At first glance, “agentic PIM” can sound like another piece of AI jargon. 

Product information management has always been about centralizing, improving, and distributing product data. Generative AI has already accelerated parts of that work, from drafting descriptions to translating content and suggesting missing attributes.

Agentic PIM represents a deeper shift than this. Instead of waiting for a person to initiate every task, AI agents can understand an objective, plan the steps, work across data and tools, and move a process forward within defined guardrails.

The gap between experimentation and scale remains wide. McKinsey states that 88% of respondents said their organizations regularly used AI in at least one business function, yet only about one-third had begun scaling AI across the enterprise.

What Does “Agentic PIM” Actually Mean?

Agentic PIM is a product information management environment in which specialized AI agents can observe a catalog, reason over product and business context, propose or take actions, collaborate with people and systems, and monitor results.

A traditional PIM is primarily a system of record: it centralizes and governs product information. An AI-assisted PIM might generate a description when a user clicks a button. An agentic PIM would go further by becoming a governed system of action. It can recognize that a product family is incomplete, determine what is missing, retrieve evidence from approved sources, draft updates, route higher-risk decisions for review, and prepare approved information for the right channels.

It’s important to note here that this does not mean removing people from the PIM process. “Agentic” describes purposeful, multi-step action, not unlimited autonomy. Normalizing units may be safe to automate, but complex acts like publishing a sustainability claim or changing regulated technical information should still require expert validation.

Why is PIM a Natural Home for AI Agents?

AI agents need more than a language model. They need trusted context: taxonomies, attribute definitions, variant relationships, approved terminology, supplier sources, channel requirements, permissions, and decision history. 

A PIM already contains much of this information, and can act as the perfect foundation for training and supporting AI agents.

Without that foundation, agents can produce individually plausible but collectively inconsistent results. A content agent may describe an unvalidated feature. A localization agent may translate content that is still changing. A channel agent may publish an outdated record. The problem is not simply model accuracy; it is coordination around a shared source of truth.

Agentic PIM addresses both sides of the AI shift. Internally, agents help teams collect, enrich, govern, and syndicate product information. Externally, that data must be comprehensive and machine-readable enough for AI search engines, shopping assistants, and buying agents to understand. Forrester reported in January 2026 that 94% of business buyers were using AI in their buying process.

What Does Agentic PIM Look Like in Practice?

AI agents can support work across every stage of the product lifecycle. Rather than functioning as isolated tools, specialized agents can collaborate around a shared, governed product record, moving work forward while keeping human experts involved in decisions that require judgment or carry greater risk. 

Consider a manufacturer onboarding a supplier catalog containing spreadsheets, technical PDFs, and product images. A supplier data agent could classify products, map source fields to the company’s product model, normalize measurements, identify duplicates, and flag uncertain mappings. High-confidence, low-risk changes could move forward automatically, while exceptions are routed to a product expert with source evidence attached.

An enrichment agent could then identify missing dimensions, compatibility details, certifications, or use-case attributes. 

A content agent might create different descriptions for a distributor portal and a direct-to-consumer website, using the approved brand voice and each destination’s requirements. 

A localization agent could adapt that content for different markets, while a compliance agent isolates regulated claims for human approval. The agents perform specialized roles, but work from the same record and follow the same sequence.

The process can continue after publication. Suppose a marketplace changes its taxonomy or begins rejecting a required image field. An activation agent could detect the failures, group affected SKUs, identify the root cause, propose a corrected mapping, and prepare products for republication. A market intelligence agent could then analyze search behavior, reviews, channel performance, returns, or AI-discovery signals and recommend improvements to the core record.

That feedback loop matters because product information is never finished. Buyer language changes, channel requirements evolve, and new use cases appear. The National Retail Federation estimates that nearly 20% of all online sales (worth $850 billion) are returned. 

Product information is not responsible for every return, but preventing avoidable confusion around fit, compatibility, dimensions, materials, or usage can have material value.

What Makes Agentic PIM Trustworthy?

A reliable agentic PIM needs an “AI harness”: infrastructure that determines what the model can access, what context it receives, which tools it may use, and what controls apply. That means role-based permissions, business rules, source attribution, confidence signals, approval workflows, audit trails, observability, and reversibility.

It also requires orchestration. Supplier ingestion must happen before enrichment; validated enrichment should precede content creation; approved content should precede activation. When agents operate independently, every use case adds another data copy, rule set, integration, and failure point. When they share a governed foundation, their value compounds.

This discipline matters because agentic technology is easy to overhype. Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls. 

The better starting question is not “Where can we add an agent?” but “Which product information bottleneck has a clear outcome, trustworthy data, and defined decision boundaries?”

From Data Entry to Strategic Orchestration

Agentic PIM does not eliminate product experts. It changes where their expertise is applied. 

Repetitive tasks such as mapping supplier fields, identifying missing attributes, adapting content for different channels, and monitoring publication errors can be handled by agents operating within governed workflows. Human expertise remains central, but it is applied where it has the greatest value: setting policies, reviewing exceptions, validating sensitive claims, and improving the rules that guide automation.

As catalogs become larger, channels multiply, and product information is consumed by both people and machines, PIM must coordinate far more than data storage. 

Agentic PIM brings that coordination into everyday product operations, continually identifying gaps, preparing recommendations, routing decisions, activating approved information, and feeding market signals back into the product record. 

The result is a product information practice that can respond more quickly to new products, regulations, channels, and buyer expectations while maintaining clear oversight. 

Over time, the product record becomes a living operational asset: richer, more useful, and more trustworthy with every interaction.

For years, product data enrichment has largely been a manual exercise.

Marketing teams wrote product descriptions. Merchandisers categorized products. Product managers filled in technical specifications. eCommerce teams hunted down missing images, dimensions, certifications, and compatibility information.

