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I feel very strongly that there needs to be an EXPERT/HUMAN between work produced by a junior (human or AI) and the actual client deliverable. No matter what the subject is. Tech, finance, communication etc etc. Everyone and their mother is fully capable of going to chat gee pee tee and maybe getting the right answer. The expertise of knowing what is right and WHY is more important than ever.
But I'm a tech guy. A fractional CTO. NOT a CFO. I am not the finance expert. Learned a bit while spending the last year building basically "Claude Code for accountants" in my role as VP of Product for an accounting firm.
When my client uses it, every output gets reviewed by a real accounting pro before anything ships. The accountant catches mistakes, the system logs the corrections, and over time the error rate keeps dropping. That part I feel good about.
One day the tool surfaced something that ended up saving one of my client's clients about 10% of their annual operating budget. Combine that with some understaffing on the client side, and the ask came in: would I temporarily step in as fractional CFO for that client?
I do have a finance degree. I've done a lot of startup financial modeling and business planning. I'm the only person who really understands what the tool did and didn't do. I raised the obvious "are you sure you want me, not an actual CFO?" question. They wanted me.
I'm as tired as you all are of explaining to people why they're unqualified to do your job. So I feel slightly embarrassed telling you I said yes.
The engagement went well. Client was thrilled.
But the part that's nagging me: on the bookkeeping side, the AI's work has an actual CPA reviewing every output. There IS a human-in-the-loop checkpoint. It's just not me. On the CFO side, when I was sitting in that seat, there was no equivalent rubber-stamp. The system was: me, with AI tools, making decisions that affected a real business.
I'm not sure if this is a real ethical issue or a really persistent case of imposter syndrome. Maybe both.
Should I have said no?
$600m FMCG. Recently did some acquisitions and now our working capital needs help. How do you analyse which metrics to use and how to chase quick wins? Seems like there are quite a few metrics related to working capital
Interesting that this comes up when interviewing for private equity companies. What's CFO's have worked in private equity and what's different about that role versus family business , public company, privately owned , etc?
Spent the last few weeks in rooms with three different CFOs at mid-to-large industrials. Different sectors, different geographies. Same conversation, almost word for word:
"I cannot tell my board what our margin looks like next quarter, because I don't know what the tariff schedule will be next month. And nobody in my organization can model it fast enough for me to make a decision before it changes again."
That's the actual problem right now. Not tariffs themselves — companies have dealt with tariffs forever. It's the cadence. Policy is changing on weekly timescales, but enterprise planning still runs on quarterly cycles. The gap is where margin goes to die.
Some numbers that have been making the rounds in finance circles:
So CFOs are asking questions supply chain has never been built to answer in real time:
The honest answer in most companies right now is: we don't know, and we'll get back to you in three weeks with a deck. By then the tariff has changed twice.
This is what's driving the quiet rise of scenario-simulating supply chains. The idea isn't new — Monte Carlo, digital twins, agent-based modeling have all existed for years. What's changed is the urgency and who's funding it. It used to be a supply chain VP's pet project. Now it's a CFO line item.
A few things I'm seeing companies actually do:
1. Tariff exposure dashboards owned by FP&A, not supply chain. The data lives in supply chain systems, but the surface where the CFO interacts with it is owned by finance. This sounds like a small org change. It isn't. It's the only way the answers get used.
2. Pre-built scenario libraries. Instead of building a custom model when a tariff announcement hits, companies are pre-modeling 20–50 plausible policy scenarios in advance. When news drops, you're picking from a library, not building from scratch. Cuts response time from weeks to hours.
3. Probabilistic sourcing decisions. Instead of "we will dual-source from Vietnam," it's "we will hold optionality on three regions and shift volume dynamically based on landed cost and lead time, re-evaluated monthly." This requires contracts that didn't exist five years ago.
4. Margin-at-risk reporting alongside VaR. Treasury has been doing Value-at-Risk on FX and rates forever. Supply chain is starting to produce the equivalent for input costs. CFOs love it because it speaks their language.
5. Quarterly board reporting that includes scenario fan charts. Not point forecasts. A spread. "Here's our base case operating margin, and here's the P5–P95 band given tariff volatility." Some boards are starting to require this.
