Workforce composition is changing rapidly. Beyond human factors such as demographics and the rise of the gig economy, digital automation is transforming organizations' workflows and processes – and the nature of work itself.
Though these categories denote levels of complexity, it is better not to think of automation technologies as stages through which an organization must progress. Instead, each technology is best suited to particular types of work and may be used in concert to achieve larger goals.
Intelligent automation (IA) is a term increasingly applied to this concept of combining multiple automation technologies to solve complex business issues. For example, organizations are looking to use RPA with machine learning, NPL and digital character recognition to help address regulatory compliance challenges and process high-volume, low-complexity insurance claims.
Doomsday predictions about automation's impacts to the workforce have been in the headlines for the past few years, with total job loss a significant concern. For example, in 2014 Gartner research director Peter Sondergaard stated, “Gartner predicts one in three jobs will be converted to software, robots and smart machines by 2025.” Yet while there are pockets of extensive automation within the industry, generous estimates cannot put the average rate of automation above 5% – significantly behind the rate required to achieve replacement of a full third of the workforce in 7 years.
While initial predictions were for automation to result in wholesale replacement of human workers, that is not what we are seeing play out in immediate timeframes. Instead, these technologies are being used to enhance or support the work of human employees. Automation capabilities can help remove the burden of repetitive administrative work or provide information to help individuals make better decisions, allowing employees to focus on value-added tasks. For example, many of the basic contact center transactions can be completed by RPA, while a machine learning process can provide a human employee with suggested responses to customer complaints in real time. This enables the employee to focus on solving more complex customer issues, while reducing the risk of error.
There is no question that the number of employees required to perform certain functions will decline as a result of digital automation. However, automation will also create and increase demand in other job areas. One obvious area of growth is in deploying and training these systems to work in unique business contexts. Individuals will also be required to manage teams of AI, perform quality assurance, and address errors or complex issues as they arise.
The technology exists to achieve the type of workforce transformation predicted in years past – yet other barriers stand between organizations and the vision of a wholly automated office environment. Human behavior, as well as the speed at which organizations can invest and adapt to significant changes in both technology and process, form roadblocks. Many financial institutions are also still making the shift from a strategic plan with a 1- to 3-year timeframe to a broader strategic plan with the longer time horizon needed to engage in meaningful technological innovation and transformation.
While automation has been viewed as a way to increase efficiency and reduce costs, especially in the back office, it is not the only route to achieve these goals. Many financial institutions have instead been prioritizing areas such as standardizing and centralizing processes, addressing the challenges of an aging workforce and looking to broader questions of digital innovation.
Digital automation in its many forms will increasingly contribute to workplace productivity. The current question is not whether automation will affect the workforce, but how, to what degree, and at what point will we reach the equilibrium between human and robotic workers.
Financial institutions pursuing the use of intelligent automation are encouraged to consider four key areas during the transition:
As the industry transitions toward greater automation, it is important to remember that this is not an all or nothing proposition: there is no need to choose between an all-machine or all-human workforce. Instead, organizations need to seek ways to integrate automated operations with legacy people processes so as to achieve the greatest value and productivity from both types of resources.