Today, AI can automate much of that work.

Large language models can generate compelling product descriptions in seconds. Computer vision can identify product attributes from images. AI can classify products, normalize supplier data, translate content, summarize customer reviews, and even suggest missing attributes based on similar products.

But while AI has dramatically changed how enrichment happens, it hasn’t changed what makes product data valuable.

In fact, as AI increasingly becomes part of the buying journey itself, the quality of your product data matters more than ever. 

Rich, structured, accurate product information determines whether AI systems can understand, compare, and recommend your products.

What is AI product data enrichment?

AI product data enrichment is the process of using artificial intelligence to improve the completeness, quality, consistency, and usefulness of product information.

Rather than relying solely on manual effort, AI can help organizations:

• Generate SEO-friendly product descriptions
• Fill in missing product attributes
• Categorize and classify products
• Standardize supplier data
• Translate product content
• Extract information from specification sheets and PDFs
• Improve metadata
• Recommend related products
• Identify inconsistencies or missing information

AI enrichment is more than writing descriptions

One of the biggest misconceptions surrounding AI enrichment is that it’s simply an AI copywriter.

Generating product descriptions is certainly useful, but it’s only one piece of the puzzle.

Your catalog should also include:

• Complete technical specifications
• Consistent attributes
• Product relationships
• Compatibility information
• Certifications
• Dimensions and materials
• Use cases
• Customer-facing benefits
• Rich metadata
• Structured product identifiers

AI can help enrich each of these areas, turning incomplete product records into comprehensive product experiences.

The richer your product data becomes, the more opportunities customers, and increasingly AI assistants, have to discover the right product.

Best practices for AI-powered enrichment

1. Audit the current catalog

Before introducing AI, understand the condition of your data. Select a representative sample across categories, markets, suppliers, and price points. Review it for missing attributes, duplicate values, inconsistent units, outdated claims, weak descriptions, and conflicts between channels.

Do not evaluate every field equally. Care instructions may be critical for apparel, while voltage, compatibility, and certifications matter more for electronics or industrial equipment. Prioritize attributes according to customer intent, regulatory needs, channel requirements, and commercial value.

The result should be an enrichment backlog showing which fields are missing, which products are affected, what source information exists, and which improvements matter most.

2. Define the target product record

AI works best when it has a clear destination. Define what a complete record should contain for each product category before generating or extracting content.

That model might include: 

• Identifiers
• Taxonomy 
• Specifications 
• Dimensions 
• Materials 
• Compatibility 
• Benefits 
• Use cases 
• Media 
• Safety information 
• FAQs 
• Channel-specific content

Set allowed values and formatting rules. Decide which measurement units to use, how colors will be standardized, and which claims require evidence.

This structure gives AI boundaries. Instead of producing free-form text, it can populate approved fields, map supplier terminology to a controlled vocabulary, and flag information that does not fit established rules.

3. Apply AI to the right tasks

Different AI techniques suit different jobs. Large language models can rewrite descriptions, generate FAQs, and adapt content for specific audiences. Natural language processing can extract specifications from manuals. Classification models can assign categories, while translation models can localize content.

A workflow may combine several capabilities. AI could extract a material from a supplier PDF, convert a measurement, map the product to your taxonomy, and draft a customer-friendly benefit.

Preserve traceability throughout. Teams should know whether a value came from a trusted source, was transformed by a business rule, or was inferred by AI. Inferred information should not be presented as confirmed fact without review.

4. Enrich for context, not just completeness

A technically complete record can still be unhelpful. Shoppers often describe needs through problems, occasions, environments, or constraints rather than exact product terminology.

For example, “width: 18 centimeters” is useful. “Fits on narrow countertops and under standard kitchen cabinets” explains why it matters. Similarly, software should be described not only by features, but also by company size, workflows, implementation requirements, and industry use cases.

AI can turn specifications into contextual content, but the facts must remain precise. Replace phrases such as “high performance” or “compact design” with measurable, verifiable detail. This gives customers clearer reasons to buy and helps search and AI systems interpret the product.

5. Keep humans in the loop

AI increases speed, but accountability remains with the business. Establish review rules based on risk. Low-risk tasks, such as reformatting units or applying approved category labels, may be automated. Claims about safety, compliance, ingredients, performance, pricing, or sustainability should receive specialist approval.

Use reusable prompts, validation rules, confidence thresholds, and approval workflows. Require source references for critical attributes and maintain brand guidelines so generated content remains consistent.

Governance should also monitor unsupported claims, repetitive phrasing, incorrect translations, and content that may be accurate but unsuitable for a market.

6. Publish consistently and measure outcomes

Enriched data should flow from a governed source of truth to every relevant channel. If specifications differ between a brand site, retailer feed, marketplace listing, and structured website data, customers become confused and automated systems receive conflicting signals. The whitepaper likewise emphasizes consistent information wherever a product appears.

Measure more than output volume. Useful indicators include attribute completion, validation pass rates, onboarding time, manual correction rates, channel rejections, conversion, returns, and customer-service questions.

Treat enrichment as a continuous loop: identify gaps, improve records, validate outputs, distribute updates, and learn from customer and channel feedback.

The future of enrichment is machine-ready product data

As AI becomes increasingly involved in product discovery, product information must serve two audiences simultaneously.

First, it needs to persuade human buyers with compelling descriptions, imagery, and storytelling.

Second, it needs to provide structured, machine-readable information that AI systems can easily interpret, compare, and recommend.

A well-governed product catalog supports stronger customer experiences, faster product launches, improved operational efficiency, and better visibility across traditional search, marketplaces, and emerging AI-powered shopping experiences.

