For several years, I’ve commented on a range of emerging technologies that will have a profound impact on white-collar work in the coming decade. I’ve now coined the term “Robotic finance” to describe this emerging focus, which includes four key areas of technology: Artificial intelligence (AI) and machine learning (ML), robotic process automation (RPA), bots utilizing natural language processing, and blockchain distributed ledger technology (DLT), each of which I describe below. Robotic finance will have a disproportionate impact on finance and accounting departments: I estimate that adoption of these technologies potentially will eliminate one-third of the accounting department’s workload within a decade.
Robotic process automation (RPA) relies on programming or the application of analytical algorithms to execute the most appropriate action in an automated workflow. RPA enables business users to configure a “robot” (actually, computer software) to interact with applications or data sources to process a transaction, move or manipulate data, communicate with other digital systems and manage machine-to-machine and man-to-machine interactions. This technology is gaining increasing notice by finance departments, with good reason: RPA represents an important step beyond simple process automation in that it uses software to execute routine but complex workflows that require judgment. Rather than making an individual check-off a step in a routine approval, a robotic system applies an algorithm that decides the next step. The system thus does what people spend an awful lot of their time doing every day: making judgments that most of the time could be done by a machine.
The application of artificial intelligence (AI) and machine learning (ML) to business computing will have a profound impact on white collar professions. This is especially true in heavily rules-based functions such as accounting. Companies recognize the transformational potential of AI and ML, but the progression and pace of the adoption of these technologies is unclear. Some applications of AI and ML are already in use but others are a decade or more away from replacing human tasks.