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

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

Why Pricing? Why Now?

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

But product data tells only part of the story.

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

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

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

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

Building a Single Source of Truth

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

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

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

The result:

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

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

The Rise of Agentic Commerce

This acquisition is especially important because commerce itself is changing.

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

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

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

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

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

This is exactly the future we’re building toward.

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

What This Means for Customers

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

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

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

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

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

Welcome to the Team, PricingHUB

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

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

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

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

Welcome to Akeneo, PricingHUB!

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

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

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

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

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

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

Bridging the Gap Between Data and Value

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

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

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

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

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

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

Data-Driven Stock Optimization

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

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

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

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

Making Products “Shoppable” for Algorithms

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

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

Conclusion: From Information to Execution

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

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

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

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

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

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

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

Here are the 2026 PX Champion Award winners.

The Digital Foundation Award

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

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

Winner: LG Electronics USA.

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

The impact was immediate:

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

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

The Product Storyteller Award

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

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

Winner: Tanner

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

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

Success was evident early:

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

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

The Market Maker Award

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

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

Winner: Petra

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

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

The result:

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

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

The Growth Architect Award 

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

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

Winner: AutoQuotes.

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

The results speak for themselves:

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

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

The PX Champions of the Year

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

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

2026 Global PX Champions of the Year

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

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

Watch Unlock 2026 On Demand

Missed Unlock 2026? 

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

Consumer search habits are undergoing a radical transformation. 

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

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

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

Why Product Data Is the Foundation of a GEO Strategy

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

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

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

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

Akeneo Product Cloud: The Backbone of Your AI Visibility

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

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

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

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

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

AI Readiness Audit

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

AI Discovery Analysis

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

A Virtuous Omnichannel Cycle

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

Immediately Actionable Recommendations

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

Conclusion

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

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

For decades, eCommerce has been built around human behavior: browsing websites, comparing products, reading reviews, and completing purchases manually. 

But the rise of artificial intelligence is fundamentally reshaping that process. 

Increasingly, consumers are turning to AI assistants and large language models (LLMs) to help them discover, evaluate, and eventually purchase products.

In fact, 37% of consumers now begin their searches on LLMs rather than traditional search engines like Google. 

The only major friction point left is trust during the purchase process itself. Today, 58% of consumers use AI for product research, yet only 17% trust AI to complete purchases on their behalf. That gap will close over time, and when it does, brands will face a simple reality: either become readable and accessible to AI agents, or risk becoming invisible.

This is where agentic commerce enters the picture.

Agentic commerce describes a process where AI agents can independently research, compare, recommend, and even purchase products on behalf of consumers. Rather than consumers manually navigating websites, intelligent agents will increasingly handle the process autonomously.

Now, in simple terms, this has one major impact: AI does not interpret your brand, and is not impressed with flashy, clean eCommerce web experiences. It only cares about your data. If your product information is fragmented, inconsistent, or incomplete, AI agents will struggle to understand your products and may bypass your brand entirely.

The good news is that agentic commerce will not replace eCommerce overnight, if at all. Instead, it will emerge as an entirely new commerce channel, one powered by machine-to-machine interaction rather than human browsing.

The question brands need to ask now is simple: when AI becomes the buyer, will it choose you?

To answer that, brands first need to understand the infrastructure powering this shift. Agentic commerce is not driven by AI alone, it depends on the protocols and standards that allow AI agents, retailers, payment systems, and platforms to communicate with each other securely and in real time.

These emerging frameworks will shape how products are discovered, evaluated, and ultimately purchased in the age of autonomous commerce.

What are Agentic Commerce Protocols?

At the foundation of agentic commerce are the protocols that allow systems to communicate with one another in real time.

An agentic commerce protocol is a standardized framework that enables AI agents to securely discover, interact with, and transact with retailers or service providers on behalf of users. These protocols support autonomous purchasing workflows from product discovery through checkout and payment.

In simple terms, these protocols act as the “rules of engagement” between merchants and AI agents.

Without standards, every AI assistant would need custom integrations for every retailer, payment provider, inventory system, and commerce platform. That would make large-scale agentic commerce nearly impossible.

Protocols like Universal Commerce Protocol (UCP) and Agentic Commerce Protocol (ACP), which we’ll dive deeper into in a minute, aim to solve this problem by creating common frameworks for interoperability.

While both protocols are designed to support AI-driven commerce experiences, they approach the future very differently.

The Open Web Model: Universal Commerce Protocol (UCP)

Universal Commerce Protocol (UCP) follows an open-web philosophy.

Under this model, any merchant and any AI agent can participate as long as they comply with the protocol standards. Strategically, UCP is designed to preserve the openness of the web while enabling AI-native commerce experiences.

Merchants maintain ownership of their checkout experience, pricing logic, and customer interactions. They also remain discoverable through search indexing and accessible across a broad ecosystem of AI assistants and services.

The core of UCP is the manifest file. Every UCP-compliant merchant publishes a machine-readable file that describes the merchant’s capabilities. This file acts like a directory entry for AI agents, telling them how to interact with the merchant’s backend systems and which tools or APIs are available for completing specific tasks.

