We’re pleased to share that Akeneo has been positioned in the Leaders Category of the IDC MarketScape: Worldwide Product Experience Management and Product Information Management Plus Applications for Digital Commerce 2025 Vendor Assessment! 

We believe this recognition reflects Akeneo’s continued commitment to helping organizations deliver accurate, enriched, and consistent product experiences across every channel. As the digital commerce landscape evolves, Akeneo remains focused on empowering businesses with flexible, modular, and AI-enhanced tools to meet both operational and customer-facing needs at scale.

What is the IDC MarketScape: Worldwide PXM and PIM+ Applications for Digital Commerce 2025 Vendor Assessment?

The latest IDC MarketScape assessment explores how technology vendors support the evolving needs of digital commerce through Product Experience Management (PXM) and Product Information Management Plus (PIM+) platforms. This evaluation, conducted by IDC’s Heather Hershey, highlights the philosophical and functional distinctions between PXM and PIM+, and evaluates 11 vendors based on their capabilities and strategies.

These systems are designed for marketing, merchandising, and digital experience teams to personalize and orchestrate product content across multiple channels. They typically include dynamic enrichment tools, asset personalization, and modular API-based architecture with embedded artificial intelligence for syndication and content generation.

PIM+ platforms, by contrast, emphasize operational mastery. These are data-first systems that support ingestion, normalization, and syndication of structured product data at scale. They often integrate with ERP, CRM, and commerce platforms, providing regulatory compliance, workflow automation, and multilingual data governance.

How the Vendor Evaluation Works

IDC MarketScape evaluated vendors based on factors such as active customer base, support for digital commerce channels, SaaS/cloud readiness, and whether platforms support both B2C and B2B use cases. A minimum of 20 active customers with annual revenues of $50 million or more and the ability to syndicate across five or more major channels were part of the inclusion criteria.

Akeneo’s Position in the 2025 IDC MarketScape

Akeneo is positioned in the Leaders Category of the IDC MarketScape for 2025. Founded in 2013 and headquartered in Nantes, France, Akeneo supports businesses in over 165 countries with a PXM solution that can also function as a PIM+ platform. Akeneo Product Cloud centralizes product data and supports use cases across in-store displays, POS systems, ecommerce, sales enablement, and customer support.

Akeneo provides enrichment through generative AI, automated translations, and collaboration workflows. Its syndication module, Akeneo Activation, connects to over 400 retailer channels including Amazon, Walmart, and Google Shopping. The Akeneo Supplier Data Manager (SDM) is available both as part of the suite and as a standalone solution to streamline data exchange with suppliers.

Key technical characteristics include:

• 100% RESTful API exposure and partial GraphQL API availability
• Cloud-native, multitenant SaaS on Google Cloud Platform
• Modular architecture built using microservices
• Workflow engine and support for universal product attributes
• Support for digital product passports and design/performance declarations

Strengths and Considerations

The report notes several strengths of Akeneo:

• Omni-channel consistency: Akeneo Product Cloud ensures consistent product information across all sales channels, both online and offline, which is essential for maintaining brand consistency and providing a cohesive customer experience.
• Advanced data enrichment and automation: The solution utilizes AI and machine learning to automate data enrichment, tagging, and categorization, thus reducing manual effort, minimizing errors, and ensuring product information is always current and comprehensive.
• Customization and scalability: Akeneo Product Cloud offers customizable workflows, flexible data models, and scalable infrastructure, making it suitable for businesses of various sizes and enabling them to grow alongside the business.

Challenges include:

• Industry-specific limitations: Akeneo does not actively target the food and beverage sector due to the maturity of the sector and the specific product requirements that Akeneo does not natively support (but it can support these requirements through its partner ecosystem if needed).
• GDSN capabilities: While Akeneo supports customers with GDSN requirements, Akeneo lacks native GDSN capabilities, which limits its ability to scale and recruit customers needing these features.
• Target market focus: Akeneo’s business model and core product offerings are better suited for midmarket and enterprise businesses rather than micro businesses and small businesses. While Akeneo has micro business and SMB clients, its advanced functionalities and go-to-market model are aimed at larger enterprises.

