The Future of FP&A: Automation, AI, and the Trends of 2024

AI has the potential to transform many FP&A functions including quicker access to higher-quality data. But to reap the benefits, organisations will need smart security and privacy policies.

How to use ai for financial analysis?

Financial planning and analysis (FP&A) teams don’t budget and forecast the way they once did—and that’s an excellent thing. Recently, the trend has been to move away from traditional static planning—which primarily looks to the past to provide limited insights into the future—toward more agile processes based on cloud systems with AI integration. The goal? Organisations must prepare for the future by increasing their ability to make up-to-date, informed decisions to quickly pivot when future industry conditions change.

And as AI becomes more sophisticated, the technology will continue to transform FP&A, allowing CFOs and financial planners to deliver deeper insights to their organisations and prepare for the “what ifs” that come their way. Finance leaders will no longer need to operate solely from gut instinct—a reality for half of CFOs, a Workday survey found—but instead from experience informed by real-time, quality data.

With the evolution of AI in finance, FP&A teams are more forward-looking.

For professionals and businesses seeking to stay ahead, understanding these shifts in FP&A is critical. This article will guide future trends in financial planning and analysis, highlighting how AI integration is not just an option but a necessity for informed decision-making and strategic pivoting in the face of changing industry conditions.


The Present and Future of FP&A  

Transforming from fixed yearly forecasts to dynamic, AI-enhanced planning, FP&A teams are evolving with technology to offer strategic insights and adapt to future financial challenges.  


The Present State of FP&A

FP&A teams once had little choice but to operate from fixed projections made at the beginning of the fiscal year. When the unexpected happened, they lacked the tools to adapt quickly. Today, technology has shattered those limitations.

Cloud-based enterprise resource systems (ERP) with AI tools have streamlined financial processes and provided quick access to updated, organisation-wide data.  

FP&A teams are now projecting more often—and more accurately. Before 2020, only 57% of companies ran daily, weekly, or monthly financial projections. Now, 80% do, aided by technology such as data visualisation tools and cloud-based enterprise planning platforms, which empower continuous, company-wide collaboration.  

With the evolution of AI in finance, FP&A teams are more forward-looking. Through scenario planning and simulations, teams are considering future economic and market events to model what-if scenarios for today.

CFOs and other finance leaders are increasingly expected to add more insight to business operations.

The expectations placed on FP&A teams and CFOs have also changed. They’re not simply expected to deliver financial reporting but to offer strategic insights that guide and check off company priorities.


The Future of FP&A

What FP&A looks like in the future will largely be influenced by the capabilities of AI. Some top trends that will shape the FP&A function include:

Advanced analytics. As AI ramps up, predictive analytics will become more accurate and integrated. These tools will deepen FP&A pros’ insights into everything from market trends and customer habits to financial projections.  

Data governance. As organisations become ever more reliant on data, the need for that data to be more accurate and secure will soar. 

Strategic partnerships. Organisations will continue to look to FP&A teams to pinpoint future expansion opportunities and help ensure the business’s financial viability.

Increased data visualisation and reporting. Interactive dashboards and data visualisation tools will become even more widespread. And as these tools become more user-friendly, data democratisation across the organisation will follow.

Scenario planning. The reality of future disruptive events such as geopolitical uncertainty, labour shortages, and natural disasters will lead FP&A to embrace scenario planning further to protect against risk.  

Integrated planning. Top companies will transition to an integrated, company-wide planning model so that everyone can understand how departmental decisions impact a company’s overall financial plan. By 2024, an expected 70% of all FP&A activity will be company-wide planning projects.


AI in FP&A - An In-Depth Guide

AI in FP&A signifies a shift towards more insightful, data-driven decision-making, with technologies like machine learning enhancing predictive analytics and automating routine tasks, ultimately reshaping the role of finance professionals in strategic planning.

Using AI-augmented financial models, teams can make fast but well-informed decision.

What is AI in FP&A?

As CFOs and other finance leaders are increasingly expected to add more insight to business operations, they also need better ways to process data more efficiently.

AI and ML technologies can assist FP&A teams in managing and analysing data and automating repetitive, manual tasks to allow finance teams more time for strategic insights.  

AI in finance also plays an increasingly important role in one of FP&A’s most sophisticated tasks—predictive analytics. The ability to input past and current data to turn out data-backed predictions about future events is paramount to the future success of any organisation.

AI supercharges this function through the ability to ingest and analyse more data, more quickly.

Difference between AI, Machine Learning, and Predictive Analytics

AI, ML, and predictive analytics are related concepts, but the terms aren’t interchangeable. Think of AI as the umbrella term with examples of how the technology can be used falling underneath it.

AI is the ability of machines to perform complex tasks that usually require human intelligence. AI is trained by humans to communicate, analyse data, recognize patterns, and make predictions at speed and scale.  

Machine learning (ML) is a subtype of AI that uses data and algorithms that focus on how machines learn and interpret data through recognition. ML algorithms can ingest large quantities of data to spot patterns or make predictions.  

Predictive analytics is a statistical technique that combines data with ML to analyse past and current data and make informed future predictions. In a 2022 Workday survey, 54% of FP&A leaders said that predictive analytics will be the biggest opportunity for AI and ML in finance by 2030. 


