Systems integrators are usually the ones asked to make catalog intelligence real: implementing PIM, commerce platforms, search, and feed tooling, then wiring them together for a client. Enrichment rarely lives in one system, so the work becomes an integration problem — connecting sources, defining precedence, designing schema, and building the governance and QA that keep data trustworthy after go-live.
How SIs approach catalog work
The durable value an integrator adds is less about any single platform and more about the model around it: which source wins when systems disagree, what the canonical schema is, who owns each attribute, and how quality is measured over time. A clean tool implementation on top of an undefined data model tends to surface the same problems later — just faster and at greater scale.
Typical enrichment priorities
- Source integration with clear precedence and conflict resolution
- Canonical schema design and attribute governance
- Workflow, validation, and QA processes that survive handoff
- Repeatable blueprints and mappings reusable across clients
- Clear ownership so data quality has an accountable home
Where AI discovery raises the stakes
Clients increasingly expect their catalogs to perform in AI search and agentic commerce, not just in on-site search. That raises the bar on structure and consistency — the integrator's schema and governance decisions now determine whether products are legible to answer engines and shopping agents downstream.
Common pitfalls
- Tool-first implementations without a data-ownership model
- Over-customization without maintainable governance
- No shared definition of "done" for enrichment
- Governance treated as documentation rather than an operating process