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.

Autumn is officially here. The leaves are changing, the temperatures are dropping, and at Akeneo, we’re welcoming a new season of innovation.

But while the calendar might be turning, one thing isn’t slowing down: the pace of AI transformation.

Earlier this year, our Summer Release introduced expanded agentic capabilities through Ziggy, putting AI to work across the product information lifecycle. Now, with the Akeneo Autumn Release 2026, we’re taking the next step.

As AI takes on more responsibility, businesses need confidence that their product data is accurate, their processes are governed, and every change happens with the right oversight.

That’s exactly what this release delivers.

We’re introducing new ways to accelerate product data operations, strengthen governance, and help organizations deliver exceptional product experiences at enterprise scale.

Let’s take a look at what’s new.

Velocity: Moving Faster From Source to Storefront

Product data teams are under constant pressure to do more with less: onboard products faster, keep information accurate, and deliver consistent experiences across an expanding number of channels.

The Autumn Release introduces new capabilities that combine AI and automation to accelerate work throughout the product data lifecycle.

With enhanced Akeneo PX Insights, businesses gain greater visibility into how their products are discovered and understood by AI-powered search engines. Automated AI Discovery and AI Readiness analyses help identify missing product information, prioritize improvements, and turn insights into actionable opportunities. Working alongside Ziggy, teams can translate those insights into governed catalog improvements at scale.

New product model suggestions also help teams bring structure to growing catalogs by automatically identifying products that could be organized into shared models and hierarchies, reducing manual effort while maintaining oversight.

And with expanded Ziggy and Shopify integration, organizations can better understand synchronization issues and address underlying product data problems before they disrupt storefront experiences. The Akeneo App for Shopify now offers Standard and Enterprise editions, with Enterprise designed for businesses managing more complex, multi-store operations that require greater scale and more frequent synchronization.

Smarter Supplier Collaboration, Without the Back-and-Forth

Supplier onboarding can be one of the most time-consuming parts of managing product information, which is why the Autumn Release introduces expanded Akeneo Supplier Data Manager capabilities.

With new Supplier Collaboration capabilities, organizations can delegate more of the work of collecting, updating, and maintaining product information directly to the people who know those products best: their suppliers.

Rather than relying on internal teams to chase down missing specifications or manually manage updates, businesses can request targeted changes and empower suppliers to take an active role in maintaining accurate product data.

But delegating the work doesn’t mean giving up control. Internal teams can review supplier contributions before accepting changes into their trusted product catalog.

The result is less administrative work, faster collaboration, and greater confidence in the information flowing into the business.

Control: More Automation, Without Losing Oversight

As AI agents become more involved in product operations, an important question emerges: who’s in control of the changes they make?

That’s why every new AI capability in the Autumn Release operates within Akeneo’s AI harness, providing the permissions, business context, traceability, and human approval workflows necessary for AI to act reliably.

Agents operate within established permissions, their actions are traceable, and people retain oversight of proposed changes.

For organizations managing increasingly complex enterprise environments, that control extends beyond AI.

New Enterprise Access & Security Governance capabilities, available with Akeneo Product Cloud Premium, help businesses automate identity and access management through Just-in-Time (JIT) User Provisioning, SCIM, and SIEM integration.

These capabilities simplify user onboarding, keep access aligned with corporate identity policies, and strengthen security monitoring without creating more administrative work.

As the IT team at La Maison Simons, an Akeneo customer testing the new functionality, explains:

“Managing access for both our employees and external partners requires flexibility without compromising security. With Just-in-Time User Provisioning, we can support different login experiences, whether through SSO or direct login, while ensuring users have the right access from the start. It’s a valuable step toward simplifying user management while maintaining the security our business needs.”

Additional Supplier Enterprise Security capabilities bring SSO and multi-factor authentication to supplier collaboration, extending secure access beyond internal teams.

Governance: Defining What “Ready” Really Means

For years, product data teams have relied on completeness scores to determine whether products are ready for market.

But here’s the problem: a product can be 100% complete and still not be ready to sell.

A manufacturer might have every required field populated but be missing critical compatibility information. A beauty brand might have a complete description that hasn’t been approved for a particular market.

With new Product Readiness and Readiness Policies, Akeneo is helping businesses move beyond checking boxes.

Organizations can now define what “ready” means for specific business objectives, channels, markets, and locales by establishing rules around product attributes, values, descriptions, and relationships.

Teams can identify exactly what’s preventing a product from moving forward, prioritize improvements, and create more consistent processes across their organization.

And as governance requirements become more complex, Akeneo Product Cloud Premium provides enterprise-scale readiness capabilities, supporting thousands of policies across extensive catalogs, brands, markets, and business units.

Making Complex Product Relationships Simple

Governance isn’t just about individual products. It’s also about understanding how products relate to one another.

For manufacturers, distributors, and retailers managing complex catalogs, those relationships can quickly become difficult to maintain.

Product Compatibility, available with Akeneo Product Cloud Premium, brings dynamic product relationships directly into PIM.

Teams can define reusable rules connecting products, spare parts, accessories, and complementary items, with compatible relationships automatically maintained as catalogs evolve.

Instead of managing endless spreadsheets or manually updating associations, businesses can establish consistent, governed relationships that scale alongside their operations.

It’s another way Premium helps enterprise organizations manage greater product complexity without sacrificing agility.

