October 21, 2025
by Yi Jin

Agentic Commerce Ecosystem Market Map 2025

October 21, 2025
by Yi Jin

Key Takeaways

The retail industry is undergoing a transformational shift toward Agentic Commerce, where AI-powered agents and platforms autonomously assist consumers across the shopping journey. In 2025, a vibrant ecosystem of companies is enabling this paradigm: from AI-driven product discovery tools that answer customer questions on product pages, to video commerce platforms making content shoppable, to AI-based infrastructure that personalizes search and recommendations at scale, to conversational interfaces (chatbots and virtual assistants) that act as personal shopping agents, and finally to retail media solutions that monetize these new touchpoints. Together, these innovations are redefining how products are found, evaluated, and purchased, offering retailers new ways to engage customers and advertisers new avenues to reach them – all while moving toward more autonomous, seamless shopping experiences.

The rise of generative AI and autonomous agents is redefining digital commerce. The term Agentic Commerce refers to a new era in which AI acts with agency on behalf of users – not just providing information or recommendations, but taking goal-driven actions in the shopping process . In contrast to traditional e-commerce tools (like basic chatbots or static recommendation engines), agentic AI systems can independently handle end-to-end tasks: finding products matching a shopper’s needs, answering detailed questions, comparing options, and even executing transactions – essentially becoming a personal shopping assistant that plans, decides, and acts for the consumer . This shift promises more personalized, frictionless customer experiences (“shop in seconds” convenience), while challenging retailers to integrate their offerings into AI-driven decision flows .

In 2025, the Agentic Commerce ecosystem has begun to crystallize into distinct categories of technology and solutions that collectively enable this AI-first shopping journey. This article presents a market map of five major segments:

Agentic Product Discovery (PDP-Native AI FAQ) – tools that embed AI Q&A and assistants on product detail pages to guide purchase decisions.
AI-Powered Video Commerce Platforms – interactive video and livestream shopping solutions that leverage AI for engagement and personalization.
AI Commerce Infrastructure (Product Graphs & Personalization Engines) – the backend platforms (product data graphs, search, and recommendation engines) that use AI to tailor content to each user.
Conversational & Agentic Commerce Interfaces – chatbots, voice assistants, and AI agents that converse with users and facilitate shopping through natural language.
Retail Media Intelligence & PDP Monetization – platforms turning these AI interactions into advertising opportunities, from sponsored answers to on-site ad networks.

In each section, we’ll highlight the key companies, linking to their websites and providing a brief analytical insight into how each contributes to the Agentic Commerce landscape. Together, these companies and solutions illustrate a commerce environment where AI is deeply embedded: product discovery becomes a dialog, content becomes shoppable and personalized, and every customer question or intent is an opportunity for both service and monetization.

As you read through this market map, a clear picture should emerge of how autonomous shopping agents and AI-driven platforms are redefining retail – and what that means for brands, retailers, and consumers in the near future.

Agentic Product Discovery (PDP-Native AI FAQ)

In this category, AI assistants are embedded directly into product pages or e-commerce sites, functioning as intelligent FAQs or product experts. They allow shoppers to ask natural-language questions about products and receive instant, context-aware answers – enhancing product discovery and confidence. The following companies provide such PDP-native AI solutions, helping customers make informed decisions on the spot:

Firework (AVA 4.0) – Firework offers a lifelike AI shopping assistant called AVA (now in version 4.0), which brands can embed on their e-commerce sites. AVA is a digital human avatar powered by Firework’s proprietary LLM, capable of answering customers’ questions, providing tailored advice based on context and history, and even demoing products in real time . By bringing an always-on “virtual sales associate” to product pages, Firework helps retailers replicate the in-store experience online, giving shoppers personalized, conversational guidance that can drive higher engagement and conversion . (Notably, Firework’s AVA also appears in the Conversational Interfaces section for its broader agentic capabilities.)
Shopify (Sidekick) – Shopify Sidekick is Shopify’s AI commerce assistant integrated into its platform. Sidekick acts as a 24/7 expert on Shopify stores, leveraging the merchant’s data to assist with both backend tasks and customer interactions. While initially geared toward helping merchants manage their stores, Sidekick effectively brings AI into the storefront through features like AI-generated content and instant answers to common customer questions. It connects data points to uncover insights and executes tasks – for example, it can automatically generate product descriptions or answer a shopper’s query about product specs by drawing on the store’s information . By combining deep commerce knowledge with advanced reasoning, Sidekick enhances product discovery and FAQ responses, ensuring customers get quick, accurate information on Shopify-powered sites.
Heyday by Hootsuite – Heyday (acquired by Hootsuite) provides an AI-powered conversational chatbot for e-commerce brands. It serves as a virtual sales and support agent, capable of fielding product questions, providing personalized recommendations, and even creating shopping carts via chat interfaces . Heyday’s platform integrates with e-commerce systems so it can pull product feeds, inventory info, and order data in real time . This allows it to deliver always-on support and tailored advice – for example, helping a customer find the right size or suggesting complementary items – mimicking an in-store associate through chat. By handling FAQs and guiding shoppers 24/7 on websites or social channels, Heyday boosts engagement and conversion while lowering the burden on human support teams .
Bolt AI – Bolt, known for its one-click checkout, has expanded into AI-driven personalization often dubbed “Bolt AI.” Bolt’s platform uses AI orchestration to streamline the purchase journey, personalizing the checkout and product discovery flow in real time. In partnership with Palantir, Bolt launched Checkout 2.0, a self-learning system that adapts to each shopper’s preferences and behavior – e.g. prioritizing their preferred payment method, surfacing relevant product add-ons, and remembering past selections . This AI-driven approach turns what was once a static end-of-journey step into a dynamic part of discovery. For instance, Bolt’s engine could answer a question like “Can I use Apple Pay?” and automatically present that option, or recommend an upsell that fits the shopper’s profile . By reducing friction and tailoring the experience, Bolt’s AI increases conversion rates (the company emphasizes outcomes like decreased cart abandonment and higher payment success) while giving shoppers a more intuitive path to purchase.
Ada – Ada is an AI customer experience platform widely used for automated chat support in retail. It enables retailers to deploy AI chat agents on their sites that instantly resolve product FAQs, order inquiries, and more. Ada’s strength lies in handling a huge volume of customer questions autonomously: its chatbots have automated over 4 billion conversations to date . On a PDP, an Ada bot can answer questions about sizing, stock availability, or shipping in real time, using both its trained knowledge base and integration to live data (like checking inventory systems) . By resolving common queries via AI (Ada reports up to 83% of support issues can be handled by its agents ), Ada not only lowers support costs but also boosts conversion – shoppers get immediate answers and personalized help (across chat, email, voice, etc.), which keeps them from bouncing due to uncertainty.
Intercom (Fin) – Intercom Fin is Intercom’s GPT-powered chatbot that works natively with the Intercom customer communications platform. Fin functions as an AI support agent that can instantly answer customer questions by drawing from a company’s knowledge base and past interactions . In an e-commerce context, Fin can live on product or help pages, addressing queries like “What’s the difference between model X and Y?” or “How do I return this item?” with conversational, accurate responses. It’s effectively an AI FAQ that’s deeply integrated with Intercom’s live chat and ticketing – meaning if Fin can’t handle a complex query, it seamlessly passes the customer to a human agent with full context. By automating routine Q&A and assisting in product discovery dialogs, Intercom Fin helps retailers scale personalized support and keep shoppers engaged during their decision process .
Zoovu – Zoovu is an AI product discovery platform that offers conversational search and guided selling tools. It enables businesses to add interactive product finders and digital assistants that ask shoppers questions and then recommend products. For example, a consumer on an electronics site might be prompted by a Zoovu “product advisor” with questions about their needs (“Are you looking for a gaming laptop or a work laptop?”) and then see a tailored shortlist. Zoovu’s platform combines AI-powered search, guided selling quizzes, and product content enrichment to make discovery intuitive . It uses natural language processing and a rich product knowledge graph to interpret what buyers mean, not just what they type, delivering relevant results and suggestions that feel like expert advice. By turning tedious filtering into a dialogue and personalizing recommendations, Zoovu helps shoppers find the right product faster, improving satisfaction and conversion (they report metrics like significant conversion lift and lower cart abandonment with their solution) .
Klevu AI Discovery – Klevu provides an AI-powered search and product discovery suite for online retailers. Klevu’s technology brings together smart site search, category merchandising, and product recommendations in one platform . Its AI engine analyzes each shopper’s behavior (clicks, searches, purchases) to personalize results – for instance, two users searching the same term may see different product rankings based on their preferences. Klevu’s solution is known for combining semantic understanding with machine learning ranking to yield highly relevant results at speed . Retailers using Klevu report notable improvements like increased search-led conversions and higher average order values, as the AI surfaces products each customer is more likely to buy. By automating much of the merchandising logic and continuously learning from data, Klevu AI Discovery ensures that product discovery on the site feels individual to every shopper, functioning as an ever-improving, AI-driven sales assistant in the search bar.

