From Numbers to Strategy – How AI Strengthens the CFO’s Role

Artificial intelligence can significantly reduce the workload of CFOs and finance teams across key areas of operations. Here are the main areas of support:

• Automation of budgeting and forecasting processes: AI analyzes large volumes of historical data and predicts future financial results with high accuracy. Machine learning algorithms identify patterns that may not be visible to human analysts, enabling more precise forecasts and more effective risk management.
• Enhancement of financial and management reporting: AI automatically generates reports and dashboards by aggregating data from multiple sources. As a result, the CFO can focus on analysis and strategic decision-making instead of spending time on manual document preparation.
• Cost optimization and FinOps: AI conducts in-depth analysis of cost structures, identifying areas of potential savings. It also supports the management of IT and cloud infrastructure costs, which is crucial in FinOps strategies.
• Financial modeling and scenario analysis: Advanced algorithms simulate various financial scenarios, supporting the CFO in strategic decisions related to investments, mergers, acquisitions, or restructuring. The ability to anticipate the impact of different scenarios helps minimize business risk.
• Financial consolidation and regulatory compliance: AI automatically reconciles and consolidates financial data from multiple organizational units, ensuring compliance with accounting and tax regulations. It also detects anomalies and potential errors, improving reporting quality and compliance.

AI technology not only automates routine tasks but also significantly enhances the quality of financial analysis. Thanks to its ability to process vast amounts of data, it can identify efficiency opportunities that might otherwise be overlooked. Automated financial consolidation eliminates the risk of human error and ensures data consistency across the organization, translating into better business decisions and increased investor confidence.

An example of an advanced AI solution supporting CFOs in these areas is OneStream Sensible ML. This technology enables the creation of highly accurate forecasts based on time series data provided by users. In the context of strategic planning, organizations can leverage 3- to even 5-year historical datasets to build long-term forecasts that support defining the company’s strategic direction. Sensible ML analyzes 60 monthly data points and creates statistical forecasting models (univariate), which can be deployed within the OneStream environment.

In operational planning and annual budgeting, where financial targets need to be translated from a high-level view into detailed plans across product categories, sales channels, or customer segments, Sensible ML can process 150–250 monthly data points. The technology automatically enriches source data with macroeconomic factors, generates features relevant for modeling, selects the most impactful variables, and compares predictive models (e.g., ARIMA, SARIMA) to ultimately deploy the most effective forecasting approach.

In more advanced use cases, such as daily or weekly demand planning or S&OP, Sensible ML can process over 250 data points, integrating internal and external data, and incorporating business events, promotions, and holidays through an event builder. This enables the creation of highly granular forecasts for specific products and locations, supporting decision-making in logistics, procurement, workforce management, and inventory planning.

Another solution worth noting in the context of supporting CFOs is the IBM watsonx™ platform. It consists of three main components: watsonx.ai, watsonx.data, and watsonx.governance. Watsonx.ai enables the development and deployment of advanced predictive and language models that can support financial forecasting, risk analysis, and the preparation of strategic recommendations. For CFOs, this means the ability to automatically generate accurate analyses based on historical data, significantly accelerating the decision-making process.

In turn, watsonx.data is a solution that facilitates the collection, processing, and analysis of large volumes of financial data from multiple sources, including ERP systems and data warehouses. This supports data centralization and enables better control over both management and operational reporting. Meanwhile, watsonx.governance allows for comprehensive monitoring and documentation of AI model performance, which is particularly important in the context of regulatory compliance and the auditability of AI-driven decisions.