For example, an AI agent could use a merchant’s manifest to:

• Search a product catalog
• Check live inventory
• Validate pricing
• Initiate checkout
• Complete transactions

This is the vision for UCP; in practice today, product information is sent through Google Merchant Center to Google, making it effectively a feed-based approach, very similar to how ACP works.

That said, the protocol is evolving quickly.

With the introduction of newer capabilities such as Catalog, announced in March 2026, UCP is beginning to support more real-time, conversational interactions between AI agents and merchant systems.

This opens the door to far more advanced use cases, including:

• Querying live inventory during conversations
• Validating current pricing in real time
• Personalizing recommendations dynamically
• Supporting interactive shopping experiences directly within AI interfaces

One of UCP’s strengths is its flexibility. Merchants can begin with a relatively lightweight feed-based setup and gradually evolve toward more advanced, real-time integrations as their infrastructure matures.

It’s also important to note that momentum for UCP is building. Google has recently announced that major platforms such as Commerce Inc, Salesforce, and Stripe will implement UCP, signaling growing ecosystem support and increasing the likelihood of broader adoption across the industry.

The Platform Model: ACP (Agentic Commerce Protocol)

Agentic Commerce Protocol (ACP) is the alternative “Marketplace” standard.

Unlike UCP’s “open web” philosophy, ACP operates closer to a platform model. Merchants need to explicitly register or integrate with the specific ACP endpoints (maintained by partners like Stripe for the payment) to be accessible. It prioritizes a highly controlled, high-quality user experience over broad, open-web discoverability.

To simplify merchant participation, Stripe created the Agentic Commerce Suite, which acts as an aggregation layer for ACP product feeds. Instead of submitting separate feeds to each agent platform, merchants can send a single feed to Stripe, which then syndicates it to supported platforms such as ChatGPT, Microsoft Copilot, and future ACP-compatible environments.

This approach significantly reduces integration complexity for merchants while helping AI platforms maintain more standardized product experiences.

Several early examples of ACP-style commerce are already emerging.

Microsoft Copilot currently offers a Checkout feature that enables users to complete purchases directly within the conversational interface. Rather than redirecting users to external websites, transactions can happen seamlessly inside the AI interaction itself.

ChatGPT also announced its “Buy For Me” feature in September 2025. However, OpenAI later scaled back aspects of the in-product transaction experience, highlighting one of the key realities of agentic commerce:

AI purchasing remains more difficult than many initially expected.

Challenges around trust, liability, consumer comfort, fraud prevention, and transaction responsibility are still significant barriers. Consumers may be comfortable asking AI for recommendations, but fully delegating purchasing authority is a much bigger psychological leap.

Even so, ACP demonstrates how platform-centric ecosystems may evolve into highly curated commerce environments where AI assistants manage large portions of the customer journey directly.

Competing Standards, Uncertain Outcomes

It is still far too early to predict whether ACP or UCP will emerge as the dominant standard.

Both protocols are in the early stages of development, adoption remains limited, and implementations are still evolving rapidly.

History shows that technological standards are rarely determined solely by technical superiority. Instead, success is usually driven by adoption, ecosystem power, and distribution.

A technically elegant protocol with limited adoption has little influence. Meanwhile, a simpler standard backed by major platforms can quickly become the foundation for an entire industry.

The future of agentic commerce will likely be shaped less by protocol design and more by which ecosystems gain traction with consumers, merchants, AI providers, and payment platforms.

For brands, the key takeaway is simple: do not wait for a winner to emerge before preparing.

How to Prepare for Agentic Commerce

Regardless of which protocol ultimately succeeds, there are several steps brands can take now to prepare for the future of AI-driven commerce.

1. Invest in Structured Product Data

AI agents rely entirely on data quality.

Incomplete descriptions, inconsistent attributes, missing specifications, and poor taxonomy structures make it difficult for AI systems to understand and recommend products accurately.

Brands should focus on creating clean, enriched, and standardized product information that is machine-readable across channels.

2. Prioritize Interoperability

Future commerce ecosystems will depend on systems being able to communicate with one another seamlessly.

Brands should evaluate whether their current commerce infrastructure supports APIs, real-time data exchange, and flexible integrations that can evolve alongside emerging protocols.

3. Improve Product Context and Metadata

AI agents need more than product titles and pricing.

Rich metadata, detailed specifications, compatibility information, sustainability details, customer sentiment, and contextual attributes will increasingly influence how products are ranked and recommended by AI systems.

Consumers will still browse websites for years to come, but increasingly, AI agents will act as intermediaries between customers and brands. Discovery, evaluation, comparison, and even purchasing decisions will gradually shift toward machine-driven interactions.

Protocols like ACP and UCP are laying the groundwork for that future today.

Some ecosystems will favor openness and interoperability. Others will prioritize tightly controlled platform experiences. Both models may coexist for years, just as marketplaces and open-web commerce coexist today.

But regardless of which standards ultimately win, the direction of commerce is becoming more clear, and it seems that it will be guided by what AI agents can interpret and understand just as much as it’s guided by what appeals to human shoppers.

Choosing the right Product Information Management (PIM) system can make or break your ability to deliver compelling product experiences across channels, especially in 2026.