Final Thoughts

As commerce environments become increasingly fragmented and dynamic, flexible and AI-augmented platforms are key to managing complexity while meeting customer expectations.

We believe Akeneo’s placement in the Leaders Category of the 2025 IDC MarketScape underscores its role in supporting businesses through the complexities of modern digital commerce. With capabilities that span both product information mastery and product experience delivery, Akeneo provides a flexible foundation for organizations seeking to scale operations, enrich product content, and adapt to changing customer expectations. As enterprises navigate the convergence of data governance and experience optimization, Akeneo’s approach offers a pathway to meet these demands with clarity, agility, and confidence.

Download the excerpt 2025 IDC MarketScape Worldwide PXM and PIM+ Applications for Digital Commerce Vendor Assessment now to learn more.

Product search and  discovery were two very different experiences in the past. You see, online search was simple. After typing something simple like “black shoes” into Google, you crossed your fingers and hoped the algorithm would find information that didn’t look like it belonged in a 2002 fashion catalog. 

Discovery, on the other hand, was mostly accidental—you’d stumble across something interesting while navigating a labyrinth of categories, filters, and miscategorized products and click ‘add to cart’. But in today’s digital world, where attention spans are short and expectations are sky-high, that kind of experience just doesn’t cut it anymore.

This is where Artificial Intelligence (AI) comes in, the not-so-silent partner behind eerily accurate recommendations, voice assistants that finish your sentences, and platforms that seem to know what you want before you do. From semantic search to generative answers, AI is fundamentally changing how we find, explore, and even think about product information.

Traditional Search and Matching Keywords 

As you might have guessed, search refers to the act of looking for a product, while discovery encompasses the results (and sometimes the unexpected opportunities) that come from that search. From the early days of digital search and eCommerce until now, most systems have relied on traditional, keyword-based search engines. These engines operate on a simple principle: they match the exact words in a user’s query with terms found in a brand’s product catalog or content database. If a customer entered the precise name of a product or category, they were usually presented with relevant results.

This approach worked reasonably well when product catalogs were small and customer expectations were even smaller. But as online inventories expanded and customer expectations evolved, the limitations of keyword search have become increasingly apparent. If a customer uses a synonym, introduces a typo, or describes the product in a way that didn’t match the catalog’s wording (such as saying “sofa” instead of “couch” or misspelling “headphones”), the search engine often fails to return useful results.

In short, traditional search and discovery helps users hunt, but it doesn’t help them explore.

What is AI Search and Why is it Important?

As customer expectations grow more sophisticated, so does the technology designed to meet them. AI-powered search engines have emerged as a powerful evolution of traditional search, one that is smarter and far better at figuring out what customers actually mean. Instead of treating every query as a literal checklist, AI search is an intelligent approach that uses technologies like machine learning and natural language processing (NLP) to understand user intent, context, and behavior.

Rather than relying solely on rules and product tags, AI search can learn from user behavior, adapt to evolving trends, and return results that align with the user’s intent, even when their query doesn’t match exact keywords. It draws insights from what people click on, what they skip, and how they refine their searches.

For example, let’s say a customer is searching for “best trail running shoes for wet conditions”. A traditional search engine would return any results that are tagged “running shoes”, “trail running”, or “shoes for wet conditions”, leaving our customer to weave through trail running backpacks, mesh running shoes, or even rubber rain boots.

But if we run the same search query with an AI-powered search engine, it will analyze thousands of customer reviews to find the trail running shoe with the most mentions of being waterproof, check the most frequently purchased sneaker in areas where muddy trail running is popular, and cross-reference return rates after a particularly rainy season to populate the most relevant products possible. Instead of simply matching specific keywords, AI offers the ability to better understand the intent behind the search, surfacing the most relevant results rather than generic products that contain a single word from the original search.

AI in Action: Retail Search and Discovery Examples

To better understand how AI is reshaping search and discovery in retail, it helps to look at real-world applications. From interpreting vague queries to delivering hyper-personalized recommendations, AI is driving improved shopping experiences. Below are several examples that highlight how leading retailers are using artificial intelligence to transform the way customers find and engage with products.

