Thousands of column inches of press coverage are dedicated to the ‘miracle’ of Artificial Intelligence (AI) and how this technology is transforming many industry sectors, from medical diagnostics, through to autonomous vehicles and smart homes. Few doubt the impact that AI could bring, but many CFOs are only starting to grapple with its potential.
Automation is high up the CFO’s agenda and AI fuelled applications hold out the prospect of better productivity as well as liberating hard-pressed finance professionals from the drudgery of transaction processing and data-intensive analytics. But many CFOs are left on the side-lines of the AI revolution. Although convinced that AI is the future, they are struggling to find the use-cases that can bring the technology to life and enthuse them now.
So how can AI be made less ‘artificial’ and more realistic?
Part of the problem is that when it comes to AI, many software vendors have left businesses to fend for themselves. They have provided links to AI capability and machine learning engines but have left it to finance functions to develop the applications for themselves. Other vendors have taken a more enlightened and supportive approach by embedding AI capability directly into the applications to make them natively more intelligent.
The difference in these two approaches can be profound. Savvy CFOs know they do not want to employ legions of data scientists and other specialists to develop AI applications; they need AI capability straight ‘out of the box’ and a rapid ROI to justify the investment.
For example, AI capability embedded at the transaction level in ERP systems can transform the cash allocation process for accounts receivable. Historically cash allocation is a manually intensive process. FSN’s research finds that 52% of finance professionals say that transaction processing swallows up too much of their time. The good news is that cash allocation can be accelerated by AI driven automation, matching cash to invoices based on previous account history and continuously refined matching criteria. Leveraging AI in this way improves productivity and enables much faster clearing of unreconciled items.
But AI also brings opportunities for enhanced productivity to information management, analytics and reporting. AI-powered predictive analytics enables finance uses to rapidly identify variances and then go on to identify the root causes, enabling finance professionals to elicit insights out of their data that may have gone undetected without the power of AI and machine learning.
Natural Language Processing (NLP) is another innovative application of AI which recognises language, contextualises spoken commands and carries out requests. So called, Digital Assistants that are familiar in the consumer world (like Siri and Alexa) are being rapidly embedded in ERP and other business systems. This new breed of digital assistant is designed to simplify processes and information requests. It brings very welcome relief and productivity in an environment where many finance users are time-poor and spend over a quarter of their day simply looking for information.
For finance professionals that are already benefitting from these technologies embedded in their finance systems, there is nothing artificial about AI.
You can hear more about the potential of AI in a short video clip here.