Building HR Data Integrity for AI Success

I hope you're doing well.  This weekend's paper, “Building the Backbone of HR Data Integrity,” calls on SWP and Talent Acquisition leaders to look beneath the dashboards and AI features and focus on the data operating model that governs how information flows across Core HR, Recruiting, Learning, and Analytics systems.

Why This Paper Matters for SWP & Talent Acquisition and HR Leaders

This paper highlights the critical data-infrastructure requirements that Strategic Workforce Planners, Talent Acquisition leaders, and CHROs must consider to make AI both practical and valuable. It emphasizes that building effective AI systems cannot be achieved solely by increasing budgets—the foundation for success lies in understanding and mapping the existing HR data infrastructure and the operating model that shapes it.

It outlines how the continuous move to the cloud has created multiple connected HR systems—Core HR, Talent Acquisition, Learning, Performance, Analytics, and Engagement—all powered through APIs and data exchange. Each system’s data operating model—encompassing inputs, outputs, and inter-system relationships—is crucial for maintaining scalability, seamless integration, and readiness for AI features.

The paper also defines core data operating decisions HR must lock down: job architecture, staffing model, position management, organizational model, compensation architecture, worker types, and effective-dating discipline.

Finally, it calls for a cross-functional team comprising SWP, Analytics, and Talent Acquisition to audit and document the current operating model across systems—ensuring the organization can fully leverage data integrations and emerging technologies.