1. Conversational AI: Amazon’s Rufus

In early 2024, Amazon launched Rufus. Trained on Amazon’s vast product catalog and supplemented with information from across the web, Rufus acts as an intelligent shopping assistant that provides real-time answers, product comparisons, and tailored recommendations. Instead of typing a product name, users can ask Rufus things like: “What should I consider when buying running shoes for flat feet?” or “What are some fun birthday gifts for a 5-year-old?”. Rufus then responds with context-aware, human-like answers and curates relevant product suggestions based on millions of product listings, reviews, and customer behavior. This turns product search into a discovery journey, particularly helpful for undecided or first-time buyers! 

2. Personalized Product Discovery: Sephora

AI can analyze user behavior across sessions to anticipate what individual customers want, sometimes before they even know it themselves. It can factor in previous purchases, time spent on certain product pages, items viewed together, and more to suggest relevant items. Take Sephora, for example. Their AI-powered “Virtual Artist” app lets users virtually try on makeup, but beyond that, their recommendation engine uses a customer’s skin tone, past purchases, and browsing habits to suggest personalized products, such as foundation in the right shade or moisturizers perfectly suited to their complexion and concerns, even if they hadn’t explicitly searched for it.

3. Visual and Voice Search for Seamless Shopping: IKEA

AI-powered visual search allows users to upload a photo, like a jacket they saw on Instagram, and find similar styles available for purchase. This eliminates the guesswork of trying to describe a visual item with keywords, transforming casual inspiration into direct shopping opportunities. An example would be IKEA’s app that features visual search so a customer can snap a picture of a table they like and get matching or complementary furniture suggestions.

Voice search, on the other hand, enables hands-free browsing. Instead of typing, customers can simply speak their queries, making shopping more intuitive and accessible. Walmart incorporates voice shopping via smart speakers, letting users add items to their cart by simply saying what they need. This technology understands context and intent, providing a seamless, conversational user experience, whether you’re multitasking at home or on the go!

The Challenges of AI Content Discoverability 

While AI significantly enhances search and discovery, it’s not without its challenges. Here are some key ones:

• Data quality and quantity: AI models are only as good as the data they’re trained on. Poor, inconsistent, or insufficient product data leads to inaccurate recommendations and search results.
• Limited data: For new products or new customers, AI has limited data to learn from, making it harder to provide relevant recommendations or search results initially.
• High initial investment: Implementing advanced AI search and discovery systems often requires significant upfront investment in technology, infrastructure, and specialized talent.
• Lack of transparency: It can be difficult to understand why an AI made a particular recommendation or search ranking, which can hinder troubleshooting and trust.
• Scalability: Handling the immense volume of real-time data for search and recommendations, especially during peak shopping periods, requires a robust and scalable AI infrastructure.

AI and PIM

AI and Product Information Management (PIM) work together to transform the search experience by ensuring that product data is not only accurate but also optimized for how users search, speak, and browse. A PIM system provides the structured foundation needed for any search engine—centralizing attributes, categories, and rich product descriptions. Artificial intelligence, especially technologies like natural language processing and machine learning, enhances this foundation by analyzing how people express their needs, interpreting search queries, and enriching product data to align with users’ intentions.

By feeding enriched and structured product information into search systems, AI in search can better understand context, anticipate intent, and support the future of search where discovery is intuitive and personalized. The result is a more dynamic and satisfying user experience, one where customers can quickly find information and uncover the right products, even when their queries are vague, conversational, or unstructured.

The Search Experience Is Evolving

AI is redefining search and discovery by moving beyond rigid keyword matching to deliver experiences that are more aligned with user intent. Whether through natural language understanding, visual search, or behavior-driven recommendations, AI is turning search into a powerful engine for exploration and engagement.

As this technology continues to evolve, the focus will shift even more toward delivering relevant, context-aware results that help users find what they need quickly and meaningfully. To support this shift, businesses will need to ensure that the underlying product information is clear, consistent, and structured enough for AI to do its job effectively. The future of search is already here and it’s driven by intelligence, not just input.

Data quality can be a wonderful thing when it goes right! But when it takes a wrong turn? Oh, it can be a real headache, or worse. Poor data quality often feels like a confusing jigsaw puzzle – and if you’re missing half the pieces, or they just don’t fit, you’re left with a problem that’s as frustrating as it is messy. 

