Why is catalogue governance critical before AI automation?

Catalogue governance establishes clear rules for product data. Without it, AI automation will amplify existing inconsistencies and errors across your catalogue, making data quality issues worse and eroding trust in the system's outputs. It's about ensuring a clean input for consistent outputs.

What are the immediate risks of automating an ungoverned product catalogue?

The immediate risks include widespread data inaccuracies, propagation of duplicate entries, incorrect product categorisation, and potentially legal or compliance issues due to unreliable data. This leads to customer confusion, increased returns, and operational inefficiencies.

How long does implementing catalogue governance typically take?

The timeframe for establishing catalogue governance varies significantly based on catalogue size, complexity, and internal resources. For a complex catalogue with tens of thousands of SKUs, expect 6-18 months of focused effort before optimal automation can begin.

What specific roles or teams should be involved in establishing catalogue governance?

Key stakeholders typically include product management, marketing, IT, sales, and potentially legal or compliance teams. A dedicated data steward or product information manager is often central to coordinating efforts, defining standards, and overseeing implementation.

Can a PIM system help enforce catalogue governance?

Absolutely. A PIM system like Akeneo is designed to enforce data standards, manage workflows, and provide a single source of truth for product information. It is a critical tool for implementing and maintaining the policies defined by your governance framework, making it easier to prepare data for AI initiatives.

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Issue 032BenchmarkAI for CommerceRef 116

Why complex catalogues need governance before automation

Before implementing AI-driven automation for product information, organisations must establish clear governance over their complex catalogues. Without foundational control, automation amplifies existing inconsistencies, leading to significant commercial and operational issues.

Ricki Larkin· AI Solutions Specialist

8 Feb 20255 min readReviewed February 2025

"Without foundational governance, AI automation often amplifies existing data inconsistencies rather than resolving them."

Written by

Ricki Larkin

AI Solutions Specialist

8 years at iWeb

LinkedIn →Speak with Ricki about your brief →

Ricki leads AI implementation across commerce, content and operational workflows at iWeb. He writes about agentic commerce, AI-assisted product enrichment, retrieval quality, governance, and the commercial difference between AI demonstrations and AI systems that survive production use. Consistently focused on practical AI applications with measurable operational payoff.

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