It wouldn’t be PXmas without a grand finale! 

For Day 12 of our 12 Days of PXmas, we’re spotlighting the real-world impact of great product experiences. This roundup brings together 12 Akeneo customer stories that show how brands are turning complex product data into faster launches, smarter workflows, and more compelling customer experiences across every channel. 

So grab a cup of cocoa, cozy up, and unwrap the ingenuity and impact behind twelve transformations from 2025.

12 Stories of PX Success

1. Tiffany & Co.

Tiffany & Co. modernized and centralized its product information management by implementing Akeneo Product Cloud, enabling the brand to: 

• Clean and structure complex product data 
• Eliminate errors and duplications 
• Support multiple market catalogs with greater precision and consistency

With a stronger governance framework and integrations across systems like Salesforce Commerce Cloud, Algolia search, and Adobe DAM, Tiffany significantly improved data accuracy and accelerated its global expansion, launching and supporting seven new markets in six months, with notable reductions in time-to-market and enhanced localized product experiences that reinforced the brand’s high standards for global customers. 

Learn more about Tiffany & Co.

2. Fossil

Fossil, the global lifestyle accessories company known for watches, jewelry, and leather goods, transformed its product information management by adopting Akeneo to replace fragmented spreadsheets and manual processes, centralizing all customer-facing product data and automating content creation with translation tools and rules features. 

This shift brought: significant improvements in data accuracy (up to an 85% increase), faster access and updates across channels, and more efficient seasonal product launches, enabling Fossil to scale its global operations and enhance the online customer experience while strengthening its competitive advantage. 

Learn more about Fossil

3. Sealed Air

Sealed Air, a global leader in protective and food-safety packaging best known for Bubble Wrap®, moved away from disparate systems and manual spreadsheets to a centralized, structured source of truth that supports scalable growth and preserves decades of product expertise with Akeneo. 

This digital transformation not only streamlined internal workflows and improved data access and accuracy, but also strengthened partner enablement by providing detailed and consistent product content, automated data sharing, and enhanced import/export capabilities; all of which were efforts that helped Sealed Air earn Akeneo’s 2025 Expansion Award for driving growth across new and existing channels. 

Learn more about Sealed Air

4. JC Perrault

JC Perreault, a family-owned Canadian retailer of high-end furniture and appliances, modernized its digital operations by implementing Akeneo Product Cloud to centralize and standardize product information that had previously lived in outdated, manual systems. 

By creating a single source of truth for product data and assets, leveraging AI for translation and enrichment, and automating workflows with integrations, the company: 

• Dramatically improved data quality 
• Accelerated product launches from two weeks to just 24 hours 
• Expanded its online catalog fourfold while ensuring consistent, enriched product experiences across both its JC Perreault and Espace 67 brands

Learn more about JC Perrault 

5. tectake

Tectake, a European home and garden retailer selling approximately 2,000 products across 80 marketplaces in 16 countries, consolidated its fragmented product data tools into Akeneo PIM to create a centralized source of truth, and implemented Akeneo Activation to streamline marketplace syndication. 

By replacing manual workflows and disparate systems with a unified platform, tectake drastically reduced marketplace integration time from months to just 15 days, automated attribute mapping with reusable templates, and gained real-time alerts that help prevent product delistings and ensure consistency across channels. This transformation not only accelerated time-to-market and improved marketplace performance but also empowered the team to proactively manage product experiences at scale, supporting strategic growth across diverse global channels.

 Learn more about tectake

6. Courir

Courir, a fashion-driven sneaker retailer and winner of the 2025 Akeneo Accelerator Award, transformed its product information workflows by adopting Akeneo and moving from manual, time-intensive processes to a centralized, AI-powered system that dramatically increased operational efficiency and digital agility. 

By consolidating product data in a single source of truth and integrating tools like the BEE App and a custom troubleshooting dashboard, Courir: 

• Cut product description and translation time by over 90% 
• Reduced manual entry by 97% 
• Sped up video uploads by 66% 
• Achieved a 96% product publication rate, resulting in faster time-to-market, higher-quality enriched product experiences, and scalable omnichannel growth online and in stores. 

Learn more about Courir 

7. Steelcase

Steelcase, a global leader in office furniture and workspace solutions, modernized its cumbersome, manual product data processes by adopting Akeneo PIM and Akeneo Supplier Data Manager (SDM), creating a centralized source of truth that empowered business users to enrich and manage data independently of IT and significantly reduced manual work across multi-regional product records and supplier onboarding. 

With automated linking of assets like images and finishes, streamlined supplier data ingestion, and improved governance, Steelcase cut annual manual effort for supplier data by over 60% and accelerated product release workflows by more than half, driving greater operational efficiency and faster time-to-market. 

Learn more about Steelcase 

8. Leatherman Tools

Leatherman, the iconic multi-tool maker, replaced its outdated legacy product information system with Akeneo Product Cloud to unify product data across teams, improve accuracy, and accelerate time-to-market, while also introducing PX Insights to bring the voice of the customer directly into the product record. 

By centralizing workflows and surfacing real-time review data and AI-generated recommendations inside Akeneo, the team uncovered new use cases, such as IT professionals using rescue scissors as cable cutters, leading to optimized content, expanded SEO and metadata strategies, and fresh go-to-market opportunities. This customer-informed approach helped Leatherman transform data into strategic insights, elevate product experiences, and open new market possibilities based on how customers actually use their products. 

Learn more about Leatherman Tools 

9. Giant Tiger

Giant Tiger, a Canadian value-focused retailer with over 260 stores, implemented Akeneo to centralize previously siloed product information and assets to improve collaboration and operational efficiency across teams. 

By replacing disconnected spreadsheets and inconsistent asset storage with a single source of truth and structured asset tagging, Giant Tiger significantly increased product data completeness from about 60% to nearly 100% online, accelerated time-to-market through faster asset search and management, and empowered its teams to focus on strategic work instead of manual tasks; efforts that earned the company Akeneo’s 2025 Leadership Award for excellence in product information and asset management. 

Learn more about Giant Tiger 

10. Tikamoon

Tikamoon, a European direct-to-consumer furniture brand known for sustainable, high-quality solid wood products, overcame data inconsistency and system limitations by implementing Akeneo Product Cloud as a centralized source of truth, replacing an outdated CMS that restricted attributes and visual storytelling. 

By integrating Akeneo with key composable technologies like Cloudinary (DAM) and Sylius (eCommerce), and leveraging AI to automate content optimization and multilingual workflows, Tikamoon greatly expanded its product galleries, improved international syndication, and delivered richer, consistent product experiences across channels and markets, all while enabling a 20% larger catalog to be managed without growing the team. 

Learn more about Tikamoon

11. ABB

ABB, a global leader in electrification and automation, revolutionized its product catalog production by implementing Akeneo alongside InBetween’s smart automation software to centralize and structure product data and fully automate the creation of its massive 600-page 501 Catalog. 

What once took 6–8 months and extensive manual effort across teams now completes in just 18 hours, with the ability to update catalogs daily, add new products instantly, and generate localized, multilingual print and web versions with consistent, accurate information. This dramatically increased efficiency, freed up resources for strategic work, improved time-to-market, and ensured high data reliability and consistency across all formats, enabling ABB to deliver richer customer experiences and targeted marketing materials faster than ever. 

Learn more about ABB

12. Henderson Food Service

Henderson Foodservice, a long-established distributor serving thousands of hospitality and foodservice customers across Northern Ireland and the Republic of Ireland, strengthened its product data operations by partnering with Akeneo and deploying Supplier Data Manager (SDM) to centralize and automate supplier collaboration on product information. 

Facing complexity from more than 360 suppliers and strict UK/EU regulatory requirements for allergens, ingredients, and labeling, the company moved from manual, inconsistent processes to a connected workflow where suppliers upload structured data directly into SDM and Akeneo’s validation rules ensure compliance and completeness before it flows into the PIM, dramatically reducing processing time per product (saving an estimated 10 minutes per item), improving data accuracy and compliance, and transforming supplier relationships into a competitive advantage with faster time-to-market and greater transparency across every SKU. 

Learn more about Henderson Food Service

That’s a Wrap on the 12 Days of PXmas

As we close out our 12 Days of PXmas, we hope these 12 customer stories have filled you with as much inspiration as holiday cheer! Whether you’re dreaming of cleaner catalogs, faster launches, better marketplace performance, or simply happier customers, there’s a lesson here for every product experience journey. 

Here’s to taking these stories into the new year. May your data be merry, your workflows be bright, and your PX goals reach dazzling new heights. Cheers to continuous innovation and a very PXmas to all!

🎶 On the 11th day of PXmas, Akeneo shared with thee…
Eleven bylines worth bookmarking!

As we near the end of our 12 Days of PXmas, it felt like the perfect moment to look back on some of the ideas that made this year such a meaningful one for product experiences. Throughout 2025, Akeneo leaders shared their perspectives across top industry publications, tackling everything from AI and trust to sustainability, returns, and the future of retail. 

For Day 11 of PXmas, we’re wrapping them all up into one easy-to-explore roundup. So grab a mug of something warm, get comfortable, and let’s dive in. 

1. How to Earn Consumer Trust in an AI-Driven World

In this Martech Edge byline, Akeneo CEO Romain Fouache examines rising consumer skepticism toward artificial intelligence and explains how brands can close the trust gap as AI becomes more deeply embedded in customer experiences. While many shoppers recognize the value of AI-powered features, Fouache notes that concerns around data usage and transparency continue to hold consumers back. 

