Considerations/Challenges for successful RPA Implementation

Considerations Challenges for successful RPA Implementation

Robotic Process Automation has been playing a crucial role in the evolution of the modern workforce. RPA helps streamline tedious processes, thus enabling teams to maximize their potential and focus on higher-value work. The demand for RPA is heightened in its ability to grasp countless skills – without human intervention, while consistently carrying out mundane tasks. In today’s increasingly competitive marketplaces, RPA also brings about unparalleled cost-efficiency.

RPA Strategy can bring about:

  • Continuous Process Improvement
  • Operational efficiency
  • Adherence to Compliance
  • Improved Customer Experience

Top considerations while selecting the Process for RPA Automation:

RPA implementation is straightforward if you have a clear and detailed roadmap i.e.  Identify processes that will yield the greatest benefits:

  • High volume: One of the key benefits of RPA is reduction of human effort. You should start automating your highest volume processes first.
  • Fault tolerant: If a process cannot handle any errors, then its automation should either be DE-prioritized or there should be a quality control process to ensure that automation errors get caught.
  • Error prone: The more manual errors in a process, the more benefits your company can get by automating such a process. Manual mistakes can cause significant customer experience.
  • Speed-sensitive: Any processes that can delay delivery of services to customers are good candidates for automation as automation can make processes instantaneous.
  • Requiring irregular labor: Since finding temporary labor is difficult. processes with irregular labor demands force companies to employ for peak demand which is inefficient. RPA bots can easily scale up or down, easily managing peak demand.
  • Rules based: Ideal processes can be described by specific rules. RPA bots need to be programmed and if the rules of the process cannot be programmed, then that process is not a great candidate for RPA.
  • With few exceptions: This is similar to the “rules based” criteria mentioned above. However, some processes have so many undocumented rules that even if they are rules based, it is time consuming to identify all rules via interviews with domain experts. Such processes are not good candidates for automation.
  • Mature: Automating a process that is changing every day is a waste of time because developers will spend a lot of time on maintenance. Stable processes are good candidates for automation.

Finally, even if a process is not a good candidate for automation, it could possibly be broken into automat-able sub-processes that yield large benefits when automated.

Risks/Challenges of RPA Implementation:

  1. Wrong use-cases for automation: Identifying wrong use cases for automation is a common mistake that challenges RPA implementation and results in lower ROI. That’s why it is important to make a case for a proof of concept (POC) before taking it ahead for automation.

For example, companies that chooses to adopt RPA in departments with the most headcount in order to generate more savings fail due to large load of changing processes and exception handling. Companies that aim to reduce headcount for immediate FTE (Full Time Employee) savings fail because it did not have the resources required to build a robust RPA solution.

2. Wrong platform: One of the top challenges of RPA implementation is choosing the wrong platform due to a lack of knowledge of all the processes. Sometimes, the deciding factor is the cost, which can result in companies choosing a platform that does not suit their business needs.

3. Lack of skilled resources: With the growing popularity of RPA, the demand for skilled resources has been on the rise. Your RPA deployment can hit a roadblock if there is a shortage of skilled resources in your team.

4. Automating processes end to end: Sometimes there are processes that cannot be completely automated with RPA. These processes then require the use of Machine Learning algorithms, which can be an added cost to the company and to the project.

5. Not enough support from Business: Most RPA implementations fail due to a lack of required support from the businesses for workflow diagrams, plan B for all what-if scenarios, and business rules for several types of data processing by the bot.

6. Cultural change: RPA implementation requires a cultural and mindset shift within the organization, starting from the senior leadership. More than often, misguided information about RPA and its impact can create fear among people that they might lose their jobs.

7. Not following best practices: If the team ignores best practices during RPA implementation, then this can potentially result in more time being invested in debugging the code and making it difficult for teams to re-use the workflow.

8. Inadequate support from the vendor: As with any other technology, having support from the RPA platform vendor is critical for the success of your project since they have the expertise in utilizing the tool and have worked with several customers on different RPA implementations.

9. Unclear expectations: Not knowing the expectations of the team, management and other stakeholders involved in the RPA implementation can hamper its progress. Without a clear goal, it is difficult to measure the success of technology.

10. Technical ambiguity: Sometimes RPA deployment doesn’t lead to expected results due to ambiguity among the technical staff. When people fail to ask important questions related to operating requirements during the implementation, then the automation deployment can go for a toss.

11. Lack of ownership: When people are unaware of their roles and responsibilities in the new automated environment, it can create disconnected dots. This leads to a lack of ownership and accountability among various teams.

12. Lack of infrastructure: Companies fail to consider their infrastructure and choose to invest in RPA. This is one of the biggest challenges of RPA implementation since, without the proper infrastructure to deploy RPA, you cannot get the desired results.

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