Finance AI strategy & research notes

The AI Mistake That Actually Worries Me Isn't the Dramatic One

The scary AI failure stories are the dramatic ones—a hallucinated number in a board deck. The failure mode I actually worry about is quieter and more common: small, plausible errors that never get caught because nothing about them looks wrong.

The Build vs. Buy Question Is the Wrong Question Now. It's 'Embedded, Point Solution, or Neither.'

The classic build-vs-buy framing doesn't capture how AI capability is actually showing up in finance orgs in 2026—quietly, inside tools you already pay for, whether or not you asked for it.

The Skill I'd Actually Tell a New FP&A Analyst to Build Right Now (It's Not Prompting)

Everyone's telling early-career finance people to learn prompt engineering. I think that's the wrong first investment. Here's what I'd prioritize instead, and why it's more durable.

The Least Exciting Sentence in Every AI Finance Meeting: 'Where Does This Number Actually Come From'

Every stalled AI finance pilot I've seen has the same root cause, and it's never the model. It's that nobody could confidently answer where the underlying number comes from, how it's defined, and whether it means the same thing in two systems.

Why Content Spend Forecasting Is a Terrible Fit for Off-the-Shelf AI Tools (And What Actually Helps)

Most AI-for-finance tooling is built and demoed on subscription-revenue and SaaS-style forecasting. Studio and production finance runs on a completely different shape of data, and that mismatch matters more than most vendors admit.

If AI Does the Job I Learned On, How Does the Next Person Learn It?

The scariest part of agentic AI in finance isn't that it might replace junior analysts. It's that it might quietly remove the exact repetitive work that used to be how people became good analysts in the first place.

'Continuous Close' Is Mostly Marketing. Here's the Version That's Real.

The vision of a fully automated, real-time close is oversold. But a genuinely useful, narrower version of it is quietly becoming real for teams that get the fundamentals right. The difference is in what you don't automate.

CFOs Are Starting to Ask the Question Nobody Wanted to Ask in 2024

The AI ROI conversation has shifted from 'are you experimenting' to 'show me the number.' A lot of pilots are about to have an uncomfortable renewal conversation. Here's how to actually answer the question instead of dodging it.

Your Model Risk Policy Was Written for Spreadsheets, Not Language Models

Most finance orgs still govern AI tools using a model risk framework built for regression models and rate curves. That framework doesn't ask the right questions about an LLM. Here's what to add, not throw out.

We Don't Have an Agent Problem. We Have a Judgment Problem.

Everyone's excited about agentic AI doing FP&A work end-to-end. The harder question isn't whether agents can do the steps—it's whether we've actually defined what 'good judgment' looks like well enough to hand it off.

Finance Skills the AI Economy Is Pricing: LLM Labs, Hyperscalers, and Enterprise Teams

A capability map from public hiring signals—what model companies, compute platforms, and corporate finance functions value in the AI economy beyond prompt engineering.

Compute Capacity as a CFO Supply Chain: GPU Commitments, Lead Times, and Portfolio Risk

How finance leaders should treat GPU and cloud compute as a scarce production factor—hyperscaler capex, forward commitments, contract terms, and portfolio risk.

Inference Economics and AI COGS: What CFOs Should Measure Beyond Token Price

A CFO-office guide to inference economics—AI cost of goods sold, cost per successful output, agent loop multipliers, and driver-based forecasting beyond headline token rates.

Agent Audit Trails for Close and Financial Reporting: Logs Auditors Will Ask For

What defensible agent logging looks like for month-end close and financial reporting—minimum fields, SOC 1 gaps, service-account attribution, and EU AI Act context.

COSO GenAI Controls and the AI Risk Register: What Finance Teams Need in 2026

A digest of COSO’s February 2026 GenAI internal control guidance—capability taxonomy, AI risk registers, shadow AI inventory, and ICFR implications for finance.

Model-Agnostic AI Stacks for Finance: Swap Models Without Rebuilding Workflows

Why finance teams need a portability layer between workflows and foundation models—closed APIs vs open-weight deployment, EU AI Act context, and governance when models change.

When ERP and CRM Become Agentic: What Finance Leaders Should Watch

How agentic ERP and CRM systems affect controllers and FP&A—procurement routing, pipeline automation, revenue timing, and oversight design.

Agentic FP&A: Long Context, Scenario Planning, and Close Automation

How long-context models, scenario agents, AP automation, and disclosure tooling are reshaping FP&A—and the metrics finance should track.

Multi-Agent Orchestration in Finance: MCP, A2A, and Intent-Based Workflows

How finance teams move beyond single chatbots—intent-based agents, Model Context Protocol (MCP), Agent2Agent (A2A), and the controls orchestration requires.

AI FinOps in Corporate Finance: ROI, Unit Economics, and the 2026 Reality Check

How finance teams measure AI ROI in 2026—BCG benchmarks, LLM unit economics, token pricing trends, and the KPIs CFOs are adding to corporate dashboards.

AI Agents in Finance: From Experiments to Transformation Priorities

Why CFOs are prioritizing AI agents, how agentic workflows differ from chat-only GenAI, and what enterprise research says about scaling safely in 2026.

AI Governance for Finance Teams: Model Audits, Controls, and Trust

A practical overview of governance, model, and functionality audits for AI in finance—based on AICPA & CIMA professional insights on strengthening controls and compliance.

What CFOs Are Prioritizing in 2026: AI, Digital Finance, and AI Agents

Key findings from Deloitte’s Q4 2025 CFO Signals survey—why technology transformation, automation, and AI agents top the agenda for North American finance leaders.

Embedded AI vs. Point Solutions: What CFO Surveys Say About the Finance Stack

L.E.K.’s 2025 Office of the CFO survey on adoption gaps, AP/AR wins, and why most CFOs lean toward embedded AI in finance platforms—with attribution and sources.

AI in Finance 2026: Scaling Where the Productivity Gap Hurts Most

The Hackett Group’s 2026 Finance Key Issues research on workloads, tech spend, AP and FP&A adoption—and why people and change management matter as much as models.

The Future of Finance and AI: Agentic Systems, Efficiency, and Augmented Analysis

How agentic AI, automation, and smarter tooling are reshaping corporate finance, FP&A, and accounting—and what practitioners should prioritize next.

The Future of Accounting: What AI Means for the Profession

How AI is reshaping accounting—from close automation and audit tooling to advisory work—with AICPA-aligned controls and the metrics firms should track.

Building My First AI-Powered Financial Dashboard with Python

A step-by-step walkthrough of building a simple financial dashboard using Python, with code snippets and design decisions explained.

Five AI Tools for Financial Analysis by Use Case

A use-case shortlist with named platforms—spreadsheet copilots, document extraction, BI natural language, anomaly detection, and LLM drafting—with limits and governance caveats.

Learning AI in Finance: A Practitioner’s 90-Day Map

A practical learning curve for finance professionals—aligned with 2026 CFO priorities, governance expectations, and one measurable workflow at a time.