AI-Powered Video Commerce Platforms

This category includes platforms that enable shoppable video content – whether short-form videos, stories, or live-streamed shopping events – enhanced by AI. These solutions let retailers and brands create immersive video experiences where viewers can get information, ask questions, and purchase products within the video context. AI is used for personalization (showing the right videos/products to the right user), for interactive Q&A, and for scaling content (e.g., generating video highlights or automating recommendations). The key players here are:

Firework – Firework is a leading video commerce and engagement platform that empowers brands to host shoppable short videos and live stream shopping events on their own websites . Retailers use Firework to embed TikTok/Instagram-style video reels on product pages or as interactive live shows, turning video content into direct shopping opportunities. Firework’s platform stands out for its AI enhancements: it can automatically answer viewer questions on recorded videos (using the same generative AI behind its AVA assistant) and generate real-time product recommendations from video context . For example, if a customer watches a cooking demo video on a grocer’s site, they can ask “What was the second ingredient?” and Firework’s AI will answer from the video content . Firework also pioneered the integration of video into retail media – The Fresh Market’s partnership with Firework created the first retail media network with shoppable videos on-site . In short, Firework merges content and commerce, using video to engage customers and AI to make those videos interactive and monetizable.
Tolstoy – Tolstoy is an AI commerce platform specializing in interactive shoppable videos and video-based quizzes. Brands can use Tolstoy to create choose-your-own-adventure style videos where viewers can click on prompts, answer questions, or jump to different scenes. This allows a shopper to, say, take a “video quiz” where a host asks about their style preferences and then navigates to product videos matching their answers. Tolstoy’s platform aims to boost sales through engagement: it provides features like lead capture forms within videos and branching storylines that personalize the journey . For example, a beauty brand could have a Tolstoy video ask “What’s your skin type?” and then branch to the appropriate skincare routine video. By making videos two-way and tailored, Tolstoy keeps customers watching longer and drives them toward purchase decisions. It’s especially favored by smaller e-commerce merchants looking to offer an interactive shopping journey without heavy content resources – Tolstoy’s AI can even help generate video content or captions, and its templates make implementation easy .
Videowise – Videowise provides an all-in-one shoppable video platform known for its performance optimization and AI personalization. Videowise enables merchants to add product videos, user-generated video reviews, and live video feeds to their site with minimal impact on load times (it boasts a unique compression tech to shrink video files up to 97% ). On the AI side, Videowise leverages AI-driven personalization: it analyzes viewer behavior and preferences to dynamically adjust which videos are shown and even how products are featured in a video carousel . For instance, if a viewer has shown interest in running shoes, the platform can prioritize showing shoppable videos related to running gear. By delivering tailored video experiences – and doing so fast – Videowise helps brands increase engagement (shoppers spend more time on site) and conversion rates. Its platform also provides deep analytics and even automates UGC curation (via social listening features), making it a robust solution for video commerce at scale .
Bambuser – Bambuser is one of the pioneers of live video shopping. Its platform allows retailers to host live stream shopping events where hosts (e.g., product experts or influencers) showcase products in real time to an online audience. Viewers can interact via live chat, ask questions, and purchase products directly from the stream. Bambuser’s strength is in creating an engaging, community feel around shopping – features include real-time Q&A, polls, and the ability to see products being demonstrated up close . For example, a fashion brand can run a live “shop the look” show where the host tries on outfits and responds to viewer questions like “Does that come in other colors?” Bambuser also supports one-to-one video shopping, where a customer can video call a store associate for personal advice . By combining video, chat, and voice interactivity, Bambuser brings human connection to e-commerce, which has been shown to boost conversions and basket sizes. Its analytics and recording features let brands repurpose live shows as on-demand shoppable videos too. Overall, Bambuser helps businesses connect with customers in real time and drive impulse buys through the excitement of live events .
Channelize.io – Channelize.io is an end-to-end video commerce enabler that provides tools for live shopping and shoppable videos embedded in brands’ own sites and apps. It’s a white-label platform, meaning a retailer can integrate Channelize.io’s live video player and have full control over branding and data. Channelize.io supports live show features (multi-host streaming, live chat, product spotlights) as well as on-demand shoppable video libraries . One highlight is its Stories-style video widget, letting users swipe through short shoppable videos on a website similar to Instagram Stories – a format known for high engagement . Channelize’s AI comes into play by enabling interactive elements (automated language translation for global streams, or personalized video recommendations) and by capturing behavioral data to improve each stream’s effectiveness. By offering APIs, SDKs, and pre-built integrations (for Shopify, Magento, etc.), Channelize.io makes it easy for retailers to adopt live commerce technology . In summary, Channelize.io empowers brands to host their own QVC-style experiences, fostering real-time engagement and entertainment (“shoppertainment”) that can significantly lift conversion rates and average order values.
Storyly – Storyly brings the popular “stories” format (full-screen vertical videos/images that users tap through) to e-commerce apps and websites. Storyly allows brands to create shoppable Stories that showcase products with interactive features like polls, quizzes, and swipe-up links to product pages . With Storyly, an online store can embed a carousel of Stories at the top of its app – for example, a cosmetics retailer might have a Story sequence for “How to do a summer look” where each segment highlights a product, and viewers can tap to buy. Storyly’s platform is personalization-friendly: it can target Stories to users based on their behavior or segment (showing a sports fan different content than a fashion enthusiast, for instance) . It also integrates with the retailer’s product feed and analytics, enabling features like dynamic product tags (always showing current price and stock) and measuring engagement. By using a familiar social media content style in a commerce context, Storyly helps increase user engagement and time spent, and its shoppable interactivity turns that engagement into sales. Essentially, it transforms marketing content into a direct buying opportunity with a smooth UX.
Skeepers – Skeepers offers a comprehensive suite for video-powered commerce and consumer engagement, combining live shopping, user-generated content (UGC), and influencer marketing. On the live commerce side, Skeepers enables brands to create and stream live shopping events with hosts such as experts or even enthusiastic customers (“super brand lovers”), complete with real-time chat and product highlight features . A unique aspect of Skeepers is its emphasis on community and UGC: it makes it easy for brands to solicit and publish customer video reviews, unboxing videos, and tutorials across their e-commerce site and social channels . With AI, Skeepers can automatically distribute these consumer videos to relevant product pages or in email campaigns, ensuring authentic content is used to drive conversions. It even includes a “visual commerce” capability where it auto-tags and organizes UGC. By blending interactive live events and shoppable UGC videos, Skeepers helps brands build trust and social proof – shoppers can not only watch a live demo but also see real customers using the product. This holistic approach to video commerce (from live shows to post-purchase review videos) makes Skeepers a powerful tool for increasing customer engagement, loyalty, and ultimately sales .

