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.

NetSuite has unveiled NetSuite Next, a major AI-driven evolution of its ERP platform that embeds conversational AI, agentic workflows, and natural language search directly into the suite. Building on three earlier AI showcases we’ve covered at TheCFOai.com, this release marks NetSuite’s transition from incremental feature updates to a robust AI-native ERP experience, one powered by standardized, governed data that delivers more accurate and context-aware insights than multi-m

Tipalti has raised $200M and rolled out new AI features, from natural language reporting to predictive bill approvers. While these enhancements make the platform easier to use, they feel more incremental than transformative. For CFOs, Tipalti remains a strong payables tool. But there's only so much you can do as an AP module.

Google’s new BigQuery natural language feature for querying is a leap forward. These tools let users explore complex data relationships and now, you can do it using plain English instead of SQL.

NetSuite’s $1B milestone highlights more than just Oracle’s momentum it spotlights a critical tension in today’s AI-driven enterprise tech: the tradeoff between data quality and feature specialization. While standalone tools often race ahead with the flashiest AI features, they leave companies juggling fragmented data and integration headaches. NetSuite and ERP platforms like it offer a different path: unified, structured data across business functions that can power strategi

QuickBooks AI Agents: Smart Enhancements, Not a Full Virtual Team As a longtime QB user, I was eager to explore Intuit’s newly announced AI agents. These tools are meant to automate core business workflows like invoicing, transaction categorization, financial analysis, and customer engagement. The marketing describes them as a “virtual team” — but after watching a few hands-on reviews and testing some of the features myself, I’d describe them more as smart enhancements than a

AI tools like ChatGPT and Gemini can sound confident — even when they’re wrong. One time, ChatGPT told me July 1, 2025 was a Monday (it was actually a Tuesday). These so-called “hallucinations” can be far more subtle in finance, from misquoting accounting rules to fabricating data. For CFOs, the takeaway is clear: AI outputs are a useful draft — but they always require verification.

Elkar, a new AI add-on for Excel, exceeded my expectations where other AI tools fell short. From handling consolidations correctly to reformatting a 200+ row P&L, it showed real accounting nuance. Elkar proves how AI can save finance teams time inside their most familiar tool.

A real-world EASY test of AI tools for gathering public company financials reveals major accuracy issues. Perplexity and Gemini both failed to produce reliable data, while ChatGPT performed best. A cautionary look at how generative AI handles earnings reports and SEC filings.

Getting bank data into NetSuite can be a tedious task for finance teams—especially with international statements. In this test, ChatGPT showed real promise by successfully converting U.S. and Israeli bank statements into ERP-ready format, outperforming Gemini, which refused to engage. A glimpse into how AI could ease a very manual part of finance.

I tested ChatGPT's ability to perform a basic accounting consolidation. Despite confident promises and reasonable initial questions, the AI failed to eliminate subsidiary equity and investment accounts - fundamental consolidation requirements.

This post explores how ChatGPT and a specialized accounting agent handle increasingly complex US GAAP questions—from basic revenue recognition to nuanced bill-and-hold scenarios—highlighting differences in accuracy, source interpretation, and the importance of "trust but verify" when using AI in financial analysis.