Benefits of FP&A AI

Thanks to recent advances in financial ML and AI, FP&A is more agile than ever. Using AI-augmented financial models, teams can make fast but well-informed decisions to react quickly to changing conditions or plan for disruptions before they happen. Additional benefits include:

  • Real-time scenario planning. Gone are the days when finance operated with data that might be weeks—or even months—old. AI financial capabilities allow teams to see data as it’s happening and scenario plan based on that real-time data.
  • Efficiency in Data Processing. AI greatly enhances the ability of FP&A teams to handle large volumes of data. This efficiency is vital in today’s data-driven world, where the ability to process and analyse information quickly can lead to more informed decision-making.
  • Reduced labour costs. Labour is typically one of an organisation’s biggest expenses. But companies often operate with outdated or siloed people data that doesn’t make the best use of their greatest resource. AI-equipped systems can help optimise everything from staffing schedules to recruiting processes.
  • Automation of repetitive tasks. One of the most clear-cut benefits of AI for FP&A—or any function—is the ability to reduce the time employees spend on repetitive, manual tasks, allowing them to spend more time on strategic thinking and analysis. For example, algorithms can help automate data entry and analysis, shortening the time and work involved in generating reports and drawing insights from data.
  • Error reduction. AI reduces some of the risk for human mistakes. Machines, after all, aren’t affected by a bad night’s sleep. AI financial models can quickly detect anomalies, which can reduce the cost and frequency of human errors. 


AI Challenges in FP&A

Despite its enormous potential, AI isn’t perfect. Identifying the best uses for the technology and the guardrails needed to govern its use will take time. Among AI’s key challenges are:

  • Questions of fairness. AI systems are only as good as the data fed into them. Poor quality or biassed data can lead to inaccurate predictions and analyses, which can misguide decision-making processes.
  • Data privacy issues. Organisations, especially the finance function, must be able to trust the data, AI is helping them obtain. More frameworks and regulations are needed to guide how AI can be used and by whom to ensure the data’s reliability and privacy.
  • Newness. The use of AI in finance is still evolving. The tools need to be fine-tuned and employees will require more training to understand the technology and use it appropriately. 

Integrating AI in FP&A processes offers immense benefits.

In summary, AI-driven FP&A is poised to become integral to strategic business planning. It can offer deeper, more nuanced insights into financial trends and assist in long-term strategic planning. As AI technology evolves, its ability to analyse and predict financial outcomes will become increasingly sophisticated, making it an indispensable tool for financial professionals.


The Future of FP&A with AI Integration

AI in FP&A signifies a shift towards more insightful, data-driven decision-making, with technologies like machine learning enhancing predictive analytics and automating routine tasks, ultimately reshaping the role of finance professionals in strategic planning.


Increased Efficiency and Automation  

Now is the time for CFOs to consider how AI may reshape finance, in particular when it comes to predictive analytics, increased automation, risk management, and data visualisation. Because once

trained on secure, reliable data, AI and ML models can act as major accelerants for FP&A teams, enhancing efficiency and automation in FP&A processes, thus freeing up individual planners to focus on more strategic work.  

These anticipated impacts are why experts believe AI will soon be completely integrated into FP&A and that companies must begin building safe and ethical applications that keep employees in the loop.

Fortunately, company leaders are wisely signalling that they don’t plan on ceding important decisions solely to AI. Indeed, 93% of leaders in a Workday survey believe it is important for human workers to assist in making important decisions rather than relying on AI alone.  


Companies using AI in FP&A

Several companies have already begun carefully retrofitting their FP&A tools and processes with AI. In May 2023, Microsoft announced the rollout of a new function within its Dynamics 365 Finance platform that assists finance teams with analytical tasks like cash flow analysis and financial forecasting.  

Meanwhile, Ayden, a global payments company based in Amsterdam, has also made good use of AI and ML by deploying the technologies to help process the company’s vast amount of transactional data to help identify potentially fraudulent activity.  


Embracing the AI Revolution in FP&A: Your Next Steps

As we've explored, the integration of AI and ML into Financial Planning and Analysis (FP&A) is not just a trend but a transformative shift that is shaping the future of finance. Integrating AI in FP&A processes offers immense benefits, including enhanced risk management, faster decision-making, and streamlined operations. These advancements are critical for organisations that want to stay ahead in a rapidly evolving business environment.


Key Takeaways

Enhanced Predictive Analytics. AI-driven tools provide deeper insights and more accurate forecasts, enabling better decision-making.

Increased Efficiency and Automation. AI and ML can automate routine tasks, allowing finance teams to focus on strategic planning and analysis.

Risk Management. AI's ability to process large data sets can help identify potential risks and opportunities, aiding in more robust risk management strategies.

Data Visualisation and Reporting. Advanced tools offer better ways to visualise and interpret data, making it accessible across the organisation.

Future-Proofing Your Organisation. As AI technology evolves, it will become an indispensable financial analysis and planning tool.


Join the Future of Finance with AI

With this rapidly approaching future and innovation happening every day, now is the right time for companies to integrate AI and ML into their FP&A tools. The benefits of AI in finance—better risk management, faster decision-making, smoother processes—are simply too important to ignore or delay.

Ready to leverage AI in your FP&A practices? Discover how Workday FP&A Adaptive Planning can transform your organisation's financial future.

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