As businesses expand across marketplaces, eCommerce platforms, retail stores, and emerging AI-driven channels, managing product data manually or across disconnected systems simply doesn’t hold up. Inconsistent or incomplete product information can frustrate human shoppers, but can also straight up disqualify your products from AI-driven recommendations and automated buying flows as agentic commerce becomes more and more commonplace.

In 2026 and beyond, the best PIM systems go beyond centralizing product data. They enable AI-powered enrichment, supplier onboarding, omnichannel syndication, and structured data that is machine-readable.

Choosing the right PIM, then, is a strategic investment that directly impacts customer experience, operational efficiency, revenue growth, and the ability to adapt to the future of commerce.

What Is a Product Information Management (PIM) System?

Before we get too far, let’s first start with some definitions.

A Product Information Management (PIM) system is a centralized platform that collects, manages, enriches, and distributes product data across all sales and marketing channels.

At its core, a PIM acts as a single source of truth for product information, bringing together data from multiple systems like ERP (Enterprise Resource Planning), suppliers, spreadsheets, and Digital Asset Management (DAM) tools into one unified platform.

This includes:

• Product descriptions and specifications
• Technical attributes and dimensions
• SKU data
• Localization and translations
• Channel-specific content

But nowadays, a PIM does much more than store data.

Modern PIM systems should be designed to transform raw product data into rich, contextualized product experiences. They enable teams to collaborate on product content, automate enrichment processes, and ensure consistency across every touchpoint, from eCommerce sites to marketplaces to mobile apps.

As AI agents increasingly take on the role of researching and purchasing products, the structure and quality of your product data become even more critical.

Unlike human shoppers, AI agents don’t “interpret” messy or incomplete data. They rely on clean, structured, and standardized information to make decisions.

Without a PIM, product data often lives in silos, leading to inconsistencies, errors, and delays. With a PIM, businesses can streamline operations, accelerate time-to-market, and deliver the kind of high-quality product experiences that both humans and AI agents expect.

What to Look for in a PIM System

Before diving into vendors, it’s important to understand what separates a good PIM from a great one.

1. Data Management & Enrichment: A strong PIM should centralize product data and enable enrichment through validation rules, completeness scoring, and AI-driven content generation.

2. Omnichannel Syndication: Look for platforms that easily distribute product data across marketplaces, eCommerce platforms, and retail systems.

3. Workflow & Collaboration: Modern PIMs support cross-functional collaboration with approval workflows, role-based permissions, and governance controls.

4. Scalability & Flexibility: Your PIM should scale with your catalog, channels, and geographic expansion without requiring major replatforming.

5. Integrations & API-First Architecture: Seamless integrations with ERP, DAM, CMS, and eCommerce platforms are critical.

6. AI & Automation Capabilities: Leading PIM systems now include AI for content generation, translation, and data quality improvements.

7. Vendor Support & Ecosystem: The vendor’s roadmap, support quality, and partner ecosystem can be as important as the software itself.

Best PIM Systems of 2026

1. Akeneo

Akeneo has evolved from a traditional PIM into a full Product Cloud platform, focused on Product Experience (PX) and AI-driven enrichment. It enables organizations to centralize, enrich, activate, and optimize product data across all channels, with a strong emphasis on usability and collaboration.

Recent innovations have positioned Akeneo as a leader in preparing businesses for AI-driven and agentic commerce. Its 2026 Spring Release introduced the idea of the Intelligent Feedback Loop, enabling product data to continuously improve based on real-world signals, alongside responsive catalog modeling and AI Discovery Optimization. 

Additionally, Akeneo continues to invest heavily in AI, including automated attribute extraction from assets and AI-generated product descriptions, helping teams enrich data faster and at scale.

Benefits:

• Strong AI-powered enrichment and automation capabilities
• Highly user-friendly interface with collaborative workflows
• Robust ecosystem and integrations (200+ apps, major commerce platforms)
• Purpose-built for omnichannel and AI-driven commerce

Best for:
Mid-market to enterprise brands focused on delivering high-quality product experiences at scale, especially those preparing for AI-driven or agentic commerce.

2. Bluestone PIM

Bluestone PIM is a headless, API-first PIM designed specifically for composable commerce architectures. Unlike traditional monolithic PIM systems, Bluestone allows organizations to plug product data into modern, modular tech stacks.

Recent updates have focused on GraphQL-based APIs, event-driven architecture, and real-time data synchronization, enabling faster integrations with front-end applications and AI-driven services.

Benefits:

• Fully composable and headless architecture
• Real-time data delivery via APIs
• High flexibility for custom digital experiences

Best for:
Digital-first organizations and enterprises adopting composable commerce or MACH architectures.

3. Inriver

Inriver positions itself as a revenue-driving PIM, with a strong focus on product storytelling, syndication, and digital shelf optimization. It is widely used by large enterprises managing complex product ecosystems.

Recent innovations include enhanced product marketing capabilities, improved channel syndication tools, and deeper analytics to measure product performance across channels. The platform continues to invest in AI-powered content enrichment and automation.

Benefits:

• Strong syndication network and marketplace integrations
• Focus on driving revenue and reducing time-to-market
• Advanced product storytelling and merchandising tools

Best for:
Large enterprises and global brands managing complex catalogs and multiple distribution channels.