He argues that trust is not inherent to AI itself but is earned through responsible implementation, emphasizing data accuracy and transparency as the foundation of trustworthy AI experiences. By grounding AI in high-quality product data and clearly communicating how AI is used, brands can build confidence, reduce hesitation, and foster stronger, more reliable relationships with their customers.

Read the full article on Martech Edge

2. Reducing Returns and Enhancing Customer Experience

Virginie Blot, Principal Product Marketer at Akeneo, explains how high-quality product data is essential to reducing returns while improving customer experience and sustainability outcomes in this Sourcing Journal byline. She notes that many returns stem from issues such as inaccurate sizing, misleading descriptions, or insufficient product details, making returns both a customer experience and environmental challenge rather than just an operational one. 

By investing in enriched, accurate product information and leveraging data across the product lifecycle, retailers can help shoppers make more confident purchasing decisions, reduce return-related costs, improve reverse logistics, and ultimately turn returns from a persistent pain point into a driver of customer trust, loyalty, and long-term value.

Read the full article on Sourcing Journal

3. The EU’s Digital Product Passport Will Ruin Your Ignorance 

Benoit Jacquemont, co-founder and CTO at Akeneo, highlights the imminent arrival of Digital Product Passports (DPPs) as a catalyst for sustainability and transparency in product ecosystems in this byline with The CTO Club. With European Union mandates making DPP compliance compulsory for many industries in 2026, Jacquemont frames this regulatory shift not as a burden but as a strategic opportunity for technology leaders (especially CTOs) to build more transparent, resilient, and sustainable product information architectures that meet rising consumer and regulatory demands.

Jacquemont urges CTOs to treat DPPs as a chance to modernize technology stacks, break down internal data silos, and develop cross-functional collaboration between product, engineering, and business teams. He provides strategic steps tech leaders can take to ensure their organizations are prepared for this shift and can leverage DPPs to strengthen brand trust, reduce operational waste, and support long-term sustainability goals. 

Read the full article on The CTO Club

4. The Returns Crisis: How to Balance Sustainability and Convenience 

In this Sustainable Brands byline, the Akeneo team examines the growing returns crisis as retailers navigate the tension between consumers’ desire for sustainability and their expectation of fast, convenient returns. While shoppers increasingly value environmentally responsible practices, they are unwilling to sacrifice ease and flexibility, creating a challenge for brands seeking to reduce waste without harming the customer experience. 

The article outlines practical strategies to address this balance, including delivering accurate, in-depth product information, enabling hybrid return options to lower emissions, and localizing content to minimize confusion and unnecessary returns. By improving product experiences alongside sustainability efforts, retailers can reduce return rates while meeting customer expectations and turning returns into a competitive advantage.

Read the full article on Sustainable Brands

5. The Trade War that Restructures What Sells and Why 

In a Retail TouchPoints article, Akeneo CEO Romain Fouache explores how global trade tensions and shifting tariff policies are reshaping retail economics and consumer behavior, pushing shoppers to reassess what they buy and why. As rising prices and economic uncertainty weaken consumer confidence, Fouache argues that buyers are moving beyond a sole focus on low cost toward greater emphasis on value, transparency, and product purpose. He suggests this “trade war era” may accelerate more conscious consumerism, favoring brands that prioritize quality, ethical practices, and rich product information. 

By investing in clear storytelling, transparent sourcing, and stronger product experiences, retailers can better differentiate themselves and build long-term trust and loyalty in an increasingly scrutinized purchasing environment.

Read the full article on Retail TouchPoints

6. How Better Product Experiences Can Reduce Returns, Enhance Brand Loyalty, and Protect the Planet 

The team here at Akeneo explores the post–peak-season surge in returns and the negative impact it has on both brands and the environment, noting that most returns stem from inadequate or inaccurate pre-purchase product information in this byline featured in MyTotalRetail. The article argues that reducing returns isn’t about adding friction to the process but about empowering shoppers upfront with comprehensive, accurate product content that sets clear expectations. 

By enhancing product experiences, retailers can reduce costly returns, protect customer loyalty and brand reputation, and address sustainability concerns tied to excess emissions and waste, creating a win-win outcome of fewer returns, stronger trust, and a smaller environmental footprint.

Read the full article on MyTotalRetail

7. Retail’s AI Revolution Hinges On Consumer Trust And Data Quality 

In this Forbes article, Akeneo examines how artificial intelligence is reshaping the retail industry while emphasizing that technology alone is not enough to ensure success. The piece argues that AI’s true impact depends on consumer trust and the quality, accuracy, and transparency of the data that powers AI systems, particularly as AI becomes embedded in product recommendations, personalization, and search experiences. 

Without strong data foundations and clear communication about how customer information is used, retailers risk eroding trust and diminishing the return on their AI investments. Ultimately, the article highlights a broader industry shift toward pairing advanced AI innovation with responsible data governance and transparent customer practices to drive meaningful, sustainable engagement.

Read the full article on Forbes

8. How Retailers Can Make the Returns Process More Sustainable

Akeneo CEO Romain Fouache examines the growing sustainability challenge created by high retail return rates, highlighting consumer survey data that shows many returns are driven by poor or misleading product information. He explains that inaccurate sizing, unclear descriptions, and missing reviews often lead shoppers to return products, making improved product data one of the most effective levers for reducing unnecessary returns. 

Fouache also notes that nearly half of consumers factor the environmental and ethical impact of return processes into their purchasing decisions, elevating sustainability as a key driver of trust and loyalty. Framing returns as an environmental imperative rather than just an operational issue, he argues that providing accurate, enriched product experiences upfront can reduce waste, lower emissions, and strengthen both brand performance and long-term sustainability outcomes.

Read the full article on Supply Chain Digital

9. AI in Retail Ecommerce, with Benoit Jacquemont, CTO and co-founder of Akeneo 

In this CTO Magazine interview, Benoit Jacquemont discusses how artificial intelligence is reshaping the retail and eCommerce landscape. Jacquemont emphasizes that while AI offers powerful benefits like smarter search, automated content enrichment, and richer customer experiences, its success depends on high-quality, enriched product data that fuels reliable, trustworthy AI outputs.

Jacquemont also explores the risks of rushing into AI without a thoughtful strategy, particularly the potential erosion of consumer trust if AI systems deliver inaccurate or opaque results. By combining responsible AI adoption with clear data governance frameworks, retailers can unlock AI’s full potential while keeping customer trust and long-term engagement at the forefront. 

Read the full article on CTO Magazine

10. AI-Driven Product Experiences: Personalization, Trust & Data Accuracy

In this conversation, Akeneo CEO Romain Fouache explains how AI-driven product experiences are transforming eCommerce and retail, highlighting the need to balance advanced personalization with consumer trust. He emphasizes that high-quality, complete product data is essential for AI to deliver relevant results, reduce friction, and build purchase confidence across the customer journey. 

Fouache also outlines how Akeneo evaluates product information maturity to support AI, connects AI initiatives to measurable outcomes like return rates and customer satisfaction, and advocates for a transparency-first approach that includes ethical AI use, clear communication, user consent, and data privacy. Ultimately, he underscores that data accuracy and trust are foundational to unlocking AI’s full potential in product experiences.

Read the full article on Martech Edge

11. How Retailers Can Build Trust in the Age of AI and Deal-Day Fatigue 

The team here at Akeneo explores how retailers can navigate deal-day fatigue and rising expectations for AI-enabled experiences during peak promotional periods in this SalesTechStar byline. CEO Romain Fouache explains that while discount events drive engagement, they also lead to increased dissatisfaction and returns when product information is unclear or misleading, ultimately eroding consumer trust. 

The article argues that long-term loyalty cannot be built on promotions alone and emphasizes the importance of accurate, transparent product information—especially as AI becomes more central to eCommerce. By pairing responsible AI use with high-quality product data, retailers can meet heightened shopper scrutiny, reduce returns, and turn reliable product experiences into lasting customer trust and loyalty.

Read the full article on Sales TechStar

A Season of Stronger Product Experiences

And just like that, we’re almost at the end of our PXmas countdown.

These 11 bylines may come from different publications, but they all point to the same truth: better product experiences are built on trust, transparency, and great product information. Whether you’re navigating AI adoption, reducing returns, or meeting rising sustainability expectations, the message is clear: product experiences matter more than ever. Thanks for celebrating Day 11 of PXmas with us, and stay tuned as we unwrap the final day of our PXmas journey.

As 2025 draws to a close, businesses everywhere are taking a step back to reflect on a year defined by rapid change, rising customer expectations, and unprecedented technological acceleration. It’s been a year of learning (sometimes the hard way) as organizations navigated new market realities, evolving consumer behavior, and the expanding role of AI in shaping product experiences.

But before we turn the page to a new year, it’s worth pausing to look at what 2025 has taught us: the trends that stuck, the strategies that delivered, and the insights that will shape smarter decisions in the months ahead. Here are the top takeaways every business should carry forward.

The Top 7 Takeaways From 2025

1. AI -Powered Personalization is Here to Stay

Artificial Intelligence couldn’t be contained in 2025. AI went from a shiny new trend to a daily driver across the entire commerce journey. Shoppers now rely on voice assistants to help them buy products, use visual search to discover similar styles they like, and turn to AR and VR to see how items look in their space or on themselves. This evolution has forced brands to rethink every step of the product experience, moving beyond one-size-fits-all journeys to create fluid, intelligent interactions that adapt in real time to user behavior.

A key part of that evolution is personalization, and I’m not talking about “Hello, [First Name]” level personalization! Today’s customers expect product discovery, recommendations, and content that reflect their preferences and context across every touchpoint. More than 50% of consumers say that personalized experiences make them more loyal to a brand. That’s why AI-driven tools that support tailored content and dynamic recommendations have become essential for brands looking to deliver meaningful, individualized shopping experiences.

