Finance and AI Readiness: Speaking in Facts

A few years back, someone showed me an article from CFO.com that listed spreadsheet “worst practices” – the things that make finance professionals cringe when they look at a colleague’s work. Poor segregation of data, where critical values get tangled up with complicated formulas. Poor documentation of assumptions, so nobody can figure out what the original logic was. No version control, so changes get made and lost and made again. Difficulties making modifications because the whole thing is so fragile that changing one cell breaks something three tabs away.

I read the list and started laughing, because every single item on it maps perfectly to software engineering. Poor segregation of data? That’s mixing business rules with your data layer. Poor documentation? Design debt. No version control? Every developer’s nightmare. The finance team’s spreadsheet portfolio isn’t just “using Excel.” It’s a genuine custom software development operation – sophisticated, interconnected, business-critical applications built and maintained by people who would never call themselves programmers. But that’s exactly what they are.

I bring this up because there’s a conversation happening right now in a lot of organizations about finance AI readiness – specifically, what role Finance should play in the company’s AI transformation. And too often, the answer is “write the checks and stay out of the way.” Fund the AI initiatives. Approve the budgets. Let the technical people figure it out. Which completely misses the point. Finance has been building and maintaining complex data systems for decades. They’ve been speaking in facts – insisting on rigor, demanding that numbers mean something, building structures that survive the departure of the person who built them – since before “digital transformation” was a phrase anyone used. Those aren’t peripheral skills for AI readiness. They’re foundational.

The Profession of Numbers

Data is the core asset of any AI initiative, and Finance is the functional area that has been deeply involved with data and information longer than anyone else in the building. Sellers sell. Shipping ships. Customer Service serves customers. But Finance has always been the profession of numbers – accounting for costs and revenues, tracking the value of the company and what it produces, insisting that decisions get grounded in something measurable. That pedigree stretches back to the first wave of business automation, when accounting systems and ERP platforms turned Finance into the earliest adopters of enterprise technology. They’ve been living in data ever since.

So does that mean Finance should lead your AI strategy? Probably not. Your business strategy talks about what you do, where you play, and how you win. Finance’s role in that conversation is essential but specific – it’s the rigor underneath the ambition, not the ambition itself. But the idea that Finance doesn’t have a seat at the AI table is just as wrong. And frankly, it’s the more common mistake. When organizations start building their AI roadmap, they tend to invite the technologists, the data scientists, and maybe a few business unit leaders. Finance gets invited to approve the budget, not to shape the strategy.

That’s a missed opportunity, because what Finance brings isn’t just money. It’s a set of disciplines that AI initiatives desperately need and rarely have. Data quality. Value measurement. Structure that survives the departure of the person who built it. The instinct to ask “but does this actually work?” when everyone else in the room is excited about the demo. These aren’t skills you develop for AI readiness. They’re skills Finance has been honing for decades, applied to a new context.

What Finance Brings to the Table

When I think about what Finance contributes to AI readiness, five things stand out – not because they’re new capabilities Finance needs to develop, but because they’re strengths Finance already has that most organizations aren’t tapping.

The first is the one we’ve already named: speaking in facts. The core systems that support a company’s operations – accounting, ERP, planning – have long been Finance’s domain. And the discipline underneath those systems isn’t just about running reports. It’s about insisting that metrics mean something. That the data is clean. That when someone says “we grew 12% last quarter,” there’s a defensible number behind it. A popular concept in this space is KPIs, and Finance is often best positioned to understand not just which metrics matter, but the most logical way to gather the data. When your AI initiative needs to define what “working” looks like – and every AI initiative does – Finance is the team that knows how to answer that question honestly. They can help every area of the business speak in facts, at the right level of detail, to make decisions that actually hold up.