The companies that figure this out get a real edge. The ones that don't keep getting blindsided every six weeks and burning working capital on reactive buffer inventory.
Curious what folks here are seeing. A few specific questions:
Not pitching anything, just trying to compare notes. The vendor marketing on this is so loud right now that the actual practitioner reality is hard to find.
A CFO dies and goes to the Pearly Gates. St. Peter looks at him and says, "You’ve lived a good, honest life. But we’ve updated our 'Onboarding Process.' You get to spend one day in Hell and one day in Heaven, then you choose where to spend eternity."
He goes to Hell first. The doors open and it’s incredible. It’s a sleek, glass-walled office. Everyone is using AI agents that actually work. The month-end close takes three seconds. The data from Salesforce, NetSuite, and the bank feeds are perfectly synced. There are no auditors. It’s a perpetual happy hour with top-shelf scotch. He has a blast.
The next day he goes to Heaven. It’s just thousands of people sitting at desks in a dimly lit room, manually reconciling 2022 bank statements against a CRM that doesn't have an API. They are eating cold, soggy pizza and fighting with a printer that keeps saying "Paper Jam" when there is no paper.
At the end of the two days, St. Peter asks, "So, what’s the verdict?"
The CFO doesn't even hesitate. "Look, Heaven is nice and all, but the tech stack in Hell is unbeatable. I’m going back downstairs."
He checks into Hell, and the doors slam shut. Suddenly, the glass office is gone. It’s just a pit of fire, and a demon hands him a 500,000-line CSV file with no headers and says, "The Founder needs this reconciled by 5:00 PM. Also, the V-Lookup is broken."
The CFO screams, "What happened?! Yesterday there was AI! There was automation! There was scotch!"
The Demon laughs and says: "Yesterday was the Sales Demo. Today you’re an existing customer."
Hi all, I am working on a project and genuinely curious what people in financial services verticals (banks, insurance, debt, lending etc) / ops / revenue roles think.
The product: an AI platform that helps financial services to scale their top-performing people into agents - the people who close loans, handle retention calls, onboard new members - so every customer gets that same quality of interaction.
Three names being considered:
No right answer. What I'm curious about: which feels more trustworthy? More premium? More like something you'd actually engage with in a vendor conversation?
If you work in banking, lending, insurance, or revenue-side ops, your view is especially useful. Thanks.
For many Small Reporting Companies (SRCs), the Form 10-Q is a high-friction default. But there is a streamlined alternative: SEC Form 10-S.
Switching isn't just about changing a form number; it’s about reducing disclosure volume, lowering compliance costs, and focusing your team on what actually matters for your valuation. However, the transition has specific technical nuances that most teams overlook.
In this breakdown, we cover:
If you’re a Controller or SEC Reporting Manager looking to optimize your quarterly close, this guide is your roadmap.
I am a finance guy in middle management at a large Fortune 50. Our senior leadership is pushing all departments to “leverage AI”. We have access to MS Copilot and an internal Chat GPT 5 tool that works well. The direct report of our CFO to who I report is eager to get going with AI, too.
Our finance data lives in a data lake and visibility towards what data we have (beyond own domain) is nil, no public library exists. The data lake operators use Spotfire web view tools to make data available. So with anemic access to the wide dataset, leveraging AI is cut off at the knees.
I am waiting for API end points into the data lake before we can leverage AI truly. In the interim training my team using the internal AI tool on SQL and Python (pandas) to automate day to day excel mangling.
What else can I do? Eager to move but without good and open data, AI will fail at my company?
Thanks for your ideas
I’ve been watching our AI tool line items creep up over the last 18 months. In the beginning, the Board was happy with "exploration" and "productivity gains," but the honeymoon phase feels like it's over.
Lately, the questions are getting much sharper: "Where is the headcount reduction?" or "Which specific OpEx line did this actually shrink?"
The reality is that while my team is "faster," we haven't seen a clear 1:1 correlation on the P&L yet. Are you guys actually finding ways to hard-code AI savings into your reporting, or is everyone still just "winging it" with time-saved metrics that don't actually show up in the bank account?