To make this level of personalization work, brands need visibility into how their product content performs, which is where a tool like Akeneo PX Insights helps. The solution gives teams real-time visibility into product content issues affecting performance, from missing attributes and short descriptions to pricing mismatches that hurt ad eligibility. PX Insights closes the feedback loop by revealing visibility gaps, providing actionable suggestions for improvement, and helping teams take action fast. With tools like AI Discovery Optimization and discoverability diagnostics, brands can stay one step ahead, optimizing content before competitors even realize there’s an opportunity!

2. Agentic AI Lays the Seeds For Agentic Commerce

If 2025 has taught us anything, it’s that we are swiftly approaching an age of agentic commerce, where AI agents search and shop autonomously for us consumers. In fact, a recent TechRadar report found that about one-third of U.S. consumers would let AI make purchases for them, and nearly a third have already used ChatGPT to assist in buying decisions.

With OpenAI announcing their “Instant Checkout” feature, we are seeing the discovery channel itself become. the point of purchase. This fundamentally changes how product data needs to be enriched and delivered. If an autonomous system is evaluating your catalog and deciding whether your product gets surfaced, your content must be complete, contextual, and machine‑readable. 

With predictions that around 33 % of enterprise software applications will include agentic AI by 2028, brands must act fast. As these systems become decision‑makers in the buying journey, commerce teams are no longer just designing for people but for intelligent agents too.

3. Traditional Search Is Evolving, But Still Essential

Despite the rise of AI-driven discovery and agentic experiences, traditional search still plays a central role in how people shop. In fact, consumers are still most likely to begin their buying journey with traditional search engines, using them to explore options and compare prices before making a decision. 

However, with the rise of AI-powered search that can offer a more personalized experience, shoppers are starting to expect search to understand intent and surface relevant products fast, no matter where they’re browsing. 

For brands, this evolution means search optimization now goes far beyond SEO headlines or keyword stuffing. What really determines search visibility is product data quality. From clear titles and complete attributes to rich descriptions and consistent taxonomy, search engines and shopping platforms reward content that helps their algorithms serve the right result at the right time. Incomplete, vague, or overly generic product content is a liability in today’s crowded digital shelves. As AI and agentic tools begin to rely on deeper data signals for ranking and selection, brands need to treat product data as search infrastructure. The better the content, the better the discoverability.

4. Consistent Omnichannel Shopping Isn’t Optional

The modern customer journey is anything but linear. Shoppers fluidly move between online and offline touchpoints, such as browsing on their phones and visiting a store to see a product in person. In fact, we found that, despite all of the advancements in digital commerce over the past few years, 30% of consumers still prefer to make actual purchases directly in physical retail stores.

In other words, while discovery and comparison may start on screens, many buying decisions still happen face-to-face, and vice versa. This fragmented journey means brands can’t afford to prioritize one channel over another. 

That’s exactly where Akeneo Activation comes in. As customer journeys stretch across more channels, the pressure to deliver consistent, channel-ready content at speed grows exponentially. Akeneo Activation helps brands automate the delivery of complete product data, ensuring it reaches each destination in the exact format required. Instead of juggling fragmented spreadsheets and portals, teams can streamline workflows and reduce manual effort, freeing up time to focus on strategy rather than formatting. 

More importantly, the platform enables optimized product experiences at scale, allowing teams to tailor content for each channel and boost SEO. Whether customers are shopping in-store or browsing online marketplaces, brands must deliver the same accurate product story everywhere.  In today’s diverse commerce landscape, Akeneo Activation makes scaling product experiences both achievable and effortless.

5. Customers Are Often Willing to Pay a Premium for High-Quality Product Information 

Consumers are researching more thoroughly and making buying decisions based on the quality of information they’re given. And when that information falls short, the consequences are real. In fact, 76% of buyers rank inaccurate product descriptions as a top reason for returning a product, and on the flip side, the majority of consumers are willing to pay 25-30% more for products with clear, comprehensive, and personalized product information. Whether it’s incorrect specs, missing details, or misleading images, poor product information directly impacts the customer experience and your bottom line. 

This is exactly the kind of challenge Akeneo PIM is built to solve. By centralizing product information, Akeneo allows brands to collect, enrich, and govern content in one place, then distribute it across every relevant channel with confidence. Instead of siloed spreadsheets or outdated assets floating between teams, businesses get a single source of truth that scales with them. In a market where trust is earned (and lost) in seconds, Akeneo PIM gives brands the control they need to meet customer expectations and exceed them!

6. User Reviews Strongly Influence Decisions

Consumers are paying close attention to what other people say about your products. Reviews have become one of the most powerful tools for shaping perception, offering a kind of real-world proof that marketing copy simply can’t replicate. For many shoppers, a glowing review is more convincing than any product headline. On the flip side, negative feedback can stop a sale in its tracks — even during high-intent moments like deal days. In fact, 45% of shoppers say they would abandon a purchase if they encounter negative reviews. Reviews act as a proxy for quality and satisfaction. When that is weak or negative,  consumer confidence can vanish in seconds.

The best way to counter hesitation and prevent negative feedback is by pairing strong product information with authentic user reviews. Clear descriptions, accurate specs, and detailed images help set the right expectations, reducing confusion and returns. When this content is reinforced by positive customer feedback, it creates a powerful trust signal that reassures new buyers. Together, high-quality product data and real reviews can boost credibility and create a consistent experience across every touchpoint.

7. Sustainability Is Core to Brand Value

In 2025, what a brand stands for is just as important as what it sells. Shoppers are paying attention to the values behind the products. From sustainability practices and ethical sourcing to transparency in the supply chain and detailed nutritional or allergen information, today’s consumers want to understand the full story. However, these are often the weakest links in product content. Our research shows that information tied to sustainability, compliance, and brand values remains among the least complete areas in product data, creating gaps that directly impact consumer trust and decision-making. When customers can’t easily find this information, they often interpret the absence as a lack of credibility or commitment.

On the flip side, consumers are increasingly willing to reward the brands that clearly communicate their purpose and principles with their trust and their dollars. In fact, 42% of shoppers say they would pay more for products from companies that openly share their values, with many of them willing to spend over 10% (and some, up to 24%!) more. Brands that embrace this are turning purpose into a differentiator, using clear product content to highlight ethical practices and social impact. Those who do it well are strengthening loyalty while boosting margins.

Looking Ahead: Commerce Isn’t Slowing Down

If 2025 taught us anything, it’s that commerce is evolving faster than ever. It’s driven by smarter technology, changing customer expectations, and the rising importance of trust and data quality. Brands that have adapted are creating standout experiences across every channel and platform.

The opportunities for 2026 are big, but so are the stakes. Whether it’s embracing agentic AI, streamlining omnichannel delivery, or investing in product experience as a strategic advantage, success in 2026 will be shaped by the brands laying the groundwork today.

AI may be the new engine powering digital commerce, but it’s running on a surprisingly delicate fuel: trust. 

Shoppers love the convenience of AI-powered search and chat-based assistance, but they’re not fully sold on its accuracy. In fact, only 45% of consumers trust that AI can provide reliable, accurate responses. And honestly, who can blame them? Most brands are still trying to get their product names consistent across channels, let alone feed clean data into an AI model.

That’s the real plot twist in today’s AI commerce story: the issue isn’t the AI but the data behind it. Without well-structured, complete product information, AI experiences become unpredictable, inconsistent, or even worse — confidently wrong. This is exactly where Product Information Management (PIM) steps in. Far from being a back-office tool, PIM has quietly become the foundation of AI commerce, ensuring brands deliver AI experiences customers can actually trust.

How PIM Supports AI Commerce

Most commerce stacks were never designed for agentic search or AI-generated recommendations. ERPs and MDMs excel at managing operational or generalized business data, but they aren’t built to handle the depth and contextual richness AI systems need to perform. As AI becomes more embedded in eCommerce platforms and customer experience engines, these legacy systems reveal their limits quickly.

For IT leaders and retailers trying to modernize their stack, this creates a fundamental bottleneck. AI can pull signals from customer behavior and intent, but it can’t compensate for missing dimensions of product data. When your ERP only holds base specs and your eCommerce platform holds manually uploaded descriptions, AI is forced to work with a fragmented picture. 

For retailers, this means product discovery tools struggle, onsite search becomes unreliable, and recommendation engines can’t confidently match buyers with what they’re looking for. The end result is the same on all sides: inconsistent search results and hallucinated outputs that end up destroying customer trust and undermining revenue-driving AI initiatives.

A PIM system like Akeneo PIM solves this foundational problem by serving as the single source of truth for rich, structured product content. Think of PIM as the supply chain for product information: it takes raw, unstructured inputs from suppliers, spreadsheets, and internal tools, and transforms them into organized and validated data ready for every channel. Just as a physical supply chain turns materials into finished goods, a PIM converts raw product records into high-quality, AI-ready assets through enrichment, normalization, and governance.

This structured process is essential for AI commerce. Product information needs to be contextualized and prepared for consumption by advanced systems. AI models require detailed taxonomies, consistent attribute sets, and relationships between variants. They also need product story elements like usage information and lifestyle detail. PIM becomes the operational engine, ensuring this information is both accurate and complete before it enters any AI-powered experience.