4. Pimberly

Pimberly is a SaaS PIM focused on data automation, onboarding, and enrichment, helping businesses rapidly clean and structure product data.

Recent platform updates emphasize automation-first workflows, including bulk data ingestion, automated validation, and improved supplier onboarding tools. Pimberly also continues to enhance data governance and workflow automation to reduce manual effort.

Benefits:

• Strong automation capabilities for data onboarding
• Faster time-to-value compared to many enterprise PIMs
• Clean, intuitive interface

Best for:
Retailers and distributors looking for fast implementation and improved data quality without heavy complexity.

5. Pimcore

Pimcore is an open-source platform that combines PIM, DAM, CMS, and MDM into a single solution. It’s known for its flexibility and ability to support highly customized use cases.

Recent developments include stronger support for headless and composable architectures, improved data modeling capabilities, and enhanced API performance, making it more competitive in modern tech stacks.

Benefits:

• Extremely flexible and customizable
• Combines multiple capabilities in one platform
• Open-source foundation reduces licensing costs

Best for:
Enterprises with strong in-house development teams and highly complex data or experience requirements.

6. Plytix

Plytix is a cloud-based PIM designed for simplicity, usability, and affordability, making it especially popular among SMBs.

Recent improvements include enhanced data completeness tracking, workflow automation, and improved collaboration tools, along with better integrations for eCommerce platforms like Shopify and WooCommerce.

Benefits:

• Easy to use and quick to deploy
• Affordable pricing for smaller teams
• Strong collaboration and catalog management features

Best for:
Small to mid-sized businesses looking for an accessible entry point into PIM.

7. Salsify

Salsify is a Product Experience Management (PXM) platform that combines PIM with digital shelf analytics, syndication, and content optimization.

Recent innovations include deeper digital shelf analytics, expanded retailer and marketplace syndication, and increased use of AI to optimize product content for discoverability and conversion.

Benefits:

• Strong syndication network and retailer integrations
• Focus on digital shelf performance and analytics
• Scales effectively for global brands

Best for:
Brands heavily focused on marketplaces, retail syndication, and digital shelf optimization.

8. Syndigo

Syndigo combines PIM, MDM, and content syndication into a unified platform, with a strong focus on global data distribution and compliance.

Recent enhancements include expanded global data pool connectivity, improved analytics and reporting tools, and deeper integration with retail and GDSN (Global Data Synchronization Network) ecosystems.

Benefits:

• Strong global syndication capabilities
• Advanced data governance and compliance tools
• Comprehensive enterprise-grade platform

Best for:
Enterprises needing global data synchronization, compliance, and large-scale syndication.

How to Choose the Right PIM for Your Business

There is no one-size-fits-all “best” PIM system, just the one that best aligns with your business goals, team structure, and technical ecosystem.

To make the right choice:

• Start with your business objectives (growth, efficiency, CX)
• Map your data complexity and channel strategy
• Evaluate ease of use vs. flexibility
• Consider long-term scalability and vendor support

But in 2026, there’s an additional layer to consider: how well your PIM prepares you for agentic commerce.

As AI agents increasingly influence, or even complete, purchasing decisions, your product data must be structured, contextualized, and optimized for machine consumption. This means choosing a PIM that can:

• Deliver clean, standardized, and enriched data at scale
• Support real-time updates across channels and endpoints
• Enable AI-driven enrichment and continuous data improvement
• Ensure your products are discoverable and competitive in algorithm-driven environments

Most importantly, treat your PIM as a long-term strategic investment. The right platform will position your business to thrive in a future where both humans and AI agents are making buying decisions.

Spring is a time of renewal, and for product-driven organizations, it’s a reminder that growth depends on the ability to evolve.

Today, product information must evolve as quickly as the markets it serves. New channels, shifting customer expectations, and the rise of AI-powered discovery are fundamentally changing how products are found, evaluated, and purchased. Static product data can no longer keep pace.

That’s why, as announced in Unlock Chicago, the Akeneo Spring 2026 Release introduces a new set of innovations designed to make product data more dynamic, intelligent, and actionable. These capabilities empower teams to respond faster, enrich smarter, and deliver product experiences that continuously improve over time.

At the core of this release are three key pillars, each supported by new products and features that bring this vision to life.

The Three Pillars of Akeneo’s 2026 Spring Release

1. The Intelligent Feedback Loop

Traditionally, product data has been managed as a static system of record. Teams build a data model, enrich product content, distribute it across channels, and then react when something goes wrong. 

The problem is that today’s commerce environment doesn’t wait. Markets evolve in real time, and product data needs to evolve with them.

The Intelligent Feedback Loop changes this dynamic.

Instead of relying on periodic updates and manual fixes, Akeneo transforms product data into a continuously learning system that captures signals from across the product journey and feeds them back into the PIM as actionable improvements.

