As brands continue to prioritize technology, sustainability, and transparency, legislation across the globe is evolving to support these efforts.

The European Union has been leading the charge with new regulations aimed at enabling a greener, more circular economy, particularly through one of the most impactful regulatory developments: the Digital Product Passport (DPP), a key element of the EU Strategy for Sustainable Products Regulation. This initiative falls under the broader scope of the European Green Deal, approved in 2020, which aims to make Europe the first climate-neutral continent by 2050.

While DPP is a European regulation, its effects are expected to ripple across global supply chains, making it a pivotal consideration for businesses worldwide.

But what exactly is the Digital Product Passport, how does it work, who is affected, and how can your organization prepare? Let’s dive in.

What are Digital Product Passports (DPP)?

A Digital Product Passport (DPP) is a digital record that captures, stores, and shares comprehensive product information throughout its entire lifecycle, from raw materials to end-of-life. The goal is to make key sustainability and supply chain data available across the entire value chain so that manufacturers, suppliers, retailers, repairers, and consumers alike can make better-informed decisions.

The passport is expected to include data such as:

• Product name and model: The official commercial name, model number, or SKU used to identify the product. Consistency in naming ensures traceability and prevents confusion, particularly when products are sold across multiple markets or channels.

• Place and date of manufacture: The specific location(s) and timeline of the product’s production. It may include factory identifiers, country of origin, and the batch or lot number.

• Warranty details: information about warranty periods, coverage terms, repair policies, any available extensions, and potentially also customer service contact information.

• Unique product identifier: Each product must be assigned a globally unique ID, such as a serial number, GTIN (Global Trade Item Number), or QR code, that allows it to be digitally tracked through the value chain.

• Material composition and origin: Detailed information on raw materials and components used in the product, including the types of materials, their percentage of content, and where they were sourced.

• Sustainability metrics: Quantifiable data on the product’s environmental performance, such as carbon footprint, water usage, energy consumption during production, and emissions generated during transportation.

• Repairability and durability scores: How easily the product can be repaired and how long it’s expected to last under normal use. This may also include standardized repairability ratings, expected lifespan, and availability of spare parts.

• Presence of hazardous substances: Manufacturers will be required to disclose any hazardous or restricted substances included in the product, in accordance with REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) and RoHS (Restriction of Hazardous Substances) directives.

• Recycling and end-of-life options: Detailed instructions on how the product (or its individual components) should be handled at end-of-life, including disassembly instructions, recycling center locations, or guidance on proper disposal.

The Digital Product Passport, as part of the larger European Green Deal, sets the overarching ambition to achieve net-zero emissions by 2050 and is designed to make sustainable products the norm, not the exception, across the EU.

This strategy aims to extend the lifecycle of products, ensure easier repair and reuse, and reduce waste, and the DPP in particular is the enabler of this strategy, making relevant product data accessible at every step, from design to disposal. For businesses, understanding this broader context is critical, as compliance with DPP is just one part of a much more comprehensive shift toward circular, transparent production and consumption.

For these reasons, the European Union mandates that DPPs support several strategic objectives:

1. Compliance with Legal and Environmental Regulations

The DPP serves as evidence of regulatory compliance, allowing businesses to prove adherence to current and future environmental and product safety regulations.

2. Support for a Circular Economy

By providing transparency into product components and usage, the DPP encourages product reuse, easier recycling, and reduced environmental impact, all aligning with the EU Circular Economy Action Plan.

3. Empowered Consumer Decision-Making

When sustainability and origin data are available at the point of sale, consumers can make ethical and eco-conscious purchasing choices based on accurate product information.

4. Innovation in Business Models

DPPs open up opportunities for businesses to introduce repair, refurbishment, and reuse models. Companies can move beyond the traditional take-make-waste model and embrace sustainable services.

In essence, the Digital Product Passport is a tool for traceability, transparency, and transformation, helping businesses and consumers alike to participate in a more sustainable future.

A business is only as strong as its team, and a team thrives under strong leadership! At Akeneo, we understand that behind every exceptional brand is a visionary leader who empowers their teams and drives innovation while bringing strategy to life. That’s why we awarded each of the following businesses – Giant Tiger, KoRo Handels GmbH, Kruitbosch, and Groupe Emosia – with the 2025 Leadership Award at Unlock. 

These brands are unmistakably at the forefront of internal transformation and efficiency. Faced with evolving customer expectations and growing pressure for operational agility, they made a strategic decision to streamline their processes with Akeneo Product Cloud. In doing so, they’ve not only overcome critical business challenges but also elevated the customer experience across all channels!

