From spreadsheets to smart insights: can AI really eliminate manual reporting in Finance?

The promise of AI in Finance

AI-enabled platforms can integrate data from multiple systems, perform real-time analysis, generate insights & dynamic dashboards that adapt instantly to changing business/market conditions. In Financial Planning & Analysis (FP&A), AI supports scenario modeling, forecasting, and variance analysis, reducing hours of manual spreadsheet work. The results ? Faster cycles, more accurate forecasts, and analysts who can focus on insights & decision support rather than reports preparation.

Yet, as Alexander Declerck, T&S Leader Ghent and Roeselare, highlighted, “AI in finance remains underutilized. Its current role is often auxiliary, supporting explorative phases rather than replacing core financial functions”. Why? Because the effectiveness of AI depends on robust data structures and high-quality data, a challenge for many organizations. Without complete, accurate, timely, and consistent data, advanced applications like predictive analytics cannot deliver meaningful results.

From reporting to prediction: a shift in mindset

One of AI’s most transformative capabilities lies in predictive analytics. Traditionally, financial reporting has been backward-looking, a record of what happened. Predictive analytics turns that model forward-looking, akin to an airplane cockpit giving real-time weather warnings, enabling decision-makers to anticipate turbulence and adjust accordingly. When based on quality historical data, predictive models help CFOs anticipate revenue swings, manage working capital, and identify emerging risks.

Self-learning AI models can go a step further by integrating external factors, such as market trends or macroeconomic indicators, refining forecasts over time. However, as Jonas Willems, Project Manager in Data, cautioned, “AI won’t replace strategic analysis, interpretation or stakeholder communication. Instead, it augments these functions, allowing finance professionals to focus on value-added work. The question isn’t whether AI is going to give you the right answers or insights, but rather if you’re able to ask it the right questions.”

Realistic use cases for AI in Finance

AI can make a tangible impact in several areas of finance, particularly where data management challenges persist. Key use cases include:

• Data Quality Management: Detecting anomalies, data cleansing, removing duplicates, and ensuring data completeness and accuracy.
• Automated Reporting: Aligning figures across systems and suggesting explanations for variances, significantly reducing the time needed for disclosures.
• Predictive Forecasting: Providing CFOs with early warnings of cash flow pressures and revenue fluctuations, “what-if” scenarios generation to support decision-making
• Operational Efficiency: Streamlining reconciliation and reducing manual workload in repetitive reporting tasks.

These improvements are only possible if organizations address the core data structure and governance challenges first.

The hidden pressures of AI adoption

However, AI’s ability to generate more information faster is a double-edged sword. As Stéphanie Struelens - Leader Financial Institutions puts it: “With AI generating more information and insights, there is an increased risk of stress and ad hoc requests as teams struggle to reconcile and make sense of complex data sets.”

Rather than eliminating workload entirely, AI often shifts the challenge: instead of manually gathering data, teams must now validate, interpret, and reconcile machine-generated outputs. This requires:

• clear governance structures to manage data ownership and accountability
• standardized reporting frameworks and “one version of the truth”
• disciplined data request processes to prevent chaos

Without these guardrails, AI can create more noise than clarity.

Why strategy matters more than technology

The success of AI and finance initiatives depends on more than the latest software. A clear data and analytics strategy ensures that AI tools are fed clean, complete, and context-rich data. Integrating systems to break down siloed data is equally critical. Without it, even the most advanced AI will produce incomplete or misleading insights.

“An advanced analytics strategy should align technology investments with specific business objectives, while also defining governance for finance data analytics outputs.” Says Jean-Alexis Dombret, T&S Leader in Wallonia. Only then can AI enhance, not hinder, decision-making.

People, skills and change management

AI adoption is as much about people as it is about technology. Resistance is common, especially among staff concerned about job displacement or feeling that their expertise is being devalued. According to Insaf Bouhajra, Project Manager and AI expert, “this stress is compounded by a lack of transparency from organizations on how AI will impact roles”. Change management and communicating the value of AI as an enabler are critical.

For finance professionals, the skills of the future are less about coding and more about:

• interpreting AI outputs and questioning assumptions
• understanding AI’s limitations (e.g., hallucinations and bias)
• cross-functional collaboration to bridge business, data, and technology
• ethical awareness, ensuring transparency and fairness in AI use

As Alexander Declerck summed up: “AI won’t replace finance teams, it will redefine their value. The winners will be those who master both the technology and the human judgment it still depends on.”

Ethics and the Risk of AI hallucinations

AI’s credibility depends on human oversight. As Bouhajra noted, AI hallucinations, where systems confidently present incorrect outputs, underscore the need for human verification and multiple sources. Ethics must be integrated into AI strategies, addressing not only GDPR compliance but also transparency, accountability, and job security for employees.

Practical advice for CFOs

Before embarking on advanced AI initiatives, CFOs should:

1. Start with business problems, not technology.
2. Build a solid foundation: clean data, integrated systems, clear governance. Begin small, scale gradually: pilot predictive analytics in priority areas like cash flow forecasting.
3. Adopt a “helicopter view” to focus on critical challenges.

AI adoption is a journey, not a quick fix.

AI can dramatically cut the time spent on manual reporting in finance, but only if paired with a solid strategy, integrated systems, and a culture of data-driven decision-making. Without those foundations, the promise of automation risks becoming just another layer of complexity. The real question is whether CFOs will harness AI merely as a tool for efficiency, or as a catalyst to elevate finance into the core of strategic decision-making across the organization.