An API-First Integration Layer Built for AI-Driven Ecosystems

Modern commerce environments demand a level of connectivity that older systems simply can’t support. AI engines, marketplaces, DAM platforms, OMS tools, and eCommerce systems all need access to real-time product data, and they need it through flexible, API-first connections. A PIM system designed with APIs at its core guarantees that product information flows seamlessly across the entire technology ecosystem without brittle custom integrations or expensive IT workarounds.

With an API-first PIM, businesses can easily plug into emerging AI tools, agentic search platforms, and future interfaces that haven’t yet reached the mainstream! This future-proofing is essential as AI evolves rapidly. To thrive, businesses need flexible systems that can easily integrate new technology without replacing the old. PIM makes that possible.

Don’t Forget About Data Governance

In an AI-first commerce landscape, data governance changes from an operational concern to a strategic necessity. AI systems amplify whatever they’re fed, meaning flawed or non-compliant data damages customer trust and increases regulatory risk. Accurate, reliable product data is required to meet strict standards for completeness, accuracy, and compliance before it reaches any customer-facing system.

These governance capabilities include validation rules, workflow approvals, version control, completeness scoring, and audit trails, each of which acts as a safety net preventing low-quality information from entering the data supply chain. IT leaders and retailers alike can be confident that what enters their AI systems has been vetted by both automated checks and human oversight, dramatically reducing the chance of AI confidently delivering wrong or misleading recommendations. 

For IT teams, this means fewer data-related incidents and a significantly lower risk of AI-driven errors cascading across their tech stack. For retailers, it translates into more trustworthy recommendations and fewer moments where AI surfaces outdated or simply incorrect information to shoppers.

This governance layer also builds transparency, which is becoming a core expectation from buyers. As consumers grow more aware that AI guides their product discovery, they want to know that the information they’re seeing is precise and compliant. PIM enables businesses to provide that assurance and meet upcoming regulations that will scrutinize AI decision-making and product data accuracy even more closely.

Building the Foundation for AI-Ready Commerce

AI is rapidly transforming how customers search and evaluate products, but its success depends entirely on the quality and consistency of the data behind it. Legacy systems weren’t built for this new era, and retailers and IT leaders need a foundation that can support the accuracy AI-driven experiences demand. PIM fills that gap by delivering enriched and governed product information that every AI engine, channel, and customer touchpoint can rely on.

By investing in a PIM-driven data strategy, businesses future-proof their entire commerce ecosystem. With the right product data foundation, retailers can deliver more trustworthy experiences, IT teams can scale innovation confidently, and AI systems can perform at their fullest potential. In a world where product information fuels every interaction, PIM becomes the catalyst that unlocks meaningful AI commerce.

On the fourth day of PXmas, we’re celebrating “helpers”. And no, not the kind working at the North Pole. 

When you hear the word “Akeneo,” your first thought is probably our robust Product Cloud, along with the many products and services that work with them. And why wouldn’t it be? Our solutions are designed to help businesses achieve their goals and exceed their expectations!

But behind every feature and improvement lies something even more essential: the talented individuals who make them possible. As we continue celebrating the 12 Days of PXmas, we’re focusing on the real helpers; the teams and colleagues who shape Akeneo every day. 

Their expertise and dedication fuel everything we create, from helping unlock new opportunities for the business to nurturing the growth of our teams and culture. That’s why we spoke with four colleagues about their journeys, their roles, and what Akeneo means to them. They are:

Alay Shah, Director of Revenue Technology & Operations

Janelle Mims, Manager of Learning & Development

Jesse Creange, VP of Business Development

Sarah Assous, VP of Product Marketing

In Your Own Words: Who Are You?

Alay Shah: I am a Boston-based dad trying to find good New York-style pizza here. It’s not going that well. Aside from that, I am a big gamer who likes to play RTS and RPG games to unwind.

Janelle Mims: I am the Learning & Development Professional. I am someone who values experiences, and I think Learning & Development should be an experience. I’m quirky and a heartled trainer and builder! I’m not your typical corporate leader, though I used to think I had to be. I like chaos, but the good kind in small doses! I like curiosity and playing, and I like to think I bring that to how I support people in programmes. I’m also a former actor, so I do like a good story! I tend to use humour, drama, and the occasional side-eye to bring things to life! 

Jesse Creange: Hey! I’m Jesse, I’m 31, French and Paris-based, and father of a cute, reckless, and mischievous 1 year old! I’m an entrepreneur and a nerd *(I know an absurd amount of useless Lord of the Rings and Star Wars facts*).

Sarah Assous: Had you asked me this in January, my answer would have been very different. Today, I can proudly say that I am a new mother learning how to navigate my career and family life. At work, I’m a passionate and enthusiastic professional who loves to take on new challenges while supporting my team and colleagues to grow and reach their goals.

What Inspires You On a Daily Basis?

Alay Shah: The biggest inspiration for me is when people are pushing themselves to be better every single day, professionally and personally. If we as a whole strive to get better every single day, then we can and will change the world.

Janelle Mims: Small intentional moments. I’ve done a training session before, and it was great to see the “a-ha moment”. It’s great for them to challenge people and help them say, “I don’t see this working,” and to be able to have those discussions. I like people taking ownership of their own growth and development, and figuring out what to do when it’s different than what they expected. I also really like food! I’m driven by food and perfectly seasoned dishes, or the right song at the right time, as I’m driven by music as well!

Jesse Creange: My wife, with her extraordinary calm, kindness, intelligence, and my ever-smiling son!

Sarah Assous: Smart and passionate people who aren’t afraid to go after what they want. I love seeing people grow and reach their goals, whether professionally and/or personally.

What Would You Say Your Personal Values Are And Why?

Alay Shah:

Growth: I want to constantly be learning new things and gaining new experiences. These experiences allow me to view the world from new perspectives.

Curiosity: I constantly question everything and want to learn more about why something occurs. I also do not accept “It’s always been done this way” as an answer; I need to know why.

Janelle Mims: Courage, creativity, curiosity, adaptability, honesty, and joy. In terms of why, honestly, I’d call myself a big kid in the sense that I like to approach things with a childlike point of view. Children are excited; they jump into anything. They ask questions as much as possible, they’re honest, and they can find joy in the smallest of things. I think this mindset is extremely important because these values help me find the excitement in any part of the journey.

Jesse Creange: 

Curiosity: I love learning new things, even tiny ones. I can lose hours a week on Wikipedia.

Excellence: I have a natural tendency to always do the maximum when I do something… and often end up over-architecting things. 

Pleasure: I need fun at work. I like conviviality, jokes, food, drinks, passion, and friendly relationships with customers and prospects.

Autonomy: the combination of freedom and ownership in the service of collective effort. I enjoy when people drive their own success. Not to be confused with being a lone wolf!

Sarah Assous: Honesty, trust, embracing change, kindness, and positive attitude. I strongly believe in the law of attraction and that everything happens for a reason. These values have shaped and changed my life many times and have helped me be who I am today.

Why Did You Choose This Career Path?

Alay Shah: I actually sorta ended up in Revenue Operations and Technology. I started off in Financial Management, then moved over to IT supporting the GTM teams, and eventually moved into the Sales Ops team. From there, I stayed on the operational side of the business and never looked back.

Janelle Mims: As clichéd as it sounds, it found me. I am a former actress (many years ago!) and took an office job, because starving artists need to eat, in hospitality – specifically lifestyle concierge. I took it upon myself to show new hires the ropes, make sure they knew our systems and processes. Apparently, I did it well, because our global Head of Learning and Development brought me on her team, and that was that! I think the hospitality background translates really well into L&D as it helps us keep people at the center of everything we do.

Jesse Creange: The three things I love doing the most are:

1. Debating (with a pinch of bad faith and mischief)
2. Playing games (all kinds)
3. Building stuff

I think it naturally led me to entrepreneurship and sales 10 years ago!

Sarah Assous: I began my career in the London Insurance market in product innovation (very different from today, I know…). This experience helped me realise that there was an opportunity to be in a role where I could help shape the product while also having additional impacts within an organisation – I discovered that product marketing could help me do exactly that.

Working in an environment with constantly evolving markets and new innovative technologies excites me. That’s why I chose to predominantly focus my career on the B2B space and specifically businesses where I am able to put myself in both the buyer, user, and consumer’s shoes

I love being able to approach a problem and solution through different lenses, especially when I, as a consumer, can benefit from the outcome! 

What Does It Mean To You To Be In A Position Of Leadership?

Alay Shah: My view on leadership is that I am here to support the team and ensure they are successful. My goal as a leader is to have my team grow and learn skills they can use here at Akeneo and beyond in their professional careers. 

Leadership also means we take full accountability for failures and mistakes. We do not delegate failure, only success.

Janelle Mims: The first word that comes to mind is impact. I am constantly thinking of the impact I’m having and want to have on Akeneo and those within. That includes ensuring I’m leading by example, demonstrating the right values and behaviors, from communication to collaboration and more. Actively celebrating the wins and owning and learning from our mistakes. It’s being crystal clear in my expectations, and holding myself accountable and challenging others to do the same. It’s actively seeking how I can support and improve. Being in a position of leadership requires a lot of reflection to ensure you’re setting others up for success.

Jesse Creange: I am very grateful for the trust the Executive team, the Board, and all our internal customers put in the vision we presented as a team. I am also grateful for the warm welcome and excitement coming from the BDR, the Education, and the Enablement teams. It means everybody aligns on the diagnosis and the solutions we presented; it means it resonates with all. It’s the foundation for success.

Sarah Assous: For me, leadership is about having a clear vision and setting meaningful goals so that every team member understands their purpose and value. 

It’s about creating a space for people to shine in their strengths and skills, contributing not only to the organization’s success, but also to grow in their careers. 