Navigating Unique Challenges on the Path to Product Excellence

To see a rainbow, you have to get through the rain, and before Akeneo, these companies weathered more than their fair share:

• Fragmented data, fragmented results: When your product information lives in five places at once, nobody knows which version to trust! For KoRo and Giant Tiger, product info lived across multiple tools such as spreadsheets, ERPs, and shared drives, making it difficult to maintain consistency. Both companies struggled with fragmented systems that led to unreliable product data and inefficiencies.
• Inefficiencies in everyday workflows: Spreadsheets and endless copy-paste routines? Not exactly a recipe for speed. Teams spent too much time on repetitive tasks like locating images, updating spreadsheets, or transferring data between systems. At Groupe Emosia and Giant Tiger, this slowed down product launches and distracted teams from strategic priorities.
• Limited access and limited alignment: Without centralized platforms, teams lacked visibility and had trouble working together efficiently. Groupe Emosia’s disconnected workflows and Kruitbosch’s multi-partner ecosystem made collaboration especially difficult without a single source of truth.
• Barriers to scalable growth: Growing pains are real, especially when your systems weren’t built to scale. Legacy systems made it tough to expand into new markets or channels. Kruitbosch struggled with adapting product content for B2B and B2C audiences across regions, while KoRo needed a better structure to scale operations globally.
• Unreliable data with risky decisions: Guesswork doesn’t belong in product data. Without proper control over updates, teams faced issues with outdated or incorrect product specs. KoRo lacked version tracking and change history, increasing the risk of compliance errors. Giant Tiger faced similar challenges as their catalog expanded.

Despite what ‘hypebeasts’ and overenthusiastic tech gurus would have you believe, modern growth doesn’t just depend on hustle or even great products; it hinges on how well you manage and deliver information. With powerful tools like Product Information Management (PIM) and Digital Asset Management (DAM), companies can unlock new levels of efficiency and consistency while delivering top-tier experiences for customers. From global brands to fast-scaling startups, those embracing these systems and ensuring that they work well together are setting themselves up for faster growth.

But what exactly do these powerful solutions do, and how are businesses achieving rapid growth through their implementation? The 2025 Digital Commerce Benchmark Study,  created in partnership with Ntara and Bynder, offers fresh insights into how PIM and DAM together are driving success across industries, along with other factors.

Let’s take a sneak peek into the report to see what the data says about the impact of these technologies, and why investing in them might be the smartest move your business makes this year!

What’s PIM and DAM?

Before diving into how these systems fuel business growth, it’s important to understand what they actually do.

Product Information Management (PIM) is a software solution that helps businesses store, organize, manage, enrich, and distribute product data across all sales and marketing channels. It serves as the single source of truth for product information, ensuring accuracy, consistency, and efficiency at every touchpoint.

Digital Asset Management, on the other hand, refers to the system and processes used to centralize, manage, and deliver digital assets such as images, videos, graphics, and documents across teams and throughout the product lifecycle. DAM ensures that everyone has access to the right content, in the right format, at the right time.

Better Results Start with Customer Centricity

89% of businesses say that improving the customer experience is their top priority when it comes to how they bring products to market. This customer-first mindset is echoed in how companies are choosing and upgrading their technology: PIM, DAM, ERP, CMS, and CRM, and eCommerce systems are among the most frequently planned investments, especially in sectors like consumer brands and industrial manufacturing.

These investments help to streamline internal operations, and they help these customer-focused organizations create more seamless and personalized experiences for buyers. Businesses that pair systems like PIM and DAM tend to lean more toward customer-centric thinking, making tech decisions that prioritize the end-user experience rather than just internal cost savings.

And it’s paying off: despite economic uncertainty, 41% of businesses reported strong performance over the past year, and 51% expect growth in 2025. The companies seeing the most traction are those that understand that great product experiences start with customer needs, not internal processes.

Maturity Isn’t Just About Age—It’s About Smart Tech Moves

Digital maturity is the result of deliberate investment in the right systems, strategies, and tools. Even companies with relatively high digital maturity often hesitate to call themselves “ahead” – a clear sign that keeping up with shifting buyer expectations and evolving channel demands requires continuous investment.

But there’s a clear pattern: as digital maturity increases, so does competitive confidence. Among companies that rated themselves at the highest level of digital maturity, 100% reported confidence in their ability to compete – none felt behind.

One of the strongest signals of smart digital investment? Companies with both PIM and DAM in place report 36% higher digital maturity and 39% greater confidence compared to those without. And that confidence translates into action: these businesses are better equipped to launch products faster, scale across channels, and deliver exceptional customer experiences.

