teams, is starting 2026 with more than product momentum. The company has been named one of Built In’s Best Places to Work in 2026, earning a spot among the top midsize employers in New York City and ranking within Built In’s top 100 companies overall.

The recognition places Stensul alongside organizations setting the pace for how modern tech companies attract, retain, and support talent—at a time when both marketing and work itself are being reshaped by AI.

Culture as a Competitive Advantage

Built In’s annual Best Places to Work awards spotlight companies whose compensation, benefits, and workplace programs stand out in an increasingly competitive labor market. Now in its eighth year, the program uses a data-driven methodology that evaluates employers on pay transparency, benefits offerings, and company-wide cultural initiatives.

For Stensul, the honor reinforces a strategy that treats culture as more than an HR initiative. Rachel Meranus, Stensul’s Chief Revenue and Marketing Officer, described the recognition as validation of a people-first philosophy that directly supports innovation and customer impact.

As marketing teams face mounting pressure to move faster and do more with fewer resources, Stensul’s internal culture mirrors the value proposition of its platform: empower teams, remove friction, and let creativity scale.

Standing Out in an AI-First Job Market

The award also reflects a broader shift in how companies are evaluated by talent. With AI-driven search and discovery increasingly influencing how candidates research employers, workplace recognition now carries added weight beyond traditional employer branding.

Maria Christopoulos Katris, Founder and CEO of Built In, emphasized that Best Places to Work recognition has become a signal not just to people, but to algorithms. In an era where AI tools help candidates compare roles, culture, and growth opportunities, awards like these shape how a company’s story is interpreted and surfaced.

For tech and MarTech companies competing for specialized talent, that visibility matters. Culture, benefits, and values are no longer soft differentiators—they are discoverable signals that influence hiring outcomes.

Momentum Heading Into 2026

Stensul enters 2026 with growing relevance in enterprise marketing, where speed-to-market and operational efficiency are top priorities. Being recognized as a Best Place to Work strengthens the company’s position as it scales—helping attract the kind of talent needed to support innovation without sacrificing cohesion.

The recognition also underscores a trend across B2B tech: high-performing platforms increasingly depend on high-trust cultures. As automation and AI absorb more routine work, companies that invest in people, autonomy, and clarity are better positioned to compete.

For Stensul, the Built In award isn’t just a badge—it’s a signal that the company’s internal operating model is keeping pace with the future of work it helps marketers navigate every day.

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Awards season has always been fueled by speculation—who will win, who will be snubbed, and which moments will dominate the conversation the next day. Now, that speculation is getting quantified. Polymarket, the world’s largest prediction market, has announced a first-of-its-kind partnership with the Golden Globes®, becoming the awards show’s exclusive prediction market partner.

The collaboration introduces real-time, market-driven insights into one of entertainment’s most closely watched events, blending cultural debate with live probability data across Golden Globes events, digital platforms, and editorial coverage.

Turning Awards Buzz Into Live Market Signals

At its core, the partnership formalizes something fans already do instinctively: predict outcomes. Polymarket translates those collective expectations into live markets, offering a constantly updating snapshot of what audiences believe will happen—before and during the show.

As part of the deal, Polymarket branding and live market insights will be integrated into the official 2026 Golden Globes viewing party, giving attendees and viewers a new layer of context as categories unfold. Rather than relying solely on pundits or social chatter, fans can track real-time probabilities shaped by thousands of participants.

Shayne Coplan, Founder and CEO of Polymarket, positioned the partnership as a natural extension of awards culture. The Golden Globes, he noted, have always been about anticipation and debate. Prediction markets simply formalize that energy, turning opinions into data.

A New Engagement Model for Live Entertainment

For the Golden Globes and its owner, Penske Media Corporation, the move reflects a broader push to deepen fan engagement across entertainment, fashion, and pop culture. Craig Perreault, President of Penske Media Corporation, described the partnership as opening a “new frontier” in how audiences interact with content—one that strengthens emotional investment in films, shows, and creators.

