With the explosion of the robotic process automation market (RPA), many CEOs are in the midst of preparing their organization for transformation and welcoming a new generation of staff—the digital worker.
According to recent IDC research, the number of software robots entering the workforce will increase by 50 percent by 2021. It won’t be unusual to see organizations give software robots to every employee to augment their day-to-day activities.
But despite the promise that trillions of dollars are expected to be saved by deploying these digital workers, many RPA projects fail to fully deliver on their promise. To completely realize the return on investment, we need to onboard digital workers appropriately and keep a close eye on their performance to ensure they are being used to their full potential.
Here are ways to make sure your digital workers are skilled for their jobs.
1. First, do you even need a digital worker? Given all the hype in the market, this may seem counter-intuitive, but not every process is qualified for RPA. Even worse, picking the wrong process will only lead to frustration as you try to make your digital worker perform a task that it is technically unable to do. Here’s how to judge:
• The process follows rules-based, rather than judgment-based decisions.
• The process is repetitive and possibly prone to human error.
• Input data is digitized or can be with the help of OCR and document capture.
2. Know and document the processes. To ensure the success of our human colleagues in operations, sales, customer service, R&D, engineering or other areas in our business, we document processes so people know what to do. For a digital worker, it is even more critical processes are fully documented as it is the foundation for properly training it. Of course, this is totally dependent on knowing your current business processes in great detail. Many organizations think they know how their processes work, but in reality it is far from the truth. Fortunately, there are intuitive analytic tools to help you understand and quantify process behaviors so digital workers can be prepared for deployment based on fact and not on opinion, bias or assumptions. These same tools can also help identify high-value automation candidates, quantify automation’s potential impact, and ensure automation efforts do not produce any unintended consequences.
3. Provide appropriate training. First-generation RPA bots focused on automating high-volume, relatively simple processes involving structured data that did not require human intervention. As enterprise demands have evolved and AI capabilities improved, digital workers are increasingly tasked in more complex environments where humans are part of the process, and where some cognitive reasoning may be employed. As with our human workforce, if we want our digital workers to handle increasing complexity and process sophistication, they will need more training. This training is realized through the addition of content intelligence – the ability to understand and extract valuable data from business documents, and process intelligence – the ability to understand, analyze and monitor end-to-end business processes to raise the Digital Intelligence of bots to enable them to understand, reason, and learn continuously.
Their Digital IQ continuously increases by monitoring and learning from variations in invoice forms, data and how exceptions are handled. Eventually, they can ultimately achieve straight-through processing without any human intervention.
4. Avoid duplicate work. Just as you wouldn’t hire people to do the same job or allow job functions to overlap causing redundancies or conflict, the same goes for digital workers. However, unlike their human counterpart who can speak up, digital workers will tirelessly repeat whatever you ask them. This makes it more critical that the digital worker creators utilize the right AI enabling tools to ensure they are properly designed to avoid conflicts and deliver benefits. With advanced Digital Intelligence skills you’ll be able to:
• Spot redundant processes that you may be unaware of.
• Identify human processes that align well with automation.
• Identify robotic processes optimizations that can free up digital worker cycles — making even the most productive digital workforce even more productive.
• Discover inefficient human-digital worker hand-off or vice versa.
• Provide quantifiable data on the financial impact of digital workers by the process.
• Compare human vs. digital labor in terms of cost, accuracy, efficiency, and duration.
5. Monitor performance. Human workers regularly receive performance evaluations, and it’s important for digital workers’ performance also to be monitored and corrected. It’s another common reason why RPA projects fail to meet expectations – they aren’t monitored effectively after deployment and get stuck performing broken or poorly executed processes. Process visibility allows teams to identify, analyze, and correct issues such as bottlenecks, compliance risks, or mis-sequenced execution, especially in mixed mode scenarios where bots incorporate human assistance.
6. Give them a promotion. By having proof of digital workers’ performance and cost impact, you’ll be able to give bots a “promotion” and expand them enterprise-wide. Digital Intelligence solutions are providing specific pre-packaged AI skills in a way that is easy to train and consume to build and extend the digital workforce quickly.
However, scaling from tens to hundreds, or even hundreds to thousands of bots requires significant command and control to ensure automation remains synchronized across every process and the business system it touches. Having detailed analysis and a clear monitoring system allows you to have a central viewpoint to all you busy bots and the contributory role they play in business processes, even those that cross different business silos.
The future of work entails a growing digital workforce that will take on more reasoning and decision making, allowing them to go much further than simple automation. IDC estimates that today machines conduct 29 percent of evaluating information and reasoning and decision making, and will be up to 36 percent in two years. It’s important that CEOs and your teams are prepared for this new class of workers and create their path to success.