Each year, the Akeneo Expansion Award honors visionary customers leveraging Akeneo Product Cloud to fuel remarkable growth and transformation. The 2025 recipients, including Sealed Air Corporation, FELCO, and Digital Factory InVivo, are no exception. These leading brands show how a strategic approach to Product Information Management (PIM) solves many existing data management problems and can unlock global markets and unparalleled efficiency. New channels, fresh audiences, and complex challenges become opportunities when product data is put to work.

Despite operating in diverse industries, these companies faced remarkably similar hurdles before implementing Akeneo. But today, they offer valuable insights for any business looking to turn product data into real growth.

Growing Pains: The Pre-Akeneo Struggle

Before Akeneo, our award winners were stuck with product data systems that just couldn’t keep up with their ambitions. Their biggest hurdles included:

• Inefficient manual processes: Managing extensive product portfolios relied heavily on manual methods and spreadsheets, making processes time-consuming as the companies scaled. Launching a new product could take a month – not exactly agile.
  
• Siloed and inconsistent data: Data was often managed in separate systems, leading to siloed information and difficulty providing consistent product information across channels. Importing data from disparate sources led to unreliable product data.
  
• Limited accessibility and collaboration: Existing systems just didn’t cut it—teams struggled to access or update product info quickly, and manual data sharing made it tough to keep channel partners in the loop, let alone support them efficiently. Dependence on technical teams for data management and integration slowed things down and left little room for business users to take the lead.
  
• Difficulty scaling and expanding: Outdated legacy systems lacked the flexibility needed for modern eCommerce. Expanding globally was difficult, customizing content for different channels was slow, and keeping up with new regulations just piled on more complexity.
• Risk of losing institutional knowledge: For companies like Sealed Air, key product knowledge lived in the heads of seasoned team members. Without a system to capture it, that expertise was at risk of being erased.

These challenges stood in the way of global growth and kept brands from delivering the consistent customer experience today’s shoppers expect.

The Power of Akeneo’s Product Cloud

Akeneo Product Cloud gave these companies the springboard they needed to turn growing pains into growth wins! And while each brand’s journey was unique, several common themes emerged in how they leveraged Akeneo, including:

1. Creating a single source of truth: The first major win? Bringing scattered product data together to create a single, centralized product data repository. This shift eliminated the need for manual spreadsheets and ensured data reliability and consistency across all channels. As Digital Factory InVivo noted, Akeneo made managing their large, complex catalog a whole lot simpler by providing them “with a centralized and structured platform”.
   
2. Automation in action: All three winners leveraged automation to improve their workflows significantly. FELCO cut product launch time from one month to just 24 hours by automating publishing, and Digital Factory InVivo sped up catalog integration and updates, reducing ticket processing time from over a day to just a few hours. In fact, Digital Factory InVivo saw efficiency gains overall equal to 1.5 full-time employees in just three weeks.Thanks to Akeneo’s rule-based automation and smart features, AI-driven product descriptions streamlined processes and freed up valuable resources for all of our PXM Champions.
   
3. Seamless integration and data syndication: Akeneo’s composable infrastructure also played a key role in these transformations, making it easy to plug into the rest of the tech stack via APIs. The winners connected Akeneo with everything from ERP systems to DAM tools like Cloudinary, ETL tools like Kiboko, and eCommerce platforms like Shopify. This enabled smooth data sync, streamlined syndication, and stronger collaboration with channel partners. FELCO even retired its legacy GS1 system by managing compliant data directly in Akeneo using reference entities – a major win for both efficiency and flexibility.
4. Enabling global reach with multilingual capabilities: For brands expanding across borders, Akeneo’s multilingual and localization data capabilities made a big difference. FELCO tapped into native genAI tools to translate product descriptions with local relevance, and scaled from 10 to 170 markets in under six months, while new product launches across the board accelerated from months to just days or hours. Digital Factory InVivo tailored catalogs and enriched content to meet customer expectations and stay compliant with country-specific regulations.
5. Empowering teams with improved data quality: Akeneo gave business teams the reins with a structure that’s built for autonomy, not bottlenecks. With robust control and validation tools, teams could manage product data confidently, leading to stronger consistency, better SEO, and cleaner content all around. Digital Factory InVivo saw a significant expansion in attribute management after adopting Akeneo, growing from 40 to 650 attributes, while FELCO’s channel partners gained easier access to enriched product data, strengthening relationships and boosting sales effectiveness.
6. Transforming for the future: With the right strategy and the right technology in place, these PXM Champions are setting themselves up for long-term success. Sealed Air used AI to preserve decades of product knowledge, while FELCO enabled the online sale of spare parts and Digital Factory InVivo launched new eCommerce platforms and digitized partner catalogs, creating an omnichannel platform that connects their entire value chain.