Turning Over a New Leaf in Product Experience

Every new season brings an opportunity to rethink what’s possible.

And with the Akeneo Autumn Release 2026, we’re helping businesses do exactly that.

By connecting intelligent automation with product readiness, supplier collaboration, compatibility management, AI discoverability, and enterprise governance, Akeneo is making it easier to manage increasingly complex product ecosystems without multiplying manual work.

Ready to turn over a new leaf in your product data strategy?

Explore Akeneo’s latest release and discover how we’re helping businesses deliver smarter, more connected product experiences from source to sale.

Snowleader was not short on product knowledge. It was short on hours.

The outdoor retailer manages around 450,000 SKUs across more than 400 brands, with as many as 17,000 new products arriving during a seasonal launch. Writing every description from a blank page was never going to scale comfortably.

So the team introduced AI-generated product descriptions built from structured product data. Prompts were configured by product family and brand tone, while the content team remained responsible for reviewing and refining the results. 

According to Snowleader, the approach reduced the time spent writing descriptions by around 20%. That is a meaningful win: less repetitive work, faster launches, and more time for storytelling and optimization. 

It is also a good example of what a successful first AI use case looks like.

But it is not the end of the story.

Once one agent starts delivering value, the next questions arrive quickly. Could another agent onboard supplier files? Could one enrich missing attributes? Could a Quality Agent validate the result before the Content Agent acts? Could a Localization Agent prepare the approved copy for every market?

The pilot proves that AI can complete a task. What comes next determines whether it can become part of a product operation your teams can actually rely on.

The Gap Between Adoption and Impact

Businesses are not waiting to experiment with AI. McKinsey’s 2026 State of AI survey found that nearly nine in ten respondents reported regular AI use in at least one business function.

However, scaling is a different matter; the same survey found that only 44% said AI was being scaled across their enterprise, and just 37% attributed any EBIT impact to it. McKinsey also found that the highest-performing organizations were more likely to redesign workflows around AI rather than simply insert AI into existing processes.

That distinction matters for product data teams.

A pilot usually works with a carefully selected product family, a limited number of sources, and a well-understood workflow. Production introduces less tidy conditions: conflicting supplier values, unusual product structures, updated channel requirements, new markets, approval delays, and claims that require legal or technical judgment.

The model has not suddenly become less capable. The job has simply become real.

The Second Agent Changes the Conversation

One agent can often operate as a contained project, but as soon as you add another, the dependencies become more visible.

Imagine that your Supplier Data Agent updates a product specification. Should the Enrichment Agent act immediately, or wait for validation? Should the Content Agent regenerate the description? If the content changes, should every translated version be updated? Should the Syndication Agent pause publication until the new information is approved?

Without a shared operating foundation, agents may use different copies or versions of the same product information. The Content Agent may act before the Quality Agent finishes its review. The Localization Agent may translate content that is still changing upstream. Each agent can appear successful in isolation while the full process becomes harder to control.

What Orchestrating Multiple AI Agents Requires

Moving beyond the pilot requires more than connecting an agent to additional data. Four capabilities become especially important:

One shared product record: Every agent needs access to the same governed source of product truth. If attributes conflict or ownership is unclear, the agent may confidently act on the wrong value.

Clear sequencing: Supplier ingestion, enrichment, validation, content generation, localization, and activation are connected stages. An agent needs to know not only what to do, but when it is safe to do it.

Shared business context: A generic model does not know your taxonomy, approved terminology, brand voice, product-family rules, or marketplace requirements. That knowledge has to be structured so it can be reused across agents rather than recreated inside every prompt.

Oversight and traceability: People still need to review decisions involving uncertainty, compliance, technical accuracy, or brand judgment. Teams must also be able to see which source an agent used, what it recommended, who approved it, and what ultimately changed.

This lifecycle approach reflects broader guidance such as the NIST AI Risk Management Framework, which encourages organizations to incorporate trustworthiness and risk management throughout the design, deployment, use, and evaluation of AI systems, not only at the pilot stage.

What to Do After a Successful Pilot

Resist the temptation to move directly from a polished demonstration to the entire catalog.

Start by testing the agent against a representative mix of product families, suppliers, exceptions, and channels. Confirm that it can retrieve the right context, identify authoritative sources, and handle imperfect inputs.

Next, define the decision boundaries. Which actions can happen automatically? Which require review? Which must remain human-led? Make sure reviewers receive the evidence behind a recommendation, not only the final answer.

Finally, measure the process rather than the novelty. Compare manual effort, cycle time, accuracy, acceptance rates, review time, publication success, and downstream impact before and after deployment.

A pilot should lead to one of four decisions: expand, refine, redesign, or stop.

Your first agent shows what AI can do. The choices you make after the pilot determine whether the next agent becomes another isolated experiment, or part of a connected product operation where every new capability builds on the data, governance, and lessons already in place.

It is the same reflex every November: 30% off the entire site. The revenue chart looks impressive by Cyber Monday. Then January arrives, and the margin report tells a different story.

The real question is not how much to discount. It is which price, for which product, at which moment of its lifecycle. Answering it requires something most retailers still lack on Black Friday: product data and pricing working from the same page.