This approach mirrors trends already playing out in sports and finance, where live data overlays and real-time analytics have reshaped how audiences experience events. Applying that model to awards shows signals an evolution in entertainment media, where passive viewing gives way to participatory experiences.

Why This Matters Beyond Awards Season

The partnership also highlights the growing mainstream appeal of prediction markets. Once considered niche tools for political forecasting or crypto-native audiences, platforms like Polymarket are increasingly intersecting with culture, media, and marketing.

For brands and publishers, real-time prediction data offers a new way to capture attention during live events—especially as second-screen behavior becomes the norm. Instead of scrolling aimlessly during commercial breaks, viewers can engage with live probabilities that evolve moment by moment.

It’s also a sign of how entertainment properties are experimenting with interactive formats to stay relevant in an on-demand world. Live events remain one of the few moments that reliably command collective attention, and adding data-driven interactivity raises the stakes for keeping audiences locked in.

What to Expect at the 2026 Golden Globes

The 83rd Annual Golden Globes will air on Sunday, January 11, 2026, broadcast live on the CBS Television Network and streaming on Paramount+ in the U.S. Polymarket insights will appear not only at live events but across the broader Golden Globes digital and editorial ecosystem.

As “Hollywood’s Party of the Year®” continues to expand its scope—now celebrating film, television, and podcasting—the addition of prediction markets signals a shift toward more dynamic, participatory storytelling.

For viewers, it means awards night won’t just be about watching winners announced. It will be about seeing expectations rise and fall in real time—turning collective opinion into a live narrative that unfolds alongside the show itself.

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Brick Marketing is doubling down on something it’s been doing longer than most agencies have existed: teaching marketers how to do the work themselves.

The Boston-based digital marketing firm has announced an expanded suite of marketing education resources, aimed at helping businesses, in-house teams, and individual professionals sharpen their skills and improve real-world performance. The update builds on more than two decades of education-led consulting, now tailored to a market reshaped by generative AI, evolving search behavior, and rising expectations for measurable ROI.

At a time when “AI-powered marketing” often means black-box tools and vague promises, Brick Marketing is taking a more grounded approach—focusing on practical instruction, clear frameworks, and hands-on learning that teams can apply immediately.

Education Built for How Marketing Actually Works

Brick Marketing’s expanded offering combines full-day training classes, free educational webinars, and its long-running Wear Em Down Marketing strategy eBook. Together, the resources are designed to support organizations at different stages of growth—from companies building their first SEO foundation to mature teams adapting to Generative Engine Optimization (GEO) and AI-influenced discovery.

What stands out is the emphasis on applicability. Rather than abstract theory, the programs are structured around real business needs: how search visibility is earned today, how content authority compounds over time, and how AI is changing—not replacing—the fundamentals of marketing strategy.

This focus reflects a broader industry shift. As search engines increasingly blend traditional rankings with AI-generated answers, marketers are being pushed to rethink how visibility is achieved. Education, not just tooling, is becoming a competitive advantage.

Full-Day SEO, GEO, and Content Marketing Training

At the center of the expansion are full-day training classes covering SEO, Generative Engine Optimization, and content marketing. These sessions are available both in person at Brick Marketing’s Boston office and virtually across the U.S., making them accessible to distributed teams and remote-first organizations.

The curriculum blends foundational concepts with advanced execution, using live walkthroughs, real examples, and clear explanations of how each tactic contributes to long-term brand authority and discoverability. Participants are guided through frameworks they can implement immediately—without needing enterprise-level tools or outside agencies to get started.

Class sizes are intentionally limited, a notable contrast to large-scale online courses that prioritize reach over engagement. Attendees are encouraged to ask questions, interact with examples, and work through real scenarios that mirror their own marketing challenges.

Registration and scheduling for the SEO, GEO, and content marketing classes are now open.

Preparing Marketers for the Generative Search Shift

Brick Marketing’s inclusion of Generative Engine Optimization reflects how quickly the search landscape is changing. As platforms like Google integrate AI-generated summaries and conversational results, marketers can no longer rely solely on traditional ranking signals.