As product discovery becomes more conversational and machine-mediated, enriched product data will support immediate commercial goals and long-term readiness. The objective is simple: create product records that people can understand, systems can process, and teams can trust.

Historically, wholesale distribution has trailed behind other sectors in digital transformation, often relying on legacy systems that can feel daunting to upgrade. However, modernizing your tech stack does not have to be an overwhelming overhaul. By implementing artificial intelligence (AI), distribution executives can turn operational challenges into significant growth opportunities.

Advanced algorithms can transform the core pillars of your business, optimizing how products are searched, discovered, and purchased to meet the demands of today’s B2B buyers.

How AI can help distributors across search, discovery, and purchasing

Upgrade search and discovery with AI in distribution workflows

B2B buyers now expect the same frictionless search experiences they encounter in consumer retail. In 2025, 64% of B2B buyers were Millennials or Gen Z—digital natives seeking top-tier digital experiences. For wholesale distributors, this means upgrading static catalogs into dynamic, intuitive discovery platforms. 

To effectively upgrade your search and discovery workflows, implement the following operational upgrades:

• Standardize your foundation. Clean, organized product data is the prerequisite for intelligent search. Before algorithms can surface relevant products to buyers faster, your data must be uniform, accurate, and comprehensive. Audit your existing data for inaccuracies or inconsistencies, and make a plan to resolve them so you’re giving your product information management system (PIM) the most up-to-date information possible.
• Implement intelligent search capabilities. Replace rigid keyword-matching with semantic search algorithms that understand buyer intent, industry-specific jargon, and product synonyms. This functionality allows buyers to search using their own terminology, incomplete SKUs, or partial part numbers, instantly surfacing matches and eliminating the “no results found” dead ends common in legacy systems.
• Optimize the discovery phase. Machine learning algorithms can analyze past buyer behavior, regional trends, and purchasing patterns to personalize the catalog experience for each account. By automatically grouping complementary items, suggesting relevant alternatives for out-of-stock products, and uncovering hyper-relevant cross-selling opportunities directly on the product page, this technology guides buyers to items they need but didn’t explicitly search for.

Beyond just fixing search bars, treat your platform’s search query data as a direct feedback loop from your customers. Regularly reviewing failed search analytics can reveal emerging market demands or pinpoint exact areas where you can refine your catalog taxonomy.

Empower smarter purchasing decisions

Procurement is the engine of wholesale distribution, and inefficiencies here impact your bottom line. By integrating predictive analytics and agentic commerce models into your purchasing workflows, you can replace reactive ordering with a proactive strategy. This shift automates complex decisions, drives revenue, and optimizes your overall market positioning.

Consider how intelligent tools can refine your procurement strategies:

• Automate forecasting. Purchasing managers can use predictive analytics to anticipate seasonal demand spikes and supply chain bottlenecks before they happen. By analyzing historical data and market trends, these tools help maintain optimal inventory levels and prevent costly stockouts.
• Streamline vendor management. Sophisticated algorithms actively evaluate supplier performance by monitoring pricing fluctuations and tracking delivery times. This objective data allows your procurement team to negotiate better terms and pivot when a vendor underperforms.
• Accelerate routine purchasing. Transition routine reordering to autonomous AI agents that execute purchases based on predefined risk parameters and real-time inventory triggers. This tool shifts manual data entry to the algorithm, freeing your procurement specialists to focus entirely on strategic sourcing and high-level vendor negotiations.

To maximize the return on investment for these purchasing tools, sync your predictive forecasting data directly with your distributor marketing and promotions calendar. This integration ensures your sales teams highlight products that your system guarantees will be in stock, aligning demand generation with inventory reality.

Preparing your team and technology for AI adoption

Introducing new technology is only half the battle. The true challenge lies in change management. Executives must carefully guide their IT, operations, and sales personnel through this digital transition to ensure high adoption rates. Constructing an optimized tech stack that supports advanced analytics requires both operational shifts and targeted upskilling across departments.

Here is how you can help different departments navigate this digital integration:

• IT: Shift your IT team away from simply maintaining legacy servers and toward managing advanced data architectures. Establish clear data governance policies and build tech stacks designed to support continuous analytical processing.
• Operations: Empower your warehouse and logistics managers to use AI-driven inventory mapping and automated routing systems. Transitioning these groups from manual tracking to managing algorithmic workflows makes for faster fulfillment times and reduces physical bottlenecks on the floor.
• Sales: Explain that integrating AI into sales workflows saves time that sales team members can spend improving the customer experience instead of sifting through data. For example, as MDM’s distribution sales training guide notes, “Before sales calls, sales representatives can use AI to predict customer purchasing behavior, allowing them to present more personalized, data-driven up-sells, cross-sells, and add-on recommendations.”

Consider forming a committee of early AI adopters from IT, operations, and sales. This dedicated group can pilot new tools, document best practices, and champion the technology to their more hesitant peers, drastically reducing internal resistance.

Embracing AI fundamentally upgrades how wholesale distributors manage search, discovery, and purchasing. By standardizing data and empowering your teams with predictive insights, you create a frictionless experience that benefits both your buyers and your bottom line. As these technologies continue to evolve, regularly audit your tech stack and upskill your workforce to maintain your competitive edge.

For more than two decades, digital visibility meant ranking on Google.

Brands optimized web pages, targeted keywords, and competed for clicks. If you earned a spot near the top of the search results, you had a strong chance of winning the customer.

That model is rapidly changing.

Today, shoppers are increasingly turning to ChatGPT, Gemini, Perplexity, Claude, and other AI assistants to help them research products, compare options, and make buying decisions. 

Instead of scrolling through pages of links, they simply ask:

• “What’s the best espresso machine for a small apartment?”
• “Which running shoes are best for marathon training?”
• “What’s the most durable outdoor dining set under $1,500?”

