Genixly team
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November 26, 2025
Artificial Intelligence and Business Analytics: The Emerging Trends That Reshape the Enterprise Approach to Data
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Agentic AI in ecommerce is transforming online retail. Explore the new paradigm where AI agents reshape buying journeys, automate decisions, and drive growth.
Agentic AI in ecommerce is emerging at a moment when online retail is reaching its limits. Shoppers face overwhelming product choices, inconsistent search results, and interfaces that require excessive effort. Retailers, in turn, struggle to keep pace with growing catalogs, shifting demand, and customer expectations that change faster than their systems can adapt. Traditional automation can no longer bridge the gap — it reacts, but it doesn’t understand intention.
The solution is a new class of intelligent systems: agentic AI for ecommerce. These AI agents can reason, plan, and act autonomously, comparing products, interpreting constraints, filtering noise, and completing complex tasks on behalf of both shoppers and merchants. Instead of forcing customers to navigate the journey step by step, agentic commerce lets them delegate the work entirely.
This article explains the new ecommerce paradigm driven by AI agents. You will learn how agentic AI reshapes discovery, pricing, operations, and support; which capabilities make it fundamentally different from past ecommerce AI; and what retailers must do now to prepare for a world where agents — not interfaces — drive the majority of shopping decisions.
Agentic AI refers to a new generation of systems that can understand a goal, determine the necessary steps to achieve it, and execute those steps without constant human intervention. In ecommerce, this means AI doesn’t just answer questions or offer suggestions — it can take initiative and complete tasks on behalf of both merchants and customers.
Want to learn more about ecommerce and its latest achievements? Follow our Glossary of Ecommerce Terms.
Most ecommerce automation still revolves around fixed rules: display a product recommendation, trigger an email, route an order. These systems are helpful, but they operate within narrow boundaries and depend on users to drive the experience.
Agentic AI breaks that pattern. Instead of waiting for instructions, agents can:
This ability to reason and act autonomously is what separates agentic AI from conventional automation or chat-based assistants.
From the customer perspective, agentic AI changes the flow of ecommerce interactions, too. Instead of moving through pages and filters, shoppers can delegate tasks — finding suitable alternatives, comparing options, or building a basket — to an agent that understands preferences and constraints.
For merchants, this means their digital storefront is no longer the only interaction surface. Agents, on behalf of the customer, may explore the catalog, evaluate availability, or assemble product combinations often without direct website navigation.
A clear example of this shift is happening right now in front of your eyes. Start browsing for something on Google, and you will immediately spot the new AI mode. Google’s AI Overviews can summarize products, compare options, and surface recommendations directly within the results page, often before a shopper ever clicks through to a retailer’s website.
These AI-driven panels pull from structured data, reviews, and product feeds to present a curated set of options tailored to the user’s query. In practice, this means discovery now begins with an intelligent system that interprets intent — not with traditional category pages or filters.
Traditional ecommerce AI tools focus on improving specific steps — better recommendations, smarter search, faster support responses. Agentic AI expands the scope. It connects those steps into a coherent sequence and handles them autonomously. Instead of enhancing individual moments in the journey, it participates in the journey itself.
As you can see, the rise of agentic AI in ecommerce is already here. And it is not on paper. It’s measurable, reshaping how customers discover products and how retailers capture demand.
Recent analyses suggest that agentic commerce is already influencing billions in retail activity. A study published by Digital Commerce 360 estimates that AI shopping agents may now drive $73 billion to $292 billion in annual GMV, depending on conversion assumptions.
Traffic data reinforces this shift. OpenAI’s ChatGPT now directs referral volume comparable to nearly 20% of Walmart’s total referral visits, indicating that ecommerce AI agents are becoming influential discovery surfaces.
For retailers, this means the earliest form of agentic AI ecommerce traffic is already shaping purchasing decisions — even before most merchants start optimizing for it. Except for big market players, of course.
The clearest sign of momentum is that major ecosystem participants are already building agentic AI for ecommerce directly into their platforms.
On the customer side, for instance, Amazon introduced Buy With AI / Buy For Me, allowing an AI agent to compare, evaluate, and purchase products on behalf of a customer.
Salesforce comes from the opposite side. The platform is rolling out an AI toolset — Agentforce — that can help merchants and store administrators. Their platforms claim to provide autonomous merchandising agents that analyze performance, adjust promotions, and generate content.
Microsoft goes even further. The company is working on an enterprise foundation for AI agents through its “Agent Factory” patterns and interoperability protocols like MCP and A2A.
When the largest market players coordinate around the same idea, it signals that agent-driven ecommerce is rapidly becoming the new standard.
