The financial planning process is on the verge of a transformative shift, driven by the integration of artificial intelligence (AI) and machine learning (ML).
Traditional financial forecasting simplified the process of looking at data manually from previous years and quarters, and projecting a growth or decline of a certain percentage. Now AI can bump forecasting and financial planning up to the next level, allowing organizations to make faster, more effective, data-driven decisions with greater confidence.
According to Gartner, 58% of financial functions were already using AI in 2024, a number that is expected to rise to 90% by 2026, with at least one AI-enabled solution deployed per case. And by 2027, 90% of descriptive and diagnostic analytics in finance will be fully automated.
The Role of AI Within FP&A
AI is moving financial planning from a backward-looking exercise to a forward-thinking, predictive process.
Dynamic Forecasting
Traditional methods typically involved analyzing past performances and making educated guesses about future trends. With AI’s advanced ML algorithms’ capabilities to find patterns in the data and how datapoints are connected, firms can now make future financial forecasts with far greater accuracy.
By analyzing vast datasets, ranging from market trends, such as interest rates, CPI and commodity prices, to internal financial data, like marketing expenditure, AI can generate real-time forecasts that are more responsive to market uncertainties and other variables. This capability allows businesses to be more agile, and adjust their strategies to optimize outcomes based on the most current and relevant data.
For financial forecasting, a majority of the time, data is available periodically, in a weekly or monthly cadence. Time-series forecasting algorithms, a concept of statistical and machine learning, are well suited to solve budgeting and forecasting use cases.
Scenario Planning
Scenario planning is an essential aspect of financial planning that helps businesses prepare for potential futures. AI enhances this by providing more detailed and accurate scenario analyses.
AI can model how different economic conditions, regulatory changes or market shifts could impact a company’s financial health. For example, a business can generate best-case or worst-case scenarios for demand forecasting, by using multiple business levers, like inventory levels, inflation rate or discounts. This enables businesses to develop more robust strategies that can be implemented quickly as conditions change, reducing the risks associated with market volatility.
Moreover, AI-driven scenario analysis allows companies to simulate the impacts of various decisions before they are made, helping avoid costly mistakes. This dynamic forecasting ensures that financial planning is not just a static annual exercise but a continuous process that evolves in real-time with the business environment.
AI Agents
Traditionally enterprise applications are, at their core, rule-based systems. They follow predefined workflows and require structured data and human input for decision-making. AI agents, on the other hand, can plan and execute actions based on dynamic context without relying on hard rules. One of the most immediate and impactful applications of AI in finance is the automation of repetitive and time-consuming tasks. AI agents bring intelligent reasoning, real-time analysis and decision-making capabilities. It can be used for anomaly detection to identify unusual patterns in financial data, automate the generation of financial reports in a coherent format and to analyze variances between actuals and forecasts, identify drivers, suggest adjustments for future planning and generate scenario-based forecasts in financial forecasting.
Leveraging Generative AI for Strategic Insights
Generative AI, a subset of AI that can create new content or predictions based on existing data, is beginning to make its mark in financial planning. For instance, GenAI models can analyze contracts and CRM data to identify discrepancies, streamline the contract review process and prevent downstream accounting errors.
It has lots of potential to empower finance functions, such as:
● Produce personalized financial insights and analysis based on specific needs and historical actions or financial reports
● Answer topics like top-performing products, gross profit for a division or alternative roll-ups, in response to natural language queries.
● Generate and compare multiple financial scenarios which assist executives in strategic decision-making
Challenges in Implementing AI in Finance
AI adoption in finance does not come easily, because financial systems contain vast amounts of sensitive data. A a result, they are more susceptible to data breaches. Integrating AI systems with other components, such as cloud services and APIs, can increase the number of entry points that hackers might exploit. Hence, most of the finance executives cite data security as a top challenge.
Limited AI skills is another hurdle: Most of the finance orgs don’t have the skillset to leverage AI in planning and budgeting activities. In early stages, high costs, staff resistance, lack of transparency and uncertain ROI dominate. Other hurdles stay constant, such as data security and finding consistent data. As companies expand their use of AI, the potential for bias and misinformation rises, particularly as finance teams tap GenAI. Integrating AI solutions and tools into existing systems also presents its own challenges.
AI-Driven Decision Making
As AI and ML continue to evolve, their role in financial planning will only grow. The ability to continuously adapt to new data, automate routine processes and generate predictive insights positions AI as a critical tool for financial leaders. By embracing it, businesses can transition from reactive financial management to proactive, data-driven decision-making that not only mitigates risks but also identifies new opportunities for growth.
The integration of AI and ML into financial planning represents a fundamental shift. As companies continue to adopt these technologies, the planning process will become more agile, accurate and aligned with the rapidly-changing business environment. The time to embrace is now, as AI holds the key to staying competitive and thriving in an increasingly complex and uncertain world.