Key New Features

• Responsive Catalog Modeling & Enrichment: Responsive Catalog Modeling & Enrichment enables your catalog to adapt automatically to market demands. By analyzing retailer requirements, rejection logs, and search trends, it suggests both missing attributes and the exact values needed to complete them. This eliminates manual research, reduces bottlenecks, and dramatically accelerates time-to-market, turning reactive fixes into proactive optimization.
• PX Insights – AI Discovery Optimization: Akeneo connects AI-driven discoverability insights directly to your PIM structure. Instead of vague recommendations, teams receive clear, actionable guidance on which attributes to create or refine based on how AI engines interpret their products. This bridges the gap between insight and execution, making AI optimization tangible and achievable.
• Flexible AI Sourcing – Bring Your Own LLM: Enterprises can now integrate their preferred AI providers directly into Akeneo. Whether using OpenAI, Claude, or another model, this feature ensures organizations can scale AI-driven enrichment while maintaining compliance, security, and control.
• Extension Platform: With built-in backend hosting powered by Upsun, Akeneo now enables full-stack extensibility within its ecosystem. Teams can deploy custom applications, like ERP integrations or regulatory checks, without managing external infrastructure, reducing complexity and accelerating innovation.

2. Velocity: Accelerating Time to Market

The second pillar is all about removing friction at every stage of the product lifecycle so teams can move from idea to execution faster and with greater confidence. 

In practice, this means simplifying complex workflows, reducing reliance on technical resources, and automating time-consuming processes that traditionally slow teams down. 

Whether it’s onboarding new users, enriching product data, activating new retail channels, or equipping sales teams with the right information, Akeneo’s latest innovations are designed to streamline each step. By embedding AI assistance, standardizing integrations, and making product data more accessible across the organization, our goal is to enable teams to spend less time navigating systems and more time driving growth, ultimately accelerating time-to-market and improving overall business agility.

Key New Features

• Ask Ziggy, the PIM AI Assistant: Ask Ziggy is a new AI-powered assistant embedded directly in Akeneo PIM. It provides contextual, real-time guidance on configurations, workflows, and catalog data, helping users navigate complexity and make better decisions faster. By reducing reliance on technical experts, Ask Ziggy accelerates onboarding and empowers more teams to work efficiently within the PIM.
• Activation – Custom Channels: Custom Channels revolutionize retailer onboarding by replacing manual processes with a template-driven approach. Simply upload a retailer file, and Akeneo automatically creates a reusable, governed activation channel.This dramatically reduces onboarding time, improves compliance, and eliminates the need for one-off engineering work.
• Akeneo Digital Showroom: Designed for sales teams, the Digital Showroom provides secure, on-demand access to enriched product data, complete with single sign-on, flexible exports, and engagement analytics. Sales reps can instantly answer buyer questions, prepare for meetings faster, and uncover new opportunities, all powered by consistent, high-quality product information.
• Target Plus API Connector: This certified connector enables seamless integration with Target’s exclusive third-party marketplace. By automating product syndication and order management, it ensures compliance while simplifying operations, making it easier than ever to expand into new channels.

3. Trust & Governance with Extensibility

As product data becomes more dynamic, interconnected, and increasingly powered by AI, maintaining control, security, and consistency becomes both more challenging and more critical. Without the right guardrails, faster workflows and automated enrichment can introduce risk, ranging from inconsistent brand messaging to compliance issues and data inaccuracies. 

The 2026 Spring Release addresses this by embedding governance directly into every stage of the product data lifecycle, ensuring that innovation doesn’t come at the expense of control. With capabilities like secure supplier collaboration, controlled staging environments, and AI enriched with built-in brand context, Akeneo enables organizations to scale their operations confidently. 

Teams can collaborate more openly, automate more processes, and adopt AI more broadly, while still maintaining full visibility, auditability, and alignment with brand, regulatory, and organizational standards.

Key New Features

• Secure Supplier Collaboration (Contributor Portal): The enhanced Contributor Portal enables secure, structured collaboration with suppliers. With managed accounts, controlled access, and auditable workflows, businesses can streamline product data collection while maintaining full governance.
• Staging Catalog: The new Staging Catalog provides visibility into supplier-submitted data before it enters the live catalog. This creates a controlled environment for validation and approval, protecting data integrity while improving transparency.
• GenAI – Brand Context Injection: Akeneo’s GenAI capabilities now incorporate structured brand data (such as tone of voice and regulatory requirements) directly into content generation. This ensures AI-generated content is not only fast, but also accurate, compliant, and aligned with brand standards across every market.
• Activation – Channel Readiness  Guide: The Channel Readiness  Guide centralizes marketplace requirements, giving teams a clear understanding of what data is needed before activation begins. By enabling proactive data modeling, it reduces rework, improves alignment across teams, and accelerates time-to-first activation.

Turn Product Data Into Your Competitive Advantage

Spring is a season of progress; a time to refresh, reset, and set the stage for what’s next. And in many ways, that’s exactly what the Akeneo Spring Release is all about.

It’s about giving your product data a fresh start, transforming it from something you manage into something that actively works for you.

Because when your product information can adapt, improve, and scale alongside your business, everything else starts to move a little faster, too.

Ready to see what’s new? Explore all the innovations in the Akeneo 2026 Spring Release and discover how you can turn your product data into a true engine for growth. Or, if you’re already convinced, reach out to an Akeneo expert directly to get started today.

Agentic commerce is moving fast. Every week seems to bring a new feature, a new protocol, a new launch, and a new prediction about how buying behavior is about to change forever. 

