Robotic Process Automation (RPA) is one of the most followed technologies in recent history and a lot of businesses are embracing it at a rapid pace. The business benefits of RPA are varied including minimized costs, enhanced compliance, improved productivity and better customer experience. Seamless integration, scalability and the assured delivery of positive ROI are what inspire businesses to invest in RPA. However, there are several RPA implementation challenges that need to be faced. While it’s not possible to avoid facing these challenges, businesses can minimize their impact by taking the right approach.
Let’s look at the key challenges that businesses usually face while implementing RPA –
- Managing change – Whenever there is a major operational change in an organization, it’s natural for the employees to get stressed. This is especially so when the change incorporates the use of a technology as this might cause significant change in the responsibility of the employees. Hence, it’s important to ensure thorough and frequent communication between the employees and the executive sponsors. This not only ensures that professionals are informed regarding what they are expected to do, but also helps in successful adoption.
- Choosing the right processes – RPA provides a lot of automation capabilities and hence, it’s considered to be ideal for tasks which are rules oriented, high in volume, repetitive and don’t demand human intervention or judgment. For instance, copy-paste tasks or data migration can be smoothly handled using RPA. On the other hand, tasks which require a lot of human judgment, like interacting with customers and developing relationships with them, are best handled manually.
- Technical ambiguity – Another significant challenge that most of the organizations face is the technical ambiguity. To ensure proper implementation, the technical professionals must be ready to satisfy the operational and technical requirements. They should have a thorough understanding of the architecture of the target environment and how the infrastructure assets can be protected. They should also address questions like how the application and network access will be provided and whether the IT interface will support IT change management and automation development. While finding answers for these questions, organizations will be able to achieve enhanced efficiency.
Since now we are familiar with the key challenges of RPA implementation, let’s look at an approach that you can use to mitigate them.
- Discover – Before automating a process, it’s important to create a repository of all the processes as well as a detailed view of the activities of the users. The contextual data can be collected taking the help of three groups of experts – the domain experts, the process experts and the operational experts.
- Define – The initial team and the automation consultants can collaborate and optimize the entire process. By eliminating the redundant steps and identifying the steps which can be optimized further, they can improve performance and simplify processes. They can also define a ‘man to machine ratio’ and create the right balance between tasks that are performed by the virtual workforce and tasks which are handled manually.
- Design – During this stage, an appropriate model needs to be defined and the overall impact on the employee skill sets and existing processes should be evaluated. The team can design the operating model by following the initial steps and referring to an RPA use case that defines the roles and governance criteria clearly.
- Develop – After the design is complete and a mechanism for handling exceptions has been established, the team needs to develop a definite model of execution. Usually enterprises follow the agile methodology since this helps improve the team collaboration and the delivery speed. Development and deployment of the solution involve rigorous testing and short sprints.
- Deploy – In the concluding stage the RPA bot is put into production. Although thorough testing has already been completed, it is wise to directly monitor the bot for one shift after going live. All the activities of the bots are monitored and recorded. Analysis of the data can identify opportunities for further improvement.