Good news: there’s a solution. If anything truly conquers data quality problems, it’s AI. And when it comes to putting AI to work, Akeneo leads the way. As a pioneer in AI-powered PIM, Akeneo helps brands turn messy, inconsistent product data into polished, high-quality assets that drive great customer experiences. Whether it’s cleaning up errors or accelerating your time-to-market, Akeneo’s AI transforms complexity into clarity. Curious to see how?

What is Artificial Intelligence?

First, let’s define the simple, but powerful, two-letter acronym. Artificial Intelligence (AI) refers to the ability of computer systems to perform tasks that typically require human intelligence, like learning, reasoning, and problem-solving. Essentially, it’s about machines thinking and acting smarter! 

The 5 Most Common Data Quality Problems

Now that we know what AI is, the real question is, what can it actually do for your product data? As it turns out—a lot. From spotting errors to enriching product listings automatically, AI doesn’t just tidy up your catalog, it streamlines your workflow and frees up your team for higher-impact work.

Now let’s break down five data quality headaches that AI is surprisingly good at solving, and how Akeneo Product Cloud makes it all click into place:

1. Inconsistent Product Data

Product data inconsistencies, like conflicting specs or mismatched attributes, often emerge when information travels across systems, regions, or teams. Distributors might receive differing inputs from suppliers, creating confusion and delays. Retailers then deal with inaccurate listings that lead to stock errors or missed sales. For consumers, it all results in frustration, mistrust, and higher return rates.

The Solution

Akeneo Supplier Data Manager (SDM), solves this at the source. It facilitates real-time collaboration between distributors and suppliers to ensure product information is aligned, complete, and up to standard from day one. Data requirements (whether from your PIM, ERP, or other systems) are surfaced through an intuitive UI, making it easy to guide suppliers and enforce consistency through AI-powered automation and rule engines.

Akeneo’s AI strengthens this process by classifying products intelligently, spotting inconsistencies, and auto-enriching records with the correct values and linked assets. The result? Less cleanup, more confidence, and product data that’s consistent across every channel.

2. Incomplete Product Data

When product data sheets are missing key information like specifications, usage instructions, or safety details, it’s more than just a formatting issue. Incomplete data makes products harder to find in search, harder to trust, and much easier to skip. For suppliers, it means delays in processing and potential compliance risks. For distributors and retailers, it leads to poor product discoverability and missed sales. And for consumers, it ultimately results in cart abandonment.

The Solution

Akeneo PIM simplifies the challenge of filling in the blanks. Acting as your single source of truth, it allows teams to manage, enrich, and sync data across every channel with accuracy. AI accelerates this by detecting gaps, recommending attribute values, and efficiently organizing products into categories, so you can focus on the big picture.

With Akeneo’s latest enrichment features, the process goes even further. Our AI can automatically extract critical details (such as color, material, or category) from assets like images and PDFs, and use them to populate relevant attributes in your PIM. These insights feed into the correct fields automatically, helping teams work faster, eliminate manual entry, and bring your complete, high-impact product content to market without delay.

3. Duplication Product Data

Data duplication isn’t always obvious, but it can wreak havoc on catalogs and reporting. It often creeps in during imports, transfers, or catalog updates, and snowballs into skewed metrics, messy reports, and a cluttered customer experience. Suppliers may submit the same product more than once, and retailers may see sales data split across duplicates. Consumers? They’re left wondering why the same product appears twice with different info or prices.

The Solution

With AI-driven data cleansing and deduplication in Akeneo PIM, you can automatically identify and eliminate duplicate entries as product data is imported, saving your team hours of manual effort and ensuring your catalog stays clean and accurate from the start.

But what about complex product relationships like configurable items, bundles, or component-based products? That’s where Composable Products comes in. This new, powerful feature lets you model real-world product relationships without creating redundant records or workarounds. From pairing electronics with accessories to managing modular furniture sets, Composable Products simplifies linking items while keeping your data clean and duplication-free. That means fewer errors and a system that evolves with your merchandising needs.

4. Poor Quality Supplier Data

Sourcing product information from external parties leaves you at the risk of receiving outdated or incorrect data. Suppliers may struggle to validate key information, leading to expensive returns, contractual disputes, and strained relationships. For distributors and retailers, poor-quality data slows processing times, delays time-to-market, and erodes brand trust. And as you can imagine, consumers get the wrong impression or the wrong product altogether.