The second is structure for scale and sustainability. This one is underrated. In the rush to deploy new technology – and AI is accelerating that rush considerably – organizations build things that work great for a demo but collapse under real-world conditions. Too reliant on one person who understands the logic. Too fragile to survive a change in volume or scope. Finance has seen this movie before, because they lived through it with every wave of enterprise technology since ERP. They know what it takes to build systems that can be handed off to new teams, that survive turnover, that scale with growth instead of breaking under it. That institutional memory is worth more than most AI strategy decks.

Third: identifying value. This might be Finance’s single most important AI contribution, and it’s the one that gets the least attention. Every department in your company is excited about AI. Product teams see breakthrough possibilities. Marketing sees personalization at scale. Operations sees predictive everything. But excitement isn’t a business case. Finance understands how shareholder value is actually created for your company – the levels of risk and investment the organization is comfortable with, the difference between revenue that looks good on a slide and revenue that shows up in the financials. When a product team wants to embed AI into a physical product, they’re often stepping into unfamiliar territory – new revenue patterns, new cost structures, new metrics for go/no-go decisions. Finance needs to help those teams build business plans that account for how digital products actually behave, not how widget sales used to work.

Fourth: building sustainable teams. The range of skills needed to pull insights from all the data an AI-enabled organization generates is broader than most people realize. You need people who can see patterns, people who can design the right questions, people who can build the models, and people who can turn the output into action. The technology stack underneath all of this keeps shifting – what was essential infrastructure three years ago is table stakes today. Finance’s contribution here isn’t technical. It’s organizational. Building teams with enough breadth that they don’t fall apart when one person leaves. Creating structures that develop skills over time rather than depending on a single hire who knows the latest tool.

And fifth: partnering across the organization. There’s a reason “business partner” has become such a common aspiration in finance circles – Finance is one of the few functional areas that works closely with every other department. They see the whole picture. They understand how money flows across the organization, which means they understand how decisions in one area affect outcomes in another. For AI readiness, that cross-functional view is invaluable. When the AI conversation stays siloed – marketing doing its thing, operations doing its thing, nobody comparing notes – Finance is naturally positioned to be the connective tissue. Not because they own AI, but because they already work across every boundary that AI needs to cross.

You’re Already Programmers (You Just Don’t Know It)

Let’s go back to that CFO.com worst practices list for a moment, because it tells us something important about finance AI readiness that most people miss.

The article was aimed at finance professionals, warning them about common spreadsheet mistakes. But read it through a software engineer’s eyes and the parallels are striking. “Poor segregation of data” – mixing critical values with complicated algorithms in the same cells – is exactly what developers call tangling your business logic with your data layer. “Poor documentation of assumptions” is design debt. “Difficulties in making changes” because the whole structure is too fragile? That’s what happens when you don’t build in modularity. And “now it’s here, now it’s not” – losing track of changes made while testing different assumptions – is a version control problem that every development team in the world recognizes instantly.

The author’s advice? Treat the development of a spreadsheet – any spreadsheet – more like writing a term paper with footnotes and a bibliography. In other words, treat it like what it actually is: software development.

This isn’t a criticism of finance teams. It’s the opposite. Your finance organization has been doing software development for years – building complex, interconnected, business-critical applications that drive real decisions. They’ve been doing it without formal training in software engineering, without development environments, without most of the tools that professional programmers rely on. And the systems they’ve built work. They’re imperfect, sure. But they work, and they’ve been working for a long time, because the people who built them understand the data and the business logic deeply enough to compensate for the tooling they don’t have.

Now layer AI into that picture. Finance teams are already using AI-enhanced features in their planning and forecasting platforms – many of them adopted those features the moment they became available, because they solved real problems. AI copilots for financial modeling are showing up in tools Finance already owns. Automated anomaly detection in audit workflows. Predictive analytics in cash flow management. This is shadow AI happening in Finance right now, and it’s happening because Finance people are comfortable with data, comfortable with complex systems, and comfortable adopting tools that make their work better. They don’t need to be convinced that AI is useful. They need to be recognized as people who already understand it – practically, if not theoretically – and whose instincts about data quality, structure, and sustainability are exactly what the rest of the organization’s AI efforts need.

