(Gumbariya/Shutterstock)
The financial planning process is on the verge of a transformative shift, driven by the integration of artificial intelligence and machine learning. 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. Leveraging AI can propel forecasting and financial planning to the next level, allowing organizations to make faster, more effective, data-driven decisions with greater confidence.
According to Gartner, 58% of finance functions are already using AI in 2024, and this number is expected to increase to 90% by 2026, with at least one AI-enabled technology solution deployed. By 2027, 90% of descriptive and diagnostic analytics in finance will be fully automated.
Dynamic Forecasting
AI is moving financial planning from a backward-looking exercise to a forward-thinking, predictive process. Traditional methods typically involved analyzing past performances and making educated guesses about future trends. However, with AI, its advanced ML algorithms and capabilities to find the patterns in the data and how these can be connected, can now predict future financial forecasts with greater accuracy.
AI and ML play important roles in financial planning (Phongphan/Shutterstock)
By analyzing vast datasets, ranging from market trends, such as interest rates, CPI, and commodities 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, adjusting their strategies to optimize outcomes based on the most current and relevant data.
For financial forecasting, the majority of time data is available periodically, e.g, weeks, months, Time-series forecasting algorithms, a concept of statistical and machine learning, are well suited to solve budgeting and forecasting use cases.
Enhancing Scenario Planning
Scenario planning is an essential aspect of financial planning, helping businesses prepare for various 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,e.g., inventory levels, inflation rate or discounts etc. 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 to 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.
The AI agents are lining up (IM Imagery/Shutterstock)
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 , for financial forecasting it can analyze variances between actuals and forecasts, identifies the drivers, suggests adjustments for future planning, and generates scenario-based forecasts.
Leveraging GenAI 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, generative AI models can analyze contracts and CRM data to identify discrepancies, streamlining the contract review process and preventing downstream accounting errors.
It has lots of potential to empower the finance functions:
- A personalized financial insights and analysis based on their specific needs and historical actions or on-demand narrative financial reports’
- Natural language queries for irregular users or executives, it can answer topics like top-performing products, gross profit for a division or alternative roll-ups;
AI and ML introduce unique challenges in finance (Virrage-Images/Shutterstock)
- 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 finance systems contain vast amounts of sensitive data, 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 skill set which leverage the 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 more challenges
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 these technologies, 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, turning what was once a backward-looking discipline into a forward-looking strategy. As companies continue to adopt these technologies, the financial planning process will become more agile, accurate, and aligned with the rapidly changing business environment. The time to embrace AI-driven financial planning is now, as it holds the key to staying competitive and thriving in an increasingly complex and uncertain world.
About the author: Abhishek Vyas is a product manager with 18 years of experience
in enterprise planning, machine learning, generative AI, conversational AI, machine learning, and analytics. He specializes in engineering and product management disciplines and has broad-based experience in retail, e-commerce, banking, financial planning, and workforce planning. Abhishek holds a master’s degree in computer science from Symbiosis International University, Pune, India. Connect with Abhishek at [email protected].
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