AI Can Expose Weak Finance Systems Faster

AI may be the finance industry’s favorite new fix, but a TechRadar Pro Perspectives piece argues it can just as easily make existing problems worse. The article, written by the Chief Technology Officer and Co-Founder of Farseer, says finance teams are under pressure to use AI for forecasting, reporting, scenario planning, and budgeting — even though many still run on fragmented systems, disconnected data, and spreadsheet-heavy processes.

The core argument is simple: AI is only as good as the systems, processes, and data behind it. In finance, that matters because many organizations still depend on sprawling spreadsheets for critical planning and analysis. Those tools may be familiar and flexible, but the piece says they were never built to serve as the foundation for modern financial planning and analysis at large enterprises.

The spreadsheet problem in finance

According to the article, many companies still manage forecasting models, budgeting processes, and reporting workflows across large numbers of spreadsheets, often with limited governance and uneven accuracy. Data gets copied between systems, formulas change over time, and assumptions become hard to track.

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Adding AI to that setup does not remove the underlying issues, the author says. It can intensify them. If an AI model is trained on or pulls from inconsistent data sources or outdated spreadsheets, it will simply produce bad answers more quickly. The piece leans on the old rule of “garbage in, garbage out” to describe the risk.

What real AI readiness looks like

The article says too many organizations judge AI readiness by access to tools rather than by the quality of the infrastructure feeding those tools. It argues finance leaders should instead ask whether:

• financial data is consistent across systems
• teams can trust the numbers they use
• planning, reporting, and forecasting are standardized
• there is a single source of truth for business performance

If those questions do not have clear answers, the piece warns, AI may add complexity instead of value.

It also frames data quality as a strategic issue, not just an operational one. Companies with centralized platforms, integrated data environments, and standardized planning processes are better positioned to get useful results from AI, while others risk turning AI projects into costly experiments.

The article closes with a blunt point: for finance teams still dependent on fragmented systems and spreadsheet-driven workflows, the priority should be fixing the foundation first, because AI will not repair weak finance infrastructure — it will expose it.