Defining Agentic Shopping and the New Commerce Era

Ecommerce Technology

September 29, 2026

12 minute read

Written By: Lizzie Davey

Defining Agentic Shopping and the New Commerce Era

It might seem like forever ago now, but the prospect of an AI tool influencing our purchases was once very sci-fi. Now, it happens on your average pantry-restock Tuesday.

Consumers aren’t just searching for products anymore; they’re increasingly being guided toward them by AI. From Amazon’s Alexa for Shopping to ChatGPT, virtual shopping assistants now sit at the center of the buying journey.

At least 64% of shoppers were already using AI-powered tools to discover or research new products, according to Salsify and the Digital Shelf Institute’s “Ecommerce Pulse Report: Q4 2025."

And, according to Salsify's "2026 Holiday Pulse Report," 51% of shoppers are comfortable with an AI shopping agent automatically discovering, researching, and purchasing gifts.

This type of agent falls under agentic commerce: It's the product of generative AI (GenAI) getting better at understanding shopper preferences, finding them answers and products they're looking for, and, crucially, taking action on their behalf.

What Is Agentic Commerce (and Why Does It Matter)?

Agentic commerce refers to an evolution of ecommerce powered by agentic AI (semi- or fully autonomous systems that can make decisions and take action on their own).

Unlike traditional AI that simply recommends, agentic systems can actually execute. They can restock inventory, personalize offers, trigger workflows, and make purchasing decisions based on consumer preferences and contextual data.

As Steve Engelbrecht, CEO of Sitation, put it at the 2025 Digital Shelf Summit (DSS), “The difference of what agentic means is that it starts to take on an ability to do real work.”

GenAI and agentic AI often overlap because they share data inputs and intelligence, but brands must now think beyond content creation to content consumption and decisioning.

That's where agentic commerce fits in. It's part of the new three-shelf reality — physical, digital, and agentic — that brands must adapt for.

The Shopper-AI Relationship

Consumers are no longer loyal to a single brand or channel. They’re loyal to whatever meets their needs in the moment.

Salsify’s research found that:

72% of shoppers have purchased from a new brand instead of their go-to favorite.
55% switch brands for a better price or promotion, but 68% will pay more for a brand they trust.
32% say ratings and reviews are the most helpful factor when making buying decisions and are more influential than price, images, or product descriptions.

That last point signals a massive shift. Shoppers actually rely on each other (even complete strangers) to make shopping decisions. And increasingly, so does AI.

Creating Next-Gen AI Shopping Experiences

Large language models (LLMs) like ChatGPT cite Reddit more than 40% of the time, according to Statista. These tools “feed” on authentic, human conversations, like ratings, reviews, Q&As, and community discussions.

Basically, your product data and customer feedback are actively training data that shape how AI sees (and recommends) your brand.

This is an important piece of the puzzle because agentic commerce sits at the intersection of human authenticity and machine intelligence. It’s where data, trust, and autonomy merge to create the next generation of shopping experiences.

Where Agentic Commerce Fits in the Modern Buyer Journey

Salsify’s research reveals that AI is already transforming how shoppers navigate each stage of the customer journey.

64% of shoppers use AI to discover or research products.
54% use chatbots, such as ChatGPT, to help them decide what to buy.
22% of shoppers have purchased a product from an AI recommendation.

Adoption is highest among Gen Z (79%) and millennials (77%), but even 36% of baby boomers are turning to AI to help them make purchasing decisions.

Agentic Shopping in Action

Most shoppers aren’t using AI tools to buy autonomously (yet), but agentic commerce encompasses the next step of the process, where agents act on purchase intent rather than just influencing it with recommendations.

For example, say you tell your AI assistant that you’re running low on your favorite skincare product. Instead of prompting you to search, the system already knows your preferences, compares verified reviews, checks what’s in stock, and completes the purchasing journey on your behalf.

Best Practices for Adapting AI

With virtual shopping tools playing such a big part in product discovery and decision-making, brands must adapt accordingly.

Keep these three elements top of mind.