I. The Blanket Discount Trap

A blanket discount treats every product the same way. Your shoppers do not. According to BCG (2025), about 77% of consumers say they sometimes or often delay purchases earlier in the year to take advantage of year-end sales. Part of your Black Friday volume is demand you would have captured at full price, just a few weeks earlier.

The damage adds up fast. Margin disappears on products that never needed a discount. Prices drop to match a competitor, even when the products are not truly comparable.

Underneath sits a structural problem. The product lives in the PIM. The price lives in a spreadsheet. Black Friday is the moment that disconnect gets stress-tested, at scale and under time pressure.

If your pricing team cannot see what makes a product different, how can it decide what that product is worth?

II. Stage 1, Before the Rush: Lock In a Solid Everyday Price

Black Friday starts in October. In the EU, any advertised discount must be based on the lowest price applied in the previous 30 days. The European Commission’s 2026 sweep found that 30% of the 314 online traders checked presented discounts incorrectly. Your October prices set the reference for your November promotions.

Akeneo PricingHUB handles these price regulations natively: your teams set each constraint once as a business rule, and it applies to every price and promotion. Compliance stops being a last-minute check.

Trust is fragile too. Simon-Kucher’s 2025 Holiday Shopping Report found that only 46% of US shoppers believe advertised discounts fully reflect true savings. A credible discount starts with a credible everyday price.

Keep your price ladder intact

Picture a grocery or DIY retailer with two rules: keep a 20% margin on organic products, and price its private label 15% below the equivalent national brand. Without structured data, the pricing team tags thousands of SKUs by hand before it can set a single rule. When attributes like “organic” or “private label” already live in the PIM, the rule is set once and applies across the whole catalog. Your price ladder survives the promotion.

Get your range right before you discount it

Now take a running shoe sold in 15 sizes, 3 colors and two versions, Standard and Pro. Size 42 should never cost more than size 43. The Pro should always sit above the Standard. With a clean product structure, these consistency rules apply automatically, so your discounts start from a coherent range rather than a patchwork.

III. Stage 2, The Promotion: Discount Where It Pays

Every discount should earn its place. BCG (2025) recommends targeted promo plans that guide consumers toward assortments where the retailer has surplus or margin advantage. In practice: discount where stock is high or margin can absorb it, and hold your price everywhere else.

Pick the products shoppers actually compare

Price elasticity measures how much a price change shifts what people buy. On Black Friday, a TV is highly elastic: shoppers compare it across retailers, and a small price gap moves volume. The HDMI cable sold with it is not. Few people compare cable prices, so a discount there gives away margin for almost no extra sales.

Products also influence each other. Discount one TV, and part of its extra volume comes from a similar model you still sell at full price. This transfer, called cannibalization, is easier to anticipate when the system knows which products are true substitutes. That knowledge comes from product attributes: screen size, technology, brand, range.

Launch in hours, not weeks

Picture a seasonal campaign where merchandising, product marketing and pricing each work from their own files. Reconciling product lists with promotional prices can take up to 14 days of manual alignment. When product and pricing teams work from the same data, there is nothing left to extract or reconcile. The same campaign can go live in hours.

The right price on the right channel

Your stores, your website, your country sites and your marketplaces do not face the same competition. Each deserves its own price. When optimized prices flow back into the PIM, they reach every channel alongside the right product content, with no manual exports.

IV. Stage 3, Under Competitive Pressure: Defend Your Premium

It is Black Friday week, and your best-selling white t-shirt is priced at €15. A competitor drops its white t-shirt to €5. A disconnected pricing tool sees only the gap and matches it, wiping out €10 of margin per unit.

Now add product context. Your t-shirt carries the “organic cotton” attribute. Instead of slashing the price, your team enriches the product page to highlight that quality and holds the €15. The difference between destroying margin and protecting it is product context.

V. Stage 4, After the Peak: Clear Stock Without Burning Margin

Black Friday ends, but the stock does not disappear. Some products leave the week with more inventory than planned, often in specific stores or regions. A blanket markdown before Christmas would repeat the same mistake twice.

A planned markdown starts at category level, not SKU by SKU. It accounts for the stock left on each product, in each location. It also measures how a price cut on one product shifts demand across the whole category, so a markdown does not simply pull sales away from similar products still at full price. The result is a discount path that clears remaining stock before the winter sales while protecting as much margin as possible.

Products that sell well keep their price. Slow movers get the discount they actually need.

VI. AI Recommends, Your Teams Decide

None of this means handing Black Friday to a black box. Automation does not mean losing control. The AI surfaces patterns and recommends prices. Pricing experts validate them and turn them into lasting business rules.

Black Friday is also a workload peak. Thousands of prices to check, adjust and push to every channel, often by hand and against the clock. Akeneo PricingHUB automates that execution: one of our European retail customers now rolls out its optimized prices more than 50 times faster.

Automation does not mean handing Black Friday to a black box. The AI recommends prices and explains every one of them. Your teams validate, adjust and keep the final say, supported by a dedicated team of pricing experts.

Conclusion

Black Friday rewards retailers who know exactly what they sell and what it is worth. When product data and pricing are connected, every discount becomes a deliberate choice: which product, which channel, which moment. That is how Black Friday becomes a controlled decision rather than a bet on your margin.

