Executive Summary

Enterprise leaders are approaching AI with growing realism, recognizing that long-term value depends far more on data maturity, operating discipline, and change management than on tools alone. The discussion consistently framed AI as the last step in a broader transformation journey, not the first. Organizations that rushed into AI without unified data, clear ownership, or defined use cases are seeing stalled pilots, cost overruns, and internal resistance. In contrast, teams that invested early in ERP modernization, data pipelines, and governance are now positioned to extract meaningful insights and automate core workflows.

A recurring insight was that AI success is incremental and operational, not transformational overnight. Most organizations are operating across multiple stages simultaneously—modernized in some areas, fragmented in others. Leaders emphasized that measurable gains are coming from targeted use cases such as invoice processing, financial close activities, procurement optimization, and decision support, rather than broad “AI everywhere” strategies. These wins build credibility, develop internal capability, and create momentum for larger initiatives.

Looking ahead, competitive advantage will come from sequencing and discipline: defining business problems first, preparing clean and trusted data, setting financial guardrails, and actively managing the human impact of automation. AI is increasingly viewed as a productivity layer embedded into core systems, not a standalone capability—one that rewards organizations willing to invest patiently, govern rigorously, and scale deliberately.