For brands, that creates a real problem. It’s hard enough to stay informed without getting distracted by the sheer volume of noise. The important thing is not to track every release, it’s to stay close to what is actually changing customer behavior. 

The customer still sits at the center of the buying journey. Agents will increasingly support, shape, and eventually automate parts of the commerce journey, but they are still working on behalf of a human being with preferences, doubts, habits and limits. To win in this environment, you need to influence both the system and the human behind it. 

This is also why it is important to separate real shifts from hype. 

Platform Innovation vs. Consumer Trust

At the moment, platform innovation is moving faster than user trust. New capabilities appear all the time, and some of them are genuinely important, but many look more disruptive in a product demo than they do in the real world.

According to Forrester research, while one-third of younger consumers use ChatGPT for product discovery, completing purchases within answer engines remains their least-adopted behavior. The gap is clear: consumers trust AI to browse and recommend, but not yet to buy on their behalf. This explains why ChatGPT’s agentic checkout, launched with much fanfare in December 2025, was quietly pulled just three months later.

Helping someone research a product is one thing. Ensuring they have enough trust to allow an agent to buy it for them with confidence is another. Shopping and buying are not the same. One is exploratory, while the other involves trust, intent, retailer preference, fulfillment expectations, and a willingness to hand over control.

That’s why fully agentic commerce is harder than many people first assumed.

At ChannelSight, we have spent more than a decade working at the intersection of brands, retailers, and consumers. One thing we have learned over that time is that the buying journey is never static. It needs constant optimization; it’s not something you configure once and forget. It’s shaped just as much by confidence and trust as it is by technology. 

What the Data Shows

Across the market, eCommerce traffic patterns are beginning to shift as more product discovery happens inside conversational interfaces rather than along the traditional search-to-shop path. But that shift has not yet flowed through to lower-funnel purchase behavior in the same way. 

That tells us that while discovery is changing quickly, buying behavior is changing more slowly. The technology may be capable of more, but consumers are not automatically ready to follow. 

So, while agentic commerce is absolutely real, it is still early. There will be plenty of developments that look like major turning points in the moment, but not all of them will stick. Behavior is what decides that, not headlines. 

What Should Brands Actually Do Now?

1. Get Your Product Content In Order

If your product data is incomplete, inconsistent, or poorly syndicated across retail channels, you’re making yourself harder to discover in exactly the places that are becoming more important. In an LLM-driven world, structured and reliable content matters even more.

This is where having a strong product information foundation becomes critical. Akeneo Product Information Management (PIM) helps brands centralize, enrich, and standardize product data so it’s complete, consistent, and ready to be consumed by both humans and machines. Instead of fragmented or conflicting information across channels, you create a single, reliable product record that can be easily understood, indexed, and surfaced by AI-driven systems.

Akeneo Activation extends this by ensuring that enriched product content is not just well-managed, but effectively distributed. It enables brands to syndicate product information across retailer sites, marketplaces, and digital touchpoints, helping ensure that wherever agents or customers are searching, the right content is present, accurate, and optimized. 

In a world where discovery is increasingly mediated by algorithms and agents, being visible depends on being both structured and everywhere your customers, and their agents, are looking.

2. Pay Close Attention to Discoverability

Product discovery is clearly moving towards conversational and LLM-led environments. Whether that becomes fully agentic will depend in part on how relationships develop between LLMs, retailers, and commerce platforms, but the immediate priority remains clear: your products need to be visible, understandable, and easy to recommend in these environments.

ChannelSight’s Agent Discoverability tool helps brands see which products and retailers are actually being surfaced across LLM experiences, where the gaps are, and where action is needed:

3. Rethink How You Create Content

Aspirational product pages still matter, but they’re no longer enough on their own. Brands need content that answers real questions, reflects actual use cases, makes product differences clear, and gives systems something useful to interpret and surface.

That means:

• Sharper metadata and better-structured product information
• Stronger and more frequent customer reviews
• Improved FAQ and Q&A content
• More clarity around what makes your product different and why someone should choose it
• Content written to answer questions, not just inspire

Consider incentivizing reviews more aggressively (cashback promotions, etc.) and adapting your content strategy to focus on Q&A-style content rather than purely aspirational product pages. Hype up your product USPs and what differentiates them from competitors—this is what LLMs will surface when consumers ask comparison questions.

In short, the brands that do well here will be the ones that are easiest to understand.

How ChannelSight Is Helping Brands Navigate This Shift

At ChannelSight, we are watching these shifts closely through traffic patterns across our clients owned and earned channels, and through the tools we are building to help brands understand and improve discoverability in AI-driven environments. 

We have also launched Conversational Commerce as part of the ChannelSight AI suite. It allows brands to create product-specific and product-agnostic brand interactions through chat interfaces, then connect those conversations directly to relevant purchase journeys. That reduces friction, limits distraction, and keeps the user moving. 

And these experiences don’t have to begin on a website—they can be triggered from email, social media, and display advertising too. Think of it like your brand store growing by 10,000 square meters and becoming conversational.