About Akeneo PricingHUB

Our platform is designed to optimise prices throughout the product lifecycle for retailers and e-commerce businesses. Our objective? To optimise your prices in a simple, automated and data-driven way. Our intuitive solution is centred around AI, a consumer-centric approach and pricing expertise.

👉 Meet our pricing expert here 

The most revealing moment in an AI project often arrives five minutes after the demo ends.

The product description provided looks polished, or the product recommended looks accurate and relevant.

But then someone asks: Which source did it use? Is that claim approved in every market? Does it have the current price? Who reviews the change before it reaches a live channel?

Suddenly, the future of commerce looks less like science fiction and more like a missing attribute in a spreadsheet.

That tension sits at the heart of IT departments across the globe today, and what we set out to learn more about in our annual survey of IT and technology decision-makers. 

On paper, confidence is high: 95% of IT leaders say their organization is ready for AI-driven commerce, and 87% expect AI budgets to increase. 

But here’s the catch: 89% also spend more than a quarter of every AI project preparing data. 

AI Is Exposing Infrastructure Problems It Did Not Create

Nearly two-thirds of IT leaders say their department now owns AI strategy, meaning they’re the ones responsible for making AI work inside the systems the business already relies on: ERP, PIM, DAM, supplier feeds, commerce platforms, marketplaces, and regional workflows.

A team may be able to vibe-code an impressive prototype over a weekend, but it cannot vibe-code trusted product information, resilient integrations, permissions, approvals, and audit trails across brands and markets.

AI did not create brittle integrations, local taxonomies, manual mapping, or disconnected workflows, however it is making their cost harder to ignore. Every new use case inherits that technical debt unless the underlying product information foundation is modernized.

“AI-Ready” Data Still Requires a Great Deal of Human Work

While 95% of IT leaders say they are ready for AI commerce and 92% rate their data quality and structure as good or excellent, 53% spend between 26% and 50% of an AI project preparing data. Another 36% spend more than half.

That preparation means chasing missing attributes, reconciling duplicate SKUs, normalizing units and taxonomies, checking regional prices and compliance fields, resolving conflicting values, and adapting information for different channels.

It is repetitive, time-consuming work the wider business rarely sees. When it is done well, nothing dramatic happens; the AI simply produces an answer that is not wrong. Yet that invisible effort is often what holds the project together.

A Policy Is Not the Same as Operational Governance

Almost every organization surveyed (99%) either has an AI governance policy or is developing one. But a policy cannot always answer the questions that matter when AI touches live product information:

Who changed the record? Which model made the recommendation? What data did it use? Who approved the output? Where was the change published?

That traceability, sometimes called decision lineage, matters when a change involves a price, warranty, compliance claim, promotion, or product description. Governance becomes useful when it is built into workflows through permissions, human review, monitoring, and audit trails, not when it exists only on a board slide.

Automation Solves One Scale Problem, But Can Create Another

In our survey, we found that IT automation and IT operations outrank customer-facing use cases such as conversational AI and personalized search as investment priorities. That makes sense: managing millions of attributes, products, channels, and markets has already exceeded what many teams can handle manually.

The complication is that every narrowly focused agent added to solve one bottleneck becomes another component to coordinate. Without a shared foundation, multiple agents may enrich, translate, validate, and activate the same records without understanding what the others have changed.

This is where an AI harness becomes useful. It is everything around the model (permissions, memory, guardrails, approvals, and orchestration) that allows it to act reliably inside business systems. 

Product Discoverability Is Becoming a Data Problem

Seventy-nine percent of organizations are investing in or piloting Answer Engine Optimization (sometimes called Generative Engine Optimization) as product discovery shifts toward AI.

But appearing in an AI-generated recommendation is not simply a new version of ranking for a keyword. An assistant must understand the product, compare it with alternatives, verify its attributes and claims, and trust that the information is current.

Incomplete titles, missing identifiers, inconsistent specifications, unsupported claims, or stale availability can keep a product out of the consideration set entirely.

The Real Constraint Is the Foundation

Enthusiasm for AI is not fading. Budgets are growing, ownership is becoming clearer, and organizations are actively investing.

What is holding AI back is the gap between a promising model and the operational environment required to trust it. More funding will not remove the data-preparation tax when every new project begins with the same incomplete attributes, inconsistent taxonomies, regional variations, and fragile integrations.

The next meaningful AI milestone may not be a dazzling demo. It may be a far less cinematic moment: an agent updates the correct product record, follows the right rules, routes the change to a human, and publishes accurate information to the right channel.

That is the moment AI stops being an experiment and starts becoming infrastructure.

Download the full report, The Agentic Commerce Reality Check, to explore all 10 hard truths from IT leaders and assess whether your product information foundation is ready for agentic commerce.

It has been a big year at Akeneo.

We’ve expanded what Akeneo Product Cloud can do. We’ve brought new technologies and expertise into the company. We’ve introduced agentic AI capabilities designed to change how product teams work. 

We’ve continued accelerating our presence in the U.S, and we’ve worked with brands including LG Electronics USA, Tiffany & Co., Sweaty Betty, and more to transform their product operations.

Now, there’s another recognition to share.

Akeneo has been recognized as a Leader in the first-ever 2026 Gartner® Magic Quadrant™ for Product Information Management Solutions.

Gartner evaluates providers based on Ability to Execute and Completeness of Vision. For us, being positioned as a Leader in the inaugural Gartner® Magic Quadrant™ for Product Information Management Solutions is an important moment to us, but it is not one we see as ours alone.

