5 Trends That Will Shape the 2027 eCommerce Landscape

Remember when the biggest innovation in online shopping was being able to buy something without leaving your couch?

We’ve come a long way since then.

Today, you can upload a photo of a pair of shoes to find something similar, ask an AI assistant to compare the best options for your budget, and get a personalized recommendation before you’ve even opened a retailer’s website.

And if 2026 taught us anything, it’s that artificial intelligence isn’t just changing how we shop. It’s changing what shopping actually looks like.

As we head into 2027, we’re moving into an era where search engines are giving way to conversations, product pages are becoming more contextual, and the line between human shoppers and AI-powered buying agents is starting to blur.

But don’t worry, we’re not predicting a world where robots are wandering around shopping malls with credit cards. At least, not yet.

Instead, we’re looking at the technologies, consumer behaviors, and industry developments already reshaping commerce to understand what comes next.

So, grab your metaphorical crystal ball. Here are seven eCommerce trends that we believe will define 2027.

1. Agentic Commerce Moves From Experimentation to Execution

Last year, we talked about the first steps toward agentic commerce: a future where AI assistants could help consumers move from discovering a product to actually purchasing it.

In 2027, that future is becoming much more tangible.

Think about the difference between asking an AI assistant to recommend a suitcase and asking it to find a carry-on that meets your airline’s size restrictions, fits your budget, arrives before Friday, and can be purchased from a retailer you trust.

The first requires a recommendation. The second requires an AI agent that can understand multiple constraints, compare real-time information, and potentially take action on your behalf.

And consumers are increasingly open to the idea. According to DHL’s 2026 eCommerce Trends Report, 29% of shoppers say they’d be happy to let AI make shopping decisions or purchases for them within the next five years.

The technology is evolving to support that shift, too. Google’s Universal Commerce Protocol is helping establish common standards for agent-driven shopping, while emerging checkout integrations are making it possible for purchases to take place within AI-powered experiences.

Of course, this doesn’t mean traditional eCommerce websites will disappear overnight. Shoppers will still want control over their purchases, and retailers will remain central to payment, fulfillment, and customer relationships.

But brands will increasingly need to think beyond optimizing their websites for human visitors.

In 2027, your next customer might be an AI agent shopping on behalf of a human. And that agent will need accurate, structured product information to decide whether your product makes the cut.

2. Product Discovery Shifts From Keywords to Intent

For years, eCommerce discovery has largely revolved around keywords.

Want a new winter coat? Search for “women’s waterproof winter coat,” scroll through a few pages of results, and hope something catches your eye.

But what if you could simply explain what you needed?

“I’m looking for a warm, waterproof jacket for walking my dog in the rain, but I don’t want anything too bulky, and I’d like to spend less than $150.”

That’s the kind of nuanced request AI-powered discovery is increasingly designed to understand.

And shoppers are already embracing it.

According to Adobe’s September 2026 Digital Insights research, referral traffic from AI platforms to UK retail websites increased by a staggering 1,439% between October 2024 and August 2026. By August 2026, AI-referred traffic was converting at a rate 20% higher than non-AI referred traffic.

Meanwhile, Salesforce’s 2026 State of Commerce research found that shoppers increasingly begin their purchasing journey with conversational AI, with the use of agentic search as a first step growing 200% year over year.

This shift introduces a new challenge for brands: how do you make sure your products are discovered when customers aren’t searching for your brand name, specific product category, or even a conventional keyword?

Enter Answer Engine Optimization (AEO), also called Generative Engine Optimization (GEO).

Rather than focusing exclusively on rankings in traditional search results, businesses need to make their product information understandable, relevant, and trustworthy to AI-powered discovery systems.

That means detailed attributes, structured specifications, clear product relationships, accurate availability, and content that explains what a product is actually good for.

Because when an AI assistant is trying to find the perfect winter coat, it needs more than a catchy product description and a few flattering photos. It needs to understand why that particular coat is the right answer.

3. Every Shopper Becomes an Expert Shopper

Remember when buying a complicated product meant spending hours researching specifications, reading reviews, watching comparison videos, and hoping you understood enough to make the right decision?

AI is starting to change that equation.

In 2027, expect consumers to become increasingly sophisticated shoppers, not necessarily because they know more about the products themselves, but because AI can bring expert-level knowledge into the buying process.

This becomes particularly interesting in B2B eCommerce, where purchasing decisions often involve technical specifications, compatibility requirements, certifications, and complex product configurations.

An AI assistant could help a buyer identify the right industrial replacement component based on operating conditions, existing equipment, and required safety certifications.

But here’s where things get interesting for brands and retailers: as shoppers become more informed, product experiences will need to become more contextual.

Rather than presenting every customer with the exact same product page, commerce experiences could increasingly assemble information around what a particular shopper is trying to accomplish, whether that’s highlighting a product’s dimensions, sustainability credentials, compatibility, or total cost.

The result is a potential shift away from one-size-fits-all product experiences toward shopping journeys built around individual intent.

However, there’s a catch: AI can only provide expert guidance when it has access to expert-level product information.

In a world where every customer can ask increasingly sophisticated questions, incomplete product descriptions and missing attributes will become harder to hide.

4. Smarter Pricing Becomes Part of the Product Experience

Let’s be honest: everyone loves a good deal.

But today’s shoppers aren’t simply looking for the lowest price. They’re becoming increasingly strategic about when they buy, where they compare, and whether the price they’re seeing actually represents good value.

In fact, Akeneo’s 2026 PX Pulse survey found that 79% of consumers have delayed a purchase because they expected the price to drop, while 77% have noticed the same product listed at different prices across retailers or platforms.

And with AI making it easier to compare products and prices in seconds, that scrutiny is only going to intensify.