AI Commerce Infrastructure (Product Graphs & Personalization Engines)

This category encompasses the behind-the-scenes platforms that power agentic and personalized commerce experiences. These include AI-driven product data “graphs” or catalogs, search engines, and recommendation/personalization engines that ensure each shopper sees relevant products and content. They form the foundation enabling conversational assistants and dynamic UIs to perform well. Key players and their roles:

Firework (AI Product Graph) – Beyond front-end video and chat, Firework also provides an AI Product Graph as part of its infrastructure. This is essentially a fine-tuned knowledge graph of products, built by Firework’s AI to connect product attributes, content, and customer interaction data. It powers capabilities like AVA’s ability to answer detailed product questions and give informed recommendations. Firework’s approach to AIGC (AI-generated content) combines Retrieval-Augmented Generation (RAG) and model fine-tuning : the AI pulls in live data (promotions, reviews, inventory levels) and learns from customer queries to keep its product knowledge up-to-date. The result is that Firework’s AI assistant and video experiences are backed by a robust, adaptive product graph that can deliver accurate, context-rich answers in real time . In practice, if a user asks AVA “Which of these cameras has image stabilization?”, the AI Product Graph has the structured data to respond correctly. In summary, Firework’s AI Product Graph acts as the intelligence layer linking raw product data to meaningful customer-facing insights, enabling more agentic interactions on the surface.
Dynamic Yield (Mastercard) – Dynamic Yield, now part of Mastercard, is a prominent personalization and experience optimization platform. It offers an “Experience OS” that uses AI to tailor content for each user – from product recommendations and search results to homepage banners and email campaigns. Dynamic Yield’s core is its personalization engine that takes into account behavioral and transactional data to segment users and trigger the right experience. For example, a returning visitor who often buys running gear might see a running-focused banner and recommended shoes on the homepage. The platform can perform A/B tests and multi-armed bandit optimizations to continuously improve those decisions. Dynamic Yield’s AI is purpose-built for personalization, aiming to treat each customer “like they’re your only customer” by dynamically adapting the digital storefront to their needs . Many enterprise retailers use Dynamic Yield to achieve lift in conversion and customer engagement by deploying hundreds of micro-personalizations (e.g., different product sorting rules for different segments) simultaneously. As an infrastructure piece, it plugs into the site/app and operates in real time, making it a backbone for agentic experiences – ensuring that any AI assistant or interface is feeding the user with the most relevant options at every step.
Bloomreach Discovery – Bloomreach Discovery is an AI-powered product discovery suite that encompasses site search, category page optimization, and product recommendations. It is built on Bloomreach’s Commerce Experience Cloud, which ingests a retailer’s product catalog and user behavior data to create a highly intelligent search index. Bloomreach’s AI understands user queries in a shopping context (including long, conversational queries), thanks to its blend of keyword and vector search techniques . It also continuously learns which results lead to clicks or purchases, adjusting ranking algorithms on the fly. Thus, if many users who search “office chair for back pain” tend to click a particular ergonomic chair, Bloomreach will start ranking that item higher for similar queries. Additionally, Bloomreach Discovery personalizes results: two users typing “headphones” may see different top products if one values budget and another values high-end audio (the system knows this from their past behavior) . The platform also includes AI-driven merchandising rules – e.g., boosting products with higher margin or ample stock (if the retailer chooses). In summary, Bloomreach provides the smart search and product filtering that power agentic commerce interfaces, ensuring that whether a user is typing in a search box or asking a voice assistant, the underlying engine serves up exactly what they’re looking for (often before they explicitly know it).
Algolia – Algolia is a highly scalable search-as-a-service platform used by many e-commerce sites for lightning-fast product search and discovery. Algolia has incorporated AI and machine learning (such as its NeuralSearch capability) to go beyond traditional text matching. It offers semantic search that understands synonyms and context, and an AI-powered ranking that can learn from user engagement . For example, Algolia can recognize that a query for “laptop for photo editing” should prioritize high-RAM, high-storage computers even if those keywords aren’t explicitly queried. It also provides tools for personalization – combining a user’s online behavior with the search algorithm to boost relevant results (if a user often buys Nike, Algolia can rank Nike items higher for their searches). Known for its speed (millisecond-level responses) and developer-friendly API, Algolia often serves as the search backbone that agentic commerce experiences rely on to retrieve relevant products in real time. If a conversational agent asks Algolia, “Show me something like this red dress but cheaper,” Algolia’s AI can perform that nuanced search by understanding “like this” (similar style) and filtering by price. Overall, Algolia’s ability to deliver fast, personalized, and relevant search results makes it a crucial infrastructure element for any AI-driven shopping platform .
Nosto – Nosto is a personalization platform tailored for online retail, particularly popular with mid-market and boutique e-commerce brands. Nosto’s AI analyzes myriad data points (clicks, past purchases, time on page, etc.) to build customer profiles and then automatically present personalized product recommendations and content. Common Nosto use-cases include “Related products” carousels, “Complete the look” bundles, and dynamic email recommendations – all generated by its algorithms. For PDPs, Nosto can showcase “Customers also liked” suggestions that are computed via predictive analytics on what combinations of products are often viewed or bought together. It also excels at personalized merchandising: for instance, category pages can be sorted differently for each user (someone identified as budget-conscious might see lower-cost items first, while a brand-loyal user sees their favorite brands on top). According to Nosto, its AI-driven recommendations use behavioral, transactional, and predictive data in real time to put the right products in front of the right customer . The impact is higher conversion and AOV (Average Order Value) – Nosto often publishes statistics like uplift in sales due to its personalization. By automating countless micro-personalizations, Nosto frees up marketers and ensures every shopper’s experience is uniquely optimized. This kind of individualized curation is vital for agentic commerce: it means the “agent” (be it a website, chatbot, or email) knows what to pitch to each user without manual rules .
Constructor.io – Constructor is an AI-first product discovery platform designed specifically for large e-commerce retailers. It offers a suite of solutions for site search, autosuggest, product recommendations, and browse optimization, all driven by machine learning models tuned to retail KPIs (like revenue per visit). Constructor’s differentiator is its focus on maximizing business results through AI: its algorithms consider not just textual relevance, but also learns which products lead to more purchases or higher order values, and re-ranks results accordingly. For example, if a search query has 10 relevant results, Constructor can promote the ones that are most likely to convert (using training data from past shopper interactions). It uses NLP to handle natural language queries and typos deftly – customers can search conversationally or with complex phrases and still get precise results . Constructor also provides a rich autosuggest that can guide shoppers with predictive suggestions (“Did you mean…”) and even personalized query suggestions. Many retailers credit Constructor with boosting conversion by making search and navigation hyper-personalized and optimized for revenue, essentially turning the search bar into a smart sales assistant. As agentic commerce grows, having a powerful engine like Constructor to interpret user intent (including voice or chat queries) and serve the best product options is a huge asset. In short, Constructor.io delivers AI-optimized search and recommendations that help shoppers find what they want (or didn’t know they wanted) quickly, benefiting both the user and the retailer’s bottom line .
Coveo – Coveo is an enterprise AI search and personalization platform used across e-commerce, service, and workplace contexts. In retail, Coveo’s platform can be thought of as the “brain” that powers intelligent search, recommendations, and personalization across all channels. Coveo ingests data from catalogs, customer behavior, and even in-store interactions (if available) to create a unified relevance profile. Its AI then delivers 1-to-1 personalized experiences – for example, search results and product listings that adapt in real time to each shopper’s intent and context . If a user is browsing a fashion site and consistently looking at sustainable brands, Coveo can boost those brands in search suggestions and recommend content (like a blog post on eco-friendly materials) to nurture that intent. Another strength of Coveo is its analytics and machine learning loop: it constantly analyzes what users are clicking or ignoring to self-tune the relevance of results. Additionally, Coveo can factor in business goals, like promoting higher-margin items in recommendations when it doesn’t compromise relevance, effectively marrying personalization with profitability . For agentic commerce, Coveo provides the reliable relevance infrastructure so that an AI agent interface can confidently pull back the “right” answer for the user (“Which of these has the best reviews?” or “Find me accessories for this product”) without a human in the loop. By delivering conversational, context-aware search and proactive recommendations, Coveo helps make the shopping experience feel truly individualized and responsive to each moment of the customer journey .