Consumers, in turn, are adopting conversational and agent-driven interfaces at a remarkable speed. New data from Adobe shows that generative AI-powered shopping is growing at an extraordinary speed, with traffic to retail sites from AI-driven interfaces rising 4,700% year over year in July 2025. Growth has been consistently strong throughout the year as well: generative AI traffic was up 1,100% in January 2025 and 3,100% in April 2025 compared to July 2024, when activity was still too minimal to serve as a reliable baseline.
In practical terms, this means more shoppers now arrive through AI-mediated journeys rather than traditional browsing. And they show stronger engagement than traditional shoppers:
These patterns suggest that shoppers feel more confident — and more supported — when interacting through AI shopping agents rather than navigating storefronts manually.
Ecommerce has outgrown manual comparison and multi-tab navigation. Catalogs expand faster than consumers can process, delivery options multiply, and pricing fluctuates hour by hour. This creates a perfect environment for agentic AI ecommerce systems that reduce friction and guide shoppers toward faster decisions.
For merchants, this marks a transition away from an ecosystem where every journey begins on the website. Increasingly, it starts with an AI agent interpreting intent — and that makes agent-readiness a competitive advantage. So what does all of this lead to?
The agentic AI paradigm shift in ecommerce extends far beyond the automation of shopping journey steps and admin routine workflows. It reshapes the competitive landscape, the role of brand identity, and the future of retail visibility. Let’s talk about that.
As we’ve mentioned above, one of the biggest shifts is how shoppers arrive at products. AI agents don’t browse; they decide. Instead of stepping through filters or scrolling through pages, they extract what matters — price, attributes, availability, reliability — and present only what fits.
This transforms discovery into a mediated experience. Traditional SEO loses weight, and visual design matters less if a shopper never touches the interface. The new entry points are conversational assistants, personal agents, and autonomous shopping tools that interpret intent and jump directly to relevant products.
In this environment, visibility depends on how well a product can be understood by machines. Rich attributes, consistent metadata, clean descriptions, and structured catalog information are no longer optimizations — they are the prerequisites for being included at all.
The old ecommerce paradigm forced retailers to optimize for human search behavior. Agentic commerce demands a different model. Generative search engines and agent-driven journeys require content that machines can parse, reason over, and confidently surface.
This transition mirrors the move from keyword-based SEO to a broader strategy that also includes:
Success depends less on optimizing for search results and more on ensuring the product is fully “agent-readable.” Although SEO still plays an important role in product discovery, its part in the overal product visibility decreases.
If you want to stay visible in an AI-driven discovery landscape, getting comfortable with GEO is no longer optional. Read our guide on Generative Engine Optimization for Ecommerce to discover a working strategy.
Retail media networks have grown by capitalizing on shopper attention inside owned storefronts. Agentic AI changes this model as well. If users rely on agents to evaluate products, the amount of time spent browsing traditional interfaces declines. Less browsing means fewer opportunities to display ads, weakening the foundation of retail media economics.
As a retailer, you already need to rethink how you monetize visibility. The value may shift toward structured data licensing, agent-friendly placement, preferred integration relationships, or pay-for-performance mechanisms inside agentic ecosystems.
Branding historically influenced consumer choice through imagery, storytelling, and emotion. Agentic AI prioritizes utility and relevance. If an agent is tasked with finding the “best value option,” emotional differentiation matters less than objective fit.
This creates a new risk: brands that rely heavily on lifestyle positioning or visual identity may struggle if agents filter products before a human sees them. Products that don’t offer clear attributes, specifications, and differentiated qualities may be de-emphasized.
Retailers need to adapt by making brand value legible to agents — not just appealing to humans.
As AI agents handle more of the work, the traditional ecommerce storefront becomes less central to the journey. Some retailers may find themselves functioning as back-end utilities — reliable suppliers of data, inventory, and fulfillment — while agents control the customer relationship.
This “invisible infrastructure” scenario doesn’t eliminate the value of retailers, but it changes where influence sits. Those who invest in agent-readiness can thrive in this new dynamic. Those who don’t may lose visibility while more adaptive competitors capture the agent-mediated demand.
If you haven’t yet embraced the new paradigm, there’s no cause for alarm. Agentic AI remains early in its development, but the direction of travel is already evident.
As autonomy improves and interoperability standards mature, ecommerce will move toward a model where agents interact with one another far more often than humans interact with storefronts. The next wave of innovation won’t be defined by new interfaces, but by new forms of coordination between intelligent systems.
The most profound shift ahead is the rise of fully autonomous transactions. Shopper-facing agents will communicate directly with retailer-side agents to compare options, negotiate offers, and complete purchases. Human involvement will shrink to defining preferences, approving budgets, and refining constraints, while the agents handle the operational work.