We feel it reflects years of collaboration with customers who challenge us to solve increasingly complex product-information problems, partners who extend and strengthen the Akeneo ecosystem, and teams across Akeneo who keep pushing the Product Cloud forward.

A Recognition Built With our Customers and Partners

The first Gartner® Magic Quadrant™ for Product Information Management Solutions marks a significant moment for the PIM category itself, but for Akeneo, it also gives us an opportunity to recognize the ecosystem that has helped shape where we are today.

Our customers bring the real-world complexity: global catalogs, intricate data models, thousands of attributes, multiple markets and channels, changing regulations, and increasingly AI-driven commerce experiences.

Our partners bring the implementation expertise, integrations, services, and market knowledge that help turn technology into real business outcomes.

And that collaboration shows up in the feedback we receive. Akeneo has a 4.7 out of 5 rating based on 140 customer reviews on Gartner Peer Insights™.

One Engineering Manager in the retail industry shared:

“Akeneo’s flexibility in modeling complex product data is a major strength. The attributes and family structure make it easy to scale and manage large catalogs.”

And a Director of Operations in manufacturing noted:

“From the start, the team engaged in demonstrating Akeneo’s features and how it aligned with our needs and solved our pain points we experienced with the previous platform.”

Their skills, feedback, ambition, and expectations continually shape what we build next.

So, to our customers and partners: thank you. Thank you for challenging us, collaborating with us, and helping us keep raising the bar for what product information can do. We see this milestone as one we share with you.

From AI Assistance to Agentic Action

Our Summer Release introduced Agentic Ziggy, an agentic layer embedded within Akeneo Product Cloud to help teams move from simply managing product information to acting on it.

Customers are already using Ziggy to surface insights, simplify complex updates, and automate time-consuming work. At tectake, for example, Ziggy generated 200 automation rules in minutes, which was  work that would previously have taken around two hours manually.

Growing Momentum Across the Market

That product momentum is being matched by business momentum.

Akeneo continues to expand across key markets, including the U.S., as more organizations look to product information as a foundation for faster operations, stronger digital experiences, and emerging AI-powered workflows. That growth also creates a valuable feedback loop, helping us sharpen the roadmap and build for the real-world complexity our customers face.

And We’re Not Finished Yet – Stay Tuned!

Recognition in the first-ever Gartner® Magic Quadrant™ for Product Information Management Solutions comes at an exciting point in Akeneo’s journey.

This year has already brought strategic acquisitions, new customers, expanded capabilities, and an entirely new agentic layer within Akeneo Product Cloud.

And there is more coming.

Akeneo’s Autumn Release is coming soon, with additional functionality designed to expand what teams can do with their product information and how quickly they can turn it into action.

We’ll have much more to share shortly.

For now, we’re proud to see Akeneo positioned as a Leader in the first-ever Gartner® Magic Quadrant™ for Product Information Management Solutions, and grateful to the customers, partners, and teams who have helped us get here.

Download the 2026 Gartner® Magic Quadrant™ for Product Information Management Solutions and explore Akeneo’s position as a Leader.

——

Gartner, Magic Quadrant™ for Product Information Management Solutions, Andrew Davies, Eric Ahlm, Angel Berrios, Darren Livingstone, 12 October 2026.

Gartner, Peer Insights and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

For a manufacturer managing complex technical products, multilingual content, frequent acquisitions, and millions of possible product configurations, giving every team fast access to reliable product information is not a small data project.

DwyerOmega is using Akeneo’s Model Context Protocol (MCP) to connect product information from its Akeneo PIM to Microsoft Fabric, Power BI, AI tools, SharePoint, and AWS. The result is a growing operational layer that makes product data easier to access, analyze, improve, and activate.

Instead of asking every product manager or marketer to learn another system, DwyerOmega brings governed product information into the tools teams already use. Behind the scenes, MCP also helps the company replace development-heavy integrations with more flexible workflows for data extraction, content enrichment, quality monitoring, and acquisition onboarding.

The challenge: trusted data was available, but not always accessible

DwyerOmega had already established the Akeneo PIM as the source of truth for product information. The next challenge was making that information useful to teams with different responsibilities, habits, and technical skills.

Cyrus Grimes, Director of Product Content Management at DwyerOmega, had been extracting enrichment data through API connections to build Power BI dashboards. The approach worked, but it carried a cost.

Product managers run business units and balance many priorities. Marketing and SEO teams need to know where descriptions, metadata, images, and supporting content are missing. Sales operations need visibility into available catalog SKUs. These stakeholders benefit from Akeneo data, but they do not all work in the PIM every day.

“The very concept of opening another software program to access information, I felt, was a blocker.”

The pipeline itself was also reaching its limit. Multilingual content compounded the volume of every full load, and the extraction into Microsoft Fabric was not stable enough to support the next stage of growth.

The solution: an MCP-powered product data pipeline

Using Akeneo MCP with Codex inside Microsoft Fabric, Cyrus rebuilt the pipeline around a structured table design. MCP lets the AI-assisted development environment work with Akeneo and Fabric through a single interface, explore the catalog structure, create the required tables, and troubleshoot the extraction.