And while there’s a lot of really exciting ways your organization can take advantage of AI today, we’ve seen a lot of misinformation floating around about the impact AI will have on the retail industry. So, we decided to separate fact from fiction by dispelling the five most common myths we’ve heard about the most talked about technology of 2025, AI.

Myth 1: AI is a magical, fix-all solution

It’s tempting to view AI as a magic wand that can instantly resolve any business challenge we point it to. In reality, the power of AI lies in its capability to be used to address specific pain points identified by an organization as part of a larger strategy.

For instance, in the fashion industry, it can be nearly impossible to not only predict upcoming trends but then also react quickly enough to take full advantage of the market shift. A well-trained machine learning algorithm can ingest billions of data points from social media, user reviews, and market data in a matter of minutes and generate intelligent recommendations for patterns, styles, and colors. Some AI art generators could even assist in creating mock-ups of these products, or visualizing how they’ll look on different body sizes and shapes.

But who’s training the algorithm with the right target audience to ensure the AI generator isn’t creating a pink, sparkly button down shirt that’s popular in the Gen-Z market for their professional men’s shirt line? AI can help generate SEO-friendly and channel-specific product descriptions, but who’s identifying which channels and markets content needs to be curated for? Who’s communicating production and launch timelines to marketing and support teams, and ensuring everyone has the information and assets they need for a cohesive launch? 

AI is not a one-size-fits-all solution; at least for the foreseeable future, it’s not quite as easy as some people may think to just “ChatGPT it”. Which leads us nicely to our second myth.

Myth 2: AI will entirely replace human jobs

With Microsoft laying off more than 6,000 people in favor of automation and Duolingo coming forward as a “AI-first company” that will replace contract workers with machine learning algorithms, it’s not hard to see why people are panicking about AI taking their job.

But the truth is that AI needs to be viewed as a collaborator, not a competitor. While it’s true that AI has the potential to automate certain repetitive and routine tasks, its primary impact is on augmenting human capabilities and transforming the nature of work instead of entirely replacing the work itself. AI can help to optimize supply chains and reduce time-to-market, but it can’t (and shouldn’t) entirely replace the need for skilled engineers to design and create innovative products.

In actuality, the growth of AI is expected to create over 97 million jobs by the end of 2025; these roles will be more focused on data analysis and AI development and implementation, but will still require the empathy, creativity, and critical thinking of human beings that AI can’t quite replicate (yet). The adoption of this new technology should be seen as a reallocation of responsibilities that can actually lead to a more dynamic and productive workforce.

Myth 3: AI implementation is expensive and time-consuming

One of the most common excuses we hear about avoiding AI implementation is that businesses often believe that this process requires substantial upfront costs without immediate results. The truth is, while AI projects can be resource-intensive initially, cloud-based AI platforms and pre-built solutions have made it more accessible and cost-effective for many businesses. 

Cloud-based platforms eliminate the need for massive hardware investments and provide access to powerful computing resources on a pay-as-you-go basis, meaning a reduced up-front cost and an accelerated results timeline. Similarly, pre-built AI solutions are often tailored to specific industry needs, coming with predefined models and algorithms that can be customized to suit a business’s requirements, reducing the time it may take to implement.

Dwyer Omega is a global manufacturer and distributor serving diverse industrial needs across leading eCommerce marketplaces and distributor networks. To meet rising expectations and move at the pace of digital commerce, they needed to radically accelerate their time to market and eliminate the inefficiencies of manual product workflows. 

Their solution? Akeneo PIM paired with the Akeneo App for SAP Commerce Cloud, a combination that delivered speed, simplicity, and scalability from day one.

The Challenge: Manual Chaos and Mounting Delays

Before Akeneo, Dwyer Omega’s product teams were mired in manual processes. Onboarding and enriching product data meant wrestling with spreadsheets, which made launching new items slow and cumbersome.

Product syndication cycles dragged on for months, costing the business valuable time and commercial momentum. Meanwhile, technical teams found themselves constantly managing integrations and troubleshooting platform inconsistencies, diverting attention from strategic initiatives.

Their legacy systems were simply not built for the agility the business demanded.

Why Dwyer Chose the Akeneo App for SAP Commerce Cloud

The decision to adopt the Akeneo App for SAP Commerce Cloud was driven by three strategic priorities:

• Seamless connectivity: The app offered a direct, prebuilt integration between Akeneo and SAP Commerce Cloud, ensuring both systems could evolve together without additional development.

   

• Out-of-the-box value: With rapid deployment capabilities, the app reduced the need for internal IT resources and allowed the team to focus on business outcomes rather than plumbing.