This model reduces friction to almost zero, especially for replenishment, routine purchases, or multi-step buying journeys. It also creates a new competitive landscape where the strength of a retailer’s agent becomes as important as the design of their storefront.
General-purpose assistants are useful for broad tasks, but ecommerce rewards specialization. The next generation of agentic AI systems will rely on domain-specific agents that excel in particular areas, such as:
These specialists will outperform broad models because they understand the rules, constraints, and edge cases of their domain. Retailers that build or integrate these vertical agents will gain an operational advantage that compounds over time.
As agentic commerce scales, the number of active agents will eventually exceed the number of human shoppers they support. This is not a theoretical milestone — it reflects how often decisions need to be made, checked, and optimized in modern retail.
For retailers, this means preparing systems for continuous agent activity: more catalog queries, more availability checks, more price updates, more workflow triggers. The agentic AI ecommerce infrastructure must be built for a world where most “traffic” comes from intelligent systems, not people.
The traditional sequence of “visit website → browse → checkout” will feel increasingly outdated. In its place, ecommerce will rely on a deeper orchestration layer where agents coordinate across protocols, APIs, and interconnected data graphs.
The storefront remains important, but it becomes one surface among many. The real engine of value moves into the background — the new environment where AI agents reason over structured data, call tools, negotiate with peers, and ensure that the customer’s goal is achieved seamlessly.
Retailers who prepare for this shift will operate with greater precision, lower friction, and more adaptive workflows. Those who don’t may find themselves invisible in a world where agents decide what gets surfaced, compared, and purchased. The good news is that there is still time to get ready.
The path forward is clear. It centers on strengthening data, building internal capabilities, and preparing for a world where AI agents are the primary intermediaries.
As a retailer, you must ensure your catalogs can be understood — and trusted — by autonomous agents. This requires the following minimum:
Without high-quality data, AI agents won’t understand that you offer the best-in-your-niche products, and they simply won’t surface in agentic decision flows.
The need for retailer-owned intelligent agents is nonnegotiable. Your own agents become the voice of your catalog in conversations you may never directly witness.
Internal AI agents act as the connective tissue between your data, your rules, and the external agent ecosystem. They can explain product nuances, enforce merchandising logic, prioritize margin or inventory goals, and present your catalog in the best possible light — all while communicating transparently with shopper-side agents.
These systems don’t just automate tasks for administrators; they defend brand identity, enforce compliance, and safeguard commercial strategy.
As we’ve shown, the influence of traditional SEO is shrinking. To stay visible in an agent-driven landscape, you’ll need to prepare for the shift toward GEO and GXO by making your content fully machine-readable. That means focusing on:
Your visibility will depend less on how your product looks on a webpage and far more on how well agents can understand and evaluate it.
Although automation takes on more of the operational workload and frees up valuable time, it also introduces new responsibilities. Greater autonomy means a growing need for oversight, and you must put systems in place that clearly define and monitor agent behavior. That begins with strengthening governance through:
Robust governance ensures that agents act responsibly, predictably, and in alignment with business expectations, even as their capabilities grow.
There is no doubt that browsing time will decrease as agents make more decisions. This change results in a situation where traditional retail media loses leverage. The good news is that you can offset this shift by exploring new value models, such as:
Monetization must adapt to where attention — and decision power — moves!
The transition toward agentic AI in ecommerce is already underway. As an early adopter, you have a chance to shape the standards and capture the first wave of agent-driven demand.
Waiting, however, means losing visibility to those who prepare earlier. In the worst-case scenario, it threatens far worse consequences, such as becoming invisible to the systems that increasingly determine what customers see.
Agentic AI is pushing ecommerce into a new phase — one where decisions, comparisons, and routine actions happen long before a shopper reaches a storefront. This shift doesn’t remove the role of merchants; it reshapes it. Retailers move from designing pages for people to designing ecosystems that intelligent agents can understand, trust, and act upon.
The transition will not be defined by a single breakthrough. It will come from steady, pragmatic steps: strengthening product data, adopting interoperable standards, building internal agents, and creating governance that allows autonomy without losing control. The brands that succeed will be those ready to serve both customers and the intelligent systems acting on their behalf.
So, agentic AI in ecommerce is not just a technology trend. It is the next competitive frontier — the new paradigm that everyone should embrace. Ones who invest early will become those whom agents look to first when making decisions for millions of shoppers. If you are interested in technical aspects of this topic, follow our Guide to Agentic AI Architecture and Patterns in Ecommerce.
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