The Discipline to Say No

Here’s a contribution that nobody puts on the “AI readiness” slide deck, but it might be the most valuable thing Finance brings to the table: the willingness to kill experiments that don’t deliver.

Every department in your organization wants an AI budget right now. Product Development sees intelligent features. Marketing sees hyper-personalized campaigns. Operations sees predictive maintenance that pays for itself in six months. And some of those ideas are genuinely good. But most AI experiments fail – not because the technology doesn’t work, but because the business case was never solid, the data wasn’t ready, or the problem being solved wasn’t worth the investment. Someone has to be the adult in the room who asks “what does success look like, and how will we know when we’ve achieved it?” And someone has to be willing to say “this isn’t working, and we should stop spending money on it” before the sunk cost fallacy takes over.

That someone is usually Finance. Not because finance people are killjoys (though I’ve heard that accusation once or twice), but because they’ve spent their entire careers building frameworks for evaluating investment under uncertainty. They know how to set go/no-go criteria before the money starts flowing. They know how to measure incremental value rather than getting seduced by projections. And they have the organizational standing to deliver uncomfortable news without it being perceived as territorial or political.

This is the structure-for-sustainability instinct applied to AI portfolio management. Without it, you get what I’ve seen in too many organizations – a collection of AI pilots that nobody has the authority or the framework to shut down, consuming resources and attention while delivering marginal value. Finance doesn’t just fund AI initiatives. Finance provides the discipline that separates the experiments worth scaling from the ones that should be gracefully retired. That’s not a support function. That’s a strategic capability.

None of this is meant to suggest that Finance should run your AI transformation. That’s not the point, and most finance leaders would be the first to say so. The point is that when organizations think about AI readiness – who contributes, who participates, who has a seat at the table – Finance tends to get cast in a supporting role. Approve the budget. Report the results. Stay in your lane.

But Finance’s lane is wider than anyone gives it credit for. The data rigor, the value frameworks, the sustainability instincts, the cross-functional view, the willingness to say no when the numbers don’t work – these are exactly the capabilities that AI initiatives need and rarely have enough of. Your finance team has been building these muscles for decades. AI just gives them a bigger stage to use them on.

In the first article of this series, we made the case that AI readiness is an organizational capability, not a technology initiative. Finance is the proof. These are people who would never describe themselves as technologists, but who have been building complex data systems, enforcing data quality, and measuring value creation since before anyone in the company was talking about artificial intelligence. The skills were always there. The question is whether your organization is smart enough to use them.

Next up – Sales and Marketing, and the richest source of unstructured data in your company: the customer voice.

If you’re rethinking how your functional areas contribute to AI readiness – or just want to keep up with practical ideas for digital transformation – join our mailing list and we’ll keep the conversation going.

Related Articles

• How Finance Teams Can Succeed with AI – HBR piece from Vlerick Business School researchers on digital-maturity diagnostics for finance functions, with a focus on benchmarking AI readiness
• How AI is Shaping the Evolving Role of Finance Controllers – PwC’s take on five key issues for controllers navigating AI adoption, including data quality governance and cross-functional collaboration
• CFOs Evolving into Strategic Architects Amid AI Transformation – FERF/CrossCountry research finding 77% of CFOs deeply involved in enterprise strategy, yet AI governance and talent gaps persist

Recommended Books

• Reimagine Finance: The CFO’s Leadership Playbook for the Age of AI, Data, and Digital by Tariq Munir – Practical guide for CFOs navigating digital transformation with frameworks for building data-driven finance functions
• Transforming Finance in the Age of AI by Daniel Villani – A CFO’s blueprint for AI readiness covering data, governance, and people, drawn from Fortune 1000 experience
• Don’t Think So Much by Jim MacLennan – A practical guide to digital transformation that connects operations, customers, products, data, and teams into a framework that works