Stay Focused on What Actually Matters

The space will keep moving quickly. There will be more launches, more claims, and more shiny distractions. But the brands that come out ahead will not be the ones reacting to every announcement. They will be the ones that stay close to real customer behavior, invest in discoverability, and focus on the parts of the buying journey that genuinely matter. 

Agentic commerce is here to stay. Every platform wants to show how quickly the buying journey is changing, and every week seems to bring a new reason for brands to rethink their strategy. 

That is the real opportunity. The job right now is not to get swept up in every new release. It is to work out what is actually changing customer behavior, and what is still just potentially dressed up as inevitability. 

The brands that understand that difference—and act accordingly—will be the ones that thrive.

Like millions worldwide who eagerly anticipate their favorite holidays, we at Akeneo share a similar excitement for our flagship event: Unlock. 

From April 13 – 14, 2026, Akeneo will welcome customers, prospects, and PX experts to the “Windy City”, Chicago. Located at the famous Willis tower, Unlock showcases the latest innovations in PX through dynamic keynote presentations, hands-on technical workshops and expert-led practice sessions, focusing on the theme of the event: The Great Restack: Welcome to the New Age of Commerce.

Amidst this “New Age of Commerce”, many brands over the last couple of years have faced challenges with AI integration, a centralized system for product data, and streamlined communication. All of these issues, if not tackled, can have a hidden cost, affecting your brand further down the line. 

Steelcase, a global leader in office furniture and accessories, knows this challenge well. Before transforming their product data strategy with Akeneo Product Cloud, Steelcase faced many of the same operational hurdles that countless organizations encounter today.

The Challenge: Untangling the Complex Web of Product Data

For more than a century, Steelcase has led the office furniture industry with insight-driven design and a commitment to creating inspiring, people-centered workplaces that boost productivity and engagement.

Today, Steelcase operates at the intersection of architecture, furniture, and workplace technology, positioning itself as a problem-solver for organizations building the offices of the future.

But even the most innovative companies can face operational friction when their product data ecosystem becomes too complex.

Before adopting Akeneo Product Cloud, Steelcase encountered several familiar challenges:

• Manual, spreadsheet-based management of surface material data, which studies suggest wastes up to 12 hours per week hunting down information, leading to as much as 30% of total revenue loss due to inefficiency and misalignment. 
• Inefficiencies in linking digital assets to product finishes. Reflecting how incorrect product information is the reason 40% of shoppers return their items.
• Time-consuming updates across multi-regional finish records. With a PIM, managing product data is up to 6× faster than in a spreadsheet.
• Managing supplier data involved a significantly high manual workload, which in business

These challenges made it clear that Steelcase needed a more scalable and centralized way to manage product information.

The Turning Point: Flexibility & Scalability with Akeneo

By implementing Akeneo Product Cloud, Steelcase transformed fragmented product information into a structured, scalable data ecosystem.

The impact was immediate, and Steelcase has been able to:

1. Create a centralized system for product data

After implementing Akeneo PIM and Akeneo’s Supplier Data Manager (SDM), Steelcase was able to create a streamlined, single source of truth for all of their product data. This allowed Steelcase to achieve a 62% reduction in annual time spent loading supplier data, from 33.36 hours to just 12.14 hours. After years of “entirely dependent on IT”, Steelcase was now able to use the Akeneo system to cleanse and validate data, for the best results possible.

2. Automate asset linking for images, textures, and color representations

While it sounds like a simple issue, linking digital assets to the right products once requires hundreds of manual hours which are spent better in other tasks. But with Akeneo, Steelcase saw a 63.64% decrease in time spent per product family, cutting it from 22 minutes to just 8 minutes. 

3. Optimize time-to-market for product releases

One of the most impactful benefits of Akeneo Product Cloud was the dramatic improvement in product launch speed.

With automated workflows and improved data accuracy, Steelcase achieved:

• 63.61% reduction in product release hours
• Reduced release preparation time from 16.68 hours to just 6.07 hours

This increased agility allows Steelcase to respond faster to market demands, minimize missed sales opportunities, and deliver innovative products to customers sooner.

The result is a mature, scalable product data foundation that enables Steelcase to move faster, collaborate more effectively, and deliver better product experiences to customers.

Steelcase to Share Their Story at Unlock 2026

Steelcase will be sharing their story live at Unlock 2026, offering firsthand insights into:

• What their product data landscape looked like before Akeneo
• How their workflows transformed after implementation
• How they see AI shaping product data and commerce in the years ahead

And Steelcase is just one of many conversations happening in Chicago.

Other sessions at Unlock 2026 will explore:

• Product keynotes on the future of Akeneo Product Cloud and the vision behind The Great Restack
• Hands-on technical workshops covering data modeling, governance, and enrichment workflows
• Expert-led keynote sessions on preparing for the future of agentic commerce

If traveling to Chicago isn’t possible, you can still participate through Unlock Digital, a completely free virtual event that brings the same expert insights and customer stories online taking place live from April 21-22.

You can register now for both Unlock Chicago and Unlock Digital.

SAP S/4HANA and SAP Commerce Cloud are powerful, enterprise-grade platforms. They’re designed to handle massive volumes of operational and commerce data, support complex global business models, and scale as organizations grow.

