A Profession at an Inflection Point
Financial models are the most important decision-making tools in finance. As with so many professions, financial modeling now stands at an inflection point as artificial intelligence (AI) is transforming how models are built, tested, and applied.
It is not an exaggeration to say that the financial modeling landscape is changing daily. The next generation of finance professionals will have to understand the discipline of financial modeling, to be sure. But they will also be faced with the task of how to lead teams that combine people, data, and AI tools to make informed decisions.
To be clear, the fundamentals of financial modeling remain unchanged: A strong 3-statement model links the income statement, balance sheet, and cash flow statement to generate a forecast in order to derive an insight. What is changing is the toolkit available to the modeler.
This shift is a central focus of the ModSquad, a new web series that I am co-hosting with Paul Barnhurst, aka the FP&A Guy, and Giles Male, Founder of Full Stack Modeller and an accredited Master Financial Modeler. The premise of the series is to test, review, and evaluate the growing library of AI modeling tools in real time. New tools making big promises are being released regularly, so understanding how AI can support our work, while also avoiding pitfalls, has become essential for finance professionals.
Financial Modeling in the Age of AI: New Tools, New Challenges
The past year has brought an explosion of AI-enhanced modeling tools. As we discuss on the ModSquad, most of these tools can “fill in the blanks.” That is, they are excellent at reading instructions and populating a schedule, and even building a model. What the tools lack is context: The ability to ask the right questions, validate results, and tell the story of the business in a way that guides confident decisions.
Last month, Microsoft introduced Agent Mode, a tool within Copilot in Excel. Agent Mode is intended to help users interact with their spreadsheets using natural language, performing multi-step tasks that go beyond single formulas or charts. Unlike the Copilot chat interface (which gives suggestions or assists), Agent Mode directly edits or augments the Excel workbook by creating or modifying sheets and inserting formulas.
This week, I had the chance to load up the Agent Mode and test it. The experience was remarkable: With English-language prompts, it could build a basic model in seconds.
This is one of the wildest developments I have witnessed in my decades of modeling experience. Watching a machine generate a financial model on command is both exciting and unsettling.
Here is the caveat: These tools make mistakes, sometimes significant ones. While AI can speed up the model building process, it cannot (at least yet) understand an industry, assess a company’s dynamics, or tell its story.
Why Modeling Skills Matter More Than Ever
I believe strong modeling skills have never been more important. Paradoxically, as the AI tools get better, finance professionals need to be better at questioning them.
You will have to challenge the AI around certain concepts:
- Are the generated numbers accurate and reasonable?
- Does the model capture the right relationships and drivers?
- Are the assumptions aligned with reality?
A financial model is about more than linking cells. A strong modeler should not be spending all their time behind a screen. In fact, if half the work lies in building the mechanics of the model, the other half lies in the research executed before you even open your laptop.
AI can be extremely helpful if you already know what you’re doing. Ideally, you will interact with your AI tool like you would with a colleague who can collaborate with you. But a novice would be ill-advised to submit a model to their boss or client solely by relying on AI. Modeling is a journey just as much as it is a destination. It is about getting smart about a company or an opportunity so that you can apply your insights. Without proper understanding, the risk of serious errors and potential embarrassment is real.
Balancing Power and Responsibility
The power and the risk of AI is a challenge that my financial modeling colleagues and I are navigating.
As new tools emerge, we rigorously test and push them, not only to see what they can do, but also to identify their blind spots. Our findings to date suggest that while AI can generate a model based on past financial data, it struggles with prediction.I can’t ask the tool, “What do you think will happen?”
Therefore, adopting these tools responsibly means embracing a hybrid approach: Use AI to accelerate the mechanics, but rely on professional judgment and knowledge to check assumptions, interpret results, and ensure the narrative is coherent and credible.
The Model as a Dynamic Narrative
A powerful financial model needs to tell a story. You build a model for your boss or client to illustrate where a company has been, where it stands, and where it might go; the AI can speed up the data preparation, historical schedules, and sensitivity testing.
Though AI may make it faster to execute calculations, model strategy remains a deeply human endeavour. AI cannot yet research the industry context or understand competitive dynamics or interview senior leadership. That responsibility still belongs to the modeler.
The more I explore this new territory, the more I believe that the professionals who thrive will be those who can use AI as a partner to accelerate technical work while retaining control over the strategy, the substance and the story.
Building Leadership in the AI-Enabled Era
Expanding the Modeler’s Skillset
Finance leaders of tomorrow will require fluency in people management, technical skills and machine-generated insights. In addition to managing teams and outputs, professionals will have to learn how to design effective prompts, test the results, and then integrate these components into their models.
These new abilities should complement, rather than replace, the fundamentals of financial modeling. Leaders who understand the nuances of financial modeling and can work in partnership with the AI will be best equipped to guide teams and communicate insights.
Critical Thinking and Judgment
Even as AI accelerates calculations, the most valuable human qualities remain: Curiosity, critical thinking, and good judgment.
A financial modeler is more than someone who can build beautiful spreadsheets. A great modeler is, at times, a listener, a problem solver, a coach, a referee, and a communicator.
Financial modeling is not about finding “the right answer.” Rather, good models contain insights that shape how we explore possibilities. While AI can expand the breadth of that exploration, only we can decide which assumptions to test, which scenarios to prioritize, and how to use our models to inform critical decisions.
Conclusion
Financial modeling has always been about more than formulas in Excel. It has been the language of business storytelling. AI does not change that truth, but it may enhance it, which is why I do not believe that financial modeling roles are going to disappear any time soon.
As the financial modeling profession evolves, we must stay current to stay relevant. We have to be prepared for the changes that are coming. Those who can combine modeling excellence with fluency in AI will shape how organizations assess opportunities, mitigate risks, and chart their paths forward.
Financial modeling is entering a new era. While leadership, discipline, and technical skills remain as essential as ever, the future of modeling lies in the partnership between human insight and intelligent technology. Those who embrace that partnership will not only thrive as practitioners but also emerge as leaders.
