AI leadership that ships.
Field notes on Chief AI Officer leadership, AI implementation, agentic workflows, and operating leverage for mid-market firms.
The Financial Model Council: How CFOs Should Use AI to Catch Beautifully Wrong Models
AI can build a clean financial model quickly. The harder problem is whether the assumptions are commercially true. CFOs need AI review councils, not just AI model builders.
Read post →The Query Your CPA Firm Has Never Been Able to Ask
Most CPA firms have the data to answer their most important billing questions. It's sitting in three disconnected systems. Here's what changes when you connect them.
Read post →Your AI Agents Are Consistent. Your Workflow Design Is Still Broken.
Most mid-market firms drop an agent into an unchanged process and call it transformation. They get speed inside a broken design. Here's what workflow redesign actually looks like — and why it's the only place AI ROI lives.
Read post →Your AI Mandate Isn't Failing Because of the Model
Most companies blame the model when their AI mandate stalls. The real problem is almost never the model. It's that the data the agent needs to do anything useful is locked behind tools, silos, and access gaps nobody owns. Here's what's actually breaking enterprise AI — and the middle path that gets you moving this quarter.
Read post →The Covenant Monitor: From Month-End Discovery to Continuous Headroom
Most CFOs find out they're close to a covenant breach at month-end, when the controller emails a number. Here's what the workflow looks like when an agent owns the watch instead.
Read post →The CFO's Guide to Measuring AI ROI in Finance
Most AI deployments in finance produce time savings that never reach the P&L. Here's how to measure AI ROI properly, close the productivity-capture gap, and govern costs before agentic workloads blow your budget.
Read post →The CFO's AI Implementation Guide: Architecture That Actually Works
Most finance AI deployments fail — not because the technology doesn't work, but because CFOs automate old processes instead of redesigning around them. Here's the architecture that actually delivers measurable value for mid-market finance teams.
Read post →The CFO's AI Implementation Guide: Governance First
Most CFOs find out they have an AI governance problem the same way — their auditor asks a question they can't answer. Here's the implementation framework that fixes it before that conversation happens.
Read post →91% of Firms Use AI. Only 34% Feel It. Here's the Gap.
The gap between AI usage and felt transformation is not a technology problem. It is an ownership problem — and the fix is sequencing, not more tools.
Read post →Stop Using AI to Build Your Financial Models
The 'AI drafts, human finishes' workflow for financial modeling is backwards. CFOs should own the assumptions and use AI to challenge them against realized data — not outsource the judgment and inherit the errors.
Read post →The Board Doesn't Want an AI Forecast. They Want to Know Where Yours Was Wrong.
Every FP&A vendor is selling AI that builds your financial model faster. The board isn't asking how fast you built it. They're asking where you were wrong last quarter and what you're doing about it. Here's the reframe that actually matters.
Read post →The Board Pack Is Still a 3-Day Job. Here's the Workflow That Fixes That.
Most CFOs have tried AI for board pack preparation and found it underwhelming. That's because they automated the last step — narrative polish — while leaving data extraction, reconciliation, and formatting entirely manual. Here's what the full workflow actually looks like when you fix it properly.
Read post →Your AI Mandate Stalls at the Data Layer, Not the Model
You bought the Copilot licenses. The agent reads your inbox but can't touch the GL. Most finance AI mandates fail at the data layer, not the model. Here's the fix you can ship this quarter without waiting for the platform team.
Read post →Stop Using AI to Build Your Financial Model. Use It to Challenge It.
Every FP&A vendor sells AI as a model builder. It's the wrong half of the workflow. The leverage is in the role swap: the CFO keeps the pen, the agent backtests the assumptions, and the board gets a memo grounded in your numbers, not industry averages.
Read post →How Mid-Market Finance Teams Run Month-End on AI (Without the Hallucinations)
AI keeps drifting your board pack and getting numbers wrong, so you go back to doing it by hand. The fix is not a smarter tool, it is a smarter shape. Here is how finance teams run month-end on AI with the same report and the same numbers every time.
Read post →How to Stop AI From Hallucinating Your Financials: A CFO's Guide
AI hallucination in finance isn't a product defect. It's what happens when a probabilistic tool gets handed a job that needs a guaranteed answer. Here's the distinction that fixes it.
Read post →The Deal Room Is the Last Place PE Has Put AI
S&P Global says only 31% of PE firms have AI integrated into due diligence — and that is the highest-adoption category across the deal lifecycle. FTI Consulting says 95% of funds report AI initiatives meeting or exceeding the original business case. Both numbers are about the same firms. They are saying AI is working at the edges of a PE business — IR automation, DDQ drafts, portfolio dashboards — and not yet where the buy/no-buy call gets made.
Read post →Anthropic Just Shipped 10 Finance Agents. The Real Story Is Underneath the Marketplace.
Anthropic shipped 10 finance agents, Microsoft 365 add-ins, and an agent marketplace. The marketplace gets the headlines. The MCP connectors are what's useful.
Read post →Claude Built a 24-Month Financial Model for a $40M CPA Firm. Three Things It Found.
Episode 1 of AI for Finance: a driver-based forecast, three scenarios, and the patterns the model surfaced that weren't in the management report. Live interactive demo included.
Read post →How One Construction Firm Turned Open Zoning Data Into 5,000 Leads at $5 Each
We built a funnel on top of open zoning data and generated 5,000 construction leads at $5 each. Here is the build and where the pattern generalizes.
Read post →85% of Companies Want AI Agents. 21% Are Ready.
Deloitte surveyed 3,235 leaders across 24 countries. The headline number is 85% planning to deploy AI agents. The number that actually matters is 37% — and it tells a different story.
Read post →Mid-Market AI Should Start With KPIs, Not A Giant Operating Model
IBM is right that the Chief AI Officer role is becoming an operating model. But for mid-market firms, the first move is not to implement the whole model. It is to pick one measurable outcome and sprint toward it.
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