The Solution

Akeneo tackles this problem head-on with smart workflows and AI-backed validation tools. Instead of relying on manual review, you can define what “good data” looks like from the start. Suppliers work within shared templates, with real-time guidance and automated checks via SDM, so issues are caught early, not when a product goes live.

Meanwhile, Akeneo PIM continuously tracks data quality metrics like completeness and relevance. AI flags anomalies and automates routine tasks like formatting or attribute mapping. Add in robust permissions and workflow controls, and your teams can trust that the data entering your system is usable and aligned across departments and regions.

5. Outdated Product Data

Product data isn’t a “set-it-and-forget-it” situation. When prices, features, or availability shift and updates lag behind, the fallout hits everyone. Suppliers may spread outdated info, hurting margins. Business teams are bogged down by manual corrections. Customers lose interest when they constantly see out-of-stock items still on display and gain an experience that feels anything but modern.

The Solution

Akeneo’s PIM helps brands stay ahead by enabling quick, controlled updates across every touchpoint. You can respond to changing trends or business needs at speed, whether you’re launching new products or tweaking existing ones for different markets. AI speeds this up by automating tasks like attribute updates, asset swaps, and content changes across your catalog.

To ensure accuracy and control, Akeneo’s new channel-level access control feature allows different teams (regional or brand-specific, or channel-based) to manage only the data they’re responsible for. That means fewer mistakes, faster updates, and full visibility over who’s changing what. Combined with AI-powered enrichment, your catalog stays fresh and accurate, no matter how fast your business moves.

Say Goodbye to Dirty Data

Like we said before, messy product data is like a puzzle. With the wrong pieces jammed in, it can be frustrating to work with it and nearly impossible to solve. From inconsistent specs to outdated listings, every mismatch sends your operations, partners, and customers slightly off track.

But with Akeneo’s AI-powered Product Cloud, the pieces finally fit! From smarter supplier workflows to automated enrichment and real-time updates, Akeneo helps you turn scattered data into a complete, high-quality product story, ready for every channel, every time.

Ready to let AI take your product data from messy to market-ready? Let’s talk.

Printed books took more than 50 years to become widely available across Europe. The concept of the Internet dates back to the 1960s, yet it wasn’t until the 20th century that it permeated every-day life.

In contrast, ChatGPT reached 100 million users in just two months, with over 13 million unique users every single day.

Artificial intelligence came on the scene at unprecedented speed, and it leaves many of us struggling to make sense of what it really means for the way we live, work, and connect. Between the breathless promises of AI evangelists and the dire warnings of skeptics, it can be hard to separate hype from reality and even harder to see clearly how this powerful technology can be used effectively (and responsibly) in business.

With that in mind, let’s dive into the good, the bad, and the ugly when it comes to real-life AI use cases to give you a better understanding of AI applications and how we can all navigate this brave new world.

The Biggest Opportunities for AI

1. Data Cleansing & Enrichment

One of the most practical use cases for AI is cleaning and enriching product data at scale in order to ensure consistency and accuracy across even the largest catalogs. By automatically standardizing formats, categories, and naming conventions, AI helps maintain uniformity across product listings, which is essential for both internal organization and a seamless customer experience. It can detect and correct errors, merge duplicate entries, and flag missing or inconsistent information, such as harmonizing “navy” and “dark blue” or filling in missing dimensions or materials based on similar products.

Beyond cleaning, AI also enriches product data by enhancing listings with additional, customer-relevant information. This includes generating more descriptive titles and keyword-rich descriptions, suggesting tags and filters that reflect how customers actually search, and adding attributes that improve discoverability and SEO. Together, these capabilities help brands deliver a more navigable, trustworthy, and conversion-friendly shopping experience, turning a cluttered product catalog into a clear, customer-centric asset that drives better business results.

2. Data Analysis & Personalization

Machine learning algorithms can ingest vast amounts of customer data to produce data-driven insights into customer preferences and behavior. Consumer brands can use AI to analyze customer purchase histories and browsing patterns to offer highly personalized product recommendations and marketing content based on what similar customers have purchased in the past.

