Common Reasons Why Most RPA Automation Projects Fail

Common Reasons Why Most RPA Automation Projects Fail

In business, most significant decisions spring out of strong necessities. Robotic Process Automation (RPA) projects are no different— these are generally backed by an organization’s hopes and dreams to increase efficiency in some facet of their workplace’s ecosystem. Plus, RPA projects take serious commitment, especially in terms of capital. According to Deloitte, just one bot can cost between $4,000 to $15,000, but when done right you can reap substantial rewards in the form of optimized critical processes, cost reduction, and increased profitability. With RPA, keeping businesses afloat during pandemic, such as COVID-19 also becomes seamless. Because it gives the opportunity to automate processes that must continue to happen for the company to keep moving forward.

Here lies the problem though: An Ernst and Young report shows that up to 50% of RPA projects fail during or after implementation. There are many reasons for this, most contain underpinnings of unrealistic expectations and lack of prior preparedness.

Reasons Why Most Automation Projects Fail

Targeting the Wrong Processes

The quickest way to accelerate RPA project failure is by trying to automate the wrong processes. It is akin to expecting a fish to climb a tree. RPA should not be used to automate high-level cognitive tasks or broken processes.  If it is a process that changes frequently, requires cognitive decision making or creative thinking on a case-by-case basis, then it is a good idea to let humans handle it. Craig Le Clair, a Forrester analyst, uses “Rule of Fives” to identify the best processes  for RPA-lead automation.

  • Five or fewer decisions during the process
  • Five or fewer number of applications involved
  • 500 or fewer keystrokes per bot

Lack of Cross-Department Support

An RPA project’s success is heavily reliant on cross-department collaboration. You need support from fundamental pillars of your organizations like IT, HR, and the key people handling different aspects of data analytics. Many organizations fail to encourage communication between these critical departments when all of them have valuable information and insight on how RPA will be able to make their job functions easier. Besides, you must bring IT into the conversation because they hold the roadmap for your organization’s tech ecosystem, and you do not want their future to be incompatible with the RPA implementation.

Also, getting the buy-in from other departments will make sure you have strong voices supporting, sharing, educating, planning and aligning your entire workforce against the common goal that the RPA project rollout is trying to achieve — this is especially critical if your employees are afraid of automation and are reluctant to change.

No Eagle’s Eye View Governance

An RPA implementation is not a one-and-done type of deal where you put the newly recruited bot in motion and forget about it. Organizations must have a core group that would oversee the entire deployment process as well as builds strong governance for the entire RPA project.

This is your organization’s Center of Excellence (CoE)  — a team that clearly defines the roles and responsibilities for implementing automation safely and efficiently throughout the workplace. It also establishes clear standards, policies, and procedures governing the robotic processes. They also handle variables such as the timeframe for deployment, budget, while addressing possible obstacles.

Plus, a CoE’s goal is to deeply integrate RPA into your ecosystem to where it seems organically a part of your company.

Not Planning for Scalability

With RPA projects, you must pre-emptively plan for scalability. If it is unable to grow as your business grows, then it may impede your success. RPA bots that are implemented must be able to have their workload scale quickly and easily, to the point where it might just mean adding another bot as you expand. While monitoring and continually optimizing the bots can help keep scaling sailing smoothly, it is imperative that you think big from the start.

Most RPA projects fail as a result of human error and not the error in technology. Being aware of the common pitfalls of RPA projects can empower you to proactively adopt best practices to ensure that your project doesn’t end up in the 50% statistics of failed RPA projects — because that is an expensive setback that many might never recover from.


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