True leadership is about sharing knowledge, sparking curiosity, guiding, and fueling innovation. As a leader, I aim to create a collective passion and push the boundaries to achieve great things together.

What’s The Akeneo Value That Speaks To You The Most?

Alay Shah: Humble Hunger.

Janelle Mims: I know I’m supposed to say Humble Hunger as it directly correlates with Learning and Development, but the answer to this question changes for me often. Currently, it’s Diligent Benevolence. We’ve gone through a lot of change, and I think all the values are important, but at the moment, this is the one that resonates the most.

Jesse Creange: Humble hunger!

Sarah Assous: Picking just one is certainly not easy, as I think they all speak to me in different ways. If I have to pick one, I would say Humble Hunger.

Wrapping Up the PXmas Spirit

The stories shared by our colleagues remind us that innovation is powered by people. Their passion continues to shape the Akeneo experience from the inside out, making our solutions stronger and our culture richer.

As we make our way through the 12 Days of PXmas, we’re grateful for the helpers who make every day at Akeneo brighter. Here’s to the people behind the product and the shared mission that carries us into the new year!

If you’ve ever watched your product data bounce between teams, tools, and channels like it’s running its own obstacle course, you know how exhausting the journey can be. A description gets updated in one system but not another. Images go missing. Attributes get duplicated. By the time everything syncs up, you’re already behind schedule.

That’s exactly the kind of chaos Akeneo’s Autumn Release sets out to solve. This season, we’ve introduced a major wave of AI capabilities designed to create a smooth, intelligent product data flow where information moves cleanly between systems, stays consistent across channels, and adapts automatically to whatever your team needs next.

Instead of wrestling with manual updates or chasing down scattered content, we’ve developed AI functionality that now helps your product data stay organized, enriched, discoverable, and ready for every touchpoint. It’s a smarter, more connected way to work, letting teams focus on creativity and strategy while the product data takes care of itself.

Let’s take a closer look at the AI capabilities now available in Akeneo and how they unlock better performance across every step of the product lifecycle.

1. Intelligent Asset Management with Akeneo DAM

Finding the right product image shouldn’t feel like detective work. But if your teams juggle multiple campaigns, brands, or seasonal refreshes, asset chaos can creep in quickly. Akeneo DAM changes that.

With AI-driven categorization and sorting, Akeneo DAM automatically analyzes your product images and assigns keyword tags based on what’s actually in the asset, not on someone’s interpretation of what “should” be tagged. That means your tags become more consistent, more complete, and far easier to search.

Instead of teams sifting through folders or guessing which filenames match a product, they can instantly pull up the right asset in seconds. The result is more time spent crafting engaging, conversion-ready experiences and less time lost digging for content.

Best of all, Akeneo DAM is fully integrated within Akeneo Product Cloud. Product managers, marketers, and eCommerce teams collaborate in the same environment, uniting product information and product assets in one powerful workflow. When your data and your visuals live together, and when AI keeps everything clean and organized, the creative process becomes smoother for everyone.

Learn more about Akeneo DAM

2. Centralized AI Configuration

With AI becoming an essential part of enrichment, translation, and content creation, teams need confidence and control over how their models behave. That’s where Akeneo’s new centralized AI Configuration hub comes in.

Instead of juggling prompts in multiple places or guessing why an output looks the way it does, teams can now manage all AI settings, including prompts, translation rules, enrichment models, and more, in a single space. Even better, they can preview AI-generated results in real time, experiment with different prompts, compare outcomes, and refine configurations instantly.

This level of transparency builds trust. Teams understand why the AI produces certain results and can tune the system to match their brand voice, product strategy, and customer expectations. As a result, organizations can scale AI confidently across their catalog without losing control or consistency.

Learn more about Akeneo’s Centralized AI Configuration Hub

3. AI Discovery Optimization

Search is changing. Shoppers are increasingly turning to generative AI like ChatGPT, Google Gemini, or Perplexity to research, compare, and choose products. And while this shift opens exciting new opportunities, it also raises a tough question:

How do you make sure your products appear and rank well within these emerging AI-driven discovery channels?

That’s what PX Insights helps to solve.

With PX Insights, brands can understand how their products perform across both traditional search engines and new AI solutions by helping your team to understand how AI platforms interpret and surface your products versus competitors, which product attributes influence ranking, and where your data may be missing, unclear, or inconsistent.

Even better, PX Insights analyzes customer reviews and feedback to generate structured, conversational content that resonates with both shoppers and AI models. You optimize your content once and benefit twice: improved AI discoverability and stronger SEO performance.

By ingesting visibility data from AI-driven channels alongside familiar platforms like Google Shopping, PX Insights provides a truly holistic view of discoverability. Teams can see which products appear in search, how they rank, and why. And, because PX Insights operates as a feedback loop within Akeneo Product Cloud, you can instantly act on the insights to enrich data, close gaps, and measure improvements continuously. Every enrichment effort becomes more strategic and more impactful.

Learn more about PX Insights

4. Expanded AI-Powered Data Extraction

Great product content often already exists; you just need help unlocking it. Akeneo’s expanded AI Extraction feature does exactly that, automatically generating product attributes and descriptions from media links, regardless of where they’re stored.

Whether your assets live in a DAM, CMS, or standalone folder, AI can now extract rich product information from videos, PDFs, images, and more. That means:

• No more manual data entry from spec sheets
• No more rewatching videos to capture key details
• No more copy/pasting from PDFs

Akeneo’s AI pulls the essential product details directly from the asset and converts them into usable, structured attributes or compelling descriptions.

This frees your teams to focus on creativity, brand storytelling, and customer experience rather than tedious extraction work. Raw content is transformed into product narratives that resonate without slowing down your workflows.

Learn more about Akeneo’s AI-Powered Data Extraction

5. Automated Catalog-to-Channel Mapping with AI

Every channel has its own product data requirements, and manually aligning your internal attributes to each destination can be one of the slowest parts of channel expansion. Akeneo’s AI-powered catalog-to-channel mapping changes that.

Instead of relying on spreadsheets or complex rule sets, AI now intelligently matches your internal data model to the format each channel expects. Whether you’re onboarding a new marketplace, expanding into a new region, or supporting a new partner, AI handles the configuration so your teams don’t have to.

This automation means faster channel launches, fewer errors, and more agility to expand into new markets whenever opportunity strikes. 

Learn more about Akeneo’s AI-powered catalog-to-channel mapping

6. AI Models of Your Choice Integrated into Akeneo

A particularly exciting addition this season is the introduction of the Akeneo MCP Server, a new layer that encourages experimentation and innovation in product data intelligence.

This environment opens the door to early-stage capabilities that explore how AI and large language models (LLMs) can help detect inconsistencies, surface recommendations, or enhance data governance. Through the new “Bring Your Own LLM” playground, users can connect their preferred AI models, whether for compliance, enrichment, analytics, or quality assurance, and see how they interact directly with product information.

This marks an important step toward a future where product data ecosystems can improve themselves. By allowing AI agents to analyze and interact with product data, Akeneo is laying the foundation for systems that help teams uncover insights faster, maintain accuracy effortlessly, and continuously enhance the product experience.

Learn more about Akeneo’s MCP Server

The AI-Powered Product Data Flow

As your product data moves through your organization into campaigns, onto channels, across markets, it shouldn’t feel like you’re constantly steering it back on track. With the Autumn Release, AI steps in as the force guiding that flow, keeping information clean, consistent, and always headed in the right direction.

These capabilities aren’t here to replace your team’s expertise. It’s meant to take on the repetitive work, surface the insights you might have missed, and help every system speak the same language so your teams can spend more time doing the high-impact work only humans can do.

With a smarter, more automated product data flow, your business gets faster launches, richer customer experiences, and a whole lot fewer bottlenecks. And the best part? It all happens naturally, in the background, as your product data simply moves the way it should.

Learn more about Akeneo’s latest functionality released in our Autumn Release, or reach out to a PX expert today to get started.

The Digital Product Passport (DPP) is coming. And for many manufacturers, it initially sounds like nothing more than a cost driver: labels, software, data maintenance – additional expenses required by regulation.

But seeing the DPP only as a compliance obligation means missing out on major potential.

TL;DR

• The DPP scan gives you a legitimate, high-intent touchpoint directly on the product—and can both reduce costs and increase revenue.
• Three practical playbooks show where the value comes from: Fix (help customers solve issues), Buy (recommend compatible parts and accessories), and Book (drive service appointments).
• Optimize based on value per scan and track repeat scans.
• Keep revenue-driving data separate from compliance data, but make both easily accessible.
• Start with a small pilot: 10 SKUs, one playbook, two QR placements, and measure results for 30 days.
• Update content regularly and visibly to encourage repeat scans
  

The Digital Product Passport: Cost Driver or New Revenue Stream?

Yes, the Digital Product Passport costs money. In practice, most expenses fall in the lower cent range per product. QR codes cost around €0.10 to €0.15; NFC tags around €0.25 to €0.30. Including platform and license costs, you typically end up around €0.40 per device at typical volumes.

But once you place a QR code on the product, you’ve already fulfilled three of the four key conditions for effective user activation:

• Right audience: reaching the person who actually uses the product
• Right place: putting it directly on the product
• Right time: producing the right information during use or when something goes wrong

The missing ingredient is: useful, action-oriented content.

And this is where it’s decided whether a scan delivers only compliance or real business impact.

Where Does Revenue Come From and How Do We Measure It?

Every scan can create value through three different mechanisms:

1. Fix:

The customer solves a problem without contacting support.
This lowers tickets, returns, and call center volume.
Measured by:

• Ticket deflection
• First-Time-Fix (FTF)
• Mean Time to Resolve (MTTR)

Even a modest deflection rate around 15% can have a large financial impact.