   

• Business-centric approach: Instead of constantly fixing connections between tools, Dwyer Omega’s teams could devote their time to improving product experience and fueling go-to-market initiatives.

The Integration Advantage: Unifying Innovation and Execution

With the Akeneo App for SAP Commerce Cloud in place, Dwyer Omega benefits from unified innovation, as the integration ensures that both Akeneo and SAP Commerce remain compatible as they evolve, without the need for custom development. 

Operational harmony has also improved, with product data flowing automatically from Akeneo PIM to SAP Commerce storefronts, minimizing friction and reducing complexity for internal teams. 

Ultimately, this integration has become a strategic enabler, forming the backbone of Dwyer Omega’s go-to-market operations and empowering the business to move faster and more efficiently across global marketplaces.

Tangible Results: Speed, Scale, and Smarter Workflows

Since implementing Akeneo PIM and the Akeneo App for SAP Commerce Cloud, Dwyer Omega has achieved significant gains in efficiency, speed, and team productivity. Product activation, once a lengthy and cumbersome process that could take months to complete, has been dramatically accelerated. Now, new products flow across internal systems and external sales channels in near real-time, enabling the company to respond faster to market demands and opportunities.

This speed is matched by scalability. With features like bulk editing and automation, Akeneo has replaced time-consuming, manual processes with streamlined, intuitive workflows. The days of digging through spreadsheets and managing disconnected tools are gone. Instead, teams can make sweeping updates across large product catalogs with just a few clicks, significantly reducing the burden on internal resources.

As a result, product managers and other stakeholders are more empowered and engaged in their work. Freed from repetitive tasks and data wrangling, they can now focus on what really matters: curating compelling product experiences, driving strategic initiatives, and accelerating go-to-market efforts. This newfound efficiency and alignment across teams has not only improved operational performance but also laid the groundwork for continued innovation and global growth.

Looking Ahead: AI-Driven Innovation

Dwyer Omega isn’t stopping here. The team is now exploring Akeneo’s AI-powered capabilities to push their product operations even further. With built-in translation tools and generative AI features on the horizon, the company is poised to streamline content creation and localization like never before.

Ready to accelerate your time to market?

Discover how the Akeneo App for SAP Commerce can streamline your product activation process and keep your systems in sync.

👉 Explore the app on the Akeneo App Store

While a PIM lays the groundwork for managing internal product data, scaling efficiently requires a solution that brings your suppliers into the fold. 

That’s where  Supplier Data Manager (SDM), Akeneo Product Cloud, powered by Akeneo PIM, comes in. Together, they provide a centralized system for supplier collaboration, enriched with automation and AI, that helps you activate products across all your channels faster, more accurately, and with fewer manual touchpoints.

Let’s explore how this combined solution transforms supplier collaboration and why now is the moment to unlock its full potential.

1. Centralize Supplier Collaboration at Scale

Keeping all your product and supplier data in one place fundamentally transforms the way teams collaborate. With SDM, your suppliers no longer rely on messy spreadsheets or slow email exchanges. Instead, they interact with a self-service portal that’s directly connected to your product data ecosystem, with customizable workflows, tiered access, and error detection at every step.

This centralized approach:

• Speeds up onboarding by aligning your teams and suppliers in one workspace.
• Reduces errors with AI-powered mapping, shared templates, and built-in validation rules.
• Empowers suppliers to submit data confidently, with tailored permissions and guided workflows.

The average distributor uses SDM deals with 300 to 500 suppliers. With Akeneo SDM, customers like The Agent have cut onboarding time by more than 50%, while doubling the number of products activated per season. 

See how The Agent scaled faster and smarter with Akeneo SDM. 

2. Reduce Errors, Returns, and Manual Touchpoints

Inconsistent or incomplete supplier data can ripple through your entire product experience, from misclassified products that never appear in search to incorrect attributes that frustrate buyers and increase return rates.

Akeneo PIM + SDM helps eliminate these issues by getting product data right at the source, the moment the supplier submits it.

Together, they help you:

• Detect and resolve issues early using real-time error reports at the point of supplier upload 
• Validate data quality through mandatory fields and rule-based validations 
• Create a feedback loop that visually guides suppliers toward better data every time 

For example, Steelcase uses Akeno PIM + SDM to support the high volume of onboarding of spot buy and custom product categories that previously required heavy manual cleanup. With SDM’s automated validation and enrichment tools, they now catch data issues earlier, reduce internal workload, and get more products into their PIM faster.

Discover how Steelcase achieved a 62% reduction in annual time spent loading supplier data. 

