Reimagining the Operating Model for Finance: Generative AI in FP&A

Today's Finance leaders are operating within a dynamic business environment, characterized by complexity, uncertainty and an exponentially increasing need for agility and innovation. The influence of finance leaders over an organization’s growth trajectory has grown rapidly over the years, as they play a pivotal role in steering their companies through potential economic disruptions and geopolitical uncertainties, even as they continue to identify avenues to drive growth and improve operational effectiveness.

With this imperative to balance growth and manage risk, CFOs must leverage newer technologies to power business innovation and transformation in Finance.

Gen AI has immense potential to uncover productivity across the finance value chain and enhance organizational agility. The key to successful technology implementation is identifying strategic functions and processes where it can unlock the maximum business value, instead of falling prey to “fomo” or “action bias”.  In the recently conducted TCS Future of Operations Survey, 90% C-suites cited that the #1 criteria for successful technology deployment is alignment of the right technology to the right processes.

The Case for Generative FP&A

Generative AI excels in tasks like handling large datasets, analyzing complex patterns and generating detailed reports and finance leaders must identify processes where it can unlock the maximum value. One of the key finance functions which is ripe for disruption by generative AI is financial planning and analysis (FP&A) as the critical tasks covered 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, Gen AI can offer efficiency, scalability and improved natural language understanding in the domain of FP&A. They can enhance the FP&A function by generating natural language text, automating report writing, and providing insights from large datasets. 

 Generative FP&A 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.

 Some of the key use cases for Gen AI in FP&A include:

• Quarterly Business Reviews (QBRs) and Board Meetings: For organizations that have not evolved to fully interactive dashboards for QBRs and Board Meetings, GenAI can be used to develop the supporting presentations automatically with the latest enterprise data. These GenAI-created presentations can include the initial identification of issues and potential recommended actions based on a given set of financial objectives.
• Automated financial report generation: Generative AI 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 be used to generate customized financial reports tailored to needs of specific stakeholders. Automating financial report generation reduces manual effort and ensures greater efficiency and consistency.
• Financial forecasting: Generative AI tools can forecast financial trends by analysing a vast array of diverse data sources including financial reports, news articles and social media posts. Gen AI models, when trained properly, can generate various market scenarios by simulating market conditions, which aids organizations in strategic planning and pre-emptively addressing potential financial challenges and capitalizing on emerging opportunities.
• Document Analysis: Generative AI 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, Gen AI tools can accurately extract actionable insights from diverse financial documents, which reduces manual efforts and improves overall efficiency.

Re-designing the operating model for success

Reaping the rewards of these new technologies will require organizations to fundamentally redesign their operating model to support entirely new ways of working and interacting with machines and data. The core foundational elements of the new digital operating model should enable the 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.

For example, in the context of financial processing and analysis, Cognitive and Generative AI tools can be leveraged to automate data convergence and self-healing of data issues. For example, cognitive platforms 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. It can apply experiential learning by prior customer behaviors or seasonality to predict major P&L and B&S changes. 

Finance professionals can then intervene to detect anomalies and teams can be established to train AI platforms to improve results and efficiency. This will enable CFOs to focus more on business strategy, while time-consuming and repetitive tasks are automated using these cutting-edge tools. It is only fair to expect that a digital-focused operating model in finance would position a firm for a significant competitive advantage.

While Generative AI’s potential is promising, most organizations today are in the early stages of experimentation with this technology. An operating model which can utilize Gen AI to its full potential needs to be aligned to the organization’s long-term strategies, goals and objectives. It should have well defined key performance indicators (KPIs) and reporting mechanisms, and a structure that facilitates decision-making processes. At the same time, it should take into careful consideration the ethical, legal, and privacy implications to ensure responsible use of AI technologies in financial decision-making.

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