Unmasking AI Readiness: Hard Truths from IT Leaders on Commerce Data Foundations
Akeneo — 2026 — AI & Technology
A global survey commissioned by Akeneo finds widespread confidence among IT leaders about AI readiness for commerce but uncovers substantial operational gaps in data quality, governance, and integration that undermine that confidence. While most respondents report good or excellent data quality and readiness, a large portion of AI project effort is consumed by manual data preparation, and many organizations lack the operational controls (audit trails, decision lineage, orchestration) necessary to let AI act reliably across ERP, PIM, DAM and supplier feeds. The research concludes that durable AI value in commerce depends less on model sophistication and more on disciplined investment in a reusable product information foundation, structured systems, and an “AI harness” that provides permissions, monitoring, and auditable workflows.
Key Statistics
- 95% of IT and technology decision-makers believe their organization is ready for AI
- 87% of IT leaders anticipate an increase in their AI budgets over the coming years
- 63% of IT organizations now own the AI strategy
- 89% of organizations spend over 25% of their AI project effort on data preparation (53% spend 26–50% and 36% spend more than 50%)
- 65% of organizations leverage structured enterprise systems like PIM and ERP to power AI models
Key Takeaways
- Conduct a comprehensive data audit to map product data sources, identify inconsistencies and duplicates, and prioritize remediation efforts before scaling AI initiatives
- Invest in master data management and structured systems (PIM, ERP, DAM) as the single source of truth to reduce manual data preparation and enable trustworthy AI consumption
- Allocate dedicated budget and staffing for ongoing data preparation and maintenance, treating it as an operational cost rather than a one-time project
- Establish clear data ownership, SLAs, and decision-lineage processes to operationalize AI governance and ensure traceability and compliance for AI-generated changes
- Design and deploy an AI harness: define agent orchestration, RBAC-based data access policies, and AI SRE practices (monitoring, incident response, audit trails) to enable reliable, actionable AI
Cite as: Akeneo. (2026). Unmasking AI Readiness: Hard Truths from IT Leaders on Commerce Data Foundations. Retrieved from https://research.agilebrandguide.com/reports/akeneo/unmasking-ai-readiness-hard-truths-from-it-leaders-on-commerce-data-foundations