2. Buy:

After scanning, users see accessories or spare parts that are guaranteed to fit their exact model. This removes uncertainty and reduces returns.
Measured by:

• Scan-to-cart rate
• Contribution margin
• Accessory AOV

3. Book:

The scan leads directly to a service booking like maintenance, installation, upgrade and becomes more predictable

Measured by:

• Scan-to-book rate 
• No-show rate 
• Service AOV.

The Key Metric: Value per Scan

Formula:
(Accessory revenue + service revenue + avoided support costs) ÷ number of scans

Example

• 1,000 scans × 15% deflection × €12 per ticket = €1,800
  
• 1,000 scans × 3% scan-to-cart × €50 AOV × 40% margin = €600
  
• 1,000 scans × 1% scan-to-book × €120 × 50% margin = €600
  

→ €3,000 total value/month → €3 per scan

Best Ways to Utilize the Digital Product Passport (DPP)

Playbook A: Fix it fast

Many companies make it unnecessarily hard for users to get help. Hidden contact forms, overloaded hotlines, generic chatbots …

The DPP scan turns that around by offering immediate, product-specific help right where the problem occurs.

How it works

• The scan opens a page dedicated to that exact product.
• At the top: a clear button such as “Solve the issue”.
• A short, guided flow with images or 10-second videos leads users toward a solution.
• If the customer doesn’t contact support within 48 hours, it’s counted as successful deflection.
• If they do need help, support receives pre-filled details such as serial number or error codes.

Why it works

The scan combines high intent, context, and trust.
Users want a quick fix and are willing to follow short, targeted instructions.
According to Zendesk CX Trends 2024, 51% of users prefer self-service for urgent issues.

Key KPIs

• Ticket deflection
• First Time Fix (FTF) rate
• Mean Time to Repair (MTTR)
• Repeat scan rate
  

Pro tip:
If you can identify the issue clearly, show the relevant replacement part at the end to turn a Fix flow into a Buy opportunity.

Playbook B: Sell accessories & spare parts

Not every purchase has to go through Amazon. Context is a conversion driver and context is automatic in a DPP scan.

Instead of browsing a generic shop, users see 3–5 highly relevant suggestions: Required accessories, wear parts, safety upgrades, etc.

How it works

• Show a small, curated selection (3–5 items).
• Highlight compatibility clearly (“Guaranteed fit for [model]”).
• Provide a straightforward “Order now” button.
• Offer optional bundles (e.g., filter + seal + cleaning spray).

Why it works

Confidence in compatibility significantly boosts conversion rates and reduces returns.
It also keeps customers in your ecosystem rather than pushing them toward Amazon or third-party sellers.

Key KPIs

• Scan-to-cart rate
• Parts Average Order Value (AOV)
• Margin per scan
• Return rate

Playbook C: Sell service

Some products only deliver full value with proper installation, calibration, or regular maintenance.

The DPP scan lets you surface these needs at exactly the right moment—e.g., “Maintenance due in 12 days” or “Safety recall active.”

How it works

• The CTA leads to a booking page with real-time appointment slots.
• Clear pricing and service levels reduce hesitation.
• Automated reminders help prevent no-shows.
• After completion, service results can be logged in the DPP.

Why it works

It converts unpredictable, reactive service requests into predictable, recurring revenue.

Key KPIs

• Scan-to-book rate
• No-show rate
• Service AOV
• On-site First-Time-Fix rate

Pro tip:
Offer only three scheduling options:
“Later today”, “Tomorrow morning”, or “Choose a date.”
More choice slows people down.

Revenue Data vs. Compliance Data

Revenue-driving data includes:

• Compatibility
• Accessory recommendations
• Bundles
• Wear parts
• Warranty
• Recall status

This information changes frequently and is ideal for A/B tests and optimization.

Compliance-related data includes:

• Product and material information
• Serial/batch numbers
• Ownership history
• Repair and maintenance logs
• Sustainability details

This data must be consistent, traceable, and auditable.

Why separate them?

• Revenue data needs fast updates.
• Compliance data needs control and stability.
• Ownership lives in different teams:
  • Marketing/Product/Service → revenue
  • Regulatory/Quality/Legal → compliance

A good DPP experience uses revenue data to guide action, while compliance data stays accessible in the background.

QR Placement: Where Should the Code Live?

General guidance

• Packaging: Great for onboarding and pre-purchase info.
• Sticker: Flexible placement; useful when aesthetics matter.
• Point of sale: Helps with in-store comparison.
• On the device: Best for encouraging repeat scans.

Recommendation

Use at least two placements.
Packaging gets thrown away; POS displays disappear; the device remains.

Important Note

• Static content leads to one-time scans.
• Updated content leads to repeat scans.

Show updates clearly with timestamps (“Updated 3 days ago”) to build a scanning habit.

How to Organize Internally

A scan project touches nearly all central functions. Assigning clear roles like the ones outlined below helps to avoid bottlenecks:

• PIM/Data quality: Product management
• UX/Conversion: Marketing & Customer Success
• Service processes: Support
• IT/Security: Access, signatures, monitoring
  

Pro-tip:
Form a cross-functional DPP task force with shared KPIs such as scan rate, deflection, and scan-to-cart rate. 

Common Pitfalls—and How to Avoid Them

“Everything for everyone”

A single flow for customers, technicians, and retailers serves no one. Choose one persona and one primary goal per scan.

Long texts

PDFs on mobile kill conversions. Use snackable steps, max three actions per step, plus GIFs/short videos (10–15 s).

No attribution

Track with UTMs and event logs: page views, help start/completion, cart, booking confirmation.

The Next 5 Steps Starting Today

1. Set up DPP MVP fields in your PIM

Include mandatory fields plus revenue fields like compatibility, accessories, wear parts, warranty, recall status, error codes, media links, CTA text.

2. Choose SKUs for the pilot

Prioritize high-ticket or high-accessory products.

3. Create a landing template

Different versions for customer, technician, retailer—each with exactly one main action (Fix, Buy, or Book).

4. Test QR codes in 3 locations

Packaging, device, POS—measure for several weeks.

5. Build a dashboard with 6 KPIs

Scan rate, FTF, deflection, scan-to-cart, scan-to-book, value per scan.
Add a public goal (e.g., “value/scan ≥ €3.00 within 30 days”).

Conclusion

The DPP may be mandatory, but it’s also one of the most powerful product touchpoints available.

Use it to reduce support costs, sell the right parts at the right time, and turn service into predictable revenue.

The magic word is context.
If the scan delivers the exact help a customer needs in that moment, you earn trust and conversion.

Start small, measure value per scan, and scale what works.

PIM provides the data, PX provides the context, and the DPP opens the door.
What you do next is execution.

Note on QR/GS1 Digital Link & Exit Option

Products can use standard GS1 Digital Link (QR/NFC). You can change the target of the link at any time – homepage, promotions, manuals—without recalling or relabeling products. Only requirement: mandatory DPP information must remain accessible.

This article does not replace legal advice. Only the current delegating acts and sector-specific regulations apply.

It’s been about two weeks since the team here at Akeneo announced a new Accelerator for SAP S/4HANA during our Autumn Release, and we felt it deserved its own call-out. Why? Because nearly half of Akeneo’s customers who use an ERP rely on SAP. 

That makes this announcement both exciting and essential for enterprises seeking a faster, more flexible, and future-proof way to synchronize product information between Akeneo Product Cloud and SAP S/4HANA.

And the timing couldn’t be better. As Digital Product Passport (DPP) requirements come into effect in 2026, we’re entering an era where product data must be richer, traceable, and more transparent than ever before.

That means that product data can’t simply flow one way anymore. It must move in both directions, from suppliers and manufacturers into the PIM (Product Information Management system) and then back out to consumers, partners, and regulators. Businesses using SAP and Akeneo together will be better prepared to meet these requirements, and our new Accelerator is designed precisely to make that happen. 

What is SAP S/4HANA?

SAP S/4HANA is SAP’s modern enterprise resource planning (ERP) suite, designed to run on a real-time, cloud-based architecture. It’s where many of the world’s largest and most complex organizations manage their core business operations, everything from finance and logistics to product and pricing data.

In this ecosystem, Akeneo PIM plays a complementary role. While SAP serves as the backbone for operational and transactional data, Akeneo empowers teams to enrich, organize, and distribute product information across every customer-facing channel, from eCommerce platforms and mobile apps to print catalogs and in-store experiences.

Together, SAP S/4HANA and Akeneo PIM create a bridge between operational efficiency and customer experience excellence. And now, with the new Accelerator, that bridge just got stronger.

What is the Akeneo SAP S/4HANA Accelerator?

The Akeneo SAP S/4HANA Accelerator is designed to make connecting SAP and Akeneo faster, more flexible, and easier to maintain than ever before.

In the past, Akeneo’s SAP integrations followed a recurring model: we owned and maintained the connection, and customers relied on us for every update or change. While this approach worked, it created dependencies that slowed down innovation and limited flexibility.

The new Accelerator model changes that entirely. Instead of a fixed connector that requires recurring updates, the Accelerator is a delivered framework; a blueprint customers can build on and tailor to their specific needs. It is the foundation for integration between SAP and Akeneo, built upon SAP guided best practices, but now the control of configuration and customization lies where it belongs: in the hands of the customer.

Built on the SAP Business Technology Platform (BTP) and powered by Akeneo’s preconfigured iFlows, the SAP S/4HANA Accelerator helps customers and system integrators reduce complexity, speed up time-to-value, and scale with confidence. The result is deeper, more flexible connectivity between two highly configurable systems without the ongoing maintenance burden.

