Adding intelligence to robotic process automation

Robotic Process Automation (RPA) is a low-code, low-cost option for the service providers in the connectedness industry to automate high-volume manual processes, delivering cost, efficiency, accuracy, and transparency. By automating a large part of day-to-day activities, service providers can drive accuracy, improve employee morale and productivity, and ensure reliability and consistency of operations. However, to drive the intended benefits from their RPA initiatives, service providers need to understand the difference between the three primary levels of RPA maturity: Basic RPA, Cognitive RPA, and Intelligent RPA.

The Basic RPA relies on easy-to-implement and understanding fundamental technologies such as macro scripts and workflow automation. It is rule-based, does not involve much coding, and uses an ‘if-then’ approach to processing. Cognitive RPA, on the other hand, is a knowledge-based approach. It uses complex technologies such as natural language processing (NLP), text analytics, and data mining to automate parts of the process that Basic RPA cannot. But service providers can primarily benefit from Intelligent RPA, which uses AI/ML technology for decision making. With AI/ML, Intelligent RPA can go beyond data processing (gathering, sorting, calculating, and reporting), automate processes based on continuous analysis of incoming information, and learn to act smarter over time. This is especially beneficial for service providers dealing with large volumes of unstructured data. Furthermore, Intelligent RPA can gather insights and improve them over time while working together for the best results.

The insight elaborates on the three maturity levels of RPA and how to adopt them across the customer engagement lifecycle to help build out and deliver high-value use cases.

Three main levels of RPA maturity

Leverage Robotic Process Automation (RPA) to accelerate business process transformation and innovation

Robotic Process Automation or RPA is widely used by companies around the globe to streamline their business processes. RPA creates software robots to automate the processes that are highly manual, voluminous, repetitive, and rule-based. Process automation increases work quality, minimizes errors, and allow organizations to scale rapidly.

The service provider’s operational landscape has many mundane processes like service fulfillment, service assurance, billing, revenue management, and network management. By adopting RPA, service providers can quickly and easily automate the manual and tedious processes, without much investment or hassle. As a result, service providers can reduce cost, improve data quality, boost customer service, and drive operational efficiency.

Figure 1: Some examples of processes from the Order-to-Activate(O2A) cycle suitable for RPA

RPA is being widely accepted across industries and serves as a guide to help service providers in their RPA implementation, right from business process assessment till rollout. Topics covered in the insight:

• Why is RPA becoming popular in the telecommunication industry?
• Preview of telecom processes with RPA potential
• The RPA journey of a communications service provider – How to implement RPA successfully?
• Common challenges faced during RPA implementation

With most Digital Service Providers (DSPs) investing in digital initiatives today, transformation is no longer a choice but a must-have!

According to a Celonis study, “Most organizations are struggling with transformation initiatives because they are diving into executions without understanding what to change first. In a rush to innovate, 82% admit that they do not review their internal business processes while setting initial goals for a transformation program”.

Also, DSPs experience increasing process inefficiencies due to the ever-changing landscape. Traditional approaches to handling these processes are more focused on process discovery. They do not provide an accurate view of these processes and the real bottlenecks. Hence DSPs need to embrace a data-driven process optimization approach to look beyond discovery.

This whitepaper details on leveraging a process optimization framework for the DSPs to set business objectives across the transformation journey. It helps identify critical processes, accelerates cost savings by 60%, and improves customer satisfaction by 30%.

Fig. Key steps of process optimization approach

To stay relevant in today’s digital world, the service providers in the connectedness industry should simplify their business and transform themselves into a digital organization.

The road towards digital transformation is a business-critical one, and the service providers embarking on this journey will need to consider how each aspect of their business can be optimized to fulfill the new digital objectives. To optimize the existing processes and keep pace with the competition, service providers should bring the power of RPA in digital transformation.

Automation is not new and Robotic Process Automation (RPA) with its highly evolved level of sophistication, has made it a lot easier to automate processes across a variety of systems and technologies and reap tangible ROI in a very short time. RPA is a delightful journey, and the end-to-end lifecycle needs to be planned across the below phases to accelerate digital transformation.

Leverage bot development framework to empower RPA Centre of Excellence with bot velocity

Most service providers across the globe have been leveraging robotic process automation (RPA) to increase their operational efficiency and are at different stages in their journey. RPA helps an organization automate repetitive and mundane tasks using the development and deployment of bots or software robots.

But scaling RPA and increasing bot velocity to make it an organization-wide success is a big challenge. One definite solution to this challenge is setting up a robust RPA Centre of Excellence, which not only defines the best practices but also strives to achieve the organization’s goal.

An ideal RPA CoE has the following 7 components:

This insight focuses on the third component, i.e., bot development, specifically on the bot development framework. It throws light on best practices to create a robust development framework for driving bot velocity by standardizing development across the organization. By following these guidelines, organizations can reduce the bot development, testing, deployment, and review time.