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.

In this review, I test DealSage.io, an AI-powered platform for managing M&A deal pipelines. Built by former JP Morgan and Palantir execs, it streamlines due diligence, financial analysis, and deal tracking with smart data extraction and intuitive workflows. In my test, it handled messy PDFs, flagged errors, and saved hours of manual work, a sleek upgrade from Excel for dealmakers.

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.

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.

A review of OpenAI’s recent paper on AI hallucinations, which argues that benchmark and evaluation incentives are the root cause effectively blaming the “test” for the model’s guessing behavior. While the explanation is elegant, it's a copout. I suggest the field needs deeper exploration into “IDK models”. If hallucinations can't be eliminated can AI companies design a sustainable, trustworthy tradeoff between confidence and hallucination?

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

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.

NetSuite’s new AI Connector Service is more than just another feature — it’s an architectural shift. By supporting the Model Context Protocol (MCP), NetSuite is giving customers the flexibility to bring their own AI models into ERP while keeping governance and structure intact. As I’ve written before, AI is only as good as the data you feed it. That’s why this move plays to NetSuite’s greatest strength: clean, connected, normalized data across finance, operations, CRM, and HR

With so much hype in the AI world, prediction markets like Polymarket offer a refreshing way to cut through the noise. Instead of opinions, people put real money behind their beliefs — giving us a more grounded snapshot of which AI models are viewed as the top performers. Polymarket currently ranks Google’s Gemini at the top, followed by OpenAI’s ChatGPT and Elon Musk’s Grok. But these standings are based on Arena benchmark scores — not revenue or usability. For business le

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.

Trying to make sense of the dizzying pace of change in artificial intelligence? Mary Meeker’s latest report, Trends — Artificial Intelligence, is a great place to start. At over 300 pages, it’s dense, data-driven, and surprisingly accessible. It gives executives, investors, and even curious technophiles a comprehensive look at what’s happening in AI which if I had to sum the report to a single word it would be "unprecedented".

This blog explores how AI is transforming the role of CFOs and finance teams, driven by my personal journey to stay ahead of the curve and embrace the next wave of financial technology.