3. Unlock Everyday Efficiency with AI-Powered Enrichment

While SDM without AI can help set up a foundation for distributors trying to industrialize an onboarding process and provide suppliers a guided workflow for catalog sharing, the benefits of AI within SDM automate and optimize every stage of the collaboration process, even during setup.

With built-in automation and intelligent enrichment, teams spend less time fixing data and more time optimizing product experiences.

Here’s how AI with Akeneo SDM adds value in real-time:

• Auto-categorization ensures every product is consistently classified across multiple hierarchies, saving teams from repetitive corrections
• Attribute extraction pulls details like color, material, and fit directly from product descriptions, reducing manual input and error risk
• Multilingual normalization handles translations and synonyms effortlessly, keeping global product data consistent and ready for any channel

Find the Right Package for Your Business 

Ready to scale smarter? 

The future of supplier collaboration is centralized, automated, and collaborative. With Akeneo Supplier Data Manager, you gain faster onboarding, better data quality, and more powerful product experiences across every channel.

Now is the time to take advantage of Akeneo PIM + Supplier Data Manager. Suppliers are more tech-savvy. eCommerce demands are higher than ever. And your teams deserve to focus on strategy, not spreadsheets. 

Want to keep up with the latest innovations and best practices?
Subscribe to our Monthly Product Update Newsletter for expert tips, product updates, and customer stories. 

But what even is AI? It feels as if we’ve reached a point where businesses are just slapping the phrase “Powered By AI” on anything and everything without any consideration as to what that actually means. 

So let’s take a step back from all the noise and hype, and go back to basics by diving into the fundamental aspects of AI, exploring its definition, workings, historical context, and its specific implications for the retail sector.

What is Artificial Intelligence (AI)?

At its core, Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, understanding natural language, and even recognizing patterns. AI systems are designed to simulate human cognitive functions, making them incredibly versatile.

How Does AI Work?

AI operates on the principle of machine learning (more on this later), where algorithms and models are trained on vast datasets to improve their performance over time. Machine learning involves the following key components:

• Data Collection: To begin, AI systems require substantial amounts of enriched and cleansed data, which serve as the foundation for learning. In the retail industry, data can include customer preferences, purchase history, browsing patterns, inventory levels, product information, and more.
• Training Models: After collecting and consolidating all of that data, AI models are fed the thousands, or potentially millions, of data points and trained to recognize patterns, make predictions, and optimize processes in the data set through iterative training. This is when your team should be closely monitoring the data ingested by the AI model to minimize biases, inconsistencies, or inaccuracies.
• Inference: After the training phase, AI systems can make decisions, predictions, and recommendations based on existing and new data. For instance, eCommerce platforms employ recommendation engines that use AI models to suggest products to customers based on their browsing and purchase history, or based on the purchase patterns of similar customer profiles.

Why is AI Important?

While AI is no magic wand, the significance of AI lies in its ability to revolutionize various aspects of business and society based on enriched, accurate data. Here are some key reasons why AI is important:

• Efficiency: AI can automate repetitive tasks, improving efficiency and reducing the risk of errors. AI will not entirely replace human jobs; it’s a collaborator, not a competitor. This technology can help to optimize supply chains or help customer service teams intelligently route inquiries to the right person, but it can’t entirely replace the need for skilled engineers to design and create innovative products or service reps to answer complex problems.
• Personalization: Personalization with AI is all about delivering tailored experiences to individual customers based on their preferences, behavior, and historical interactions at scale. It’s a game-changer because it shifts the shopping experience from a one-size-fits-all approach to a highly customized one. 
• Customer retention: AI doesn’t just help organizations provide personalized shopping experiences at initial purchase; AI can also be used to keep customers engaged over time by suggesting complementary products, sending relevant updates, and recognizing when a customer is due for a replenishment or replacement.
• Predictive analytics: By analyzing vast amounts of historical data and considering factors such as seasonality, economic indicators, and consumer preferences, retailers can utilize AI to accurately forecast demand for their products. This insight allows them to optimize inventory levels, ensuring that they have the right products in stock at the right times. As a result, retailers can reduce the costs associated with overstocking or understocking, minimize the risk of product shortages, and ultimately enhance their profitability by aligning supply with demand more effectively.

What’s the History of AI?

AI has a rich history that dates back to ancient times, but its modern development can be divided into several key phases:

Birth of AI (1950s-1960s): The term “Artificial Intelligence” was coined in the 1950s. During this period, early AI pioneers like Alan Turing, John Von Neumann, and John McCarthy laid the groundwork for AI as a scientific discipline. But during this inception phase, AI’s credibility was already under scrutiny due to its groundbreaking nature.

AI Winter (1970s-1980s): Progress in AI faced setbacks during this period due to high expectations and limited computing power. Funding and interest in AI dwindled, leading to what is known as the “AI winter.”