Rather than presenting ten blue links, AI engines synthesize an answer by evaluating products, interpreting specifications, weighing trust signals, and recommending the options they believe best satisfy the request. 

Product discovery is moving beyond the website and into AI-powered environments where your product data must speak for itself.

That means a product can rank highly in traditional search and still fail to appear in AI-generated recommendations if its underlying product information is incomplete, inconsistent, or difficult for machines to interpret.

What Is AEO?

Answer Engine Optimization (AEO), sometimes referred to as Generative Engine Optimization (GEO), is the practice of structuring your content and product information so AI-powered answer engines can confidently understand, cite, and recommend your products.

Unlike traditional SEO, which focuses on improving rankings within search results, AEO focuses on increasing visibility inside AI-generated answers.

That’s an important distinction; SEO helps users discover your website, while AEO helps AI discover your products.

To do that successfully, AI must be able to interpret your product data the way a human would. Instead of simply matching keywords, AI understands entities, relationships, attributes, and user intent. 

When someone asks for a “compact espresso machine for a small kitchen,” the AI isn’t searching for those exact words—it translates the request into structured attributes like dimensions, appliance type, countertop footprint, and intended use case. If your catalog doesn’t contain that information in a machine-readable format, your product may never be considered.

Why Product Discovery Is Changing

AI-powered shopping assistants are changing how people buy.

Instead of researching across multiple websites, comparing dozens of product pages, and reading countless reviews, consumers increasingly expect AI to perform much of that work for them.

That means visibility depends less on how well your website performs and more on how understandable, complete, and trustworthy your product information is.

This trend extends to B2B organizations as well, which have historically adopted new digital behaviors more slowly. According to Forrester, 89% of B2B buyers now use generative AI during the buying process, making AI-powered discovery one of their primary self-service research channels.

Why Standard AEO Tools Aren’t Enough

The growing popularity of AEO has led to a wave of tools that measure AI visibility such as Profound, SEMrush, and Adobe’s LLM Optimizer.

These platforms are valuable because they can tell you whether your brand appears in AI-generated responses, identify competitors that are winning more recommendations, and highlight gaps in your visibility across different prompts and answer engines.

The problem? Most of them stop there.

They diagnose the issue, but they don’t solve it.

Traditional AEO tools generally don’t enrich missing product attributes, govern product information, align supplier data, manage enrichment workflows, syndicate corrected content across channels, or create a continuous feedback loop between AI discoverability and product data quality.

In other words, they measure visibility but don’t improve the product information that drives visibility.

As AI models evolve and buying behavior changes, maintaining AI discoverability requires continuous optimization, not just reporting. That’s why many organizations are pairing AI visibility tools with Product Information Management (PIM) systems that can actually improve the completeness, consistency, and quality of their product catalogs.

The Four Pillars of AEO

Building an effective AEO strategy is built on four foundational pillars.

1. Structure: Be Understandable

AI engines rely on structured, machine-readable product information to understand what you’re selling.

Complete schema markup, standardized attributes, accurate categories, identifiers, and relationships help AI connect your products to customer intent. Without this structure, AI is forced to guess, and AI rarely guesses in your favor.

2. Authority: Be Credible

AI doesn’t simply trust what your website says.

Instead, it validates your information across multiple trusted sources, including knowledge bases, retailer listings, review platforms, business directories, media coverage, and other third-party references.

When those signals are consistent, AI gains confidence in recommending your products. When they conflict, your credibility and visibility declines.

3. Freshness: Be Relevant

AI favors products supported by recent, active information.

Fresh reviews, updated specifications, current pricing, recent marketplace activity, analyst mentions, and ongoing customer engagement all contribute to stronger trust signals.

An outdated product record may still exist online, but it becomes increasingly difficult for AI to recommend with confidence.

4. Relevance: Be Recommended

Finally, AI rewards context, not just technical specifications.

Instead of vague marketing language, products should include specific, extractable facts, clear answers to common customer questions, and use cases that reflect how buyers actually search.

For example, describing a product as a “compact espresso machine ideal for apartments” provides far more context than simply listing its dimensions. Rich, contextual product content makes it easier for AI to match your products to conversational queries and recommend them confidently.

AEO Starts With Product Data

Many organizations think AEO is simply another SEO initiative.

In reality, it’s a product data challenge.

AI cannot recommend products it doesn’t fully understand. That means brands need machine-readable, complete, consistent, trustworthy product information that can be interpreted across every AI-powered buying experience.

As AI-powered product discovery continues to grow, success won’t be determined solely by who ranks highest in search results. It will be determined by whose product data AI trusts enough to recommend.

Summer is the season of momentum. 

It’s when businesses prepare for peak selling periods, launch new campaigns, and look for ways to move faster without sacrificing quality. But for many product teams, the reality is that managing product information, syndicating content, and preparing assets for every channel can still feel like a manual, time-consuming process.

That’s why we’re excited to introduce Akeneo’s Summer Release 2026, a release designed to help organizations work smarter, move faster, and unlock a new era of product experience management. 

Welcome to the era of the Agentic Product Cloud.

Meet Agentic Ziggy: Your AI Operational Layer

The headline of the Summer Release is Agentic Ziggy, and it’s a big one.

Agentic Ziggy introduces an AI-native workspace where teams can manage product experiences through intelligent agents rather than manual configuration. Instead of navigating complex menus or relying on technical specialists, users can guide outcomes through natural language interactions, letting coordinated AI agents handle the heavy lifting of product data workflows.

Think of it as augmenting your team with an intelligent layer that understands your objectives, recommends actions, and helps execute work across your entire product experience ecosystem. Whether you’re enriching data, preparing products for activation, or continuously optimizing experiences, Agentic Ziggy acts as an extension of your team, taking on repetitive, time-consuming tasks so your people can stay focused on the decisions that actually drive growth.

