aiSOX.ai

Huge congrats to the Numeric team on their $51M Series B! Excited to see them expanding from close management to a comprehensive platform tackling end-to-end finance workflows. Solving disconnected data problems for enterprise accounting teams is exactly what the industry needs 🚀 We'll be tracking their progress at aiSOX.ai to see how this platform evolves and what it means for the accounting tech landscape. Exciting changes ahead! For transparency: we stay independent and vendor-agnostic at aiSOX, and we cover all tools on a level playing field – if you’re building in close / audit / controls and want to be on our radar for future coverage or content, feel free to reach out. Vanessa Kramer Contributor, aiSOX

I want to extend a big congratulations to Yogi Goel and the Maxima team on their recent raise – huge signal for where AI in accounting is heading. As part of aiSOX.ai, I had the chance to speak with Yogi and see the product up close, and it’s clear they’re pushing hard on what “agentic close” can actually look like in the real world. For transparency: we stay independent and vendor-agnostic at aiSOX, and we cover all tools on a level playing field – if you’re building in close / audit / controls and want to be on our radar for future coverage or content, feel free to reach out. Nabil Hajjar ACA Contributor, aiSOX

A huge shout-out to Jotham Ty for launching aisox.ai. It's a fantastic new resource that maps the accounting AI landscape directly to its SOX impact. What I find most valuable is its focus on classifying the type of AI, a distinction that is critical for any finance leader. "AI" is not a monolith, and for audit purposes, you must know what's under the hood. Here's the new landscape as I see it, which aisox.ai helps clarify: The "AI-Washed" (Deterministic Rules): This is just a complex IF-THEN workflow, branded as AI. It's 100% deterministic and easy to audit. The "Predictor" (Predictive ML): The classic workhorse. It predicts things based on historical data (e.g., "this invoice is 99% likely a 'Software' expense" or "this transaction is an anomaly"). Auditing this involves testing the model and its data. The "Creator/Agent" (Generative AI): *This is the new frontier*. This AI understands intent and creates new content. This can range from summarizing complex contracts and explaining flux analysis to generating entire workflow automations or drafting complex accruals. As aisox.ai points out, the SOX control testing for each is night-and-day. But how do you audit #3? How do you get an auditor comfortable with a non-deterministic tool? As a technologist in this space, here are the pro-tips I use for evaluating any "AI" vendor: - Ask them to "classify" their AI. Is it deterministic, predictive, or generative? Or a hybrid? A vendor who can't answer this clearly is a huge red flag. Their answer dictates your audit approach. - Ask "where is the audit trail?" For any type of AI, you need a log. What did it do? What data did it use? For GenAI, this is even more critical: What was the prompt? What was the output? How do you prove it's operating as intended? - Test for "AI-washing." Ask: "What happens if I don't use the 'AI'?" If the product is just a series of pre-built IF-THEN rules, it's deterministic. That's not bad - it's often good for compliance - but it shouldn't be sold as a magic, learning AI. - The "Bounded vs. Unbounded" Test (Predictive vs. Generative) Ask yourself: Is the AI's output bounded (a choice from a finite list) or unbounded (net-new content)? ➡️ Predictive ML is a powerful "finder" or "classifier." It sifts through massive amounts of data to give you a bounded answer, typically a classification or a number. ➡️ Generative AI is a "creator." Its output is unbounded. It writes a new flux analysis. It summarizes a 10-page lease. It generates the code for a new workflow. The set of possible answers is, for all practical purposes, infinite. It's creating new sentences and logic that didn't exist before. As a technologist with a passion for AI in accounting, this is my own process. I'll go right to a vendor's public resources—their website, their docs—to evaluate how practical their AI is and what category it really falls under. https://aisox.ai/

Howdy followers! We're starting a founder-to-founder podcast series and would love your input on which topic you'd like us to prioritize. Would love to hear from you 📣 📣

Welcome new followers! It’s amazing to see so many fellow accountants diving into AI now, so wanted to start aiSOX.ai to help our industry sort through the many non-spreadsheet options out there now 😊 To summarize aiSOX.ai’s vision, we want to: 1️⃣ Give accountants a central and dedicated place to explore current AI tools and use cases in their areas (organized by SOX cycle) 2️⃣ Provide SOX guides to help teams adopt these AI tools faster I’m excited to get this page going and hopefully this generates relevant value to our followers. On that note it’d be great if you can take a quick sec to vote below and guide us on what to prioritize first. More to come soon! Jotham

So…my new site/side project has morphed into a solid directory of accounting AI solutions and use cases 😁 Accountants: if you're looking for AI tools, aiSOX.ai will be regularly updated with the latest AI offerings in our space (enterprise financial close only, to start). I'll also continue adding SOX guidance for each use case as my vision is still the same...more compliance guidance = quicker AI adoption 💪 SOX Leaders: Thanks for taking meetings with me so far to contribute to this resource. I promise to continue chipping away and adding more application control test guides! If anyone has AI solutions/use cases they'd like added, please comment or DM! (@Founder Friends: will reach out to you/your teams shortly to confirm details in this directory) https://aisox.ai/library