Conversational & Agentic Commerce Interfaces

This category covers the front-end interfaces where consumers interact with AI agents or chatbots for commerce. These range from general-purpose AI answer engines now adding shopping capabilities, to big tech AI assistants integrated into shopping platforms, to specialized retail chatbots and voice assistants. These interfaces are the realization of “agentic commerce” that consumers directly experience – they allow natural conversations that can span product discovery through to purchase. Key examples:

Firework AVA 4.0 (Agentic Commerce) – Firework AVA appears again here as a prime example of an agentic commerce interface. As described, AVA is a conversational AI sales assistant that lives on retail websites, greeting users and engaging in natural language dialogue about products . Shoppers can ask AVA anything from “What’s the difference between these two TVs?” to “Do you have something in blue under $100?” and get instant answers or recommendations. AVA’s human-like avatar and adaptive persona (customized per brand) make the experience feel like chatting with a knowledgeable store associate. Importantly, AVA can take actions like adding items to cart or guiding the user to checkout, fulfilling the promise of an agent that doesn’t just inform but also helps transact. By integrating with inventory, CRM, and the aforementioned product graph, AVA 4.0 is a conversational layer that connects all parts of the shopping journey in one interface . Its presence in this category highlights the trend of AI moving from backend to direct customer-facing roles – Firework’s AVA shows how a brand can deploy an AI agent on their site to drive sales 24/7 in a scalable way.
Amazon Rufus – Amazon Rufus is Amazon’s new generative AI shopping assistant, embedded in the Amazon mobile app and website for U.S. customers . Rufus is trained on Amazon’s extensive product catalog, customer reviews, and community Q&A data, enabling it to answer a wide range of shopper questions in a conversational manner . For example, a user can tap the Rufus icon and ask, “I need a gift for a 5-year-old who loves science – under $50,” and Rufus will parse this intent and respond with curated suggestions, complete with reasoning (“How about this chemistry set? It’s age-appropriate, within your budget, and has great reviews for educational value.”). This marks a significant evolution of the search bar into an AI chat. Amazon reports that Rufus helps customers save time and make more informed purchase decisions by delivering detailed, context-specific answers and product recommendations instantly . It’s effectively like chatting with an expert who knows every product on Amazon and all relevant info (price, specs, stock, reviews). By integrating Rufus, Amazon is leveraging its unparalleled data trove with AI to maintain its edge in discovery – and as a side effect, setting user expectations that shopping can be as easy as asking a question and getting a personalized, shoppable answer . Rufus embodies agentic commerce at scale, arguably bringing the concept to the masses through the world’s largest retailer.
Perplexity AI – Perplexity is an AI answer engine (akin to an AI-powered search engine) that has recently launched Perplexity Shopping, a suite of shopping-specific features. Originally known for its ability to answer questions with cited sources, Perplexity is now transforming into a one-stop shopping research assistant. When users ask Perplexity a shopping query (e.g., “What is the best laptop under $1000 for graphic design?”), the AI now presents rich product result cards with images, specs, pricing, and links to buy . It pulls in real-time product data via integrations like the Shopify integration (which provides up-to-date info from thousands of Shopify stores) . Perplexity also introduced “Buy with Pro”, a feature for Pro subscribers that enables one-click checkout within the interface – meaning the AI can not only tell you what to buy but also execute the purchase without you leaving the chat. Another innovative feature is Snap to Shop, a visual search that lets users upload a photo of an item and find similar products . With its new Merchant Program, retailers can feed their product info directly to Perplexity, ensuring the AI has accurate data . Perplexity’s move into shopping demonstrates the rise of neutral AI shopping agents that aren’t tied to a single retailer. It aims to provide unbiased recommendations and even comparisons across sellers , acting truly on the consumer’s behalf. For users, this is like having an expert researcher who can find products from anywhere online, answer follow-up questions (“Is this one compatible with X?”), and handle checkout – an embodiment of agentic commerce outside the walled gardens of Amazon or Google.
Google Gemini Shopping (AI Mode) – Google is integrating its upcoming Gemini AI into search and shopping experiences via what it calls “AI Mode” in Google Search . Google’s conversational shopping experience will allow users to have back-and-forth dialogues with Google’s AI about their shopping needs, harnessing the power of Gemini (Google’s advanced multimodal model) combined with Google’s Shopping Graph (a database of over 50 billion product listings) . In practical terms, a user might type or voice a request like, “Help me find a good smart TV for gaming, under $500,” and the AI Mode will engage in a chat – perhaps asking a clarifying question or directly showing a selection of TVs with an explanation for each pick . The interface provides inspiring visuals, smart guidance, and reliable product data in one view . Additionally, Google is adding agentic features such as price tracking and agent-guided checkout: the AI can remember a user’s budget constraint and alert them or even auto-purchase when a desired item’s price drops into range . Another feature is virtual try-on via user photos – for fashion items like clothing, users will be able to see themselves wearing a product, thanks to AI, directly within the conversation . Google’s long experience in search and the sheer breadth of its index mean its AI shopping assistant could be extremely powerful. By bringing commerce into its conversational AI, Google is aiming to retain users who might otherwise ask ChatGPT or another agent for product advice. For the consumer, Google’s AI Mode could become the go-to personal shopper that knows everything on the web (and can leverage Google’s services like payments, Shopping, Maps for availability in nearby stores, etc.). It’s agentic commerce at an Internet scale, with Google’s familiar “one-stop” convenience plus new interactive AI capabilities .