AI can also be utilized to evaluate and analyze customer reviews at scale, identifying common themes, pain points, and unexpected use cases in how customers actually interact with a product. If a significant number of reviews mention using a kitchen appliance for a purpose not originally marketed (such as using a blender to make nut butters) AI can surface this insight and communicate it back to the product and marketing teams. These teams can then update product descriptions, highlight these additional use cases, and even adjust imagery or instructions to better reflect how customers are really using the product.

3. Market Expansion

While AI is no magic wand, it can act as a powerful key to unlock new global markets and sales channels by enabling brands to create tailored, localized content at scale. Entering international markets requires adapting product information to resonate with local languages, cultural expectations, and shopping behaviors. AI can help brands overcome these barriers by automatically translating product titles, descriptions, and specifications, as well as adjusting measurements, currencies, and shipping details to align with regional standards. This makes it easier for customers in different countries to understand, trust, and engage with your products, paving the way for a smoother and faster market entry.

However, it’s important to remember that AI-generated translations are not a one-and-done solution. They still require human oversight to ensure accuracy, relevance, and sensitivity to cultural nuances that machines may miss like idiomatic expressions, regional slang, or imagery that could carry unintended connotations in certain markets. That said, AI drastically reduces the time and cost associated with creating localized content, democratizing access to global audiences that may have previously been out of reach for smaller or resource-constrained brands. When used thoughtfully alongside human expertise, AI empowers companies to expand into new regions with confidence, delivering customer experiences that feel authentic and tailored, no matter where the customer is.

4. Efficient Customer Service

In the retail industry, AI-powered chatbots and virtual assistants have revolutionized customer service by enabling brands to provide immediate, around-the-clock support no matter the time zone, language, or volume of inquiries. These tools can handle thousands of conversations simultaneously, offering customers quick answers to routine questions such as order tracking, return procedures, store hours, or product availability. For global businesses, this means customers can interact in their preferred language and receive help at any time of day, creating a seamless and accessible experience that builds trust and loyalty.

However, while AI excels at handling repetitive or straightforward requests, it should not — and cannot — replace human agents when it comes to resolving complex, sensitive, or highly specific issues that require empathy, discretion, or deep product knowledge. Instead, AI is most effective as a first line of support: triaging inquiries, resolving the most common and predictable issues, and intelligently routing more challenging or nuanced cases to the appropriate human teams. This collaboration between AI and human agents not only improves operational efficiency but also ensures customers feel genuinely heard and valued when it matters most.

5. Inventory Management

Utilizing AI-driven demand forecasting allows retailers to significantly improve their inventory management by making smarter, data-backed decisions about stock levels. AI algorithms can be trained on vast datasets that include historical sales data, seasonal patterns, market trends, promotional calendars, and even external factors such as weather forecasts or economic shifts. Unlike traditional forecasting methods, which often rely on static spreadsheets and broad assumptions, AI can dynamically analyze these variables in real-time, identifying patterns and anomalies that human planners might miss. For instance, it can predict a spike in demand for certain products during holidays or special events, while also accounting for factors like regional preferences or the impact of competing promotions.

By producing highly accurate predictions of future demand, AI helps retailers strike the right balance between supply and demand, effectively reducing the risk of costly overstock — which ties up capital and storage space — or understock, which leads to missed sales opportunities and dissatisfied customers. In addition, AI-driven forecasting can adapt quickly to unexpected changes, such as sudden drops in consumer confidence, supply chain disruptions, or viral trends that cause demand to surge overnight.

The Biggest Risks of AI

AI is exciting, but it’s dangerous to view this technology through rose-colored glasses. Let’s take a look at some of the risks that AI can pose to an organization (and no, it’s not robot world domination).

1. Brand Differentiation

A strong brand identity creates an emotional connection with customers. It goes beyond mere product features and taps into the values and personality of the brand. But as AI continues to grow in popularity, with a third of all desk workers saying they utilize large language models daily at their job, we run the risk of creating a sea of similar, template-based, algorithm-generated content. Maintaining a distinctive brand identity and voice becomes an even more crucial factor as content generation becomes more automated.

