The temptation to let AI do all the heavy lifting is understandable, but in finance, handing over everything to AI can become a costly mistake. AI has made it faster and easier to generate financial analysis and insight. But knowing whether those insights make sense still requires a CFO’s judgment. While AI is an incredible tool, the need to understand financial and accounting foundations is now more essential than ever.
A New Way of Working
AI has changed how CFOs can approach building financial models and ongoing analysis. Where we used to start with blank spreadsheets and work through every formula, we can now use AI as a thought partner, starting with the goal rather than the mechanics. This concept highlights another major shift in the role of the CFO – where the CFO is becoming a tech stack architect, moving finance far beyond reporting into shaping how businesses operate and scale.
But the fundamentals haven’t changed. Founders still need to know exactly what their data looks like, where it comes from, and what they have and what the gaps are. There’s also a subtler risk, that we call cognitive offloading, where AI is doing so much of the work founders and finance professionals either forget how to do it themselves or, perhaps even worse, never really understand what the AI is doing in the first place. AI can build the formulas, but too much reliance on it for doing the thinking and understanding can be a very costly error.
Nonetheless, the opportunity to leverage AI to transform the office of the CFO is very real. CFOs who use AI well spend less time in the weeds and more time on strategy, letting AI flag issues, summarize data, and handle financial analysis that used to consume days, even weeks.
CFOs need to stay vigilant about spotting outputs that look entirely correct but aren’t. AI is remarkably good at producing work that appears polished and accurate on the surface, and without a solid grounding in accounting and financial fundamentals, it’s easy to mistake a confident-sounding answer for a correct one which could lead to costly errors for the business.
Clean Data Is Everything
Before AI can add value, the underlying data needs to be right. AI is probabilistic, so unstructured data tends to produce inconsistent results: ask the same question five different ways of structured data and you’ll get the same answer every time, but ask it of unstructured data and you’re likely to get five different answers.
Think about data structure from day one, across the whole business, not just in finance. AI can help pulling contracts, flagging unbilled items, building revenue recognition schedules, and creating amortization schedules for intangibles. But it only works well when the source data is structured cleanly and consistently from the start, which lets that same data be presented differently for investors, management, and departments. The CFO’s value lies in knowing how each stakeholder will use the data to make decisions, and building the systems that deliver clean, rich data to support them.
From Reports to Real Decisions
The value of AI in finance isn’t only speed but also clarity. AI can create interactive dashboards that let founders and executives explore their numbers, helping founders and finance executives concentrate on outcomes, analysis, and action.
This may change how startups relate to their finance function. Founders often think of finance late, engineering and sales drive them and excite them, but the most effective founders treat their CFOs as a strategic partner from the start. AI accelerates that partnership, giving CFOs faster, cleaner insights, and the ability to ask better questions, understand the real drivers of performance, and make decisions backed by data rather than instinct alone. With AI in their toolkit, the CFO closes the gap between where the business is and where it needs to go.
Human Judgment Is Still the Point
Opportunity also requires caution, at least in the short term. AI is very good at creating outcomes that look right. But before you’re convinced, you need the expertise to know whether the output actually is right.
The approach that works is trust but verify. Ask AI to create an audit log of everything it changes; learn to create second agents to quality control the first; tell it to play devil’s advocate and find the holes; use more than one LLM as you’ll find different LLMs are good at finding different issues or inconsistencies. There’s no value in being told your output is great but enormous value in finding out where it breaks.
The nature of finance work is changing rather than disappearing. It’s about knowing what goes into your models, ensuring the data is right, and being able to audit the output. The expertise has just shifted. It has not gone away.
Why Attivo
Knowing how to use AI in finance is one thing. Knowing what good looks like, across dozens of companies at different stages, is another entirely. At Attivo, we are embedded in our clients’ finance functions as both operational and strategic partners. We make sure data is structured correctly, systems produce trustworthy outputs, and AI is used in ways that actually move the business forward.
Getting AI right in finance isn’t just about the tools. It’s about the judgment to use them well.
