I want to be honest with you upfront.
When I first started hearing about artificial intelligence transforming finance, my reaction was skepticism. Not because I doubted the technology, but because I had seen too many “transformational” tools come and go over the years. Each one promised to revolutionize the way finance teams work. Most of them added complexity without adding insight.
But over the past two years, something shifted. And I say this as someone who uses these tools daily, thinks carefully about where they add genuine value, and has changed the way I approach my own work as a result.
Artificial intelligence in finance is not hype. But it is also not what most people think it is.
What has actually changed.
The most significant change is not in what finance teams can calculate. We have always been good at calculation. The change is in how quickly we can move from raw data to meaningful insight, and how much of the routine cognitive work can now be handled by machines.
Tasks that used to consume hours of an analyst’s time, summarizing variance drivers, drafting commentary for management reports, restructuring data from multiple sources into a coherent view, can now be done in minutes with the right tools. This is not a small thing. It fundamentally changes what a finance team’s time is worth and what it should be spent on.
In my own work at Motorola Mobility, where the planning cycle integrates financial data across five geographies, we have embedded intelligent tools into the quarter-end routine, from close and consolidation workflows to the first draft of variance commentary. They do not replace the judgment that goes into interpreting the numbers. They handle the mechanical work that surrounds it, and that alone changes what the team can focus on.
The shift from producer to interpreter.
For most of my career, a significant portion of a finance professional’s value came from their ability to produce things: models, reports, forecasts, reconciliations. The production itself was skilled work that required training, precision, and experience.
Artificial intelligence is compressing the time and effort required to produce. Which means the competitive advantage is shifting decisively toward interpretation. Toward the finance professional who can look at a forecast, understand what it is actually saying about the business, and translate that into a clear recommendation for leadership.
This is not a threat to finance professionals. It is a promotion. The routine production work was never the highest-value thing we did. It was simply the unavoidable cost of getting to the analysis. Now that cost is falling rapidly, and the analysis can take center stage.
But this shift only benefits the finance professionals who are ready for it. Those who have built their identity around being the person who produces the report, rather than the person who understands what the report means, will find the transition more difficult.
Where I have seen artificial intelligence add the most value in practice.
In my own work, the areas where I have found intelligent tools most genuinely useful are not the ones I expected.
The first is in synthesizing large volumes of information quickly. Finance leaders are constantly processing inputs from multiple directions: market data, internal performance metrics, leadership priorities, team updates, external benchmarks. Tools that can help organize and summarize this information free up mental bandwidth for the thinking that actually requires human judgment.
The second is in communication. Writing clear, well-structured financial commentary, executive summaries, and business cases is time-consuming work. Intelligent tools have become a genuine accelerator here, helping to draft a first version that I can then shape, sharpen, and make my own. The final product is still mine. But the blank page problem largely disappears.
The third is in scenario thinking. Being able to quickly model multiple scenarios, stress-test assumptions, and articulate the financial implications of different strategic choices is core to what a Financial Planning and Analysis function exists to do. Tools that make this faster and more flexible directly improve the quality of decision support we can offer to leadership.
The risks that do not get talked about enough.
For all the genuine value artificial intelligence brings to finance, there are risks that I think the profession needs to take seriously.
The first is over-reliance. A model, whether built in Excel or generated by an intelligent system, is only as good as the assumptions behind it and the judgment applied to interpreting it. Finance professionals who accept outputs without interrogating them are not doing their job. The technology changes the speed of production. It does not change the responsibility for accuracy and judgment.
The second is the erosion of foundational skills. If junior finance professionals skip the hard work of building models from scratch, wrestling with messy data, and producing analysis under pressure, they may develop the ability to use intelligent tools without developing the underlying understanding that makes those tools useful. There is a difference between knowing how to prompt a system to produce a forecast and understanding what drives a forecast. The profession needs both.
The third is data integrity. Intelligent tools are powerful amplifiers. They amplify good data and good thinking. But they also amplify bad data and flawed assumptions, often at a speed and scale that makes errors harder to catch. The finance fundamentals around data quality, validation, and control matter more, not less, in an environment where analysis can be generated and distributed rapidly.
What I believe the future looks like.
The finance teams that will be most effective in five to ten years will not be the largest or the most technically sophisticated in a traditional sense. They will be the ones that have figured out how to combine human judgment with intelligent systems in a way that genuinely accelerates decision-making for the business.
The Chief Financial Officer of the future will not need to understand the technical architecture of the tools their team uses. But they will need to understand what those tools can and cannot do, where human oversight is non-negotiable, and how to build a team culture that embraces technology without losing its critical thinking.
And the finance professionals who will thrive are the ones who approach artificial intelligence with curiosity rather than anxiety. Who experiment, learn, adapt, and figure out how to use these tools to become more useful to the business, not just more efficient at the tasks they were already doing.
I am still learning. The landscape is moving quickly and anyone who tells you they have it fully figured out is not being honest. But I am genuinely excited about where this is heading.
Finance has always been about helping organizations make better decisions. The tools are getting faster and smarter. The purpose stays exactly the same.