Structured, AI-readable product data. Brands must have product content that’s accurate, consistent, and rich enough for AI systems to understand.
Authentic user-generated content (UGC). More than 60% of shoppers leave reviews — mostly to help other shoppers — illustrating a new form of consumer social responsibility. This authentic data feeds directly into the AI ecosystem.
Integrated, end-to-end systems. Agentic commerce needs a tech stack that can automate inventory management, personalized marketing, and everything in between.

Preparing for the Agentic Era: What Brands Can Do

You don’t need to hand over complete control to the machines. Instead, aim to optimize how you and your team collaborate with AI.

Here’s how you can get ready.

Feed the Machine With the Right Data

AI is only as effective as the data it consumes. To make the most of it, create enhanced content that goes beyond basic titles and descriptions to include features such as videos, lifestyle imagery, feature comparisons, and clear alt text.

Additionally, AI agents and answer engines thrive on (and favor) FAQs and Q&A-style content for their recommendations.

This is a win-win situation for both human and AI audiences, as 87% of shoppers say that enhanced product content helps them make informed buying decisions. So, the more detailed, consistent, and human your content is, the better it performs in both AI and human search.

Still Do SEO, but Emphasize GEO and AEO

AI doesn’t care so much about keywords.

Search is moving from traditional search engine optimization (SEO) to generative engine optimization (GEO) and answer engine optimization (AEO).

These optimization methods use readability, authority, and accuracy to decide if a page is worth showing to a shopper. Another good acronym to remember is E.E.A.T. (experience, expertise, authority, and trustworthiness). This means focusing on clear, conversational copy, consistent data, and pages that demonstrate expertise.

Here’s how to do that:

Design your content to be clear and accurate.
Provide thorough FAQs, specs, and usage context.
Use natural language that mimics how consumers talk.
Make pages that answer questions so agents don’t have to guess.

Encourage Authentic Conversations

Make a concerted effort to get shoppers to share unfiltered reviews and build out community Q&As.

These real interactions help shoppers and feed AI the context it needs to recommend your products to the right people at the right time.

Do this by:

Promoting authentic reviews (even the negative ones).
Using social listening (Reddit, TikTok, forums) to understand real questions.
Building rich metadata for UCG, including tags, sentiment, and key themes.
Linking reviews, Q&A, and video demos directly to your product schema.

Prioritize Data Integrity and Speed

Follow the Philips model: Start with the end in mind, work backward, and map out your transformation goals. Funnel your AI investment into improving your workflows and speeding up your time to market using the cold, hard data you’ve already collected.

Here’s how you can create what’s known as “decisioning workflows:”

Define smart rules (e.g., “if X rating > Y, add to cart”).
Employ guardrails and thresholds (budget limits, freshness checks).
Integrate with order, inventory, and personalization systems.
Build feedback loops by monitoring what agents do, and feed that insight back into your models.

Humanize Your AI Systems

Treat your AI models as if they’re just another colleague. Train them with your brand tone, values, and product nuances, and continually give feedback.

On top of humanizing your systems, remember your shoppers are also human. They should understand why an agent chose a certain product for them, but you should also be transparent about how you’re using their data.

Tip: Pick high-frequency missions (like reorders, replenishments, and subscriptions) and measure success by trust, return rates, and feedback, as well as conversions.

The Future: Human Creativity Meets Machine Autonomy

The future of commerce isn’t AI versus humans; it’s AI alongside humans, with each side playing to its own strengths.

Agentic systems can analyze, recommend, and act faster than any team ever could, but they still rely on human creativity, empathy, and ethical judgment to get it right. The brands that win in this new era combine the much-needed authenticity of human connection with the speed of AI execution.

Agentic commerce fits squarely into that vision.

Instead of handing over decision-making to the machines and rebuking all responsibility, use it to your advantage by turning existing customer insights and product data into amazing shopping experiences.

Mastering the Agentic Shelf: A PXM Playbook for AI-Fueled Growth

Learn how to build an infrastructure of authority to ensure your brand is visible, legible, and recommendable to any consumer — human or agentic.

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Tag(s): Ecommerce Technology Artificial Intelligence

Written by: Lizzie Davey

Lizzie Davey (she/her) is a freelance writer and content strategist for ecommerce software brands. Over the past 10 years, she's worked with top industry brands to bring their vision to life and build optimized and engaging content calendars.

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