The “digital workforce” is a term used to describe the software robots and artificial intelligence solutions that businesses use for higher productivity in the workplace. By performing manual tasks that are tedious and repetitive in nature, your digital workforce can give back countless hours to your human employees. This lets them focus on more meaningful, profit-generating activities.
If you’ve just invested in a digital workforce, you might be wondering what it can do for you. Digital workers have become capable of much more than simple data entry, yet they still aren’t able to replicate all of the functionality of a human worker. Actions that require higher-level thinking, creativity, and decision-making skills are still well beyond the scope of your digital workforce.
However, that doesn’t mean that there isn’t plenty for your digital workforce to do. According to management consulting firm McKinsey & Company, 60 percent of human employees’ jobs could be automated by at least one-third. Successful adoption of digital workers will require you to align your business and IT strategies.
In general, tasks that are rules-based and require a high degree of accuracy are a good choice for robotic process automation (RPA). Some of the most common tasks that RPA digital workers perform are:
Finance: accounting, invoicing, data entry
IT: identifying and solving technical problems, creating support tickets, answering common user questions
Data and analytics: collecting and analyzing information
Human resources: tracking timesheets, onboarding new (human) employees
Procurement: processing invoices, creating new requisition orders for supplies
RPA agents represent the basic level of automation achievable by your digital workforce. For more complex, higher-level activities, you’ll also need to make use of intelligent automation—the next stage in your digital workforce, lying at the crossroads of artificial intelligence and automation.
Intelligent automation makes use of technologies such as machine learning and natural language processing (NLP) to create digital workers that could properly be described as “intelligent.” Some prominent examples of intelligent automation include self-driving cars as well as IBM’s Watson, which can analyze massive quantities of data in order to answer questions posed in English.
Many digital workers that leverage intelligent automation are capable of interacting with the public at large. For example, chatbots can use NLP to answer frequent customer support queries, which lessens the load on your human agents.