Microsoft Copilot (Shopping) – Microsoft has been infusing its Copilot AI across Windows, Bing, and Edge, and in 2025 it’s piloting a Personal Shopping Agent as part of its Copilot ecosystem . In the Microsoft Edge browser’s sidebar, Copilot can already help with shopping queries – it can automatically find coupons, compare prices, and summarize product reviews on any webpage you visit . The Personal Shopping Agent goes further: it can be embedded into a retailer’s own app or site to power an interactive, brand-specific shopping chat . Microsoft demonstrated this with “Ask Ralph” for Ralph Lauren – an AI chat embedded in the Ralph Lauren app where users could converse about outfit suggestions, gift ideas, or product details, all in the brand’s tone and context . The Copilot Personal Shopping Agent allows retailers to configure the AI with their product catalog, policies, and desired brand voice . It runs on Azure OpenAI Service, ensuring enterprise-level privacy and compliance. Key capabilities include conversational product discovery (the agent can ask the shopper questions to refine what they want, like a human store associate would) and the ability for the agent to follow through to purchase or appointment booking. For example, in an in-store scenario, a salesperson with a tablet could use the agent to quickly find product recommendations for a customer in real time . Microsoft’s vision is this agent can live across channels – web, app, in-store kiosks – providing a cohesive AI assistant wherever customers engage. By offering it as a platform (Copilot Studio for building these agents) , Microsoft is positioning itself as a facilitator of agentic commerce for many brands, rather than a direct commerce player. For consumers, interacting with a Copilot Shopping Agent means a consistent, intelligent helper whether they’re on a brand’s website at midnight or chatting in a store – it never clocks out, as Windows Central quipped .
Drift – Drift is traditionally a B2B-focused conversational marketing platform, but it is also relevant in commerce as an AI chat interface for sales and support on websites. Drift’s Conversational AI chatbots engage visitors in real time with personalized dialogues, often to qualify leads or assist with product selection. In e-commerce (especially higher-value or complex products), a Drift bot can greet a shopper with, “Hi there, looking for anything specific today?” and then, using AI trained on past conversations and FAQs, respond to open-ended questions. Drift’s AI chat has been shown to generate ~40% more engagement than static chat widgets , likely because it can handle natural language inputs and provide relevant answers 24/7. It can answer questions like “What’s the warranty on this item?” or “Do you have this in stock in blue?” automatically, pulling from its knowledge base and the site’s data. If a query is too complex, it will seamlessly hand off to a human agent, but only after gathering useful info. By acting as a virtual salesperson that proactively reaches out and guides the customer through the buying journey (“Would you like to see the size guide?”), Drift helps increase conversions and capture leads that might otherwise bounce . For the shopper, it means immediate attention and help – an experience closer to walking into a store and having someone available to answer questions. In the context of agentic commerce, Drift and platforms like it show how conversational AI can be deployed on any site to improve customer experience and sales without a full “AI Mode” overhaul – essentially bringing some agentic functionality (like real-time Q&A and guided navigation) to businesses as a service.
LivePerson AI – LivePerson is a leader in conversational commerce solutions, providing large enterprises (including many retail banks, telcos, and retailers) with AI-powered messaging bots and live chat software. LivePerson’s Conversational Cloud uses AI to understand consumer intents and manage conversations at scale across chat, SMS, WhatsApp, and other channels . In a retail scenario, a LivePerson AI agent might handle a conversation like: “I’m looking for running shoes in size 11” – it could respond with a few options and a question like “Do you have a preferred brand?” and then guide the user to purchase, all within the chat interface. LivePerson explicitly trains its AI on commerce journeys, meaning it knows when to push a promo vs. when to upsell protection plans . These bots also integrate with inventory and order systems, so they can do things like checking order status or processing a return. One notable aspect is omnichannel continuity: a conversation can start on a website chat, continue via SMS, and involve bot + human agents seamlessly – all orchestrated by LivePerson’s AI. By driving more interactions through messaging (which consumers often prefer over calling or emailing) and automating them, LivePerson’s clients see higher satisfaction and often higher conversion (as the AI can engage those who might abandon carts, offering help or incentives). Essentially, LivePerson is enabling agentic commerce through the channels consumers already use – making AI-powered assistance available on the customer’s terms (any channel, any time). This drives conversational commerce, where the shopping process feels like a dialogue, not a series of clicks, and that dialogue can be monetized and measured like any sales funnel.
Octane AI – Octane AI provides conversational commerce tools for DTC (direct-to-consumer) brands, most famous for its Shop Quiz platform. Octane enables brands to create product recommendation quizzes – an interactive Q&A on the storefront that asks the shopper about their preferences and needs, then suggests a curated selection of products. For example, a vitamin company might have a quiz: “Find Your Perfect Supplement” which asks about diet, health goals, etc., and at the end recommends a bundle of vitamins. Octane AI’s platform now leverages GPT-3.5/4 (via its AI Quiz feature) to help merchants generate these quizzes and even engage in more free-form dialogues. The quizzes collect zero-party data (data willingly given by customers about their preferences) and use it to personalize not only the immediate recommendations but also future marketing (like follow-up emails that reference quiz answers) . For shoppers, these quizzes feel like a fun, personalized consultation – Octane reports that conversion rates and average order value tend to jump when customers take a quiz, because they become more confident in the product fit. Additionally, Octane supports Messenger and SMS bots, so brands can continue the conversation beyond the website (e.g., a Messenger bot that provides a skincare quiz and then sends tailored tips). In essence, Octane AI turns what could be an overwhelming browsing experience into a guided, personalized journey – an agentic salesperson in quiz form that asks the right questions and then says “Here’s exactly what you need.” By doing so, it increases customer happiness and sales, and gives the brand valuable insights. In the agentic commerce landscape, Octane AI represents how even smaller brands can implement a form of AI shopping assistant without building complex models – using structured quizzes and the power of large language models lightly to create scalable one-to-one conversations that drive commerce.

