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.

Tipalti’s new Global Finance Outlook report, based on a survey of 2,300 finance professionals, reveals that only 7% have fully automated AP operations, while 83% report higher workloads and 66% more manual work. Despite strong belief in AI’s potential, adoption remains slow — a reminder that before AI can transform finance, teams must first build structured, unified data foundations.

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.

AI cannot currently scale to meet the demands of high-volume, low-error-tolerance business processes. Despite the Agentic AI and automation, foundational limitations remain: AI’s probabilistic nature inherently produces variance, which compounds across complex workflows. From payroll to compliance, enterprise use cases demand precision, not educated guesses. Until models overcome these structural issues, AI will remain most valuable in low-volume use cases.