For a number of years, Product Information Management (PIM) platforms, digital asset management tools and syndication engines have promised a single source of truth and have delivered it. But a new challenge is emerging, not for availability but usability. The next battleground in digital commerce is not whether product data exists, but whether it works seamlessly, intuitively and contextually within the day-to-day reality of the people who rely on it. However, even the most sophisticated product data strategy will fail if it does not align with how teams actually operate.
Most organisations today would argue they have made significant progress in managing product data yet scratch beneath the surface and a familiar pattern emerges. Merchandisers toggle between systems to validate attributes, eCommerce teams cross-reference spreadsheets with PIM outputs, marketing teams extract and rework product content for campaigns, and customer service teams rely on outdated or incomplete information because accessing the latest data takes too long. In theory, there is a single source of truth but in practice, there are multiple versions of it, fragmented across workflows, tools and teams.
This disconnect creates a hidden but significant cost, called data quality decay. Every time a user has to leave one system to find or validate information in another, friction is introduced which causes decisions to slow down, workarounds to emerge and trust in the data to erode. The result is inefficiency and lost revenue, slower time-to-market, inconsistent customer experiences, higher return rates and missed opportunities to personalise and optimise.
As organisations push toward AI-driven commerce, composable architectures and real-time product experiences, the importance of high-quality, trusted product data only increases. However, scaling these capabilities requires more than clean data models or robust integrations, it requires deep, sustained, organisation-wide adoption that depends on how well product data fits into real work, as a recent Akeneo white paper shows.
If systems force users to adapt their behaviour to switching contexts, navigating complex interfaces or manually reconciling data, adoption will stall; even the best-designed platforms become bottlenecks. Conversely, when product data is surfaced in the flow of work, embedded within the tools, processes and decisions that teams already engage with, it becomes actionable. This is the shift from system-centric to human-centric product operations.
Historically, product data platforms have been designed as systems of record; authoritative, centralised repositories where data is created, validated and governed. This is no longer enough; what we need now is systems of context, environments where product data is not just stored, but experienced.
This means combining internal product data with external signals – customer behaviour, channel performance, supplier inputs, regulatory requirements and market trends so that information can be presented in a way that is relevant to users’ role and task.
A category manager needs product attributes but also to understand how those attributes impact sales performance across channels. A marketer meanwhile needs descriptions as well as insight into which content drives conversion. And a supply chain team needs specifications as well as visibility into how product data affects availability, compliance and fulfilment.
This shift has profound implications for how organisations design their product operations. They need to move beyond the idea that there is a single right interface or workflow for managing product data. Instead, systems must adapt to the diversity of roles, processes and priorities across the organisation.
This might mean embedding product data directly into commerce, marketing, and analytics tools, or enabling role-based views that surface the most relevant information for each user, or automating data enrichment and validation in the background, reducing manual effort, or integrating external data sources to provide a complete and dynamic picture of each product.
The rise of AI only amplifies this dynamic because it promises to automate product content creation, enhance personalisation and optimise decision-making at scale. But AI is only as effective as the data it relies on and the context in which it operates. If product data is fragmented, inconsistent, or disconnected from real workflows, AI will struggle to deliver meaningful value or amplify existing issues.
But when product data is unified, contextualised, and embedded within everyday work, AI becomes a powerful accelerator. It can suggest improvements, identify gaps, and automate routine tasks and so free teams to focus on higher value activities.
Ultimately, this is about rethinking product operations as a core driver of business performance. Product data is a foundational asset that underpins every customer interaction, every channel and every decision, but to unlock its full value, organisations must move beyond centralisation alone. They must ensure that product truth is accurate as well as accessible, governed as well as usable and stored as well as experienced.
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