Retail Media Intelligence & PDP Monetization

Agentic commerce interfaces and AI-driven content also open new avenues for monetization – giving rise to “retail media” opportunities within these experiences. This category covers solutions that help retailers and brands make money from on-site AI interactions and traffic, for example by serving sponsored products, native ads, or even brand-sponsored answers in AI chat. It also includes the infrastructure of retail media networks (RMNs) where retailers use their first-party data and site real estate to let brands advertise. Key players:

Firework (Sponsored Questions) – In addition to powering shoppable videos and an AI assistant, Firework is innovating in monetizing the Q&A and video engagement on product pages. One concept Firework is pioneering is Sponsored Questions, where brands can pay to have their product answers or recommendations featured to relevant customer queries on a retailer’s site. For instance, if a shopper asks the AI assistant on a PDP, “What’s the best accessory for this camera?”, a camera accessory brand could sponsor an informative answer or suggestion, clearly disclosed as sponsored but genuinely helpful. This effectively creates a new ad inventory within conversational commerce – turning customer questions into targeted, high-intent ad impressions. Firework has already blended retail media with content by launching shoppable video retail media networks (e.g., The Fresh Market’s partnership) . With Sponsored Questions, Firework extends that to the interactive FAQ domain, ensuring that the AI not only serves shoppers but also opens a channel for brands to insert themselves usefully into the dialogue. It’s early-stage, but signals how Q&A can be commercialized in a shopper-friendly way (unlike traditional ads, these would appear as answers to the shopper’s own question). This could increase retailer ad revenues and give brands a way to engage customers right at the point of decision, all while (ideally) maintaining relevance and trust. (As this field is nascent, expect more development and perhaps case studies as Firework and others test it in 2025.)
Criteo Retail Media – Criteo is a dominant player in retail media technology, operating an end-to-end commerce media platform that connects brands, agencies, and a network of retailers for advertising. Criteo’s retail media suite enables retailers to run sponsored product ads and on-site display ads on their e-commerce properties, and allows brands to bid on those placements using Criteo’s platform. For example, on a major retailer’s site, the top slots of search results or a carousel on a PDP might be powered by Criteo, showing a sponsored item relevant to the context (and labeled as such). Criteo’s platform leverages the retailer’s first-party data (e.g. past purchase data, in-cart data) to help brands target ads at the point of purchase decision – reaching consumers right when they are searching or browsing for products . With over 225 retailer partners, Criteo offers scale to advertisers outside of walled gardens . It also provides a demand-side platform (DSP) for off-site ads, and crucially, unified measurement so brands can see sales attribution from these retail media ads. In 2025, retail media is big business (Amazon alone drives huge ad revenue, and everyone else – Walmart, Target, Kroger, etc. – is building their networks). Criteo positions itself as the one-stop platform for retail media beyond Amazon, claiming about 30% of the retail media market vs. Amazon’s ~70% . By partnering with many retailers, Criteo lets brands access multiple audiences through one interface. For retailers, Criteo’s tech handles the heavy lifting of ad serving, relevance, and bidding. In sum, Criteo Retail Media equips retailers to monetize their digital shelf and gives brands a way to get their products in front of high-intent shoppers at scale – a critical component of the modern marketing mix .
CitrusAd – CitrusAd, now part of Publicis Groupe (powered by Epsilon), is another leading retail media platform. CitrusAd provides retailers with a white-label self-serve advertising platform for on-site media, notably sponsored product listings and banners. It’s known for being retailer-friendly (no revenue share fees for the retailer) and highly transparent to advertisers. A retailer using CitrusAd essentially runs their own ad network: suppliers and brands can log in to bid on search keywords or category placements on that retailer’s site, and launch campaigns that appear natively within the shopping experience . CitrusAd’s system uses relevance algorithms and retailer data to ensure, for example, that if a user searches “toothpaste”, the sponsored products shown are indeed toothpastes (and likely ones that perform well), not random items – maintaining a good UX while monetizing prime digital real estate. It supports various ad formats (product carousel ads, hero banners, etc.) and has expanded into off-site and in-store retail media as well. The unified platform means a retailer can manage both sponsored product auctions and display ads in one place, and brands get consolidated reporting. Publicis integrating it with Epsilon means powerful data capabilities – e.g., combining loyalty card data with ad targeting. Overall, CitrusAd is a major solution enabling the “retail media network” boom across many retailers (from groceries to electronics stores). Its presence in this map underscores the importance of PDP monetization – turning those AI-rich product detail pages and search results into revenue generators via sponsored content, done in a way that’s seamless to the shopper .
Amazon Ads – Amazon Ads is the advertising arm of Amazon and the progenitor of the retail media concept. It encompasses a range of formats: sponsored product ads (which appear in Amazon search results and PDP carousels), sponsored brands and videos, display ads on and off Amazon, and more. Amazon’s ad business is huge – it commands roughly 70% of U.S. retail media spend and generated tens of billions in revenue last year. For advertisers (typically the product manufacturers and sellers on Amazon), Amazon Ads provides unparalleled targeting using Amazon’s vast shopper data. For example, a brand can target an ad to people who viewed similar items or to specific audience segments (like “fitness enthusiasts”) and have those ads show up as highly-relevant placements during shopping. The integration of ads is often native – an Amazon shopper might not even realize the first few results are sponsored because they look very similar to organic listings (aside from a small “Sponsored” tag). This means Amazon has to balance monetization with user trust, and they use quality score and relevance signals to ensure ads are actually useful (ads with higher engagement can cost less to the advertiser, incentivizing relevancy). In the emerging agentic commerce world, Amazon is baking ads into new experiences too – for instance, Amazon’s Rufus assistant includes sponsored product suggestions woven into its answers . Because Amazon has consumers’ purchase intent and history data, its ads can feel like recommendations. From a retailer perspective (Amazon itself being the retailer here), Amazon Ads exemplifies how to fully monetize an AI-driven commerce platform – every query or interaction is an opportunity to present a sponsored option that still helps the customer. For the market overall, Amazon Ads sets the bar and pace – its success is what every other retailer hopes to emulate (albeit at a smaller scale), and it’s pushing innovation in formats (e.g., shoppable video ads, conversational ads with Alexa, etc.). In summary, Amazon Ads is the heavyweight of retail media, transforming advertising by making it a native part of the shopping flow and leveraging AI to target and deliver those ads for maximum impact .