It’s important to note that this isn’t automation without oversight. Agentic Ziggy embeds governance directly into execution, ensuring teams can move faster without sacrificing the data integrity and control they depend on. Speed and data quality no longer have to be a trade-off.

For those already familiar with Ask Ziggy (the context-aware PIM assistant that helps users navigate complex configurations in real time), Agentic Ziggy builds on that foundation and takes it significantly further. Where Ask Ziggy guides, Agentic Ziggy acts.

AI-Powered Innovation Across the Entire Product Lifecycle

Agentic Ziggy is the star of the show, but the Summer Release also brings meaningful upgrades across every stage of the product experience lifecycle:

1. Smarter product modeling

With enhancements to the Data Architect Agent and the new Product Model Experience, building and evolving product architectures has become more intuitive than ever. What was once a technical exercise reserved for specialists is now a guided, accessible experience that supports faster onboarding, cleaner data structures, and better scalability.

2. Image editing without the bottleneck

Akeneo DAM now supports prompt-based AI image transformations. Teams can modify product visuals using simple text instructions; for example, swapping backgrounds, adapting imagery for seasonal campaigns, or generating channel-specific variations, all without routing requests back to a design studio or spinning up external tools. Work that used to take days of back-and-forth can now be done in seconds, directly inside Akeneo.

3. Activation that troubleshoots itself 

Syndication errors have always been a drain on time and technical resources. Intelligent Error Management changes that by translating complex retailer errors into clear, actionable guidance that’s in the user’s own language. Teams can now self-serve their way through activation issues without needing deep technical expertise or constant support intervention.

4. Optimization that drives outcomes

By combining PX Insights with Agentic Ziggy, teams can move from identifying opportunities to acting on them in one seamless flow. Surface an AI readiness recommendation, understand the priority, and take action, all without leaving your workflow.

Why This Matters Now

Commerce teams are under more pressure than ever. More products, more channels, more personalized expectations. The traditional answer (add more headcount, add more process) doesn’t scale.

The smarter answer is augmenting your team with intelligent systems that can help complete work, recommend next steps, and continuously raise the bar on product experiences. That’s what Akeneo’s Agentic Product Cloud is built to deliver.

Ready to See It in Action?

Just like summer itself, this release brings a season of possibility; new tools, new ways of working, and a genuine opportunity to get ahead. Whether you’re looking to reduce manual effort, accelerate time to market, or simply do more with the team you already have, Agentic Ziggy and the Summer Release innovations are ready to help.

Join Akeneo’s upcoming Deminar to see Agentic Ziggy and the full Summer Release in action, and discover what it looks like when your product data starts working as hard as you do.

When Amazon’s annual Prime Day event kicks off on June 23, millions of consumers will be hunting for the best deals, comparing products, and filling their carts with everything from household essentials to big-ticket purchases.

While Prime Day may be built on discounts, today’s shoppers aren’t taking deals at face value. With 74% of consumers saying economic conditions are forcing them to cut back on spending, Akeneo surveyed 1,000 U.S. consumers to uncover how people are becoming more strategic in their purchasing decisions, relying on AI tools, and searching harder than ever for genuine value.

Let’s take a look at the five key trends shaping shopper behavior ahead of Prime Day 2026, and what you can do to stay competitive.

1. AI Has Become a Critical Part of the Shopping Journey

Mainstream AI tools like ChatGPT and Gemini are reshaping the future of commerce, and it is arriving faster than most teams expect. 

Ahead of Prime Day, 43% of consumers have already used AI to support shopping tasks like finding deals, narrowing down options, and summarizing product reviews. 

What’s particularly notable here is that AI is actively influencing purchasing decisions; 20% of consumers say AI has influenced their consideration of a product, while 22% report purchasing a product based on an AI recommendation.

As AI becomes a trusted source of shopping guidance, brands must ensure their product information is complete, accurate, and structured in ways that AI systems can easily understand and surface.

If AI cannot confidently understand or recommend your product, you risk becoming invisible during the earliest stages of the buying journey.

2. Consumers are More Value-Conscious Than Ever

The economic climate is having a significant impact on shopping behavior.

According to our survey, 74% of consumers say current economic conditions are affecting their Prime Day plans. While 84% still intend to participate in Prime Day shopping, consumers are approaching purchases with greater caution and intentionality.

This means shoppers are less likely to make impulse purchases and more likely to carefully evaluate whether a product truly offers value. Every purchase is being scrutinized, and consumers want confidence that they’re making the right decision before they click “Buy Now.”

For brands, this creates a new challenge. Discounting alone may attract attention, but it won’t necessarily earn trust. Shoppers want reassurance that they’re getting quality products, genuine savings, and accurate information that helps them understand what they’re purchasing.

3. Prime Day Has Become a Multi-Retailer Event

For years, Prime Day was viewed primarily as Amazon’s moment. Today, consumers see it differently.

More than half of shoppers (55%) say they plan to shop across multiple retailers to find the best deals, while 62% compare prices across different retailers before making a purchase.

In other words, consumers aren’t limiting themselves to Amazon. Instead, they’re treating Prime Day as a broader shopping opportunity and using it as a trigger to evaluate deals wherever they can find them.

Winning requires consistency. Product information, pricing, promotions, and customer experiences must be aligned across every channel where consumers encounter your products.

If shoppers find conflicting information or a better experience elsewhere, they’ll have no hesitation in switching.

4. Shoppers Don’t Trust Deals at Face Value 

Consumers have become increasingly skeptical of promotional claims.

Only 9% of consumers say they trust deals without verifying them first.

Instead, shoppers are conducting extensive research before making purchases. 32% check price-history tools and trackers to verify discounts, while 29% review detailed product information as part of their decision-making process.