Instead, visibility increasingly depends on content clarity, topical authority, and how well information can be interpreted and surfaced by generative systems. Brick Marketing’s training addresses this shift directly, helping teams understand how SEO and GEO work together rather than treating AI as a separate channel.

This educational angle positions Brick Marketing alongside a growing group of consultancies focused on knowledge transfer, not dependency. For organizations wary of outsourcing strategy they don’t fully understand, that approach offers both transparency and resilience.

A Long-Term View on Marketing Capability

While many agencies emphasize short-term performance gains, Brick Marketing’s education-first model is rooted in long-term capability building. The idea is simple: companies that understand why something works are better equipped to adapt when platforms change—as they inevitably do.

That philosophy feels particularly relevant in 2026, as marketers navigate AI disruption, tighter budgets, and increased pressure to prove impact. Training teams to think critically about SEO, content, and generative visibility may not be flashy—but it’s increasingly essential.

For businesses looking to future-proof their marketing skills rather than chase the next tool, Brick Marketing’s expanded education suite is a reminder that fundamentals, when taught well, still scale.

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Retail marketing has a data problem. Campaigns live in one system, transactions in another, loyalty data in a third—leaving retailers with fragmented insights and fuzzy ROI. At NRF 2026: Retail’s BIG Show, Celerant Technology is positioning itself as the antidote.

The retail commerce platform provider will showcase an expanded Customer Engagement Suite (CES), a tightly integrated marketing hub designed to let retailers manage campaigns, messaging, and automation from a single interface—powered directly by real in-store and online sales data.

The pitch is straightforward but increasingly urgent: stop stitching together marketing tools, and start running engagement from the same system that runs the business.

Marketing Meets the Core Retail Engine

Unlike standalone marketing platforms that rely on APIs and periodic data syncing, Celerant’s Customer Engagement Suite is built directly into its core commerce platform. That means email, SMS, live chat, social media management, online reviews, and customer communications all draw from a single, centralized retail database.

Every campaign is tied to actual transactions—point of sale, eCommerce, mobile apps, loyalty programs, and customer profiles—without manual exports or reconciliation.

“Retailers don’t need more marketing tools; they need one connected system,” said Michele Salerno, Celerant’s Chief Growth Officer. “CES becomes the central command center for marketing, powered by the same data that runs the business both in-store and online.”

It’s a clear response to a broader industry shift: retailers are under pressure to personalize outreach, prove ROI, and coordinate experiences across physical and digital channels—all while dealing with tighter budgets and leaner teams.

From Behavior-Based Campaigns to Built-In Automation

At the heart of CES is behavior-driven marketing. Retailers can build campaigns based on what customers actually do—what they buy, browse, return, or ignore—rather than relying on static lists or third-party data pipelines.

Because CES is natively connected to the product catalog, marketers can pull SKUs, images, and inventory data directly into campaigns, reducing friction between merchandising and marketing teams. Automation rules ensure messages are timely and relevant, triggered by real shopping activity instead of guesswork.

The benefit isn’t just personalization—it’s operational simplicity. By collapsing marketing and commerce into one system, retailers gain cleaner data, faster execution, and fewer points of failure as campaigns scale.

Closing the Online-to-Offline Gap With Geo-Fencing

Celerant is also using NRF to spotlight its geo-fencing capabilities, which push CES beyond digital channels and into physical retail environments.

Through branded mobile apps, retailers can trigger automated push notifications when customers enter predefined geographic zones near stores or key locations. The goal: influence purchasing decisions at the exact moment customers are close enough to act.

Once configured, these proximity-based campaigns run continuously, driving foot traffic and incremental sales without daily intervention from store teams. In an era where physical retail must justify every visit, location-aware engagement is becoming less of a novelty and more of a necessity.