Instacart Ads – Instacart Ads is the advertising platform within Instacart’s online grocery marketplace. As more consumers order groceries and essentials via Instacart, brands (especially CPG brands like beverage, snack, and household goods companies) are investing in sponsored placements on Instacart to influence decisions at the digital shelf. Instacart Ads allows brands to bid on search keywords (e.g., a soda brand can sponsor the keyword “cola” so their product appears first) and to buy banner ads or feature spots in categories. The unique aspect of Instacart is its wealth of purchase data across many retailers – they know, for instance, if a user frequently buys organic products or has loyalty to certain brands. This data can power highly targeted campaigns (like promoting a new organic snack to users who buy organic chips). Instacart has reported that ads help brands significantly boost share of cart. From the consumer side, a sponsored product on Instacart is often marked as “Featured” and shown among the first results – it’s a subtle nudge at the moment of truth when the user is adding items to their basket. Instacart is also rolling out new ad features: for example, “Retailer Promoted Ads” where retail partners can promote their private-label products, and shoppable video ads in the app’s inspiration content. As Instacart ventures into faster AI-driven experiences (they’ve introduced features like Ask Instacart, an AI chat for food queries), we may see those answers containing sponsored suggestions (“For that recipe, we recommend [Brand] tomatoes – add to cart?”). In essence, Instacart Ads is monetizing the online grocery trip, ensuring brands have a way to get discovered in a service where there’s no physical shelf to browse. For our market map, it highlights that even in agentic commerce, wherever there is search or recommendation, there is likely a sponsored opportunity – Instacart is making sure the digital equivalent of end-cap displays and eye-level shelving exists and is optimized by AI for each shopper .
Roundel (Target) – Roundel is Target’s in-house retail media network. Through Roundel, Target offers brands advertising placements on Target’s owned channels (Target.com, Target app) as well as off-site (Target leverages its first-party data to buy ads on external sites on behalf of brands to drive traffic to Target). On Target’s site, a shopper might see sponsored products in search results or category pages, or rich media banners that feel native to the Target experience . Roundel emphasizes ads that “feel uniquely Target” – meaning they try to maintain Target’s brand-friendly, curated feel even when serving ads . For instance, display ads might use Target’s design templates. Target has 165 million+ guest profiles from its stores and site, and Roundel uses that data for targeting and measurement . A key selling point for Roundel to advertisers is the ability to connect ad exposure to actual sales (both online and in-store through loyalty/RedCard data) – so a brand can see the ROI. Roundel’s operations show how a big retailer turns its traffic into a media channel: they work with over 2,000 brands, and even extend campaigns off Target’s site by placing ads on partner publisher sites that retarget or attract Target shoppers . Recently, Target has been integrating AI into Roundel’s processes too – for example, using AI to optimize ad placement and creative across channels (there was news of AI prioritizing placements on Google, Meta, Pinterest based on performance) . In our context, Roundel exemplifies a retailer fully embracing the retail media model: any agentic or personalized feature on Target’s digital properties (like an AI shopping assistant in the future) will likely have a monetization element managed by Roundel, ensuring that as customers use new features to find products, advertisers have an opportunity to appear in those experiences in a relevant way. It’s about connecting brands to the “joy of the Target experience” in Target’s own words, i.e., making ads additive to the customer’s journey .
Kroger Precision Marketing (KPM) – Kroger Precision Marketing is the retail media business of Kroger, one of the largest supermarket chains in the U.S. Powered by Kroger’s data arm 84.51°, KPM uses extensive loyalty card and purchase data from millions of households to power targeted advertising for CPG brands across Kroger’s digital properties and beyond. On Kroger’s e-commerce site and app, KPM serves sponsored product listings, search ads, and personalized coupons/offers. Because grocery shoppers often reorder the same items, KPM can do clever things like suggest sponsored alternatives (“Switch & Save!” promotions) or highlight new products that fit a shopper’s profile (e.g., a plant-based protein ad to someone who buys a lot of vegetarian items). KPM’s tagline is about making brand advertising more effective by closing the loop between media exposure and store sales . This means if you see a promoted item on Kroger’s site or in a Kroger-triggered Facebook ad and later buy it in a Kroger store with your loyalty ID, KPM can attribute that sale to the ad – a huge draw for CPG brands that historically struggled to connect media to in-store sales. In 2025, Kroger unified KPM with its broader loyalty and insights services, indicating how central retail media has become to the retail strategy . KPM also expanded off-site, via networks like the Kroger DSP (using data to buy ads on external websites that drive to Kroger). In terms of agentic commerce, Kroger is part of an alliance (along with others like Walmart, Target via Xandr) to ensure their products show up in external AI assistants and search results – essentially providing structured data feeds and payment protocols for AI agents (this is hinted by efforts like Google’s Agent Payments Protocol and KPM’s involvement in industry moves). Summing up, Kroger Precision Marketing highlights that even in grocery – a sector with razor-thin margins – the intelligence from AI and data can unlock new revenue through ads. By having an AI-tuned retail media (e.g., an AI chatbot on Kroger’s app might someday serve sponsored suggestions for recipes or products), Kroger ensures it remains a step ahead in both serving the customer and serving the advertiser in a privacy-conscious, value-added way .

