Finance Transformation: The AI Dilemma

Former US Secretary of State Henry Kissinger famously said, “If you don’t know where you are going, every road will get you nowhere.” This is so true and apt when it comes to AI hype and how Finance functions are scrambling to find meaningful use cases. No conversation related to Digital Transformation is complete today, without references to Generative AI and Predictive analytics being part of it.

AI provides a huge potential to transform the ways of working, redefine business processes and enable massive productivity. One can find numerous successful case studies where organisations have generated real efficiencies and cost savings using AI.

For instance, a Global microchip manufacturer saved more than 5,000 hours annually just by deploying AI for their Accounts Payable Invoice processing. However, the dilemma lies in today’s predominantly ‘use-case driven’ approach.

Finance FOMO?

CFOs and Finance Leaders, in fear of missing out, rush to treat AI as a one-size-fits-all solution for Finance Transformation. This addresses some automation needs in the short-term but eventually results in a set of disparate AI deployments aggravating the Organisational ‘Technical Debt’. Gartner predicts that by 2024, half of Finance AI deployments will be either delayed or cancelled due to scale-up issues!

The majority of business processes today including Finance came into existence as a direct business need during previous industrial revolutions which were dominated by mass manufacturing. In a way, the Finance processes today mimic a typical production floor’s assembly line. A systematic ‘handover’ of the output from one sub-process to the next…in other words a chain of interdependent sub-processes. One cannot start the Monthly Forecasting process before Month-end books are closed and books cannot close until all the cash receipts from customers are applied to invoices, for instance. Read the full article at CFO Magazine Australia here.