Curious what your "AI Slide" looks like in board decks right now.
Keeping pace with new ai developments has been difficult.
I've asked my teams to share a quick roundup of what interests us and sharing it across our org. new developments, interesting analysis, tools worth looking at, and what matters to us.
nothing super formal. just a way to avoid missing progress and keeping a pulse on the industry.
Here is what we round up this week, I thought it could be useful to share here and see what others post:
1. Production readiness platform for enterprise ai agents
https://x.com/sir_aymansaleh/status/2051720862869729372
This hits the core problem with AI deployments today. Specifically for us, we aren't able to rigorously generate and test all production scenarios before launching a new ai agent into customer support.
The concept of backtesting an AI agent on your production data before launch seems very obvious in hindsight after I saw this.
2. a16z’s AI adoption data
https://www.a16z.news/p/ai-adoption-by-the-numbers
The interesting part is adoption themes clustering around workflows where the value is easier to see. but most importantly, not all ai capabilities have matured to the point of being good enough (yet) to adopt.
Legal, healthcare admin, and coding are the breakout categories. government looks to be the next massive capability improvement focus.
3. People switching from Claude to Codex
https://community.openai.com/t/introducing-the-new-codex-for-almost-everything/1379125
I’ve been seeing a lot more people say they’ve moved coding workflows from Claude over to Codex and that the new OpenAi model outperforms Opus 4.7.
Whether that holds or not, the point is your workflows shouldn't hold loyalty to any one AI coding tool and your teams should always be testing new coding models when they are released.
4. Reliability race
https://x.com/jdroege/status/2052049364579659849?s=46
This is really good framing and retelling about what ScaleAI is seeing in deployments with regulated industries.
A lot of tools can look good in a demo. But don't hold up in a hospital, bank, insurer, or government workflow.
I’ve been doing fractional CFO work for five years now and have found new clients infrequently. For those of you that do this full time, how many clients do you have at a time and how have you found new clients? I was also working in private equity so happy with the fractional work I have (which admittedly is one consistent client and ample ad hoc work), but now looking to grow this business and make it my full time thing.
That being said, I’m looking for advice on how others have grown their businesses, where you advertised (if at all). Also, how do you structure your rate? Per client? Per month? Per hour?
I am NOT promoting myself (per the rules), but genuinely curious in getting advice and insight. Thanks in advance!
What’s everyone doing to manage Claude use at your company? We have a growing list of pilot users, some super basic governance but I’m starting to see our usage tick up with dubious ROI.
I’ve spent the last decade in the C-suite, and I’ve noticed a recurring pattern. Many brilliant Controllers and Finance Directors get stuck. They have perfect technical skills, but they never make the jump to CFO.
From my side of the boardroom table, the "invisible ceiling" isn't about how well you know the GAAP/IFRS. It’s about Strategic Storytelling.
Most people bring me data; very few bring me a narrative that helps the Board make a decision. As we move through 2026, I'm seeing AI automate the "data" part faster than ever. What's left is the human element—managing stakeholders and predicting the "why" behind the numbers.
I wanted to open a thread for anyone in the middle of their finance career:
• What is blocking your path to the top right now?
• Are you seeing your roles change with AI integration this year?
• Or, just Ask Me Anything about the view from the CFO chair.
I’ll be around to answer questions for the next few hours. Cheers!
I’m facing an issue of how do I generate revenue using the my fintech and other revenue models in the market . I’m new to this space and I’m not sure how to go about revenue mostly the tech part of it . I’m just not able to understand how do I start ? Everything else is ready to be deployed & All that I’m doing right now is by myself . This is where I’m going blank.
I need some help!!!
Been doing fCFO work with seed to Series C founders for a while now and the tooling landscape has gotten genuinely noisy.
Curious what others are using day-to-day, particularly around financial data cleanup, reporting, and getting usable outputs from client ERPs (QuickBooks, Xero, mainly, NetSuite etc).
Most of what I’ve tried either assumes clean data going in, or is priced for enterprise. Neither fits the clients I’m working with.
What’s been worth your time?
What have you built? What are you building? What tools are you using to build? Keen on hearing real use cases that you rely on in your day to day.