Not a day goes by when one is not bombarded by the latest innovations around artificial intelligence (AI), robotics and machine learning (ML). The inflationary use of these terms makes many people question if they are simply catchy buzzwords ― part of a short-lived market hype. On the other hand, expectations concerning the capabilities of AI and robotics are at an all-time high. From the ultimate AI-built utopia to Skynet apocalypse ― everything seems possible. Time for a grounded look at what AI, ML and robotics actually can and should do in the area of finance process automation.
Limits and possibilities
Today’s AI-applications have their limits and cannot be compared to the power of human brain capacity ― yet. But the concepts of machine intelligence and machine learning have already been successfully integrated into products and solutions in the finance automation sector for years. “We have been doing finance process automation for more than 30 years already and have had a lot of time to create hundreds of use cases, right now it is all about leveraging our expertise and creating powerful best practice solutions including latest AI, robotics and machine learning capabilities”, says Marko Kling, head of solution architects at Hanse Orga Group.
Companies usually aspire to have fast, accurate and standardised finance automation processes. But in practice, many businesses still rely on manual tasks, such as checking invoice numbers and amounts against open items or even uploading and downloading files manually. To reduce the time spent on these repetitive tasks, companies can automate their finance processes by adopting suitable solutions that work across process chains. Typically companies start looking for an easy-to-implement solution which will have minimal impact on the company’s existing system architecture, but the importance of a solution’s sustainability and long-term cost efficiency should not be underestimated.
“While high-efficiency gains can be achieved by partly automating these areas, manual checks and actions are still necessary in case processed data does not produce unique and correct results. In finance and accounting we should never tolerate automated but wrong decisions”, says Kling. “Software solutions have to support manual checks with the latest technology aiming to shorten end-to-end processing times and creating high transparency across the entire process. Best in class solutions help to optimise process areas that are prone to human error or that cause a lot of friction. Here machine learning and robotics can truly shine”.
Making the most of existing technologies
Many businesses focus on individual processes such as cash application, collections or dispute management, in their order-to-cash cycle when looking for process optimisation. But looking at the big picture, creating automated and interactive processes based on real-time information across the full financial automation chain, is definitely worth a CFO’s time. A holistic approach covers the complete order-to-cash chain, from sales orders to cash application, collections and credit management. The next logical step is to integrate the individual process steps within an O2C Factory. The individual processes should learn and benefit from each other. “The implementation of an end-to-end O2C Factory can reduce the cycle times of our customers by up to 80%”, explains Kling.
Automated processes and decisions can substantially improve overall business performance, but how can companies track what has actually been achieved? Every company’s goal is to achieve measurable and high-quality KPIs which can be benchmarked with peer companies. Using the latest analytics solutions organisations can gain insight into all core figures, so decision makers can take appropriate actions and become a best in class company. Improved communication across different company departments is another benefit of automating processes. Without automation, processes move much more slowly, particularly in today’s multinational and volatile business structures, due to the friction caused by cumbersome manual tasks or delays in moving information along the process chain.
“It is very important for us to clearly understand our customer’s needs. Using words like AI and machine learning indiscriminately will not help us meet a customer’s demands. Do they need reporting tools or file optimisation? Are they looking to optimise only one part of their operation or are they interested in creating end-to-end solutions with little human interaction? Reliable data is everything. We always aim to give our customers the best fit for their specific problems”, says Kling. So in summary: AI, machine learning and robotics are not just buzzwords. These terms are here to stay, they have been around a while already and are becoming more important every day.