This new approach promotes agility and autonomy. Customers can configure integrations to match their SAP setup, extend them as their business evolves, and maintain them independently without waiting for product updates. 

Benefits of the Akeneo SAP S/4HANA Accelerator

The Akeneo SAP S/4HANA Accelerator delivers tangible advantages for enterprise teams looking to connect product and operational data seamlessly. From faster implementation to greater flexibility and scalability, it’s designed to simplify integration while empowering businesses to move at the speed of their markets. 

In particular, this new feature enables:

• Faster connection setup: The Accelerator provides a pre-built integration foundation that shortens implementation timelines and gets your systems talking to each other sooner.
• Greater flexibility: Every SAP environment is unique. The Accelerator allows customers to customize integrations for their particular data structures, business processes, and regional requirements.
• Improved governance: With a standardized framework and consistent data mapping, organizations can maintain a single source of truth across SAP and Akeneo, improving product data quality and governance.
• Lower maintenance: Because customers can make adjustments directly, there’s no need for recurring updates or dependency on vendor releases. The framework is stable, modular, and self-sustaining.
• Future scalability: As your business evolves, your data model will too. The Accelerator’s flexible architecture ensures it can scale alongside your product catalog, market expansion, or system evolution.
• Reduced cost of ownership: Built on SAP’s high-performance Integration Suite, the Accelerator provides a reliable blueprint for connecting SAP ERP and Akeneo PIM, reducing custom development, manual work, and maintenance costs.
• Faster time-to-market: The Accelerator automates the activation of ERP information into Akeneo PIM, so teams can quickly enrich product data and share it across all channels, accelerating the entire go-to-market process.

How the SAP S/4HANA Accelerator Works

At its core, the Akeneo SAP S/4HANA Accelerator enables bidirectional data flow between SAP ERP and Akeneo PIM. Product information moves seamlessly between the two systems using prebuilt iFlows that handle everything from data import and transformation to event-based synchronization.

From ERP to PIM

When product data originates in SAP, the Accelerator ensures it reaches Akeneo efficiently through three main stages:

1. Import batch data: Define how product data is collected and filtered from SAP S/4HANA, then send it to the orchestration flow for processing.
2. Message mapping: Transform SAP data fields into Akeneo’s format and structure so that product attributes, categories, and hierarchies align perfectly.
3. Batch orchestration: Manage the end-to-end process — authentication, data mapping, validation, and final delivery into Akeneo PIM — ensuring the right data arrives in the right place.

From PIM to ERP

Enriched product information can also flow back into SAP through event-based communication. These iFlows ensure near real-time synchronization so that updated product data in Akeneo is reflected in SAP automatically.

1. Create a subscriber to the Akeneo Event Platform: Generate a subscriber ID to receive events from Akeneo PIM.
2. Create a subscription: Select which events to subscribe to (for example, when product data is updated or published).
3. Akeneo PIM event listener: Capture events in a queue as they occur.
4. Akeneo PIM event consumer: Deliver these events back to SAP or another system of choice, ensuring synchronized and up-to-date product information across the enterprise.

The result is a robust, real-time data exchange that keeps every product record consistent, from the ERP backbone to the digital shelf.

Empowering the Next Era of Product Transparency

The Akeneo SAP S/4HANA Accelerator represents a significant step forward in unifying product information management with enterprise operations. It simplifies integration, accelerates deployment, and empowers customers to maintain control over their connections, all while laying the groundwork for a more transparent, data-driven future.

As global transparency standards like DPP take effect, businesses will need to ensure every product carries a complete, trustworthy data record; one that can be accessed by consumers, partners, and regulators alike. That means operational and marketing systems can no longer live in silos. They must work in concert to tell the full story of every product.

With the new Accelerator, SAP and Akeneo customers are equipped to do exactly that. By connecting the dots between supply chain and product experience data, organizations can ensure accuracy, traceability, and sustainability across every touchpoint.

To learn more about the Akeneo SAP S/4HANA Accelerator, you can register for our live Deminar on December 3 to see how you can create richer, more efficient product experiences with Akeneo and SAP today.

For years, B2B IT teams have had a reputation for moving toward the digital future at… well, let’s call it a “carefully considered pace.” And who can blame them? When you’re managing sprawling infrastructures, complex tech stacks, and the never-ending list of “critical priorities,” adopting new innovations can feel less like turning a corner and more like steering a cargo ship with a canoe paddle.

However, AI seems to be changing that stereotype. Nowadays nearly 30% of B2B decision-makers begin their research on AI platforms, and 78% of B2B organizations have implemented AI into at least one business functionality. Instead of inching toward transformation, many B2B organizations are suddenly finding themselves accelerating towards the digital future, sometimes by choice, sometimes by necessity.

With that in mind, let’s take a look at  why AI is giving B2B IT teams a long-overdue boost into the future, how it’s changing their day-to-day reality, and what forward-looking teams can do to stay ahead.

The Impact of AI on B2B IT Teams

AI’s growing influence across the B2B landscape is creating new pressures, new responsibilities, and new opportunities for IT leaders:

1. AI Helps IT Shift From Manual Automation to Intelligent, Adaptive Systems

Traditional workflows depend on inflexible logic: predefined triggers, fixed conditions, and carefully engineered sequences. 

AI changes that. Instead of building a thousand branches in a flowchart, IT can enable systems that continuously interpret context, predict needs, and determine the next best action automatically. 

When AI handles the repetitive, predictable aspects of workflows, IT gains the bandwidth to focus on innovation and higher-value initiatives; IT teams can shift from being reactive firefighters to proactive innovators. Instead of drowning in maintenance work, they can invest their time where it matters most: modernizing infrastructure, strengthening security posture, improving digital experiences, and exploring emerging technologies that could unlock new value for the business.

Organizations that operationalize AI in IT often report higher productivity, faster project delivery, and, perhaps most importantly, a renewed sense of purpose among their teams. When IT professionals are empowered to focus on solving business challenges rather than clearing backlogs, they’re able to contribute more strategically, partner more closely with the business, and drive initiatives that make a real impact.

2. AI Requires IT to Architect Infrastructure That Supports Real-Time Decision-Making

Whether it’s dynamic pricing, product search, personalization, or inventory forecasting, AI only performs well when it has access to reliable data in real time. That places enormous responsibility on IT teams to modernize the underlying architecture that hosts and syndicates product data. Legacy batch processes, overnight sync jobs, and sluggish APIs simply cannot support the expectations of agentic AI. Instead, IT must build environments where data flows continuously and updates propagate instantly across ERP, PIM, OMS, DAM, CDP, and other systems that fuel AI-driven decisions.

The challenge is both technical and organizational. IT needs to align data governance, system ownership, and update processes to guarantee every team contributes to a consistent and reliable flow of information.

The shift toward real-time decisioning also expands IT’s role in performance optimization. High-volume requests from AI agents create new strains on infrastructure. IT must evaluate caching strategies, compute scaling, cloud utilization, and network throughput to ensure AI operations don’t introduce friction or system instability, which leads us nicely to our next point.

3. AI Demands Seamless Interoperability Across the Entire Tech Stack

For an AI agent to retrieve pricing updates, access product content, initiate fulfillment steps, or trigger customer workflows, every underlying system must be fully interoperable. This places IT at the center of ensuring that the organization’s architecture is stitched together and deeply integrated. APIs must function consistently, authentication must work smoothly, and system dependencies must be managed intelligently. When AI calls for data or triggers an action, there can be no bottlenecks.

Interoperability becomes even more important as AI evolves beyond simple query-response patterns into autonomous orchestration. An AI agent might need to retrieve product specs from PIM, confirm stock availability via OMS, calculate delivery timelines through ERP, and adjust pricing dynamically based on a CDP data signal, all within milliseconds. IT ensures these systems can talk to each other without breaking or contradicting one another.

This need for interoperability also drives IT’s vendor strategy. Not every platform is built for AI enablement, and not every API performs equally under stress. IT must make decisions about which platforms integrate well enough to support AI at scale, which require middleware, and which need to be replaced. AI highlights integration problems that were previously invisible, and IT becomes responsible for solving them.

4. AI Relies on IT to Build Continuous Feedback Loops for Ongoing Learning

AI gets better when it can continuously learn, and IT plays a crucial role in enabling feedback loops that can continuously train AI models. IT teams are the ones responsible for creating the mechanisms that help AI understand what works, what doesn’t, and how to adjust its behavior. The value of AI compounds over time, and IT is responsible for ensuring those compounding effects actually occur.

This responsibility also extends to monitoring for bias and unintended behavior. Because AI transforms over time, IT must design guardrails that keep its learning aligned with business goals and compliance requirements.

5. AI Forces IT to Manage Tech Sprawl and Overlapping AI Capabilities

As vendors race to embed AI into their platforms, IT teams face a growing risk of redundant investments. One system offers AI search. Another offers AI content enrichment. Another offers AI recommendations. Without a strategic view, organizations quickly pay multiple times for similar capabilities. IT becomes responsible for evaluating where AI adds real value and where it overlaps.

Managing tech sprawl is also critical to maintaining performance and long-term scalability. Every new AI feature introduces additional compute demands and integration requirements. IT must prevent platforms from accumulating disconnected AI functions that inflate operating costs without improving outcomes. In this new era, tech consolidation is about both efficiency and survival.