This behavior highlights an important reality: shoppers want proof.

Consumers want to know whether a discount is genuinely meaningful, whether the product meets their needs, and whether they can trust the retailer’s claims.

The more friction brands remove from the research process, the more likely they are to earn consumer trust and conversions.

5. Product Information Matters More Than AI Recommendations

Despite all the attention surrounding AI, traditional decision drivers still dominate.

When consumers were asked what influences their purchasing decisions most, price ranked first at 55%. Reviews followed at 20%, while product information accounted for 14%.

Interestingly, AI recommendations themselves remain relatively low on the list of direct purchase influences.

What can we take away from this? AI may help shoppers discover products, but it is still product information that helps them decide whether to buy.

Consumers ultimately want trustworthy details about what they’re purchasing. They want specifications, features, dimensions, compatibility information, sustainability credentials, usage instructions, and customer reviews.

AI may introduce a product, but product information closes the sale.

Our CEO, Romain Fouache, said it best: 

Are You Ready for Prime Day 2026?

Prime Day 2026 is becoming a test of how well brands can support increasingly informed, strategic, and AI-enabled shoppers.

Shoppers are comparing prices across retailers, verifying discounts before making purchases, and relying on a growing mix of digital tools to evaluate their options. At the same time, expectations for accurate product information, transparency, and overall value continue to rise.

These behaviors are unlikely to disappear once Prime Day ends. Instead, they signal a broader shift in how consumers discover products, build trust, and make purchasing decisions. 

As AI becomes more deeply embedded in shopping experiences and economic pressures continue to shape spending habits, the ability to provide consistent, trustworthy, and accessible product information will play an increasingly important role in influencing what ends up in consumers’ carts.Want to dive deeper into the data and uncover additional insights shaping shopper behavior ahead of Prime Day 2026? Download the full Prime Day report to explore the complete survey findings and learn how brands can prepare for the evolving retail landscape.

Today, we’re excited to share some big news: Akeneo has acquired PricingHUB, a leading pricing management platform, and we’re thrilled to welcome the PricingHUB team to the Akeneo family!

This marks an important milestone in our personal journey to help brands and retailers create exceptional product experiences. But more than that, it’s a reflection of where commerce is headed, and what businesses need to succeed in an increasingly AI-driven world.

Why Pricing? Why Now?

For years, product information has been at the heart of digital commerce. Rich, accurate, and consistent product data helps customers discover products, understand them, and ultimately make confident purchasing decisions.

But product data tells only part of the story.

Pricing is one of the most powerful signals in commerce. It influences customer behavior, shapes competitiveness, and impacts profitability. Yet in many organizations, product information and pricing are managed in entirely separate systems by different teams using different processes.

The reality is that these two disciplines have always been deeply connected.

A product’s price doesn’t exist in a vacuum. Pricing decisions depend on product attributes, category structures, assortment strategies, competitor positioning, customer expectations, and market dynamics. Without high-quality product data providing context, pricing becomes much harder to optimize.

That’s why bringing PricingHUB into Akeneo feels like such a natural fit.

Building a Single Source of Truth

At Akeneo, we’ve long believed that businesses perform best when they can rely on a single source of truth for product information.

With PricingHUB joining Akeneo, we’re extending the concept of a single source of truth beyond product data to include pricing intelligence as well.

Imagine product managers, pricing teams, category managers, eCommerce leaders, and merchandising teams working from the same foundation of trusted data. Instead of operating in silos, they can align around a shared understanding of products, market conditions, and pricing strategies.

The result:

• Faster decision-making
• Better coordination across teams
• More consistent customer experiences
• Greater competitiveness in rapidly changing markets

By connecting product data and pricing more closely, businesses can focus their efforts on the products that matter most and react more quickly when market conditions change.

The Rise of Agentic Commerce

This acquisition is especially important because commerce itself is changing.

Increasingly, customers aren’t just browsing websites and comparing products manually; 77% of consumers use AI to shop nowadays. AI-powered assistants and autonomous shopping agents are beginning to influence how products are discovered, evaluated, and purchased.

In this emerging era of agentic commerce, AI systems rely on structured data to make recommendations and decisions.

Two of the most important signals these systems evaluate are product information and price.

An AI shopping assistant can’t properly compare products if descriptions are incomplete or inconsistent. Likewise, it can’t make informed recommendations if pricing data lacks context or isn’t aligned with product information.

As AI becomes a larger part of the buying journey, organizations will need more than disconnected systems and fragmented data sources. They will need a unified decision layer where product information and pricing work together.

This is exactly the future we’re building toward.

By combining Akeneo’s expertise in product experiences with PricingHUB’s pricing intelligence capabilities, we’re helping organizations create the data foundation needed to compete in an AI-driven marketplace.

What This Means for Customers

For Akeneo customers, this acquisition opens the door to exciting new possibilities.

Over time, organizations will be able to more closely align pricing strategies with their product structures, gain stronger competitive insights, and reduce friction between pricing, merchandising, category management, and eCommerce teams.

The goal is simple: help businesses make better decisions faster.

PricingHUB customers can also look forward to continued innovation and investment. PricingHUB will continue operating as a dedicated business unit within Akeneo, ensuring continuity while benefiting from the broader vision and capabilities of the Akeneo Product Cloud.

And for both customer communities, this combination creates an opportunity to unlock greater value from data that has always been interconnected.

Welcome to the Team, PricingHUB

Most importantly, we’re excited to welcome the talented PricingHUB team to Akeneo.

Their expertise, passion, and deep understanding of pricing strategy make them a perfect addition to our mission of helping organizations create better product experiences.

Together, we’re taking another step toward a future where product data, pricing, insights, activation, and AI work seamlessly together to power commerce.

