Why do RPA projects fail?

Last week, we wrote about the best processes to consider for Robotic Process Automation (RPA) projects. We are currently working on RPA projects internally; the rollout to clients will come down the road, so while we have internal knowledge, we’re presenting some of this external work at a 35,000-foot level.

Today, we wanted to discuss the flip side of best processes: namely, what makes RPA projects fail?

There tend to be five big reasons:

Misunderstanding where RPA can bring value / “The Silver Bullet” problem: This can take a few different forms, but generally it arises from senior leaders (those who can write checks for new projects and technologies) not fully understanding what RPA is and where it works best. They view it as a “silver bullet” that will help fix lots of processes and even drive new revenue, and while that’s possible, it’s not the best way to use RPA, necessarily. You could use RPA to build out more complex solutions with the aid of the robots, or you could update current tools — but thinking of RPA as a “silver bullet” won’t lead you to success.

The IT buy-in problem: You need to keep IT abreast of what’s happening, what’s going on, what the strategy is, what your goals are, and how RPA intersects with all their existing protocols and processes. If business drives the RPA process but IT isn’t informed, what happens is that when IT changes its systems, the robots stop working for the business. Communication between “revenue-focused” and “IT” is crucial here.

Automating weak processes: Garbage in, garbage out. If a process is weak and you try to automate it, it doesn’t work well. First, you need to improve the process; then you can automate it.

Pipeline management issues: Teams will chase smaller projects because certain sponsors demand them, as opposed to looking for projects with more significant ROI. Executives will also try to build the team before the processes are in place for consistent, long-term success.

Miscommunication of goals and priorities at the organizational level: This is sadly very common. Anything with automation is scary to human employees because it was likely researched and brought in, in part, because of FTE reduction. Clear communication is a must. Here’s what happens when you don’t communicate well with people about the strategy behind AI projects:

We saw this in another of the companies we spoke with when we learned that despite having integrated AI, managers were modifying the output values from the algorithm to fit their own expectations. Others in the same company would simply follow the old decision-making routine, altogether ignoring the data provided by algorithms. Therefore, human behavior is central to implementing AI.

Spend money on new technology and process and have your managers “modify the output values,” in part to assure their own future employment? That’s NOT ROI. Make sure you communicate and explain how roles can adjust and change — not firings or FTE reduction, but that human employees can ideally work on more value-add projects, and RPA will handle more of the manual, repetitive work.