Context:
- For over a decade, Product Information Management (PIM) systems have served as the system of record for commerce - reliable repositories of SKUs, attributes, and digital assets feeding catalogs, channels, and storefronts.
- The rise of generative AI and conversational interfaces are reframing both needs and relationships.
- Buyers no longer just browse - they ask, in their own words, expecting a direct answer.
Answering:
What it takes to evolve a PIM from a structured data platform into a context-aware knowledge system - one that understands not just what a product is, but how it relates to other products, what problems it solves, and how a customer might describe it.
Drawing from a real-world digital transformation project:
A practical roadmap for enriching product data with semantic relationships, intent signals, and taxonomy layers that fuel AI-driven search and conversational commerce.
A strategic approach paired with an unfiltered end-user perspective:
- What worked.
- What didn’t work so well.
- What we wish we'd known before we started.
Delivering::
- A maturity model for assessing the context-readiness of your product data
- Techniques for enriching attributes, relationships, and taxonomies for AI consumption.
- How PIM data shapes how products surface in AI-driven search.
- Lessons from the trenches — key areas of collaboration, governance, and tooling shifts required to enable the change.