We’re incredibly excited about what’s ahead, and we’re just getting started.

Welcome to Akeneo, PricingHUB!

For too long, the retail industry has been operating in a state of internal contradiction. Marketing departments meticulously craft a brand’s identity, infusing products with emotion, narrative and promise. 

Meanwhile, Pricing Managers adjust figures in isolation, often disconnected from the identity that the product is trying to project. Without communication between these two departments, your commercial strategy is much like running a marathon with your shoelaces tied together.

To succeed in today’s landscape, we must accept a new reality: price is not just another data point, but the most critical product attribute of all. By unifying product information with pricing optimization, brands can finally transition from static catalogue management to a high-performance system centred on the consumer.

I. The Product Attribute: The New Engine of Your Business Rules

This journey toward orchestration begins at the most granular level: the product attribute. Too often, pricing strategies are applied broadly across entire categories or, worse, dictated solely by what the competition is doing. This “blind” pricing ignores the unique DNA of every reference.

By using the structured data already present in your product repository (such as materials, sustainability labels, or technical exclusivity) as direct inputs for your pricing rules, the price becomes a living reflection of the product’s value.

Bridging the Gap Between Data and Value

Imagine a sports retailer selling two nearly identical running jackets. One is made with standard synthetic fabric, while the other includes certified recycled textiles and weather-resistant technology designed for extreme conditions. Traditionally, both jackets might receive the same seasonal markdown because they sit within the same category.

But with orchestration powered by product data, the system recognizes attributes like “Recycled Textile Certified” and “Advanced Weather Protection” directly from the product repository. Instead of automatically applying a blanket discount, the retailer preserves a premium price point for the higher-value product because the data supports a differentiated positioning.

The result is pricing that aligns with the actual value customers perceive. Sustainability investments are protected instead of discounted away, premium innovations maintain their margin potential, and brands create a stronger connection between the promises they make in marketing and the prices customers see at checkout.

II. Mastering the Lifecycle: From “New Arrival” to “Clearance”

However, a product’s value isn’t just about what it’s made of, but also where it stands in its lifecycle. The transition from a “New Arrival” to a “Clearance” item is one of the most significant friction points in retail operations. 

Traditionally, these shifts are handled manually or through blunt, sitewide sales that erode brand equity. A more sophisticated approach uses the product’s lifecycle status, pulled directly from the PIM, to pivot the pricing strategy in real-time.

Data-Driven Stock Optimization

When a product is flagged as “End of Life” or “Exit,” a solution like PricingHUB can instantly switch its logic from margin preservation to volume maximization. By leveraging real-time elasticity, the system adjusts the price daily to hit “zero stock” by a specific target date, clearing the warehouse without sacrificing a single cent of unnecessary margin.

III. The Era of Agentic AI: Becoming “AI-Ready”

This level of precision is becoming a requirement as we enter the era of Agentic AI. 

We are witnessing a fundamental shift in how consumers discover products, as virtual assistants and AI-driven search engines begin to browse the web on behalf of humans. For these algorithms, structured data is the only language that matters.

Making Products “Shoppable” for Algorithms

If an AI agent is to recommend your product over a competitor’s, it needs ultra-rich, structured information to understand the value proposition. But visibility is only half the battle. 

In this high-velocity environment, the price must also be perfectly calibrated at every millisecond. A solution like PricingHUB can ensure that the price syndicated to these channels is always optimized for the moment, making your products not just “findable” but truly “shoppable” for the algorithms of tomorrow.

Conclusion: From Information to Execution

Ultimately, the future of commercial performance lies in the total suppression of silos between product truth and pricing action. 

When you reconcile what you say about your products with what you ask for them, you transform your catalog from a static list into a dynamic force of nature. This is the essence of modern orchestration: a world where information and execution are one and the same. 

It is time to stop choosing between brand image and profitability. By transforming your product data into a sustainable profit lever, you ensure that your brand remains both relevant and resilient in an ever-evolving market.

Ready to turn your product data into profit? Discover how PricingHUB can help you orchestrate your commercial performance today.

At Unlock 2026, we celebrated the people and teams redefining what great product experiences look like.

The PX Champions Awards recognize organizations that are transforming product information into a competitive advantage, connecting teams, streamlining operations, and delivering richer, more consistent experiences across every customer touchpoint.

From building scalable product data foundations, to creating compelling product stories and expanding across channels and markets, this year’s winners demonstrate how strong product experiences drive real business impact.

Here are the 2026 PX Champion Award winners.

The Digital Foundation Award

The Digital Foundation Award honors teams that turned fragmented product information into a scalable, reliable single source of truth.

These organizations built strong operational foundations with Akeneo Product Cloud, improving governance, accelerating supplier onboarding, and preparing their businesses for future growth, compliance, and AI-driven commerce. 

Winner: LG Electronics USA.

LG Electronics USA transformed its product data operations by centralizing product specifications and streamlining governance in Akeneo PIM. 

The impact was immediate:

• Product data processing became 71% faster, shrinking from one week to just two days
• The data model scaled from 110 attributes to more than 1,000
• Teams reduced dependency on spreadsheets and manual processes.

Congratulations to LG Electronics USA for building a product foundation designed for speed, scale, and long-term agility.

The Product Storyteller Award

The Product Storyteller Award celebrates teams that use product content to create better buying experiences. 

By enriching and optimizing product information, these organizations help customers discover, understand, and confidently purchase products across digital channels.

Winner: Tanner

Tanner transformed technical product data into engaging digital product experience with Akeneo PIM.

By enriching product content, the team improved product discoverability, increased conversions, and returns, helping customers make more confident purchasing decisions.

