AI is only as good as the data beneath it. While everyone is focused on copilots and AI agents, the real differentiator is data quality. Using SAP's six dimensions of data quality as a framework, we explore why accuracy, completeness, consistency, uniqueness, context, and timeliness have become essential building blocks for successful AI adoption in finance.

NetSuite continues to post impressive growth, with revenues rising 14% to $1.1 billion. Oracle is also touting more than 100 AI agents and AI-powered features across the platform. But at TheCFOAI, we believe the bigger story isn't the AI features themselves—it's the data foundation underneath them. AI performs best when enterprise data is centralized, structured, and consistent, which is exactly what ERP systems like NetSuite were built to do.

Most companies don’t lack data—they lack cohesion. With multiple ERPs and inconsistent processes, finance teams struggle to create reliable insights. SAP’s Central Finance offers a middle ground: a centralized data layer without full ERP replacement. It’s a practical bridge, but also a reminder that in the age of AI, structured, unified data isn’t optional—it’s essential.

Guest piece, Ron Gold of A-Eye Level highlights a growing tradeoff: as companies like Oracle ramp up massive AI and infrastructure spending, they’re also cutting costs elsewhere often through layoffs and operational tightening. CFOs, need to consider the downstream effects in those critical tools that may experience a decline in support quality, slowing down of product developments and other risks.

Experian has launched the UK’s first credit score app inside ChatGPT, allowing users to compare credit score benchmarks by postcode and age group directly through a conversational interface. While the tool itself is simple, it highlights a larger shift: financial data and services are beginning to move into AI interfaces, turning chat-based platforms into a new entry point for financial discovery and education.

NetSuite was named a Leader in two 2025 Gartner Magic Quadrants — a meaningful recognition at a time when AI is reshaping finance and ERP. Gartner evaluates vendors on Completeness of Vision and Ability to Execute, and NetSuite’s strength comes from its unified, all-in-one architecture. As I argue in this piece, AI only delivers real insight when data is normalized, consistent, and connected — something NetSuite enforces by design. With embedded AI, open model integration, an

When your CEO “chats with the data” and celebrates a great quarter that never happened, you’ve got a data problem — not an AI problem. AI doesn’t fix bad data; it amplifies it. This article explores what good data actually looks like, and why deliberate, consistent data design is the real foundation for AI-ready finance.

The real power of finance data, and the AI built on top of it, starts from the bottom up. A strong data foundation begins with uniform naming, structured onboarding, and disciplined entry at the transactional level. Companies that build from clean, consistent inputs will unlock faster closes, better insights, and true AI leverage. Those that don’t will spend their time fixing what better design could have prevented.