Conclusion

The Agentic Commerce market in 2025 paints a picture of a retail landscape transformed by autonomy, intelligence, and interactivity. Across product discovery, content, infrastructure, conversational interfaces, and monetization, we see AI-driven agents and systems working in concert to make shopping more personalized and seamless than ever before. Several key trends emerge from this panorama:

Convergence of Discovery and Conversation: Traditional e-commerce navigation is giving way to conversational discovery. Shoppers are increasingly engaging with AI Q&A on PDPs or chatting with bots that guide them to products. This reduces friction – instead of clicking through menus or reading static FAQs, consumers can simply ask and receive tailored responses. Companies like Firework, Shopify (Sidekick), and Ada exemplify this, turning product pages into two-way dialogs. In the future, we can expect almost every product search or browse experience to have a conversational option, whether text or voice, powered by an understanding of natural language and context.
Experiential Commerce via Video and Interactive Content: The rise of shoppable video and live commerce platforms shows that online shopping is becoming more experiential and immersive. Retailers are adopting storytelling and entertainment (through video, live demos, quizzes) to engage customers in ways that mimic or even surpass in-store experiences. AI enhances this by personalizing content (like Videowise’s AI-driven content adjustments) and scaling the creation and curation of videos (as Skeepers and Storyly do with UGC and stories). Going forward, expect richer media – AR/VR try-ons, AI-generated models, live influencer commerce – to be embedded in everyday shopping. The boundaries between content and commerce will continue to blur, with every video or social post a potential storefront.
The New Infrastructure: Commerce Data Graphs and AI Algorithms: Underpinning these customer-facing changes is the modernization of retail infrastructure. The deployment of product graphs, real-time personalization engines, and AI search algorithms is enabling retailers to deliver relevant experiences instantly. Tools from Bloomreach, Algolia, Constructor, etc., ensure that the “brain” behind the storefront is smart and learning continuously. This is crucial because AI agents are only as good as the data and logic backing them. In the future, we’ll see even tighter feedback loops – every customer interaction (a chat query, a video watched, a product purchased) will feed back into training models that improve the next interaction. Retailers who invest in these AI infrastructures now will have a compounding advantage, as their systems become more predictive and prescriptive, perhaps even automating merchandising decisions entirely (true autonomous optimization for revenue and customer satisfaction).
Hyper-Personalization and the Privacy Paradox: Many of these technologies revolve around using personal data to customize experiences. The market map players show a range of approaches: explicit data gathering (quizzes by Octane AI), implicit behavior learning (Nosto, Dynamic Yield), and aggregate pattern recognition (AI search like Klevu or Algolia). The result is an expectation from consumers for personalized journeys – irrelevant content or one-size-fits-all offerings will increasingly turn customers off. However, this must be balanced with growing privacy concerns and regulations. Companies will need to be transparent about data usage and offer value (personalization) in exchange for data. Fortunately, agentic commerce can actually improve privacy – for instance, local AI on-device that personalizes without sending raw data out, or intermediary agents (like Perplexity) that act on user’s behalf without revealing their identity to every retailer. The future likely holds a blend of AI-driven personalization with consumer-controlled data sharing, possibly via clear opt-in moments (like quizzes or loyalty sign-ups that feed preferences to the AI).
Seamless Transactions and Autonomous Purchasing: As Thoughtworks noted (and as we see with OpenAI’s and Stripe’s moves), agentic commerce is heading toward end-to-end execution . That means not only finding and recommending products, but also handling payments and logistics autonomously. We see early signs: Perplexity’s one-click checkout, Google’s price-tracking auto-purchase concept, and Amazon’s continued investment in one-click and subscriptions. Over the next few years, we will likely witness invisible checkout becoming common – AI agents will negotiate and place orders behind the scenes once they have our mandate (“replenish my pantry items under the best terms each month”). This will require robust frameworks for trust and security (like Google’s Agent Payment Protocol and questions of authorization as raised by Thoughtworks ). The companies in our map are all pieces of this puzzle; when assembled, they allow a scenario where you might tell your AI assistant, “I need a full camping gear set for a trip next weekend,” and it consults various discovery engines, compares on marketplaces, finds the best options (maybe even bidding for spots on retail media on the fly), and completes the purchases – notifying you only when it’s all set. The future of shopping may be “requests” and “approvals” rather than manual searching and checking out.
Monetization and New Revenue Streams: Finally, the trend of every surface becoming an ad opportunity will continue, but in a careful, contextual manner. Retail media is booming – retailers are media owners now – and agentic interfaces are new surfaces to monetize. We can expect creative ad formats: imagine a brand paying to have their new product be the one an AI assistant recommends when appropriate (sort of like sponsored “top picks” within a chat). Or a brand crafting a rich media answer to a common question (“What’s a good skincare routine?” answered in part by a sponsored tutorial featuring their product). The key will be ensuring these come with relevance and transparency to maintain user trust. The players in retail media (Criteo, CitrusAd, the big retailers) will push the envelope on ad personalization – using AI to decide not just what ad to show, but if and how to show an ad in a way that feels helpful. This might even blur into affiliate models for AI agents (if an independent AI like Perplexity recommends a product and facilitates the sale, it might earn a commission, incentivizing it to truly find what the user wants so it gets the conversion). Ecosystem governance (to avoid conflicts of interest or undisclosed sponsorships) will be an important discussion.

In conclusion, the Agentic Commerce ecosystem of 2025 demonstrates the retail industry’s rapid adaptation to AI-centric shopping. Brands and retailers that leverage these technologies are seeing smarter growth with every interaction – more engaged customers, higher conversion rates, and new revenue from ads and data. Consumers, in turn, are beginning to enjoy more natural, frictionless, and personalized shopping journeys, whether that’s through chatting with a virtual expert, watching a shoppable video, or receiving on-point recommendations exactly when they need them.

Looking ahead, the trajectory points to commerce experiences that are increasingly predictive and proactive. Shopping will become less of a chore of searching and scrolling, and more of a dialogue or even delegation to trusted digital agents. The retailers of the future will compete not only on products or prices, but on whose AI algorithms and agent interfaces deliver the most value to customers. Those who embrace openness – integrating with platforms like the ones we’ve discussed – will thrive in an era where commerce can happen anywhere, anytime, through any interface.

The Agentic Commerce revolution is well underway, and as this market map illustrates, an impressive array of companies is already building the foundation. In the coming years, their innovations will further converge, and we may very well see the first instances of fully autonomous shopping agents that span multiple ecosystems (imagine an AI that knows your Target Circle offers, your Amazon preferences, your local store inventory – and smartly orchestrates among them). The winners in this space will be those who collaborate across the ecosystem to serve consumers’ needs in the most convenient and delightful way.

One thing is certain: the shopping experience in 2030 will look dramatically different – and far more intelligent – than it does today, thanks to the groundwork laid by the Agentic Commerce pioneers of the mid-2020s. Each category in this market map is a stepping stone toward that future, where AI is the new interface for commerce and the entire retail value chain becomes more connected and customer-centric than ever before.