Lately, I’ve been thinking a lot about how artificial intelligence is reshaping not just industries like marketing or tech, but the backbone of business itself: finance and accounting. According to the Indeed Hiring Lab’s AI at Work Report 2025, nearly half of all job skills in the U.S. now fall into what they call a “hybrid transformation zone.” It’s the space where AI handles the routine, and humans handle the reasoning. If that still sounds distant, just consider this: Microsoft Excel, when used in the cloud, now includes Copilot integration, meaning you can simply prompt the formulas and formatting you need instead of building them yourself. What once took an hour of manual setup now takes a few well-written sentences. That’s the transformation zone in action.
The Indeed report found that about 26% of jobs are “highly transformable” by GenAI, with another 54% moderately affected. That might sound abstract, but in plain terms, it means automation is no longer something that’s coming. The change is already here, and it’s touching nearly every corner of the finance world. The day-to-day tasks like reconciliations, bookkeeping, and financial reporting are becoming the domain of systems that can run 24/7. What’s left for humans is the oversight: the context, judgment, and ethical interpretation that numbers alone can’t provide.
If you map this out using Indeed’s four levels of transformation (minimal, assisted, hybrid, and full), you start to see how finance and accounting roles will evolve. The repetitive, rules-based work (like data entry or invoice processing) will move toward full automation. Tasks like forecasting, budgeting, and audit prep will fall into the hybrid zone, where AI drafts and humans validate. And the advisory and strategic parts, the work that requires trust, nuance, and business intuition, will stay human for a long time. It’s not about replacement; it’s about redistribution. The future accountant won’t be buried in spreadsheets; they’ll be the ones auditing algorithms.
But there’s another side to this shift that I think we need to talk about. Matthew Call’s recent Wall Street Journal piece, “Why AI Will Widen the Gap Between Superstars and Everybody Else,” argues that AI won’t level the playing field, it will widen it. His point is that people who already perform at a high level, who ask better questions, who think systematically, who experiment without waiting for permission, will extract far more value from AI than those who don’t. And in a field like finance, where the margin between “competent” and “exceptional” is often razor thin, that insight hits hard. The professionals who master AI early will likely pull far ahead, not because the technology is unfair, but because they’ll know how to get more out of it.
That gap could create a quiet divide inside organizations. On one side, you’ll have financial leaders using GenAI to forecast trends, model risk, and identify inefficiencies in seconds. On the other, you’ll have teams waiting for permission, still using yesterday’s tools and workflows. As Call points out, when AI is invisible, credit goes to those already trusted to do exceptional work. It’s easy to imagine a scenario where the “AI-literate CFO” becomes the new superstar, while everyone else struggles to keep up. The boss doesn’t need to do all of the work, but if they wanted to it won’t be impossible in the next 30 years. More importantly, one individual contributor doesn’t need to do the work of a whole department, but they’ll be enabled to do so if they want to.
That’s not a distant future. It’s already unfolding.
The challenge now is figuring out how to make this transformation fair and scalable for all of us. The answer, I think, lies in what both Indeed and the WSJ piece agree on: education and exposure. Companies can’t just hand people tools; they need to teach them how to think with them. That means giving junior analysts time to experiment in AI sandboxes, creating shared libraries of prompts and workflows, and rewarding employees not just for output, but for how effectively they use these systems. If organizations want more “superstars,” they have to democratize the conditions that create them.
For individuals like me, this shift feels both urgent and exciting. I’m already seeing how studying accounting and AI together changes how I think about business problems. Finance isn’t just about balancing books anymore; it’s about designing systems that balance themselves. The professionals who will thrive in this next chapter aren’t the ones who resist automation, but the ones who partner with it, those who learn how to question it, correct it, and build better versions of it. In that sense, AI is raising the bar for what it means to be a great finance professional.
