Meet Zainab Hussain, a distinguished e-commerce strategist who has spent her career helping global brands navigate the labyrinthine complexities of digital retail and operations management. As an expert in customer engagement, Zainab understands that the bridge between a brand and its consumer is built entirely on the quality and accessibility of product information. Today, we are discussing a seismic shift in the industry as Akeneo moves beyond the traditional boundaries of Product Information Management. With the launch of their Summer Release, the introduction of Agentic Ziggy marks the transition from a passive “system of record” to a proactive, agentic platform. This evolution promises to redefine how data teams work, replacing manual drudgery with a coordinated fleet of AI agents designed to handle the velocity of modern commerce.
In this conversation, we explore the profound implications of moving toward an agentic UI layer that functions as an orchestration hub for product data. We delve into how the rise of agentic discovery—where AI-driven search replaces traditional algorithms—is forcing brands to treat their data as a primary competitive asset. Zainab explains the practical benefits of this new workforce, from the instant transformation of product visuals via simple text prompts to the simplification of complex retailer syndication errors. We also touch upon the vital importance of maintaining human governance through a “propose-and-approve” model, ensuring that while the speed of operations accelerates, the brand’s strategic integrity remains firmly in human hands. Ultimately, we look at how these advancements free teams from repetitive tasks, allowing them to focus on the high-level strategies that drive true business growth.
How does shifting from a passive system of record to a fully agentic platform fundamentally change the daily experience for product data teams?
For years, the daily grind for product data teams felt like being a librarian in a library that was growing by thousands of books every single hour. You were constantly filing, checking, and manually correcting entries in what we call a “system of record,” which is essentially a static warehouse for information. With the shift to an agentic platform like Agentic Ziggy, named after Akeneo’s hydra mascot, that workflow is completely turned on its head. Instead of manually touching every piece of data, teams now manage fleets of specialized agents that enrich, govern, and orchestrate information on their behalf. It’s an emotional relief to move from the anxiety of “Did I miss a field?” to the confidence of overseeing a virtual agentic workforce that collaborates with you in real-time. This isn’t just about automation; it’s about having a system that acts with intent across the entire product data life cycle, transforming operational complexities into coordinated, intelligent actions.
As agentic discovery begins to replace traditional search algorithms, what are the most significant challenges brands face in remaining visible to consumers?
The landscape is changing so rapidly that traditional browse and search algorithms are being augmented or even entirely replaced by agentic discovery, which means AI agents are now the ones “shopping” for the consumer. When an AI agent is the one searching, your product data becomes the single most important asset you have to ensure your brand remains relevant and visible. Many product teams are already at a breaking point, struggling with the scale of becoming truly omni-channel and global while managing millions of SKUs. Dealing with the speed and complexity of this new discovery model only makes that scaling challenge more intense. If your data isn’t perfectly optimized, you don’t just lose a sale; you become invisible to the very systems consumers are using to find products. This makes the role of data quality agents essential, as they can run continuous completeness checks at a scale that no human team could ever hope to match manually.
In what ways does the introduction of an AI-native workspace help bridge the gap between identifying data problems and actually executing solutions?
In the past, PXM systems were great at showing you where the problems were, but they often left you to figure out the “how” of fixing them. You might see a thousand errors in a syndication report and feel an immediate sense of dread because each one required manual investigation and a technical fix. Agentic Ziggy closes this gap by allowing users to “Ask and Act” within an AI-native workspace where natural language intent triggers workflows without any manual configuration. For example, when a syndication manager encounters complex retailer errors, the system now translates those into clear, actionable guidance in their own language. Instead of a technical escalation that might take days, the team can isolate and resolve issues at scale through self-service workflows. It takes the guesswork out of the equation, allowing a merchandiser to see a problem and deploy an agent to fix it in seconds, rather than hours of extraction and review.
With the ability to transform visuals using simple text commands, how does prompt-based AI image editing change the speed of global campaign adaptations?
Visual content has always been a massive bottleneck in e-commerce because every channel and every region requires something slightly different. Historically, if you needed a background edit or a color variant for a specific campaign, you had to send those assets back to a creative team and wait for the turnaround. Now, with AI asset transformations embedded directly within the Akeneo DAM, teams can optimize and localize product visuals using simple text prompts. Imagine being able to create instant color variants or campaign-specific backgrounds in seconds just by typing a command. This doesn’t just speed up the workflow; it democratizes the ability to create high-quality, localized product experiences. It reduces the friction between the creative vision and the operational reality, ensuring that visuals are always ready at the speed of the market.
As product operations become increasingly automated, how do you address the concerns regarding enterprise governance and the loss of human control?
Governance is the absolute cornerstone of trust when you’re dealing with enterprise-grade product data, and it’s something that cannot be sacrificed for the sake of speed. Agentic Ziggy is built with a “propose-and-approve” model, which means that while AI agents are doing the heavy lifting and proposing updates, the humans remain the final decision-makers. Every action taken by an agent is visible, and the system uses role-based permissions and approval mechanisms to ensure that nothing goes live without explicit confirmation. This balance allows teams to move beyond mere completeness—as Tiaan Heystek from Elemis pointed out—and focus on section-level readiness and complex updates with full confidence. Building this trust is key because it allows the role of the employee to evolve from a data processor to a strategic orchestrator. Humans provide the strategy and the “why,” while the agentic workforce handles the “how” and the execution.
How does this new “system of action” impact the long-term strategic growth and competitive edge of a modern retail business?
By transforming product data from a passive system of record into a dynamic system of action, organizations can finally stop just trying to keep pace with catalog complexity and start leading. When you have fifteen years of product data expertise combined with AI-assisted execution, you unlock a level of business value that was previously out of reach. Teams are no longer bogged down by repetitive, soul-crushing manual tasks; instead, they are focused on optimizing product experiences and making the strategic decisions that drive growth. This creates a cycle of ongoing improvement where catalogs remain healthy, discoverable, and competitive regardless of how fast the market shifts. As Andy Tyra, the Chief Product Officer at Akeneo, mentioned, the winners will be those who can continuously improve their experiences rather than just periodically fixing them. It’s about building a foundation where trusted data and intelligent execution work together to create a seamless, world-class experience for the consumer at every touchpoint.
What is your forecast for the future of agentic product operations?
I believe that by 2026, the concept of manually managing product information will seem as outdated as using a paper ledger to track inventory. We are heading toward a future where “agentic operations” will be the standard for every global enterprise, allowing for a level of responsiveness that matches the instant nature of digital commerce. We will see a shift where product data isn’t just “stored,” but is actively working 24/7 to find new channels, optimize itself for new discovery agents, and self-correct based on real-time market feedback. This transition will empower employees to become true architects of the product experience, using their expertise to guide fleets of agents across millions of SKUs with a level of precision we’ve never seen before. The brands that embrace this orchestration layer now will be the ones defining the standards of the next decade, while those who lag will find it impossible to scale at the speed required to stay relevant.