The new pipeline captures changes through delta updates rather than rebuilding the full multilingual dataset. It runs automatically every morning, and a full export across all languages completes in approximately 2.5 to 3 hours without hitting concurrency limits.

MCP is not replacing Akeneo as the governed source of product information. It is helping DwyerOmega extend that trusted data into the company’s broader analytics and operational environment.

What comes next

DwyerOmega is testing richer model charts for products with extreme complexity, including one model with an estimated 100,000 to 200,000 possible permutations, and looking at ways to make Akeneo the governed source for AI agents that need accurate product context.

For Cyrus, the underlying value is structural.

The long-term opportunity is bigger than any single dashboard or automation. Instead of building another isolated integration for every new need, the company can create repeatable workflows around one trusted product foundation.

For other manufacturers, the lesson is practical: the value of governed product data grows when people and systems can use it without introducing another layer of inconsistency. DwyerOmega is showing how MCP can turn a PIM from a destination for product data into an active source of operational intelligence.

Where that foundation gets especially interesting is what DwyerOmega can now build on top of it. From giving teams greater visibility into product data quality to accelerating content creation and onboarding newly acquired product lines, MCP is beginning to change how product information moves across the business.

In Part 2, we’ll explore those workflows and the impact they’re already having.

The easiest way to build an AI platform is to keep telling yourself you are only building one agent at a time.

First, the content team creates an agent to generate product descriptions. Supplier operations builds another to map incoming data. Then eCommerce adds one to validate marketplace listings, while a different team experiments with enrichment or localization.

Each project solves a genuine problem. Each may be relatively straightforward to build. And none of them, on its own, looks like a platform.

But eventually, those agents need to work on the same products. They need to agree on which data is authoritative, apply the same business rules, complete tasks in the right order, and leave a record of what they changed and why. The architecture begins to take shape before anyone has consciously decided to build it.

That is why the build-versus-platform decision is not really about whether your engineering team can create an AI agent. In many cases, it can. The more important question is whether you are solving one contained problem, or creating the first part of a connected AI operating model that your team will need to govern, maintain, and expand over time.

When Building Your Own Agent Makes Sense

There are good reasons to build an AI agent internally.

A custom approach can work well when the use case is narrow, the inputs and expected outputs are clearly defined, and the underlying product information comes from a small number of reliable sources. It is also easier to manage when the catalog changes infrequently, only a few downstream systems are involved, and mistakes can be identified and corrected without significant business or regulatory impact.

In those circumstances, an internal agent can be a practical way to test an idea, solve a specific bottleneck, and build experience with AI.

The important question is ownership. Someone still needs to maintain the models, prompts, integrations, business rules, and monitoring as the technology and your requirements evolve. A custom build is most sustainable when the organization has both the engineering capacity and the long-term commitment to support it.

Where Complexity Begins to Compound

The challenge is rarely the agent itself. It is everything the agent depends on.

Imagine that a Supplier Data Agent updates a technical specification. Should the Content Agent immediately regenerate the product description? Does the Quality Agent need to validate the new value first? Should the Localization Agent translate the updated copy automatically? Does the Activation Agent pause publication until the change has been approved?

These are no longer model or prompting questions. They are questions about product data, business rules, workflows, accountability, and system design.

Without a shared operational layer, different agents may work from slightly different versions of the same product record. One may use an attribute value that another has already replaced. A Content Agent may generate copy before a validation step is complete. A Localization Agent may translate information that is still being revised upstream.

The result is a collection of useful automations that create delays, inconsistencies, and new points of failure when they need to work together.

What a Fleet of Agents Needs

As you move from one agent to several, four shared capabilities become increasingly important:

A shared product state

Every agent should read from and write to the same governed product record. When agents use separate copies, schemas, or data pipelines, inconsistencies can remain hidden until they cause a rejected listing, inaccurate description, or customer-facing error.

Sequencing and dependency management

Agents across the product lifecycle do not operate independently. Supplier information needs to be normalized before enrichment. Enriched data may need validation before content is created. Content may require approval before localization or activation. Those dependencies must be managed deliberately.

Shared business rules

Each agent makes decisions about what to complete, flag, publish, or translate. If the same rule is recreated across several prompts and scripts, a single business change may require updates in several different systems.

Unified observability

When something goes wrong, teams need to trace the complete chain of events. That means seeing the source information, the recommendation an agent made, the rules it applied, the person who reviewed it, and the final change made to the product record. Without that history, troubleshooting quickly becomes an investigation across systems and teams.

When a Platform Becomes the More Practical Choice

A platform becomes increasingly valuable when multiple agents need to share product data, business context, channel knowledge, approval workflows, and audit history.

It can also reduce the amount of work required to introduce each new use case. Instead of rebuilding governance, integrations, mappings, and monitoring every time, teams can reuse capabilities that are already in place.

That does not mean a platform removes the need for custom development. Many organizations will still build specialized agents, business logic, and experiences that reflect their specific products and processes.

The platform’s role is to provide the foundation around those agents: a governed product model, reusable rules, human workflows, channel connectivity, traceability, and the infrastructure required to keep everything aligned as the catalog evolves.

Ask What You Are Really Building

The build-versus-platform decision should not begin with, “Can our team build this agent?”

In many cases, the answer will be yes.

A more useful question is: “Are we building one contained capability, or the beginning of a connected system?”