This creates an interesting opportunity for retailers in 2027: bringing pricing intelligence and product information closer together.

Traditionally, pricing decisions and product content have often been managed separately. One team determines what a product costs, while another manages the specifications, descriptions, attributes, and experiences used to sell it.

But in an AI-driven shopping journey, those details are evaluated together.

5. Trust Becomes the Ultimate Competitive Advantage

Here’s a slightly uncomfortable truth about AI: just because it can generate an answer doesn’t mean that answer is correct.

As generative AI makes it easier than ever to produce product descriptions, images, reviews, and other content at scale, brands face a growing challenge distinguishing trustworthy information from inaccurate or misleading content.

We call this the AI slop trap: generating more content than ever before, without ensuring that the information behind it is actually reliable.

And consumers are paying attention.

DHL’s 2026 research found that privacy and trust concerns remain an important barrier to AI shopping adoption, with 48% of surveyed consumers expressing concerns in these areas.

In 2027, we expect first-party product data, verified specifications, trusted certifications, and transparent sourcing to become increasingly valuable.

After all, if an AI assistant is recommending a skincare product, safety accessory, or replacement automotive part, consumers need to know that the information it’s using is accurate.

And the importance of verified product information extends beyond AI.

Digital Product Passports (DPPs) are bringing new requirements for product transparency and traceability. Beginning February 18, 2027, battery passports become mandatory under EU rules for certain categories, including electric vehicle batteries, light means of transport batteries, and industrial batteries with capacities greater than 2 kWh. Requirements for additional product categories are expected to follow in later years.

For businesses, the opportunity is to build trust into their product experiences from the beginning, using accurate, governed information that can be verified, updated, and shared consistently.

Because in an increasingly AI-generated world, authenticity might become one of the most valuable things a brand can offer.

6. AI Moves Behind the Scenes, With Humans Still at the Helm

So far, we’ve focused on how AI will transform shopping for consumers.

But what about the people working behind the scenes to make all of those experiences possible?

In 2027, we expect one of the biggest shifts in eCommerce to happen inside the organizations themselves.

Instead of relying on individual AI tools to generate a product description or translate a few attributes, businesses will increasingly deploy specialized AI agents to support entire product information workflows.

It’s an exciting prospect, especially for product data teams that spend far too much time copying information between spreadsheets, fixing the same errors, and chasing missing attributes.

According to Deloitte’s 2026 Global Retail Industry Outlook, 68% of surveyed retailers expect to adopt agentic AI within the next 12 to 24 months.

But moving from an impressive AI demonstration to a reliable, production-ready workflow is a different challenge entirely.

What happens when an agent generates an inaccurate technical specification? Or publishes an unapproved sustainability claim? Or changes information that another agent is already working on?

That’s why AI governance will become an increasingly important operational discipline.

Businesses will need clear rules around what agents can access, modify, approve, and publish, as well as visibility into their actions and defined escalation paths when human judgment is needed.

And importantly, this doesn’t mean removing people from the process. It means changing what they’re responsible for.

Instead of manually completing every repetitive task, product experts can focus on guiding AI-powered workflows, reviewing exceptions, validating important decisions, and ensuring that automated systems remain aligned with business objectives.

7. Omnichannel Shopping Becomes More Connected Than Ever

With so much attention on AI assistants and autonomous purchasing, it would be easy to assume that traditional shopping channels are on their way out.

But the reality is more complicated.

Consumers aren’t abandoning physical stores, social media, marketplaces, or retailer websites. They’re simply adding more ways to discover and purchase products.

And they expect those experiences to work together.

DHL’s 2026 eCommerce Trends Report found that 63% of surveyed businesses sell through social channels, while 45% of shoppers buy through social commerce.

Meanwhile, physical stores continue to play an important role in product discovery, evaluation, and fulfillment.

The challenge for brands is making sure the experience remains consistent as customers move between channels.

A product promoted on social media needs to have the same accurate specifications as the one displayed on a marketplace. An AI recommendation should reflect current availability. A customer checking a product in-store should be able to access reliable information about sizes, materials, compatibility, and delivery options.

And all of those experiences need to be supported by fulfillment and service that live up to expectations.

The real opportunity in 2027 is creating a connected commerce ecosystem where every touchpoint tells the same trustworthy product story, whether the customer is browsing in person, scrolling through social media, or asking an AI agent to make a recommendation.

How to Prepare for 2027

If there’s one thing we’ve learned from watching eCommerce evolve over the past few years, it’s that the future rarely arrives in one neat, predictable package.

Some shoppers will enthusiastically hand their purchasing decisions over to AI agents. Others will still want to visit a store, speak to a knowledgeable employee, and inspect a product before making a decision.

Most will probably do a little bit of both.

But across all seven trends, one common thread emerges: the quality of the product information behind the experience matters more than ever.

AI agents can’t confidently recommend products they don’t understand. Personalized experiences can’t answer detailed questions without comprehensive product attributes. Dynamic pricing can’t deliver credible value when product and pricing information are disconnected. And autonomous workflows can’t scale responsibly without governance, traceability, and human oversight.

For brands, manufacturers, distributors, and retailers, preparing for 2027 starts with three priorities: velocity, comprehensiveness, and trust.

Product information needs to move quickly enough to keep pace with changing markets and channels. It needs to be comprehensive enough to answer increasingly specific customer questions. And it needs to be trustworthy enough for both humans and AI agents to rely on it.

At Akeneo, we believe the next generation of commerce will be powered by AI, but guided by human expertise and built on trusted product information.

After all, the future of shopping might involve fewer search bars, smarter AI agents, and buying journeys we haven’t even imagined yet.

But whether your next customer is a person, an algorithm, or some combination of the two, they’ll still need one thing before they’re ready to buy:

A good reason to trust your product, and that starts with the data behind it.