Getting Ahead of the Competition with a Digital Workforce

The digital workforce is coming

In fact, it’s almost here as businesses enter either the implementation stage or begin seriously investigating it. The reason for all of this interest is twofold. Firstly, digital workforces have the ability to enable business operations teams to enhance, accelerate and customize key processes that deliver clear customer satisfaction and operational improvements. Secondly, global enterprises now have the chance to operate as smaller, faster and more agile companies while still maintaining their own advantages of scale and size.

It is clear then that for business operations teams, this is great news as it allows them to focus on improving customer satisfaction and driving revenue. For IT teams though there are quite different goals and requirements. The main challenge for IT teams is the need to connect both new and legacy infrastructure together while still maintaining a high level of security and visibility. Digital workforces therefore need to be able to deliver on both fronts combining the advantages that business and IT teams need without compromising on security and visibility.

The good news for businesses is that Robotic Process Automation (RPA) is emerging as a way for enterprises to achieve these very goals. Acting as a processing constant between modern and legacy equipment, RPA can connect technology of the past with that of the future. In doing so it can ensure the goals of the business teams are met without jeopardising security or visibility.

So with the benefits clear, it is surely just a straightforward process of setting up an enterprise RPA system? Wrong. Selecting the right one is not so straightforward, but businesses can improve their chances of success by recognising and understanding the six key intelligent automation skills. This insight will ensure the right solution is selected that works for both business and IT teams. To better understand these, it is worth looking at each in more detail:

Knowledge and insight

This is the ability to interpret information from different data sources, process it and deliver helpful insights using artificial intelligence. Tasks that utilise knowledge and insights include translating languages and identifying and analysing sentiment. As a result, these skills are particularly useful for handling customer service emails, which can free up human staff to focus on either customer phone calls or more challenging issues.

Visual perception

Exactly as it sounds this skill involves the ability to read, understand and contextualise information from images and documents. For instance, this can involve something as mundane, yet challenging as extracting information from an image as part of an insurance claim. RPA technology in this instance needs the ability to adapt to environmental changes such as screen resolution, network and application performance, as well as changing elements within an application.

Planning and sequencing

Also known as “Cognitive Planning”, these skills involve the ability to cope with complex and secure orchestration, sequencing and auditing of tasks, with as little human intervention as possible. In particular, these skills enable digital workers to discover opportunities and effectively plan workflows and workloads correctly. This adaptability makes it easier for RPA platforms to swap in and out new and legacy technologies.


As with human workers, collaboration enables tasks to be completed between two or more workers or systems. In particular, this can include human works, digital workers and other systems. For example, an address change should be completed through full automation with a chatbot handing off to the digital worker, which completes the necessary back-end system updates.


Another key skill for RPA solutions is the ability to solve complex problems, using both deterministic, rules-based workflow and probabilistic or predictive approaches. Each of these leverages trained machine learning algorithms to complete tasks such as predicting credit risk. Using data collected from multiple sources and a well-trained machine learning algorithm can help assess credit risk.


This a key part of a digital worker optimises, improves and adapts to customer data. Following the emergence of Machine Learning as a service (MLaaS), RPA platforms need to be flexible and make it easier to create training datasets from application data and processing exceptions to assist these programmes.

Enterprise RPA is set to change the way businesses think and operate. Yet, success is never guaranteed. By sticking close to these six intelligent automation skills however, enterprises can digitally transform themselves safe in the knowledge that they have a key competitive advantage.  This is no time for half measures.

About the Author

Colin Redbond is Head of Technology Strategy for Blue Prism. Blue Prism develop Robotic Process Automation software to provide businesses and organisations with a more agile virtual workforce.