For a stable, low-risk use case, an independent build may be entirely appropriate. But when several agents begin acting on the same products, supporting the same launches, and applying the same business rules, the surrounding operating layer becomes just as important as the agents themselves.

Start with a genuine operational problem, use a representative sample of your product data, define where human judgment is required, and measure the new process against the one it replaces. That will help you determine whether you need a focused custom solution today, or a shared foundation that can support the next use case tomorrow.

Amazon listing problems have a habit of appearing at the worst possible moment.

A new product is ready to launch, but required attributes are missing. A variation suddenly appears as a separate listing. A specification is correct on your website but outdated on Amazon. Or a team discovers that hundreds of SKUs need to be manually updated because a marketplace requirement has changed.

It is tempting to treat each of these as an Amazon problem.

But in many cases, Amazon is simply where an upstream product information problem becomes visible.

If the product data feeding Amazon is incomplete, inconsistent, incorrectly structured, or scattered across different systems, spreadsheets, and teams, no amount of last-minute listing optimization can fully solve the problem. The more products and marketplaces you manage, the more important it becomes to fix product information at the source.

What Causes Amazon Listing Data Problems?

Amazon listing data problems often originate from poor-quality product information before that information ever reaches the marketplace. Common causes include:

• Missing attributes 
• Inconsistent values 
• Incorrect product relationships 
• Outdated specifications 
• Fragmented supplier data 
• Manual channel-specific transformations

Here are some of the problems brands and retailers should watch for.

1. Missing or incomplete product attributes

A product may technically have enough information to exist in an ERP or internal database while still lacking the information necessary to create a useful marketplace experience.

Dimensions, materials, compatibility details, ingredients, technical specifications, care instructions, certifications, or other attributes may be blank or buried inside unstructured descriptions.

That creates problems at two levels. First, marketplace requirements may make some fields necessary for successful publication. Second, even when a listing can go live, missing information gives customers less confidence that a product meets their needs.

That matters because shoppers are actively scrutinizing products; 29% of consumers review detailed product information when assessing deals specifically on Amazon, while only 9% trust deals without verifying them first.

2. Product data that does not match Amazon’s structure

Every sales channel has its own way of organizing product information.

Your internal attribute might be called “fabric,” while a marketplace expects “material.” A technical measurement may need a particular unit. A value stored as free text internally may need to map to a predefined marketplace field.

When these differences are handled manually every time products are published, inconsistencies multiply quickly.

The better approach is to maintain structured, governed product information centrally and establish repeatable mappings that transform that data into Amazon’s required format. This separates the underlying product truth from the way each individual channel needs to receive it.

3. Broken parent-child and variation relationships

Color. Size. Pack quantity. Finish. Configuration.

Variants are useful to customers, but only when their underlying relationships are modeled correctly.

If parent-child relationships are inconsistent upstream, variants can appear as disconnected products rather than intuitive choices within the same product experience. Shoppers may struggle to understand their options, while commerce teams spend time manually correcting relationships downstream.

This is fundamentally a product modeling problem. Managing product families and variations centrally makes it much easier to maintain consistent relationships as catalogs expand.

4. Conflicting or outdated product information

A product may be listed as 50 cm wide on your own website, 52 cm on Amazon, and 51 cm in a distributor catalog.

Which answer should the shopper trust?

These discrepancies frequently arise when information is stored across ERP platforms, spreadsheets, supplier files, eCommerce systems, and marketplace tools without a governed source of product information.

And shoppers increasingly have ways to spot those inconsistencies. 62% of U.S. consumers compared prices across retailers during Amazon’s 2026 Prime Day. When consumers move easily between channels, conflicting information becomes much harder to hide.

5. Weak content created from weak source data

Compelling Amazon titles, descriptions, bullet points, and rich content ultimately depend on the quality of the product information behind them.

If a marketing team starts with little more than a product code, a generic supplier description, and a handful of specifications, it becomes much harder to create content that clearly communicates what makes the product useful, distinctive, or relevant to a particular buyer.

The same challenge applies when generative AI is used for enrichment and content creation. AI can help teams generate descriptions, translate content, classify products, and adapt messaging for different channels, but it still relies on accurate and complete product context. Missing attributes, vague specifications, or inconsistent source data can quickly turn into equally vague or inaccurate generated content.

That makes strong source data increasingly important as brands scale marketplace content. Structured attributes, approved terminology, trusted specifications, and clear product relationships give both people and AI better material to work with, making it easier to create Amazon content that is accurate, differentiated, and consistent across large catalogs.

6. Marketplace growth that depends on manual work

A few Amazon listings can be managed manually.

A few thousand listings, constantly changing across markets, product categories, suppliers, and channels, are a different story.

Manual spreadsheets, copying and pasting, and one-off data transformations create opportunities for errors while making every catalog update slower.

Synchro Diffusion offers a good example of what happens when marketplace teams tackle both enrichment and activation together. The company used Akeneo’s native AI capabilities to automatically generate product descriptions, translate content, and structure product data, increasing data completeness from 40% to 90%. It then used Akeneo Activation to connect that enriched product information directly to Amazon, giving the team greater control over product data, pricing, and orders while making it easier to resolve issues at the source and publish updates faster.

Fix Amazon Listing Problems at the Source

Teams struggling with recurring Amazon listing issues should ask a different question.

