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Highlights
- Generative AI (GenAI) has immense potential to uncover productivity across the finance value chain and enhance organizational agility.
- One of the key finance functions that is ripe for disruption by generative AI is financial planning and analysis (FP&A).
- To get the desired value from GenAI, organizations need to fundamentally redesign their operating model to support entirely new ways of working.
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GenAI’s potential
Today, finance leaders’ influence on their organization’s growth is significant.
They play a pivotal role in steering their companies through potential economic disruptions and geopolitical uncertainties, and identifying avenues for growth and improving operational effectiveness. As they look to balance growth and risk in a dynamic business environment characterized by complexity and uncertainty, new technologies such as GenAI can help them make strategic decisions to power business innovation and finance transformation.
GenAI has immense potential to transform the finance value chain and enhance organizational agility. However, finance leaders should not fall prey to “FOMO” or “action bias.” They must realize that the key to successful AI implementations is identifying strategic functions and processes where the technology can unlock the maximum business value. In the recently conducted TCS Future of Operations Survey, 90% C-suites cited that the top criterion for successful technology deployment is alignment of the right technology to the right processes.
The case for generative AI in FP&A
GenAI and FP&A are made for each other.
GenAI excels in tasks such as handling large datasets, analyzing complex patterns, and generating detailed reports. One of the key finance functions that is ripe for disruption by generative AI is financial planning and analysis (FP&A) as the critical tasks under this function involve forecasting, budgeting, and analyzing financial data that helps strategic decision-making.
While traditional AI models have been around for the past decade, they struggle with complex language generation and might require more manual input for report creation. Compared to traditional AI models, GenAI offers improved natural language understanding in the domain of FP&A along with more efficiency and scalability. GenAI solutions can enhance the FP&A function by generating natural language text, automating report writing, and providing insights from large datasets.
Generative AI has the potential to revolutionize financial decision-making by automating repetitive tasks, augmenting human capabilities, and providing more accurate and timely insights for decision-makers. It can help organizations improve their financial planning processes, enhance forecasting accuracy, optimize resource allocation, and gain deeper insights into financial performance, risks, and opportunities.
Key use cases
GenAI can be a game changer for FP&A.
Some of the key use cases are:
- Quarterly business reviews and board meetings: For organizations that have not evolved to fully interactive dashboards for quarterly business reviews and board meetings, GenAI can be used to develop the supporting presentations automatically with the latest enterprise data.
- Automated financial report generation: GenAI tools can efficiently pull in vast volumes of data from multiple sources and automate the creation of well-structured and informative financial reports, including balance sheets and cash flow statements. These tools can also generate customized financial reports tailored to the needs of specific stakeholders. By automating financial report generation, organizations can reduce manual effort and ensure greater efficiency and consistency.
- Financial forecasting: GenAI tools can forecast financial trends by analyzing a vast array of diverse data sources, including financial reports, news articles, and social media posts. GenAI models, when trained properly, can generate various market scenarios by simulating market conditions. This can help organizations in strategic planning and pre-emptively addressing potential financial challenges and capitalizing on emerging opportunities.
- Document analysis: GenAI can be used to process and extract valuable information from large volumes of financial documents—from invoices and receipts to contracts and annual reports. By using natural language processing (NLP) and machine learning algorithms, GenAI tools can accurately extract actionable insights from diverse financial documents, reducing manual efforts and improving overall efficiency.
Re-designing the operating model
To enable entirely new ways of working with GenAI, organizations need to fundamentally redesign their operating model.
The new digital operating model should be built on core foundational elements that enable an organization to quickly:
- Sense: Perceive the world by acquiring and processing images, sounds, and speech.
- Understand: Analyze and understand the information collected by adding meaning and insights.
- Decide: Finalize the actions needed based on comprehension and understanding.
- Respond: Take action in the physical world based on simulation results and decisions taken.
In the context of FP&A, GenAI tools can be leveraged to automate data convergence and self-healing of data issues. They can sift through large volumes of structured and unstructured orders, contract data, and terms of payments to create a cash flow outlook or recommendations for cash conversion cycle optimization. Not just that, they can apply experiential learning from prior customer behaviors or seasonality to predict major P&L and balance sheet changes. GenAI models can also detect anomalies in financial reports and professionals can then intervene to assess and fix them. Simultaneously, teams can be established to train the models to further improve results and efficiency.
This means finance teams can focus more on business strategy, while time-consuming and repetitive tasks can be automated using these cutting-edge tools.
Most organizations today are in the early stages of experimentation with GenAI. An operating model aligned to the organization’s long-term strategies, goals, and objectives can help organizations unlock the full potential of GenAI. To understand the efficacy of the model, organizations should have well-defined key performance indicators and reporting mechanisms, and a framework that facilitates decision-making processes. At the same time, they should take into careful consideration the ethical, legal, and privacy implications to ensure responsible use of AI technologies in financial decision-making.
