Why I’ve come around on the whole AI thing
Although it's still probably coming for all of our jobs
When I started my career in corporate FP&A, we didn’t have enough people to analyze the data we needed to. All day long, layer upon layer of analysts were crunching numbers, managers checking their work, and directors translating it all for leadership. Each person had their lane, their specific piece of the P&L to protect. Miss a number? That's your neck on the line.
But, what happens to all those people when AI is crunching the numbers in seconds rather than days? Who is translating it all for leadership if AI is crafting the notes? "AI is going to take all our jobs" was my knee-jerk reaction.
But, I’ve started to come around. It’s not so much that I think all of these jobs will be done by robots. Alternatively, I think it’s going to make the best analysts 100 times better.
The Truth About AI in Finance
First, let's address the elephant in the room: AI requires skill to use effectively. It’s a skill anyone can learn, but it is a skill nonetheless.
Just like a financial model is only as good as its assumptions, AI output is directly correlated to the quality of your prompts. If you can't articulate what you need clearly and provide proper context, AI will serve up garbage. The old adage "garbage in, garbage out" hasn't gone anywhere.
Well, the "M" in LLMs stands for "models" – and as we know, models provide frameworks for reality, they don't replace it. You still need human judgment to manage these models, even if they're hyper-efficient.
So, it’s not so much that AI is going to replace you and your job. It’s more like a person who knows how to leverage AI is going to take.
As AI reshapes our industry, I'm seeing a fundamental shift in how finance teams operate. The traditional hierarchical structure is flattening, and the nature of our work is evolving.
The Rise of the Super-Analyst
Consider this: Why maintain separate analysts for sales, OpEx, margins, and EBITDA when one analyst, supported by specialized AI agents, could manage an entire P&L? Or, why couldn't a single analyst handle multiple business units or clients?
This isn't science fiction. It's already happening.
The New Finance Organization
The transformation is already underway. Here's how I see it playing out:
Data preparation and routine analysis will be automated through data pipelines and ETL workflows. We won't need armies of junior analysts maintaining spreadsheets.
Senior analysts will shift from manually maintaining forecasts to managing predictive algorithms. Their value won't come from being good Excel model builders– it'll come from their ability to tune and interpret AI models (even within Excel).
The middle management layer will thin considerably. The best managers will evolve into directors, while others might find themselves caught in no man's land as algorithms take over quality control.
Directors will need to be exceptional. They'll need to understand the details while delivering crystal-clear insights to leadership. The competition for these positions will intensify as organizations flatten.
The Future is Already Here
As technology continues to advance, this trend will only accelerate. But there's a catch: it's not a simple matter of replacement. The nature of finance work is fundamentally changing. Success will increasingly go to those who can harness technology most effectively.
And here's the crucial point: while models can suggest countless good decisions, humans will remain responsible for making the most important ones. Leadership skills, business acumen, and strategic thinking aren't going away – they're becoming more valuable than ever.
A Word of Caution
While this AI-driven transformation is exciting, it's not without its challenges and limitations:
Not Every Organization is Ready: This vision works best in companies with clean data structures and mature processes. Early-stage companies or those with messy data environments may still need traditional full-time analysts who build institutional knowledge. That said, AI-driven automation and predictive tools are becoming more accessible by the day, so even at smaller scales, bootstrapped teams have tons of tools at their fingertips.
Managers Aren't Obsolete, They're Evolving: While some managerial tasks will be automated, we still need humans to ensure AI outputs align with strategic goals. The manager's role is shifting from spreadsheet oversight to AI governance and cross-functional enablement.
Data Quality and Ethics Matter More Than Ever: Just like traditional financial models, AI systems are only as good as their inputs. Organizations need robust data quality processes and ethical governance frameworks to ensure AI-driven decisions are both accurate and responsible.
The key is to approach this transformation thoughtfully, ensuring we're not just chasing efficiency at the expense of effectiveness.
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The Bottom Line
The only constant in our industry is change. AI isn't an enemy to feared (at least not yet) – it's a tool that will separate the great finance professionals from the good ones. The question isn't whether AI will take your job. The question is: are you ready to become 100 times better at what you do?