Artificial intelligence has quickly become the boardroom’s favorite solution. From forecasting and reporting to scenario planning and budgeting, finance leaders are under growing pressure to demonstrate how AI can improve efficiency and drive better decisions.
But against the rush to adopt AI, many organizations are overlooking a fundamental truth: that AI is only as effective as the systems, processes and data that support it.
Chief Technology Officer and Co-Founder of Farseer.
This is particularly true in finance, where many teams continue to rely on fragmented technology stacks, disconnected data sources and spreadsheet-heavy workflows. While AI promises to automate analysis and surface deeper insights, it cannot compensate for weak foundations. In fact, it often does the opposite, exposing issues that previously remained hidden beneath layers of manual work.
The reality is that many finance functions are less prepared for AI than they realize.
The spreadsheet problem AI cannot solve:
Spreadsheets remain deeply embedded within enterprise finance. They are familiar, flexible and accessible. However, they were never designed to serve as the backbone of modern financial planning and analysis for large enterprises.
In many organizations, critical forecasting models, budgeting processes and reporting workflows are still maintained across countless spreadsheets, often with limited governance and varying levels of accuracy. Data is copied between systems, formulas evolve over time, and key assumptions can become difficult to trace.
Introducing AI into this environment does not eliminate these challenges. It amplifies them.
If an AI model is drawing insights from inconsistent data sources or outdated spreadsheets, it will simply generate the wrong answer faster. Automated recommendations may appear sophisticated, but their reliability is ultimately determined by the quality and integrity of the underlying information.
This is why the familiar principle of ‘garbage in, garbage out’ remains so relevant to finance teams today.
Why finance teams may be overestimating their AI readiness:
Many organizations assess AI readiness by evaluating tools. They ask whether they have access to the latest models, whether employees are using generative AI and AI agents, or whether automation opportunities exist within their workflows.
Far fewer assess the quality of the infrastructure feeding those systems.
True AI readiness starts with questions such as:
- Is financial data consistent across systems?
- Can teams trust the numbers they are working with?
- Are planning, reporting and forecasting processes standardized?
- Is there a single source of truth for business performance?
If the answer to these questions is unclear, AI adoption risks introducing new complexity rather than delivering meaningful value.
The challenge is not a lack of ambition; most finance leaders recognize the potential of AI. The challenge is that many organizations are attempting to build advanced capabilities on top of foundations that were never designed to support them.
Data quality is becoming a strategic priority:
As AI becomes more embedded within finance operations, data quality is shifting from an operational concern to a strategic business priority.
Finance teams have long spent significant amounts of time gathering, reconciling and validating data before analysis can even begin. AI has the potential to reduce that burden, but only when the underlying information is accurate, connected and accessible.
Organizations that invest in modern finance infrastructure gain a significant advantage. Centralized platforms, integrated data environments and standardized planning processes create the conditions necessary for AI to deliver meaningful outcomes. They also improve transparency, governance and trust in financial decision-making.
Without these foundations, AI initiatives risk becoming expensive experiments that fail to deliver lasting value.
Building the foundations before scaling AI:
The future of finance undoubtedly involves AI. The technology’s ability to improve forecasting, accelerate reporting and support more strategic decision-making is too significant to ignore.
However, the organizations that realize the greatest benefits will not necessarily be those that adopt AI first. They will be those that prepare for it properly.
Before automating processes or deploying new AI capabilities, finance leaders should take a closer look at the systems supporting their operations. Are they creating a trusted, connected and scalable environment for decision-making, or are they simply digitizing existing inefficiencies? AI is a powerful multiplier, but multipliers work in both directions.
For finance teams still relying on fragmented systems and spreadsheet-driven processes, the priority should not be adopting AI faster. It should be strengthening the infrastructure that allows AI to succeed.
Because AI will not fix weak finance foundations. It will expose them.
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