If you have the power to predict your organisation’s future, what will you do? Finance leaders can be the most powerful, leading your organisation to focus on the big value by sprinkling the magic of forward-looking metrics and customised predictive models.
With that magic in hand, you do not want to let any valuable data go to waste. Data is considered the most valuable asset an organisation can value and leverage. Especially with the rise of digital transformation, most executives understand how digitalisation and data go hand in hand. This new “trend” that showcased AI capabilities made data more beneficial than ever.
Hold your horses; there’s a catch. Most executives seem to not know their organisation’s five most valuable data assets or return on data (ROD). What looks like a straightforward question, it’s actually one that many executives couldn’t answer. Like asking, “Who’s your favourite artist” and your mind blackouts.
Then, how can an organisation leverage its data to the fullest potential? Of course, this would create a problem because comprehending the strategic value of data is the key to making the right AI investments. As generative AI and machine learning (ML) become more prevalent, there is a huge potential for CFOs and financial planning and analysis (FP&A) teams to see significant returns on these investments.
Every AI-driven solution has unique traits, even the general AI that we use daily. We can’t simply use Google Gemini to perform FP&A, or can we? Hence, to acquire the best outcome, merge and marry your most valuable data with AI.
You need to fully grasp one thing: AI can potentially power a paradigm shift in forecasting and metrics in FP&A. Large language models (LLMs) can revolutionise KPIs by using customised data sets. Instead of safeguarding existing value, they can become intelligent tools that create new value.
With the support of machine learning (ML), financial planning and analysis (FP&A) activities are transformed. Forecasting goes beyond providing accurate financial projections and offers valuable insights for operations and workforce planning. This means that KPIs can provide better insights into performance and support more strategic decision-making.
Forward-looking Measures
Data has become the lifeblood of digitally transformed organisations, connecting and defining their operations. As a result, the role of metrics in value protection and creation has never been more crucial. The potential of combining generative AI, machine learning, and key performance indicators (KPIs), questioning whether KPIs can evolve beyond mere measures and transform into dynamic software agents capable of learning – are all becoming the main questions.
This concept is no longer just theoretical, especially with recent years’ remarkable advancements in generative AI. With the assistance of AI, metrics can metamorphose into forward-thinking tools that facilitate forecasting and scenario planning. This enables finance leaders to adopt a more proactive and strategic approach.
Leveraging ChatGPT allows you to pose the most pertinent questions about KPIs. “What can be done to enhance your performance? Is the current data beneficial to you? How can we improve your forecasting with new data? How would you respond in a given scenario? Do you perceive this scenario as beneficial or detrimental?” These questions illustrate the potential for KPIs to become more interactive and adaptive, paving the way for a new era of data-driven decision-making.
AI can potentially assist the finance function in creating value by using more predictive and future-focused key performance indicators (KPIs). These KPIs can encompass traditional metrics like revenue, profit, and sales, as well as emerging hybrid financial customer-centric metrics such as customer lifetime value or churn rate.
All About Intelligent Forecasting
Just imagine the incredible potential of harnessing AI to unlock predictive insights. According to a global Workday survey, organisations often stumble in adopting AI due to poor-quality data. However, when finance leaders leverage AI trained on top-notch data, they have the power to revolutionise forecasting.
Instead of just aiming for accuracy and precision, forecasting can become a wellspring of valuable insights and discussions about how to best prepare for business opportunities, prioritise tasks, and respond to customer needs. By incorporating custom AI models or machine learning, forecasting becomes more prosperous and cost-effective, providing actionable insights for the entire organisation.
Consider a scenario where a company’s forecasting model continuously learns from the latest data to confidently predict the number of vehicle bays needed for customers at each location based on the day of the week, time of day, and weather conditions.
Looking ahead, the possibilities for forecasting with AI seem endless. Imagine leveraging generative AI apps built on customised AI models to explore multiple use cases for scenario planning. Additionally, emerging AI ecosystems like OpenAI and Google Gemini are poised to enable businesses to integrate data streams from platforms such as Workday with generative AI systems to create scenarios based on accurate data and specific parameters.
The potential for this kind of interconnectivity and interoperability between Workday and tailored scenarios is immense. It could play a significant role in compliance stress testing for financial planning and analysis initiatives. Furthermore, the costs associated with these advanced analytics are becoming more accessible.
Time To Rethink Our Greatest Asset: Data
Every business has its own unique scope and purpose when it comes to custom forecasting models and scenarios. However, the starting point for AI and ML planning should always be the same: you have to begin with your business needs, identify the problem you’re solving, and build from there.
Data can also be a crucial entry point. If data is an asset, then what are your most valuable assets—and how can you get greater value from them?
Answering that question is particularly challenging due to common data silos. To drive accessibility and maximise value in the era of AI, the finance function must take the lead in managing data. For the future of capital allocation, CFOs should be at the forefront; they should be the driving force behind the necessary changes.
