If you spend any time on LinkedIn or at industry conferences these days, you will inevitably be told that artificial intelligence is the silver bullet for financial planning and analysis. We are in the middle of a massive hype cycle. Billions of dollars are currently being poured into AI initiatives under the banner of “finance transformation.” CFOs and FP&A leaders are being sold a vision of the future where predictive models, automated variance analysis, and conversational agents seamlessly drive strategic decision-making.
It is a beautiful vision. But it glosses over a massive, uncomfortable roadblock that the industry is collectively ignoring: artificial intelligence does not fix a fragmented finance infrastructure. In fact, it does the exact opposite. AI exposes and amplifies the cracks in your foundation.
Let’s be entirely honest about the current state of enterprise finance. Despite decades of digital transformation initiatives and the implementation of expensive, monolithic ERPs, the vast majority of enterprise finance functions are still fundamentally being run on Excel. We have created a reality where layer upon layer of spreadsheets – each holding slightly different versions of the truth – are stitched together through manual processes and human glue. It is a system that works just well enough to get by, until it doesn’t. And at the scale of a modern enterprise, it breaks incredibly fast.
The truth that no one wants to admit in the boardroom is that there is a 100 percent chance of error when you rely on manual spreadsheet work at an enterprise scale. The only actual unknown is exactly how much that error is costing your business. When you have dozens, or even hundreds, of people trying to plan together using disparate files and email chains, the process becomes painfully fragile.
This brings us to the core problem with the current AI obsession. Many organizations are desperately trying to leapfrog their infrastructure problems by investing in AI. They want the shiny, predictive outputs without doing the grueling work of cleaning up their data layer. But when you layer advanced artificial intelligence onto fragmented, inconsistent, and disconnected data, you do not magically get better decisions. You simply get faster, more confident mistakes. The old adage of computer science has never been more relevant: if you feed garbage into the system, you are going to get garbage out.
Before a finance function can become truly intelligent, it must first become reliable. You have to build the groundwork. Think of AI as the cherry on top of a cake; you cannot put the cherry on top until you have spent the months required to actually bake the cake.
Baking that cake means replacing disconnected spreadsheets and patchwork systems with a single, unified financial operating system. It requires a data foundation capable of integrating disparate systems into a single, unassailable source of truth. Right now, in many organizations, month-end reporting has devolved into a negotiation rather than a statement of fact. Teams sit in meetings debating whose spreadsheet is correct, and executives are forced to make critical decisions before the data is even finalized.
To move forward, the industry must prioritize the data layer. When data, modeling, planning, and reporting live in one connected environment, the conversation changes entirely. Only when the foundational data is clean, structured, and modeled in a meaningful way can AI start to deliver its promised value. Once you trust the numbers, AI can instantly tell you exactly why a variance occurred: pinpointing that revenue dropped because sales volume fell in a specific region, rather than forcing an analyst to spend three days hunting for the answer.
We need to stop treating AI as a band-aid for bad architecture. Fixing the foundation isn’t as glamorous as launching a new AI chatbot, but it is the only way to shift the finance function from constantly reporting on the past to proactively shaping the future.
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Luka Mijatović is the co-founder, Chief Technology Officer, and Chief Financial Officer at Farseer, a SaaS company dedicated to optimizing business modeling, planning, and analysis.
As a cross-functional leader who actively builds and manages both technical and finance teams, Luka advocates for practical, hands-on approaches to financial modeling, forecasting, and talent acquisition. He holds degrees from the Faculty of Electrical Engineering and Computing at the University of Zagreb.
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