The Operational AI-Readiness Layer: How Organizations Can Ensure Integrity in Their Data Foundations

Global spending on enterprise AI is forecast to reach $2.59 trillion in 2026, a 47% year-over-year increase. Despite this, Precisely data shows that 88% of data leaders report their AI initiatives are held back by their level of data readiness.

The window for SAP®-run businesses to improve data readiness is narrowing: SAP®'s end of mainstream support for ECC in December 2027 makes S/4HANA migration a decision Global Business Services (GBS) leaders can no longer defer. How that migration is managed will shape AI outcomes for years afterward. 

This SSON industry report, in collaboration with Precisely, explores the fact that AI investment keeps outpacing the data foundations meant to support it, and how GBS and shared services leaders can intervene before the migration window closes. 

Key Takeaways: 

• AI spending is accelerating, but returns are not following: Every dollar invested in predictive analytics, generative AI, and agentic AI can only perform as well as the operational data feeding it. For most organizations, that data is still not optimal.
• The AI-readiness paradox: Leaders report having the infrastructure, skills, and data readiness AI needs, but then report those same three areas as holding their AI initiatives back.
• The SAP® deadline adds pressure: Roughly 30% of SAP® migration projects were already delayed or over budget in 2025, with data quality issues cited as a significant and frequently underestimated contributor.
• Governance as an enabler: Organizations with formal data governance programs report substantially higher trust in their data, and that trust is what determines whether an AI roadmap produces results or stalls at the pilot stage.
• The 'Operational AI-Readiness Layer': Learn how combining automation, data quality management, process governance, and data integration creates a closed loop that keeps AI systems fed with optimal data.
• Pactiv's success story: Reynolds Leveraged Services cut product creation turnaround in half and realized over $1.3 million in annual savings by working with the data feeding an existing process.  
• The window will not reopen: Discover why treating the S/4HANA migration as a data integrity opportunity, rather than a technical burden to survive, determines AI readiness for years afterward. 

Download the report to understand why the SAP® migration window is a chance to build AI readiness properly, and what it costs GBS teams that treat it as a technical exercise instead.