6. AI Expands IT’s Responsibility for Data Quality and Data Readiness

In many B2B organizations, product data is spread across ERP, PIM, spreadsheets, vendor portals, legacy tools, and shared drives. AI magnifies the issues buried in these systems. Missing attributes, inaccurate dimensions, outdated certifications, or mismatched hierarchies directly undermine AI’s recommendations and predictions.

IT must define how information flows, who owns it, how frequently it updates, which systems are the sources of truth, and what validation rules prevent errors from contaminating downstream AI processes. AI thrives on consistency, and IT becomes the gatekeeper that enforces it. Good governance transforms data from a liability into an asset.

With AI relying so heavily on structured, consistent, and enriched product information, platforms like Akeneo PIM become foundational to successful AI adoption. Akeneo centralizes product data, enforces data governance rules, fills content gaps, and ensures every system receives complete, high-quality information. By giving IT a single source of truth, Akeneo PIM removes one of the biggest barriers to effective AI and empowers teams to deliver the accuracy and speed today’s AI-driven experiences demand.

7. AI Requires IT to Balance Innovation With Stability and Performance

AI accelerates the pace of innovation, but it also increases operational complexity. IT teams must support new models, new integrations, new data flows, and new compute requirements without destabilizing the systems the business relies on daily. Innovate too slowly, and the organization falls behind. Innovate too fast, and the infrastructure buckles!

Balancing both demands a strong architectural strategy and continuous monitoring. AI may be the catalyst for innovation, but IT ensures the organization remains functional and secure as capabilities expand. 

Where IT Goes From Here

As AI automates routine tasks, optimizes workflows, and enables real-time decision-making, IT teams are stepping into a new era where their work is more strategic, more collaborative, and more influential than ever before.

This shift comes with new responsibilities: architecting real-time data flows, ensuring interoperability across an increasingly complex tech stack, safeguarding data quality, and maintaining the stability and performance businesses depend on. But it also unlocks new opportunities for IT to drive innovation, accelerate digital transformation, and deliver smarter, more connected experiences for every team across the organization.

And at the center of all this progress is data; the clean, consistent, enriched information AI needs to function. When the data is right, AI can finally do what it promises, and IT teams can lead the business confidently into the future.

The B2B organizations that will thrive in this new era are preparing their data, modernizing their architecture, and empowering IT to build the digital backbone of tomorrow. With solid data foundations and a balance of innovation and stability, IT turns AI’s potential into meaningful business outcomes. The future of B2B commerce is intelligent, and IT is the team that will make that intelligence possible.

For several years now, the conversation about AI’s impact on commerce has been about how it’s on the horizon; how one day it would revolutionize the way we shop, sell, and engage with products.

Well, that day is here.

About 80% of eCommerce businesses already leverage AI solutions to enhance operations and customer experience, and nearly half of all consumers have used AI tools while shopping.

But AI can only be as great as the technology and data that powers it, and 85% of AI projects fail because of low-quality or inconsistent product data. Without the right foundation, even the smartest algorithm can stumble. Because even though AI gets all the attention, it’s the alignment of data, systems, and workflows that truly makes it effective.

For IT teams, that’s where the real challenge begins. The effectiveness of AI depends on the choices IT leaders and businesses make today, the systems they connect, and the flexibility they design into their stack. So before we talk about building an AI-optimized future, it’s worth asking: what does that foundation look like when engineered for long-term innovation.

What is a Tech Stack?

A tech stack is the collection of technologies, tools, and frameworks that work together to power a company’s digital ecosystem. It includes everything from the software and programming languages used to create applications to the systems that store and process data within the infrastructure. Think of it as the digital foundation that keeps products and operations running smoothly, enabling communication between systems, ensuring performance, and supporting the overall user experience. Each element in the stack plays a specific role, and together they define how efficiently a business can operate and evolve in an increasingly digital world.

The flexibility of your tech stack determines how quickly you can adapt to new technologies. In this context, the tech stack becomes the connective tissue that ties everything together, ensuring every tool and process works in harmony. 

The Challenges of Building an AI-Optimized Tech Stack

Building an AI-optimized tech stack is far more complex than simply integrating new tools. The goal is clear, but achieving it means overcoming a series of technical, structural, and cultural challenges that can slow even the most forward-thinking teams, such as:

• Fragmented data and legacy systems: Many organizations are still running on a mix of old and new technologies that don’t naturally “talk” to each other. Forrester links fragmented data to lost revenue, slower time-to-market, and higher return rates.
• Poor data quality and governance gaps: Inconsistent or incomplete records can lead to misleading insights; according to Gartner, poor data quality costs organizations an average of $12.9 million annually. 
• Scalability and infrastructure limitations: AI workloads require compute power, storage, and architecture capable of handling real-time processing. Many tech stacks weren’t designed for that kind of scalability, forcing IT teams to modernize infrastructure while keeping operations running.
• Integration complexity: Connecting AI engines, PIMs, APIs, analytics tools, and front-end platforms can turn into a web of dependencies. Without an API-first approach, each addition risks creating new silos instead of eliminating them.
• Cultural and cross-functional misalignment: Even the best tech can fail if teams don’t align around shared goals. Silos between IT, product, and business units slow down decision-making and limit the potential of AI initiatives before they mature.
  

Key Steps to Building an AI-Optimized Tech Stack 

Building an AI-optimized tech stack is about creating a connected, flexible foundation that can evolve as fast as the technology itself. Every component, from infrastructure to governance, must support agility, scalability, and collaboration. Let’s take a look at how IT and business leaders can design a future-ready ecosystem that fuels innovation, empowers teams, and turns AI potential into real business growth.

1. Design for Flexibility and Future Growth

The pace of innovation in AI and commerce is relentless, and  inflexibility has become a liability. A system built for adaptability enables organizations to transform without rebuilding from scratch, integrating new tools and scaling operations as demand changes. Flexibility is the foundation that lets a company pivot quickly and stay relevant in a rapidly shifting landscape.

Scalable, API-driven environments make it easier to adapt to new technologies, expand into new markets, and respond to customer expectations faster, which leads us nicely to our second step.

2. Adopt Open, API-First Systems

While closed, proprietary systems might once have provided control and simplicity, an API-first approach allows data to flow freely across systems and can help eliminate silos, accelerate automation, and enhance collaboration across the business. In fact, 82% of organizations have adopted an API-first approach (a 12% year-over-year increase), and 65% now generate revenue from API programs, showing that flexibility and connectivity are essential to scaling AI-powered commerce.

By enabling plug-and-play integration, API-first design gives IT teams the freedom to innovate without heavy coding or custom workarounds. Need to replace a legacy tool or integrate an emerging AI model? APIs streamline these transitions, reducing risk and allowing you to build a stack that seamlessly integrates today’s workflows with tomorrow’s intelligent, connected tools.

3. Centralize Product Data Management with PIM

If data is the fuel that powers AI, then Product Information Management (PIM) is the engine that runs it smoothly. For IT teams, a PIM acts as the single source of truth for product data. It’s the core system where raw information is structured and enriched before it’s distributed across channels. By consolidating product records into a central hub, teams can maintain smooth data synchronization between platforms and reduce redundancy.

This consistency is crucial because AI depends on high-quality data to function effectively. Poor or inconsistent information leads to broken recommendations and inaccurate search results, which ultimately leads to (you guessed it) frustrated customers! With a centralized PIM, businesses not only enhance operational efficiency but also empower AI systems to deliver better insights and customer experiences.

4. Implement Strong Data Governance

Poor data hygiene leads to duplicated records, inconsistent information, and compliance risks that can damage both trust and performance. Establishing clear governance rules makes sure that all data entering your systems meets defined standards for completeness and accuracy.

Governance also means accountability. With AI influencing more purchase decisions and customer interactions, businesses must ensure transparency about how data is used and how AI makes recommendations. 

Ultimately, good governance is about protecting data as well as empowering it. When data is well-managed, AI systems can operate faster and more effectively, and deliver insights you can act on with confidence.

5. Embed Continuous Maintenance and Optimization

An AI-optimized stack is a living system that needs ongoing attention, so this means regular audits, system updates, and performance reviews ensure that integrations remain secure and aligned with new technologies. This proactive approach reduces downtime and keeps operations running smoothly as AI models and digital tools evolve.

Optimization goes hand-in-hand with adaptability. As buyer behavior changes and new capabilities emerge, businesses that regularly enhance their stack can seize opportunities faster than competitors scrambling to catch up. The goal is continuous alignment, making sure every system, process, and dataset supports growth in an ever-evolving digital landscape. 

6. Champion Collaboration Between IT and Business Teams

A truly AI-optimized tech stack doesn’t belong to IT alone. When business, marketing, and technical teams operate in silos, improvements slow down and data loses value. In fact, employees waste up to 12 hours per week hunting down information, leading to as much as 30% of total revenue loss due to inefficiency and misalignment. 

But when these groups work together under a shared vision, technology becomes a catalyst for growth rather than a drain on resources. This alignment allows business leaders to articulate strategic goals while IT teams translate them into scalable, technical solutions. It also encourages open communication about challenges and performance metrics, ensuring AI initiatives deliver measurable outcomes.

When collaboration becomes cultural, every department understands its role in maintaining and optimizing the stack. Data becomes more accurate, and processes are more aligned. 

Building for Intelligence, Not Just Integration

Creating an AI-optimized tech stack is about building the right environment where intelligence can thrive. When data, systems, and teams work in sync, AI becomes a capability that transforms how businesses operate and grow.

For IT and business teams, the challenge is to move beyond implementation and focus on orchestration. Success depends on aligning strategy and architecture, so AI can deliver real value. Organizations that achieve this balance build ecosystems that accelerate transformation and results.