2. Biases & Liability

Machine learning models learn from large datasets. If these training datasets contain biases or inaccuracies, the model can learn and perpetuate those biases. In the context of global brands and retailers, this can result in inaccurate, discriminatory, or unfair outcomes in various aspects of operations, from product recommendations to legal compliance and more. If the data used for training AI models is based on a customer base that skews heavily urban, you may accidentally draw insights about shopping behaviors in rural or suburban communities that aren’t accurate.

3. Privacy & Security

Collecting and analyzing customer data raises privacy and security concerns. Brands must implement robust cybersecurity measures to protect sensitive customer information from data breaches or cyberattacks, and be transparent with customers about the types of data they collect, why it’s collected, and how it will be used.

4. Technical Challenges

There’s no denying that AI is a complex technology, and implementing an AI solution into your tech stack is no small feat. You need to ensure that you have the right folks on your team to prepare your internal teams for adoption, and you need to ensure that you have the right technology in place to integrate smoothly with the new solution and seamlessly communicate product information (not to mention, you need an organized single source of truth for product information).

5. Customer Resistance

Some customers may perceive AI-driven interactions as impersonal and devoid of the human touch, or they can sometimes feel like they’re losing control over their interactions with others. In fact, 60% of consumers are concerned about how AI uses their personal data.

If customers perceive that a brand prioritizes automation over genuine customer interactions, they may be less likely to remain loyal and more inclined to switch to competitors who offer more personalized and human-centric services. It’s important to acknowledge this hesitation and alway consider the customer’s perspective when implementing new technology, ensuring that your technology is enhancing your team’s work and not overpowering.

Conclusion: Bad Data, Bad Results

Product data serves as the foundation upon which AI technology is built and trained. Just like a house’s stability relies on a strong foundation, the effectiveness of AI systems hinges on the quality of the data they are trained on. Inaccurate, incomplete, or irrelevant data can lead to unreliable AI outcomes.

But the undeniable reality is that AI is here, and it’s here to stay; so the question becomes, how do brands and retailers embrace and implement this technology without losing the trust they’ve built with consumers?

If you’re ready to explore how AI and other emerging technologies are driving the next chapter of commerce (and how your business can stay ahead), we invite you to download our whitepaper, The Next Chapter of Commerce. Packed with insights, strategies, and real-world examples, it’s your guide to navigating the future with confidence and embracing the benefits of AI while minimizing the risks.

In case you missed it, the team here at Akeneo recently unveiled a host of exciting new functionality as part of our 2025 Summer Release, and this one is packed with innovations to help you deliver smarter, faster, and more impactful product experiences.

We know that keeping up with today’s rapidly evolving commerce landscape is no small task. Customers expect rich, accurate, and engaging product information at every touchpoint, while teams are under increasing pressure to move quickly, stay competitive, and do more with less. That’s why this release focuses on empowering you with tools that bring speed, intelligence, and flexibility to your product experience strategy, particularly through the power of artificial intelligence.

There’s a lot to explore in this release, from enhancements that streamline onboarding to tools that optimize your products for AI-powered discovery. But we wanted to take a moment and highlight three of the most exciting AI-driven features now available in the Akeneo Product Cloud. These innovations are designed to save you time, reduce costs, improve data quality, and unlock the full potential of your product content.

Let’s take a closer look at how AI is helping our customers work smarter than ever before.

1. End Painstaking Product Modeling with Data Architect Agent

Building a robust product data model is no small feat. For many organizations, the process of designing and implementing a data structure can take months of cross-functional collaboration, trial and error, and costly rework. Teams often struggle to account for all the nuances of their products, markets, and channels upfront, leaving them with rigid systems that can’t easily adapt when business needs evolve.

That’s where the Data Architect Agent (DAA) comes in.

With DAA, you can generate accurate and flexible data models in just days, not months. Our AI-powered agent accelerates your time-to-value by empowering your team to explore and adapt your data structure early in the onboarding process, reducing costs and minimizing the risk of expensive rework down the line.

The Data Architect Agent provides intuitive suggestions, supports dynamic customization, and enables AI-assisted iteration, so you can confidently model your data once and adapt it forever. By combining Akeneo’s deep expertise in product data modeling with AI automation, DAA helps you cut implementation costs by up to 70% and go live in record time, all while building a flexible foundation that scales with your business.

