What should FP&A professionals consider and explore when evaluating GenAI solutions
Generative AI, or GenAI, is a hot topic in pretty much every industry. Many organizations are scrambling to invest in GenAI to keep up with those who have already invested.
GenAI certainly has its usage in streamlining and identifying efficiencies in financial tasks – from reducing manual tasks, consolidating financial data, and improving accuracy to assisting in forming data-led strategy. However, today’s reality – despite the hype – is that GenAI adoption in FP&A is still in its early stages.
For GenAI to really present value to the office of the CFO it must be governed by ethical considerations, follow company policy, and have an actual use case for the end users of your system.
In this blog, we will look at what FP&A professionals should consider and explore when evaluating GenAI tools, where the use cases exist, as well as the future of GenAI in finance.
The role of AI in FP&A is clear
AI can help capitalize on integrated data across systems in the Cloud. With this 360 data visibility, AI can easily draw data from multiple functions into a report, not just quickly but with large degrees of accuracy. With legacy systems, this kind of report generation could take large amounts of manual effort while also being prone to error.
A Gartner survey shows that a third of accountants make several financial errors per week due to capacity constraints, with 59% making errors on a monthly basis.
This highlights the main use case for AI in finance: with a global shortage of qualified accountants, and an increased need to plan and forecast, most finance teams are pressed to deal with greater workloads and this could lead to both burnout and costly financial errors – no matter how experienced your team is.
Beyond advanced reporting capabilities, there are other real use cases for AI. For example, within a Cloud-enabled ERP system, certain capabilities allow non-technical financial staff to create bespoke processes and extensions of their system without technical training in coding.
Through natural language processing, low-code tools, or even smart assistants, AI can do the hard work when it comes to coding, and let the users of your system, who know it best, create processes and extensions of your system that streamline financial processes.
Finally, AI can enhance strategic tasks such as forecasting and planning. With AI users can generate forecasts and utilize machine learning to present potential scenario models based on reports.
This doesn’t automate the strategic processes of a financial team but helps shoulder the data consolidation tasks, but still requires human logic to interpret and strategize effectively. Additionally, the time saved on manual tasks means financial teams can spend more time on forming strategy, rather than manual data consolidation.
Those currently utilizing automation will gain a competitive edge over those generating reports and consolidating data with legacy systems like Excel, with a recent report from Raconteur highlighting that 96% of employees who use generative AI for work feel that it increases their productivity
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The potential use cases of AI in finance
As mentioned, GenAI in FP&A is still in its infancy, with its current use cases only really showing a slither of what could be capable. In the long term, AI will play a role in FP&A that rapidly evolves as the technology evolves and ethical governance is ironed out.
In the coming months, FP&A professionals should take time to understand the technology, its real capabilities today, what could be capable as the tools develop, and begin to map out what you need to put in place to expand its use case for your organization to drive value.
Here are some potential use cases for AI in finance, in the future:
- Many leaders, executives, etc, will annotate an FP&A report with findings, notes, forecasts, strategies, and more. It could be possible in the future for AI to take these annotations into account as well, informing machine learning, but also being able to summarise these comments into a report.
- When fully trained on organization-specific data, there's no reason why AI can’t develop interesting strategies that humans may not think of due to bias and experience.
- AI could form quite granular reports on customer behavior, with the potential ability to derive customer personas from specific financial data. AI has proven ability to understand patterns in numbers that can be tough for humans.
- A simple dashboarding assistant can suggest and/or insert variances in data or apply conditional formatting could save a human a lot of time.
- Similarly, AI could transform financial risk assessment and management, with the ability to recognize potential risks and forecasts from data that may not be obvious to financial staff.
The reality of AI in finance
Data storytelling, today at least, still requires human oversight and direction. It is likely the role of FP&A professionals will change in the future, but by investigating AI now, including how to implement and invest in it ethically and responsibly, can help FP&A teams know how to drive value when they invest in AI.
The biggest challenge is that generative AI applications need a training dataset to make machine learning effective. Even though GenAI is moving remarkably quickly we do not yet appear to have a safe way for organizations to input their sensitive financial information to create a valid training model.
Clearly, this comes with a myriad of challenges as financial data is arguably the most sensitive for an enterprise. Considering what information is required to train the GenAI app is challenging - How do you give access to the subplans? What information do you expose? How do you protect the data?
Ultimately, you must be guided by your company policy – for example, what is your company policy on sharing sensitive financial information with an external app? How do you ensure that data is not exposed to the public internet? How do you train the app to ensure it is delivering accurate analysis?
While the capabilities of AI seem endless, many real vulnerabilities still need ethical and logical considerations for success. AI is undoubtedly a huge target for cybercriminals, particularly those organizations that implement AI in their finance teams without the proper preparations or considerations.
Unit4 can help you understand the use case for AI in your finance teams
Unit4 has multiple AI capabilities built into our Cloud platform. We're also exploring utilizing AI to help our customers communicate insights from their financial data more easily using AI-enabled FP&A tools along with smart assistance.
Talk to sales today and discover where AI can be used in your organization. To learn more about Unit4’s ERP and FP&A solutions and how we use AI, consult our website for more information.