Why This Matters Now

Celerant’s expanded CES lands at a time when retailers are rethinking their MarTech stacks. Best-of-breed tools once promised flexibility, but often delivered data silos, rising costs, and limited visibility into performance.

The alternative—unified commerce platforms with embedded marketing—is gaining traction. By tying engagement directly to transactions, platforms like Celerant aim to give retailers something many still lack: a single source of truth for customer behavior and campaign impact.

Competitors across the retail tech landscape are moving in a similar direction, but Celerant is leaning heavily on its single-database architecture as a differentiator—especially for mid-market and specialty retailers that don’t want enterprise-level complexity.

See It in Action at NRF 2026

Celerant will demo the Customer Engagement Suite at booth #4232, positioning it as a core pillar of its broader all-in-one retail platform. That platform already spans point of sale, inventory, eCommerce, mobile apps, fulfillment—and now centralized marketing.

For NRF attendees, the message is clear: the future of retail marketing isn’t another dashboard. It’s fewer systems, tighter data, and marketing that’s inseparable from how—and where—customers actually buy.

Get in touch with our MarTech Experts.

Artificial intelligence is rapidly becoming the new interface between sports organizations and their fans—and the ATP is leaning into that shift. Infosys has announced the launch of Ally, an AI-powered chatbot designed to deepen fan engagement across men’s professional tennis, while also extending its long-standing partnership with the ATP through 2028.

The move underscores how digital platforms, data, and generative AI are reshaping the sports experience, turning passive spectators into active participants. For Infosys, it also reinforces a strategy it has been executing for over a decade: using enterprise-grade AI and analytics to power fan-facing experiences at global sporting institutions.

Ally: A New Front Door to the ATP’s Data Universe

Ally is built on Infosys Topaz, the company’s AI-first offering that leverages generative AI technologies. At its core, the chatbot acts as a conversational gateway to the ATP’s vast data ecosystem, providing real-time answers to questions around match statistics, tournament draws, schedules, head-to-head records, and historical performance.

Unlike basic sports chatbots that recycle surface-level facts, Ally is tightly integrated with the ATP Stats Centre, ensuring responses are grounded in verified, up-to-date data. Fans, journalists, players, and coaches can explore player comparisons, track performance trends, and surface context-rich insights through natural language conversations.

The experience is designed to be concise and intuitive, lowering the barrier for casual fans while still delivering depth for analysts and professionals who rely on accurate, granular data.

Built for Trust, Safety, and Scale

One of the growing concerns around generative AI—particularly in live sports contexts—is accuracy. Infosys and the ATP are positioning Ally as a controlled, enterprise-grade deployment rather than an open-ended experiment.

The chatbot includes multiple guardrails, such as content filtering, removal of personally identifiable information (PII), and contextual validation to detect and prevent misinformation or AI hallucinations. Continuous learning mechanisms allow Ally to refine its responses over time, adapting as new data, players, and tournaments enter the ecosystem.

This focus on governance reflects a broader trend in sports and media organizations: AI is no longer just about novelty, but about reliability, compliance, and brand trust.

A Decade-Long Digital Partnership Deepens

The Ally launch builds on a partnership that began in 2015, when Infosys became the ATP’s Digital Innovation Partner. Over the past ten years, Infosys has played a central role in shaping the ATP’s digital infrastructure, including the ATP app, ATP PlayerZone, and the ATP Stats Centre itself.

Extending the relationship through 2028 signals that the ATP sees AI and data as foundational—not experimental—to its future. As fan expectations evolve toward personalization, interactivity, and real-time insights, governing bodies are under pressure to modernize how they package and distribute information.

For Infosys, the ATP partnership also serves as a high-visibility showcase for its AI capabilities in a real-world, global environment—an approach the company has mirrored across other sports and entertainment properties.

Beyond Fan Engagement: Sustainability and Carbon Tracking

AI isn’t the only focus of the Infosys–ATP collaboration. In 2025, the partners rolled out Carbon Tracker 2.0, an updated version of the sustainability initiative launched in 2023 to help players measure and reduce travel-related emissions.