Instead of “How do we fix this listing?”, ask “Why did incorrect or incomplete information reach this listing in the first place?”

That means establishing a centralized product information foundation, defining required attributes by product category, improving supplier data collection, governing variant relationships, validating completeness before publication, and creating reusable mappings for channel requirements.

A Product Information Management (PIM) system can provide that foundation by giving teams one governed environment where product data can be collected, enriched, validated, and prepared for marketplaces. Syndication and activation capabilities can then transform that trusted information into the formats individual channels require.

Because the most expensive Amazon listing problem is rarely one bad field. It is discovering that the same bad field exists across hundreds or thousands of products.

Fix the product information foundation, and Amazon becomes another channel where trusted, complete, compelling product experiences can be activated at scale.

For years, product information management could be treated primarily as an operational discipline: centralize the catalog, improve data quality, and make sure accurate information reaches the right channels.

Recently, that job has become much bigger.

Product information now supports everything from eCommerce and marketplaces to internal operations, partner ecosystems, customer experience, and increasingly, AI-powered discovery and automation. As those environments become more connected, enterprises need product information that can move between systems and teams without losing the governance, context, and control the business depends on.

In other words, product data is becoming infrastructure for how modern businesses operate, engage customers, and prepare for AI.

That changing role is reflected in two recent evaluations published by ISG Software Research: the ISG Buyers Guide™ for Product Information Management Platforms 2026 and the ISG Buyers Guide™ for Product Experience Management 2026.

ISG Research defines PIM around managing and using product information across front- and back-office applications and processes. PXM extends that focus to managing customer experiences through systems that support product-related activities.

Together, the two guides reinforce an increasingly important point: modern PIM and PXM decisions are also decisions about integration, governance, automation, and AI readiness.

Modern PIM has to connect the enterprise

The ISG Buyers Guide™ for Product Information Management Platforms 2026 evaluated 21 software providers. Akeneo was placed in the Exemplary category, while the Buyers Guide Insight highlighted strengths across both core platform requirements and emerging AI capabilities.

ISG Research specifically highlighted Akeneo’s performance in Integration, an increasingly important consideration when product information has to flow between PIM, commerce platforms, ERP systems, supplier ecosystems, marketplaces, and customer-facing channels. It also identified License, Use and Audit as an area of differentiation, alongside Privacy and Technology Administration capabilities that support enterprise oversight.

When product data touches more systems, processes, teams, and automated workflows, enterprises need to know that information can move without sacrificing control or transparency.

At the same time, ISG Research highlighted Akeneo’s capabilities in Generative AI and Platform Support for AI, connecting the traditional role of PIM with a rapidly emerging enterprise requirement: preparing product information for AI-enabled use cases.

View the Akeno ISG Buyers Guide Insight for PIM Platforms here.

PXM turns the foundation into an experience

Managing product information, however, is only part of the equation.

Customers never see your taxonomy or data governance model. They see the outcome: the description on a product page, the specifications on a marketplace, the search result that helps them discover an item, or the answer an AI assistant gives when they ask for a recommendation.

That is where PXM comes in.

The ISG Buyers Guide™ for Product Experience Management 2026 evaluated 14 software providers, and Akeneo again was identified as an Exemplary Provider.

ISG Research highlighted Akeneo’s performance across several areas, with what it described as its most pronounced advantages in Generative AI and Intelligent Automation. Integration, Privacy, Technology Administration, and Investment were also called out in the Buyers Guide Insight. That combination matters as product experience becomes more complex.

Teams are being asked to manage expanding catalogs, more channels, more content variations, localization, changing marketplace requirements, and increasingly personalized experiences. Intelligent automation can help teams handle that complexity more efficiently, but only when the information underneath those workflows is reliable and connected.

View Akeneo’s ISG Buyers Guide Insight for Product Experience Management here.

AI readiness starts with product data readiness

This may be the most important connection between the two Buyers Guides.

AI-powered commerce introduces a new consumer of product information: the machine.

Generative AI applications, conversational interfaces, intelligent automation, AI-powered search, and emerging autonomous agents all depend on the information available to them. If that product information is fragmented, inconsistent, inaccessible, or poorly governed, adding AI does not remove the underlying problem.

It can simply make the problem move faster.

That means AI readiness cannot be separated from product data readiness.

Before organizations can scale AI-powered product experiences, they need confidence in the foundation underneath them: accurate information, integration across the enterprise, appropriate governance, operational oversight, and workflows capable of supporting automation.

The two ISG evaluations reflect that changing requirement. Across its assessments of Akeneo, ISG Research highlights capabilities spanning Integration, Generative AI, Platform Support for AI, Intelligent Automation, Privacy, and Technology Administration.

Product information is becoming enterprise infrastructure

Enterprises increasingly need to govern product information across the organization and transform it into accurate, compelling experiences across every customer touchpoint. Treating those as separate challenges makes it harder to create the connected foundation that modern commerce (and increasingly AI) depends on.

That is the role Akeneo Product Cloud is designed to play: helping organizations make product information governed, connected, intelligent, and ready to support experiences across channels, systems, and business processes.

Because as AI becomes more deeply embedded in how products are managed, discovered, evaluated, and purchased, one principle becomes increasingly difficult to ignore:

The next generation of product experiences will only be as reliable as the product information behind them.