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I took a close look at CFO Connect’s new report on The State of AI in Finance. The adoption story is strong, but the reality is more grounded—AI is still mostly used for basic tasks, while bigger challenges like fragmented data and output reliability remain. Surprisingly, the report doesn’t meaningfully address hallucinations at all.

NetSuite’s 2026 Release 1 signals a shift in how AI is used inside ERP systems—from passive assistant to operational engine. With AI now able to execute actions like updating records, running reports, and supporting close, reconciliation, and forecasting, the real value is emerging in reducing friction across finance workflows. This isn’t about replacing finance teams—it’s about embedding AI where structured data already lives.

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’s recent Claude integration shows where AI actually works in finance. By dragging receipts into Claude, users can automatically generate expense reports and mileage calculations directly inside NetSuite—with full audit trail and human review. It’s a practical example of AI supercharging individual tasks, not replacing systems, and embedding intelligence where governance already exists.

Overview of Israeli startup trends in 2025 done by law firm Meitar and Investment house IBI S-cube point to increasing valuations, increasing rounds and a surge in AI and cyber investments.

AI adoption in Asia-Pacific’s digital payments and e-commerce markets is accelerating—led by China, India, and Southeast Asia—but the pace is uneven. A new ResearchAndMarkets report shows that while AI is driving efficiency and competitiveness, data quality, governance, and infrastructure remain the biggest constraints. As the region evolves, the winners will be those that build AI on structured, well-governed data rather than layering it onto fragmented systems.

Oracle’s Q2 FY26 results showed NetSuite Cloud ERP revenue reaching $1.0B, up 13% year over year—outpacing Oracle’s broader SaaS growth. This steady performance comes as Oracle’s stock has pulled back sharply from its September peak, highlighting the contrast between market volatility and NetSuite’s consistent execution as a core cloud ERP platform.

LPT and Tipalti have announced a strategic partnership combining payment orchestration with global AP automation—signaling a broader shift toward unified, end-to-end money-movement systems. As cross-border complexity grows, pairing revenue data with payouts highlights a key truth: AI is only as strong as the data connectivity behind it.

AI is reshaping compliance, and ComplyControl is one of the companies leading that shift. In this Q&A, founder Roman Eloshvili explains how his AI-native platform delivers real-time risk intelligence, unifies fragmented data, and was recently recognized as a technology leader in the Generative AI in Risk and Compliance.

AI security is a massive concern. That’s why we’re interviewing cyber expert Yoav Crombie about the state of the market and his new AI security product, Pragatix—an enterprise solution that includes a fully private, on-prem AI designed to keep sensitive data protected and under full organizational control.

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

The AI Trial Balance Trial: Chat GPT low effort failure, Gemini won't even try and Claude tried hard but failed harder. The results were clear: general-purpose AI still can’t handle even basic accounting reporting reliably.

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.

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.

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

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.

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.