But anyone who has worked in a real SAP environment knows the truth: integrating SAP systems into a modern product data architecture is rarely simple.

Each SAP landscape is different. Data models vary. Customizations stack up over time. And product data doesn’t live neatly in one place; it flows between ERP systems, PIM solutions, commerce platforms, and dozens of downstream channels. There’s no universal “plug-and-play” integration that works for everyone.

That’s exactly what Akeneo’s SAP Accelerators are built for.

Rather than offering rigid, black-box connectors, Akeneo provides accelerators that reduce initial integration friction while preserving the flexibility enterprises need to evolve, customize, and scale over time.

Let’s take a closer look at how they work, who they’re designed for, and why they matter.

SAP Complexity and the Product Data Challenge

SAP S/4HANA and SAP Commerce Cloud both rely on rich, highly structured data models. That power comes with complexity.

No two SAP implementations look exactly the same as business rules, extensions, custom objects, and industry-specific requirements all shape how product data is stored and used. As a result, integrating a PIM into an SAP ecosystem is never just about moving data from Point A to Point B.

A successful integration requires careful orchestration between:

• ERP systems that manage operational and transactional data
• PIM systems that enrich, localize, and structure product information
• Commerce platforms that activate that data across digital touchpoints

Shortcuts and overly rigid connectors might promise speed, but they often create long-term pain that limit flexibility, make governance harder, and slow innovation down the line.

Akeneo’s SAP Accelerators take a different approach.

What Are Akeneo’s SAP Accelerators?

Akeneo’s SAP Accelerators are integration blueprints designed to help teams connect SAP environments with Akeneo Product Cloud faster without locking them into inflexible workflows.

They’re built using SAP-native technologies and follow SAP best practices, giving enterprises confidence that integrations will scale, perform, and remain governable over time.

SAP S/4HANA Accelerators

The SAP S/4HANA Accelerators are delivered as prebuilt iFlows on SAP Business Technology Platform (BTP), using SAP Integration Suite.

Importantly, these are not monolithic connectors. They’re reusable integration artifacts that teams can configure, extend, and adapt to their own landscape.

Key capabilities include:

• Support for both batch synchronization (ERP → PIM) and event-driven flows (PIM → ERP)
• Message mapping logic aligned with common SAP data patterns
• Workflow templates for scheduling, orchestration, authentication, and filtering

This gives teams flexibility to choose the integration model that fits their business, whether they need high-volume batch processing, near real-time updates, or a mix of both.

Learn more about Akeneo’s SAP S/4 HANA Accelerators

SAP Commerce Cloud Accelerator

The SAP Commerce Cloud Accelerator provides a low-code option to synchronize rich, localized product information from Akeneo PIM into SAP Commerce Cloud.

Built on SAP Integration Suite and integrated into the SAP BTP toolkit, it enables technical teams to:

• Configure and transform product data to meet complex commerce requirements
• Reduce custom development while maintaining control over data flows
• Activate enriched product experiences faster across commerce channels

Rather than reinventing the wheel, teams get a clear blueprint that accelerates integration while leaving room for customization.

Learn more about Akeneo’s SAP Commerce Cloud Accelerator

Why Akeneo’s Accelerator Approach Works

Akeneo’s SAP Accelerators deliver value not by oversimplifying SAP, but by respecting its complexity.

Here’s what that means in practice.

1. Reduced Initial Friction

Building SAP integrations from scratch is time-consuming and resource-intensive. The accelerators provide a ready-made foundation so teams don’t have to start with a blank slate.

By handling core integration patterns, mappings, and workflows upfront, they significantly reduce manual effort and early-stage complexity, which is especially helpful when dealing with SAP’s intricate data structures.

2. Flexibility Built In

Because these accelerators are built on SAP’s native integration tools, teams retain full control over integration logic, governance, and extensions. As business requirements evolve, integrations can evolve with them without ripping everything out and starting over.

3. SAP-Native by Design

By leveraging SAP BTP and SAP Integration Suite, the accelerators ensure:

• Enterprise-grade scalability and security
• Alignment with SAP best practices
• Compatibility with existing SAP governance models

This SAP-native approach makes life easier for IT teams and system integrators, and reinforces Akeneo’s role as a true partner within the SAP ecosystem.

4. Faster Time-to-Market

With integration plumbing accelerated, teams can focus on what actually drives value: enriching product data, improving customer experiences, and launching new products faster.

When enriched product information flows smoothly into SAP Commerce Cloud and downstream channels, brands can activate compelling product experiences without unnecessary delays.

Start Smart & Scale with Confidence

The team here at Akeneo works closely with SAP and ecosystem partners to deliver solutions that reflect real enterprise realities. Instead of locking customers into rigid connectors, the accelerator approach embeds integration logic where customers and integrators can see it, govern it, and customize it.

Our goal is to provide a smart, SAP-native starting point for integrating SAP S/4HANA and SAP Commerce Cloud with Akeneo Product Cloud in order to reduce friction, preserve flexibility, and help teams scale confidently without sacrificing control.

For enterprises navigating complex SAP landscapes, that balance makes all the difference.

If you’re interested in learning more about Akeneo’s SAP Accelerators, you can learn more here, or reach out to an Akeneo expert today to get started.