More than 300 players have used the tool, collectively tracking 2.3 million kilometers of travel and offsetting 585 tonnes of carbon in 2025 alone. The initiative reflects how data platforms originally built for performance analysis are increasingly being repurposed to address sustainability and operational efficiency—another growing priority across global sports organizations.

Why This Matters for Sports, Media, and MarTech

The launch of Ally highlights a larger shift at the intersection of sports, MarTech, and AI. Fans no longer want static stats pages or one-way broadcasts; they expect interactive, personalized experiences that mirror how they engage with digital products elsewhere.

By embedding AI directly into its data infrastructure, the ATP is effectively turning its statistics engine into a conversational product. That approach has implications beyond tennis, offering a blueprint for leagues, federations, and media companies looking to unlock more value from their data while strengthening fan loyalty.

It also reflects how enterprise AI platforms like Infosys Topaz are moving beyond back-office optimization into highly visible, consumer-facing roles—where accuracy, trust, and experience design are just as critical as technical performance.

Looking Ahead

As AI becomes more deeply woven into live sports, the differentiator won’t be whether organizations adopt it, but how responsibly and creatively they deploy it. With Ally, Infosys and the ATP are betting that conversational AI—backed by verified data and strong governance—can make tennis more accessible without sacrificing credibility.

For fans, it promises a more interactive way to follow the sport. For the industry, it’s another sign that AI-powered engagement is quickly becoming table stakes.

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Shopify is making a decisive move to ensure commerce doesn’t just survive the AI era—it defines it. The company has announced a sweeping expansion of native commerce across major AI platforms, anchored by a new open standard co-developed with Google called the Universal Commerce Protocol (UCP). The initiative positions Shopify as the infrastructure layer for what it calls agentic commerce, where AI agents don’t just recommend products but actively complete purchases on a shopper’s behalf.

This is more than another integration update. It’s Shopify signaling that AI assistants, chat interfaces, and conversational agents are becoming the next dominant storefront—and that it intends to power them all.

Commerce Moves Into the Conversation

AI chats have rapidly become a discovery engine. Millions of users now ask AI tools what to buy, compare products, and plan purchases without ever visiting a traditional ecommerce site. Shopify’s bet is that commerce needs to meet customers inside those conversations, natively and seamlessly.

With UCP, Shopify merchants will soon be able to sell directly within AI Mode in Google Search, the Gemini app, Microsoft Copilot, and ChatGPT, all managed centrally from the Shopify Admin through what Shopify calls Agentic Storefronts. Embedded checkout experiences remove the friction of redirects, logins, and siloed carts—allowing transactions to happen where intent peaks.

This marks a shift from “AI as a referral channel” to “AI as the transaction layer.”

Universal Commerce Protocol: A Shared Language for AI Shopping

At the center of Shopify’s announcement is UCP, an open standard designed to let AI agents connect to and transact with merchants at scale. Co-developed with Google and already endorsed by more than 20 retailers and platforms, UCP aims to solve a growing fragmentation problem: every AI platform currently handles commerce differently, if at all.

Shopify is proposing a shared language that allows agents to understand and execute real-world checkout complexity. That includes applying discount codes, honoring loyalty programs, handling subscriptions, confirming pre-orders, and respecting selling terms like final-sale policies—all within a conversational interface.

Importantly, UCP is payment-agnostic. While it works seamlessly with Shopify Payments, it also supports any payment processor. Under the hood, it’s designed to adapt to diverse commerce stacks using REST APIs, Model Context Protocol (MCP), Agent Payments Protocol (AP2), and Agent-to-Agent (A2A) standards.

For merchants with more nuanced requirements—such as furniture retailers that need delivery date selection—UCP provides a structured way to prompt agents for missing customer inputs without breaking the flow.

In short, Shopify is trying to make agentic checkout as flexible as the real world of commerce itself.