AI Resurgence (Late 20th Century): The late 20th century saw a resurgence in AI research, driven by advancements in machine learning and neural networks. During this period, artificial intelligence witnessed significant advancements attributed to powerful new computers capable of accelerating algorithmic computations, alongside the advent of the internet and widespread data sharing. 

Modern AI (21st Century): AI has transcended the realm of theoretical research and has firmly established itself in mainstream use across a myriad of applications. Autonomous vehicles, one of the most prominent examples, rely heavily on AI technologies like computer vision and machine learning to navigate and make real-time driving decisions. Similarly, virtual assistants such as Siri, Alexa, and Google Assistant have become integral parts of our daily lives, utilizing natural language processing and speech recognition to provide information, perform tasks, and control smart devices. 

Beyond these well-known applications, AI is at the heart of numerous other innovations, spanning from healthcare diagnostics and recommendation systems in e-commerce to fraud detection in financial institutions. The 21st-century AI landscape reflects a dynamic and rapidly evolving field that continues to push the boundaries of what is possible in technology, profoundly impacting how we live, work, and interact with the world around us.

Is Machine Learning the Same as AI?

Simply put, no.

The slightly more complex answer is that machine learning (ML) is a subset of AI. AI encompasses a broader spectrum of capabilities, including natural language processing, robotics, and expert systems.

Machine learning, on the other hand, focuses on developing algorithms that enable machines to learn from data and improve their performance over time. This means that machine learning systems can adapt and improve their performance as they encounter new information, making them well-suited for tasks like image recognition, speech processing, and data analysis. 

In essence, while AI defines the overarching goal of creating intelligent machines, machine learning serves as a pivotal component, enabling these machines to acquire knowledge, make predictions, and solve complex problems by leveraging the power of data-driven learning.

What is Generative AI?

Generative AI is a subset of AI that focuses on creating new content or data, often in the form of images, text, or audio. It uses techniques such as generative adversarial networks (GANs) and reinforcement learning to produce creative outputs.

A prominent real-life example of generative AI is in the field of art and content generation. For instance, AI-powered systems can create artwork, compose music, or even write articles, showcasing the potential for AI to augment creative processes.

What are the Risks of AI?

As with any powerful technology, it’s risky to view AI through rose-colored glasses. Let’s take a look at a few of the potential risks that come with implementing AI technology.

• Differentiation: A strong brand identity creates an emotional connection with customers. But as AI continues to grow in popularity, we run the risk of creating a sea of template-based, algorithm-generated content. Maintaining a distinctive brand identity and voice becomes an even more crucial factor as content generation becomes more automated.
• Biases & liability: Artificial intelligence relies on 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. 
• 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.
• 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.
• 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. 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.

How Will AI Impact Retail?

AI has already infiltrated the retail market, and will continue to do so over the next several decades. Let’s take a look at a few of the ways AI can be harnessed by brands to improve the customer experience and impact the retail industry.

• Data cleansing & enrichment: AI algorithms can standardize product data by enforcing consistent formats, categories, and naming conventions. This ensures that all product information is structured uniformly, making it easier for customers to navigate and compare items on your eCommerce or retail site.
• Data analysis & personalization: As we’ve mentioned, AI algorithms can ingest vast amounts of customer data to produce data-driven insights into customer preferences and behavior. 
• Market expansion: AI can allow brands to reach global markets and channels by providing the opportunity to create tailored, localized content at scale. While AI-generated translations still require human oversight, this technology democratizes the ability to translate titles, descriptions, shipping sizes or timelines, and units of measure, all while considering cultural nuances or local regulations.
• Efficient customer service: Chatbots and virtual assistants powered by AI can provide round-the-clock support regardless of time zone or language. While a robot often can’t, and shouldn’t, be used to solve complex problems or troubleshoot intricate issues, this technology can be used to triage common technical questions or at least intelligently route particular questions to the right humans for answer.
• Inventory management: Utilizing AI-driven demand forecasting enables retailers to fine-tune their inventory management. By doing so, retailers can make precise predictions for order quantities, effectively mitigating costly overstock or understock situations. 

AI for Product Experiences (PX)

In a world where AI is all the buzz, it can be hard to truly understand how to utilize AI for product experiences (PX) and the benefits this can have for your business. AI for PX is all about harnessing some of the most innovative AI technologies out there today to create efficient, hyper-personalized product experiences across an array of digital and physical touchpoints.

If you take one thing away from this article, let it be this: bad product data in = bad product data out. No matter how advanced AI and technology has become you should always start by looking at the quality of your product data first. 