We’ve also introduced Flexible Attribute Expansion, which allows you to adjust product attributes on the fly. Whether you’re localizing content for a new market or meeting evolving regulations, you can now configure attribute properties dynamically without needing to rebuild your entire data model. 

Together, DAA and Flexible Attribute Expansion transform onboarding from a drawn-out blueprint stage to a fast, iterative build process.

Learn more about Data Architect Agent and Flexible Attribute Expansion

2. Be Found in the Moments That Matter with AI Discovery Optimization

Today’s consumers discover products in increasingly sophisticated ways—using natural language queries, voice search, and AI-powered shopping assistants like ChatGPT. Unfortunately, traditional product data often fails to align with how real people search, leaving your products buried and overlooked.

Enter AI Discovery Optimization, which helps you understand how your products are being discovered (or not) across a range of AI-powered commerce experiences.

With AI Discovery Optimization, your team can:

• Analyze your product data against AI-driven discovery trends to pinpoint gaps in visibility and relevance
• Surface performance insights for high-priority product listings, showing how consumers are actually searching—with natural language, voice, or AI-assisted queries
• Identify enrichment opportunities to tailor your product attributes and language to match evolving search behavior and consumer intent
• Gain a competitive edge by understanding how your products are interpreted and recommended by AI-powered shopping assistants

This newest feature brings actionable guidance directly into PX Insights, so you can optimize your product content for emerging AI shopping channels and make sure your products show up in the moments that matter most.

Learn more about PX Insights and AI Discovery Optimization

3. Unlock the Power of Your Product Assets with AI-Powered Enrichment

You already have a treasure trove of product assets—images, PDF spec sheets, instruction manuals, and more. But extracting structured information from these assets to enrich product listings is often manual, time-consuming, and error-prone. Our new AI-Powered Enrichment feature changes that.

With AI-Powered Enrichment, Akeneo can automatically analyze your product assets and extract key information such as colors, materials, dimensions, certifications, descriptive features, and more to populate product attributes or even generate product descriptions. This means your team can create products faster by eliminating repetitive manual tasks and significantly reducing the time it takes to enrich and launch new products. You’ll also achieve better data accuracy, as AI ensures that product details precisely match the source material, minimizing the risk of human error.

Beyond efficiency, AI-Powered Enrichment enables you to tell richer product stories by unlocking valuable information that’s been sitting in your existing assets, allowing you to build more complete, compelling product pages that better engage customers. And, by automating the tedious parts of enrichment, your team gains more time to focus on what really matters; strategic content creation, campaign planning, and cross-channel optimization.

By turning your static product assets into dynamic, structured data, AI-Powered Enrichment unlocks their full potential and helps you bring better products to market, faster.

Learn more about AI-Powered Enrichment

Discover the Full Power of Akeneo’s Summer Release 

While these AI capabilities are incredibly exciting, they’re just the tip of the iceberg when it comes to what’s included in the Akeneo 2025 Summer Release. This release is packed with enhancements and new features designed to help you streamline your workflows, unlock greater agility, and deliver even more impactful product experiences across all your channels.

Whether you’re looking to accelerate your onboarding, optimize your products for AI-powered discovery, enrich your product pages with richer content, or simply work smarter and more efficiently, there’s something in this release for everyone. 

For a full overview of everything that’s new, be sure to check out the Summer Release resource page for more details. And if you’d like to see these features in action, don’t miss our upcoming deminar on July 30th, where we’ll walk you through all the innovations live.

We’re excited to see how you leverage these new tools to push the boundaries of what’s possible with your product experiences, and we can’t wait to celebrate your successes as you continue to innovate and delight your customers. 

Winning Locally, Succeeding Globally—with Better Data

Our 2025 PXM Global Award winners show what’s possible when great brands take control of their product data. They’re a powerful example of how the right PIM strategy—paired with composable architecture—can turn complex challenges into scalable success. With Akeneo Product Cloud, they streamlined operations, expanded globally, and delivered rich, consistent product experiences across every touchpoint.

Inspired by their journey? Akeneo gives you the tools to clean, enrich, and activate your product information—so you can grow faster, go further, and build experiences your customers will love. Request a demo to see how Akeneo can power your next big move.