Is your most fancied FP&A RPA initiative not delivering the anticipated benefits? You are not alone. So, what are the most common pitfalls? And more importantly, how can you sidestep them and increase your odds of a successful RPA implementation?
Machine Learning provides tremendous insight regarding market trends & business drivers. These factors include market propensity, consumer demand, economic factors, weather, & transportation costs. Many companies take these variables into consideration but provide limited or time-consuming analysis. This process limits corporate agility.
By Mohammed Hussain, Finance Manager at Builder.AI
Mohammed is an advocate for clean financial modelling and process automation. His key function is "modern" problem solving by blending traditional Excel techniques with BI as a visualisation tool. He strives to produce models that are transparent and easily understood by the end user. This is achieved through efficient Workbook and Worksheet design, and VBA.Disclaimer: Financial Modelling has no strict “right” or “wrong” method of application. It does, however, have forms of best practice and this what this article attempts to highlight
According to research conducted by the Hackett Group, the market for robotic process automation (RPA) is real and growing. It has the potential to change the business process outsourcing (BPO) landscape, global business services (GBS) organizations, and broader business-specific processes.
Future coordination is a brainer - not a no-brainer. Combining the Now and the Future requires a well-prepared FP&A team. AI as Robotic Process Automation (RPA) can support with the “no-brainers”. Looking ahead requires diligence and thinking time, which RPA can provide space for. Also, computer algorithms prescribe to keep exploring for future gains. This includes looking to the Arts and the Artificial Intelligence developments.