Success was evident early:

• Reduced the time required to launch new product lines online, creating a meaningful competitive advantage
• Allowed internal teams to focus on digital marketing and user experience, and less on manual data management.
• Improved eCommerce conversion rates by presenting technical information

Congratulations to Tanner for proving great product content does not just inform customers, it drives performance.

The Market Maker Award

The Market Maker Award recognizes teams that excel at omnichannel product experiences.

These organizations successfully scale product information across marketplaces, retailers, eCommerce sites, and global markets while maintaining accuracy, consistency, and speed.

Winner: Petra

Using Akeneo PIM and Akeneo Activation, Petra centralized more than 15,000 SKUs and automated marketplace listings across channels.

The team also eliminated duplicate workflows and replaced two vendor contracts, creating a more efficient and scalable operating model.

The result:

• replaced the third-party aggregator model to reduce cost and complexity
• Leverage AI-powered tools to enrich product content
• More consistent product experiences across channels

Congratulations to the Petra team for turning operational complexity into a competitive advantage.

The Growth Architect Award 

The Growth Architect Award celebrates organizations that turn product experience insights into measurable business growth.

These teams continuously optimize their operations, using automation, analytics, and performance data to improve efficiency and maximize impact.

Winner: AutoQuotes.

AutoQuotes optimized product enrichment workflows in Akeneo PIM using automation and rules-based processes in partnership with Sitation.

The results speak for themselves:

• 71% reduction in cost per enriched SKU 
• A 55% increase in enrichment output
• Nearly 16,000 products updated in a single day. 

Congratulations to AutoQuotes for demonstrating how operational excellence can fuel scalable growth.

The PX Champions of the Year

The PX Champions of the Year award recognizes organizations that have fully embraced product experience as a strategic business capability.

These companies demonstrate end-to-end excellence across the Akeneo Product Cloud, elevating product experience management from an operational necessity to a true competitive differentiator.

2026 Global PX Champions of the Year

• Fullscript (North America)
• ASSA ABLOY (Northern Europe)
• Syncro Diffusion (Southern Europe)
• tectake (Central & Eastern Europe).

Congratulations to all of this year’s winners and nominees. Your innovation, collaboration, and commitment to better product experiences continue to raise the bar for modern commerce.

Watch Unlock 2026 On Demand

Missed Unlock 2026? 

You can now watch the entire digital event on demand on YouTube, including keynote sessions, customer stories, product announcements, and expert discussions, exploring the future of commerce in the age of AI and product experience.

Consumer search habits are undergoing a radical transformation. 

The traditional online search model, where users had to “hunt” for information by scanning dozens of blue links and manually comparing features, is giving way to a new “reception-based” model. Today, 50% of consumers and 90% of B2B buyers intentionally use artificial intelligence to evaluate brands and build their purchasing shortlists.

In this new reality, where more than 80% of searches now end without a single click to an external website, online visibility has fundamentally changed. It is no longer about ranking highly on a search results page, but about becoming the definitive answer recommended by AI. 

This is where GEO (Generative Engine Optimization) comes into play.

Why Product Data Is the Foundation of a GEO Strategy

GEO is the practice of structuring and optimizing your digital content so that AI models can understand, trust, and cite your brand in their responses. To achieve this, AI relies heavily on accurate, complete, enriched, and structured product data.

Unlike traditional search engines, which mainly match keywords, AI analyzes your product as a true entity within a knowledge graph. It looks not only for clear technical specifications (machine-readable through Schema.org), but also for context: what use case does the product address? Is it supported by recent customer reviews available online?

If your product data is not formatted for AI or key information is missing, AI faces what is known as a “data void.” 

The outcome is straightforward: the virtual agent will ignore your product or, even worse, recommend a competitor instead.

Akeneo Product Cloud: The Backbone of Your AI Visibility

To meet the GEO challenge, your Product Information Management (PIM) strategy must evolve. In the age of AI, your product data is your brand strategy.

A solution like Akeneo Product Cloud addresses these challenges on multiple fronts: it feeds AI systems with clear, structured attributes, provides the context needed to match search intent and use cases, and protects brand integrity by ensuring complete consistency of information across all sales channels, preventing AI from generating inaccurate responses.

Focus on PX Insights: The Secret Weapon for Dominating AI Search

To go even further and ensure discoverability in the era of generative AI, Akeneo developed PX Insights, a feature specifically designed to help your products stand out on AI platforms such as ChatGPT, Gemini, and Perplexity, while also strengthening traditional SEO performance.

Here are the key benefits of PX Insights for your GEO strategy:

AI Readiness Audit

PX Insights performs an in-depth technical audit to ensure that the “digital DNA” of your products is perfectly structured for AI crawlers and bots. The tool identifies missing data, structural errors, and ambiguous information that may limit your visibility.

AI Discovery Analysis

This feature provides valuable business insights for eCommerce teams. It allows you to see exactly how AI agents interpret and rank your products compared to competitors based on real consumer intent. This helps transform a product from being “invisible” into becoming a “top recommendation.”

AI Virtuous Omnichannel Cycle

PX Insights does not operate in isolation. It connects three essential sources: AI discoverability, Google Shopping performance, and customer ratings and reviews. By analyzing customer feedback, you can generate conversational product content that directly answers user queries. Every optimization therefore benefits all your channels simultaneously.

Immediately Actionable Recommendations

Unlike SEO tools focused solely on keywords, PX Insights concentrates on your data model. The insights generated integrate directly into Akeneo, allowing you to correct an attribute or enrich product content without switching tools, significantly improving responsiveness and execution speed.

Conclusion

Today’s market rewards companies that actively shape the way AI understands them.

With Akeneo and PX Insights, you are no longer reacting to algorithm changes. Instead, you align your product catalog with the way generative engines operate, ensuring that your products are consistently found, recommended, and selected whenever a customer interacts with AI.