Regardless of how advanced technology becomes, it’s the foundation of reliable, high-quality product data that truly unlocks the transformative potential of AI. As businesses embark on this journey, the wisdom of prioritizing data quality serves as the guiding star, ensuring that AI-driven product experiences reach their full potential, elevating customer satisfaction, and ultimately propelling businesses toward greater success in the digital age.

At Akeneo, our belief is simple: great product experiences begin with great people, strong values, and a clear sense of purpose. That belief extends far beyond what we build. It shapes how we operate, how we support one another, and how we show up in the world. 

Our 2024 Company Impact Report is a reflection of how we’re holding ourselves accountable to the environmental, social, and governance (ESG) standards that matter most to our people, our partners, and the communities we serve.

A Measurable Commitment to the Planet

One of the most tangible ways we’re leading with impact is in how we manage our environmental footprint. In 2024, Akeneo achieved a nearly 30% reduction in carbon emissions compared to the previous year. That’s the result of thoughtful action, from optimizing our cloud infrastructure to hosting our first Akeneo Recycle Day, which was a grassroots initiative with a simple but powerful concept: give unused laptops a second life by offering them to employees, with all proceeds donated to charity.  In the future, we plan to expand this initiative to include other unused office tech, reinforcing our belief in the principles of the circular economy.

Sustainability is also embedded into how we move. While travel is sometimes necessary, we now integrate sustainability criteria into event planning, aiming to reduce travel-related emissions by 30%. Whether it’s consolidating in-person meetings or choosing eco-friendly venues, every detail counts.

We’re not stopping there. By the end of 2025, we aim to have our transition plan certified by the Science Based Targets initiative (SBTi), a globally recognized gold standard for emissions reduction. It’s all part of a broader philosophy: progress must be intentional, measurable, and transparent.

Putting People First

Of course, Akeneo’s impact story isn’t only about carbon; it’s also about culture. One of the most important sections of our Impact Report focuses on creating a workplace where people can thrive. 

In 2024, we continued to invest in our Manager Essentials and Women in Leadership programs, helping employees build the skills they need to grow their careers. We expanded access to mental health services with prevention coaching, four individual therapy sessions per year, and company-wide learning sessions on topics like diversity, inclusion, and corporate responsibility. At a time when mental health is too often an afterthought, we’re putting it front and center.

Flexibility remains a pillar of how we work. Long before remote and hybrid work became widespread, Akeneo embraced flexible arrangements. Today, our hybrid model—typically 1–2 days in the office and the rest remote—is designed to meet employees where they are, while still nurturing the benefits of in-person collaboration. We also offer “work from anywhere” opportunities for up to one month a year, recognizing the importance of work-life balance in all its forms.

Community, Culture, and Connection

Beyond individual benefits, we’ve expanded programs that connect our employees with the world around them. Our Employee Volunteering Program grants each team member two days of paid time off annually to support the charities of their choice. In 2024 alone, Akeneo teammates contributed more than 360 hours across 11 office-led initiatives. 

The highlight? A global virtual walkathon that raised funds to donate e-bikes to mobility programs in France. It was a powerful example of what’s possible when global teams unite around a shared purpose.

Diversity, equity, and inclusion (DE&I) continue to be a focus. We now have five Employee Resource Groups (ERGs)—for women, LGBTQIA+ team members, neurodivergent individuals, parents, and their allies. These groups provide safe spaces, facilitate events, and help underrepresented voices be heard. They’re a core part of our “Purple Fire”, which is our unique blend of bold values that include Inclusive Community, Humble Hunger, Responsible Pioneers, and Diligent Benevolence.

Leading with Ethics and Integrity

Finally, no impact conversation would be complete without governance. Ethical business is a foundation we build everything else upon. From zero-tolerance policies on corruption and bribery to our ongoing work with EcoVadis, where we’ve earned a “Committed” badge and are aiming for Silver, we’re ensuring that integrity is woven into every partnership, policy, and practice.

We’ve also taken strong positions on issues like modern slavery, human trafficking, and supply chain ethics. We use international frameworks such as the UN Guiding Principles on Business and Human Rights and the International Labor Organization’s conventions to shape our approach. It’s not just about risk mitigation; it’s about being a company people can trust.

The 2024 Akeneo Company Impact Report

The 2024 Company Impact Report is a reflection of what happens when a company aligns its business strategy with its values. It’s about measurable action, intentional leadership, and a commitment to being better for our customers, our colleagues, and our communities. Whether you’re a partner, a prospective employee, or a curious observer, we invite you to explore the full report.

 Download the full 2024 Akeneo Company Impact Report and discover how we’re turning values into action—one initiative at a time.