Google AI Mode and Gemini: The First Major Showcase

UCP will power a new generation of native shopping experiences inside Google’s AI products. Shopify merchants will soon be able to sell directly within AI Mode in Google Search and the Gemini app, using embedded checkouts controlled from Shopify Admin.

Google is also introducing a Direct Offers pilot, allowing select Shopify merchants to surface exclusive deals inside AI conversations. The idea is simple: when a shopper expresses high purchase intent in an AI interaction, merchants can respond with the right incentive at exactly the right moment—without leaving the chat.

For Google, this helps close the loop between discovery and transaction. For Shopify, it extends merchant reach into one of the largest AI-driven discovery surfaces on the planet.

Microsoft Copilot Checkout Expands the AI Commerce Map

Shopify is also expanding its partnership with Microsoft through a new Copilot Checkout experience. Users can now shop directly inside Copilot, completing purchases without jumping across tabs or platforms.

As Microsoft positions Copilot as a productivity and planning assistant, commerce becomes a natural extension—especially when users are researching products, organizing events, or managing personal tasks. Shopify merchants stay in control of inventory, offers, and fulfillment, while Copilot becomes another high-intent sales channel.

Brands including Keen, Pura Vida, and Kyte Baby are already using Copilot Checkout, while Monos, Gymshark, and Everlane are preparing to sell directly through Google’s AI experiences.

Shopify Catalog Opens to Every Brand

One of the most strategic shifts in this announcement is Shopify opening its Catalog to brands that don’t run their online stores on Shopify at all.

Through a new Agentic plan, any brand—regardless of ecommerce platform—can list products in the Shopify Catalog, a massive, continuously updated dataset of billions of products. Shopify uses specialized large language models to categorize, enrich, and standardize product data so AI agents can surface the right product instantly, even from ambiguous or conversational prompts.

Once listed, that data flows through Agentic Storefronts and appears across AI channels like ChatGPT, Google AI Mode, Gemini, Microsoft Copilot, the Shop app, and future Shopify partners. Merchants set up their data once; Shopify handles the distribution everywhere discovery happens.

This effectively turns Shopify into a neutral commerce backbone for the AI web—similar to how payment networks operate behind the scenes of traditional commerce.

Why Agentic Commerce Matters Now

Shopify’s push reflects a broader industry reality: AI assistants are evolving from information tools into action-taking agents. They don’t just answer questions—they plan, recommend, and execute.

In that environment, commerce can’t rely on static product pages and search ads alone. It needs to be programmable, conversational, and interoperable across platforms. Shopify’s decades of experience handling checkout edge cases, tax rules, inventory quirks, and global payments gives it a credibility advantage as standards emerge.

Rivals like Amazon, Google, and emerging AI-native commerce startups are all exploring similar territory. But Shopify’s approach—open standards, cross-platform reach, and support for non-Shopify merchants—suggests it’s aiming to be the Switzerland of agentic commerce rather than a closed ecosystem.

From Tools to Infrastructure

Shopify executives describe this moment as a platform shift on par with mobile or cloud computing. Where previous waves focused on storefronts and apps, this one centers on interfaces without screens—conversations that can transact.

By investing in protocols instead of point solutions, Shopify is betting that no single AI assistant will dominate. Instead, commerce will flow across many agents, surfaces, and contexts. UCP is designed to make sure merchants don’t have to rebuild their business logic for each one.

As AI-driven discovery accelerates, Shopify’s role is becoming less about where a store lives and more about how commerce works everywhere.

The Bigger Picture

If successful, Shopify’s strategy could reshape how brands think about customer acquisition, SEO, and paid media. When AI agents handle discovery and checkout in one flow, the line between marketing and transaction blurs. Offers, pricing, loyalty, and product data become just as important as creative messaging.

For merchants, the promise is reach without fragmentation. For shoppers, it’s fewer clicks and more relevance. And for the industry, it’s a signal that agentic commerce is moving from theory to infrastructure.

Shopify isn’t just adapting to AI-driven commerce—it’s trying to define the rules.

Get in touch with our MarTech Experts.

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