How a typical employee maps to the Virtual Workforce, and how they don’t!
In our first blog post (here), we introduced the topic of Artificial Intelligence (AI) and Machine Learning (ML). Next, we’ll look at how you should consider these technologies in terms of the various stages of a new employee in your business and the benefits of a virtual workforce.
Consider the following typical employee journey:
In this journey, you hire an employee. In time, s/he evolves from merely following specific process instructions to understanding the process as well as the broader business operation in which the process exists. This contextual understanding allows s/he to handle standard exceptions when they occur. In time, s/he becomes the "go-to" person for unexpected and unprecedented problems and scenarios and is typically promoted to a supervisory or managerial role.
The image below maps your employee journey to today's technology:
- Robotic Process Automation (RPA) mimics the trained but inexperienced user — it can learn a process and replicate the necessary steps to completion time and time again.
- AI combined with RPA enables s/he to mimic a more experienced team member — it can address standard deviations in the process, automatically handling exceptions and intelligently interpreting data captured to process decisions.
Deploying a virtual workforce to sit alongside and assist human operators enables you to create a more flexible workforce, remove tedious tasks, protect knowledge, and ultimately results in the following business benefits:
- Reduce Training Costs — Remove the need to train new staff or temporary staff who are brought in to perform a certain function; train bots once on how to handle basic repetitive tasks and then they are always at the ready.
- Workforce Flexibility — Handle processing peaks and troughs easily without the need for recruiting new/temporary staff, simply by deploying more bots to handle the high-volume processes.
- Continuous Improvement — Create a more experienced virtual workforce capable of more than repetitive tasks through learned behavior by analyzing the steps and outcomes of ongoing and historical processing through ML and AI.
- Improve MI and Reporting — Great dashboards and management information undoubtedly makes running any business more effective. Day-to-day processing improves by identifying and removing pain points and bottlenecks, and more importantly, companies are able to make strategic growth decisions based on analysis of real-life data.
Capturing, preparing and analyzing the data takes too much time when done manually. How does RPA and ML help?
Building data collection into your RPA strategy means that bots can collect and store more processing analytics information than ever before. Bots can also be used to aggregate and prepare data from multiple places that is ready for analysis. Analysis is where ML and AI really puts that information to work by identifying trends over a much bigger data set than you could ever manage manually — whether that be a performance comparison across all entities, divisions and departments that make up your organization, or simply extending the period of time to analyze trends from weeks and months to years.
So why don’t companies do more of it?
We’ll take a look in our next blog.