How CFOs can turn AI analysis into faster decisions
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Matija Nakic is the CEO and co-founder of Farseer, a financial planning and analysis software company.
For decades, finance has operated on a familiar rhythm: data is collected, reports are compiled, forecasts are updated, and leadership teams make decisions based on a snapshot of the business that is often weeks old. But in an environment where conditions can change overnight, finance’s role is shifting from explaining what happened to helping leaders decide what happens next.
AI can accelerate this shift, but not simply by writing reports or summarizing spreadsheets faster. Its real value lies in helping finance teams answer strategic questions as they arise and evaluate the consequences of different choices. That requires clean, connected financial data and enough transparency for finance leaders to trust how the answers were produced. Without those foundations, AI will deliver faster answers, but not necessarily better decisions.
From reporting history to testing the future
What does an outcomes-based approach in finance look like?
Imagine a manufacturer learns that a key supplier expects raw material costs to increase by 12 percent next quarter. Rather than waiting for month-end reporting, finance can immediately evaluate how higher input costs would affect margins, cash flow, pricing strategies, and planned capital investments. Leadership can compare multiple responses before making a decision.
Or consider a retailer preparing for the holiday season. If consumer demand weakens unexpectedly, finance should be able to model the impact of delaying inventory purchases, adjusting promotional spending, or slowing hiring. Instead of relying on a single forecast, executives can weigh several scenarios and understand the trade-offs behind each.
This is where AI creates meaningful value. It reduces the time between asking a business question and evaluating the financial consequences of different decisions.
However, the quality of those decisions depends entirely on the quality of the underlying financial data. AI can only reason over the information it has access to, and if financial data is fragmented across spreadsheets, inconsistent between systems, or built on conflicting assumptions, the output quickly becomes unreliable. Clean, governed, and connected data isn't simply an IT concern, it's the prerequisite for finance AI that leaders can actually trust. When AI is connected to a unified financial model that links actuals, forecasts, operational drivers, and business assumptions, finance teams can explore scenarios faster while maintaining complete visibility into the calculations and assumptions behind every recommendation.
The best finance teams are asking better questions
This shift also changes how finance professionals interact with technology. Early conversations around generative AI focused on automation for things like reducing manual work, producing summaries, or accelerating repetitive tasks. Those capabilities remain useful, but they’re unlikely to define the future of the industry.
Instead, AI is becoming a tool for exploration. Finance leaders can ask increasingly sophisticated questions:
How would a hiring freeze affect profitability if revenue slows over the next two quarters?
Which customer segments are contributing most to declining margins?
What happens to cash flow if payment terms with suppliers change?
How would a proposed acquisition affect debt covenants under different economic scenarios?
The value doesn’t come from receiving an instant answer. It comes from being able to explore multiple possibilities while business conditions are still evolving. And while AI supports faster analysis, experienced professionals remain responsible for interpreting the results, challenging assumptions, and making the final decisions.
Trust is earned through financial context
As organizations become more comfortable asking AI increasingly complex questions, another challenge emerges: context. Large language models are exceptionally good at interpreting natural language, but finance depends on precise calculations, governed data, and consistent business logic. A simple question about profitability may require information from planning models, workforce assumptions, operational metrics, and accounting systems.
If those inputs aren't connected — or if the underlying data is incomplete, inconsistent, or outdated — the conversation quickly becomes unreliable. AI doesn't eliminate data quality issues; it amplifies them. Clean financial data, standardized business logic, and strong governance are what allow AI to produce insights that finance teams can explain, validate, and confidently act upon.
Ultimately, trust isn’t created because AI sounds convincing. Instead, it’s earned when every assumption, calculation, and data source can be understood, validated, and challenged.
Finance’s strategic imperative
In spite of the doomsday scenarios many have forecast, AI will not replace finance professionals; it will elevate their role. As routine reporting, data consolidation, and manual analysis become increasingly automated, finance teams can spend more time challenging assumptions, evaluating trade-offs, communicating risk, and helping leadership navigate uncertainty. The skills that matter most will not be producing another spreadsheet or static report, but applying judgment and connecting financial insights to business strategy.
