RPA Failures: 10 Most Common Reasons and How to Avoid Them

Robotic Process Automation (RPA) is the use of software (“robots” or “bots” in this scenario) to collect and interpret data to automate processes, manipulate data, and exchange it between multiple systems.

It is used to automate slow, manual tasks that take up your employees’ time and can help you scale the business processes you are already working on without additional time or resources.

Done right, RPA can be the fastest and most efficient way for you to acquire, improve, and deliver information, and it can help your business cut costs and save time for key employees. See the improvement ideas for the finance, banking, or real estate industry.

However, without the right structure, logic, and customization, RPA projects can go wrong, and many organizations fail to implement RPA at scale.

Turning to the RPA failure rate, as per EY, while RPA can change the economics and service levels of ongoing manual operations, the experts have seen 30 to 50% of initial RPA projects fail.

RPA can be great for tedious and repetitive processes, but it won’t fix a process that you don’t fully understand, or that is, oppositely, fundamentally broken. This is an essential and common mistake that usually leads to the RPA project not achieving its goals.

People are trying to implement RPA before they know how their processes work. It tends to fail as they constantly discover new exceptions and variations.

Indeed, “exceptions and variations” are not harbingers of RPA success. When evaluating candidate RPA processes, most experts recommend looking for predictable, rule-based processes that repeat regularly. On the other hand, methods that change or don’t follow a predictable path tend to be poorly suited for RPA.

RPA tends to fail in two scenarios:

1. either the process being automated is not as robotic as it was initially intended,
2. or the resulting automation is running in a much more dynamic environment than previously thought.

In any case, the tool requires much more maintenance and continuous development.

More work on maintenance and continuous development is not the metric you bragged about. This contradicts in some way why you are considering RPA in the first place: RPA aims to improve the efficiency of processes and reduce the likelihood of human error.

Let’s take a closer look at the 10 main reasons why RPA projects fail.

Despite the obvious benefits, companies often face the issue of why RPA projects fail. It is common for companies to be unaware of the challenges associated with automating robotic processes. According to Forbes, 54% of technology disruptions are due to poor management, while technical issues account for only 3%.

This is the biggest mistake I see happening.

It essentially means that you have automated the wrong process.

If the process requires decisions on a case-by-case basis, you still want people to be actively involved.

Suppose the environment in which the automation operates is more dynamic than expected. In that case, the RPA toolkit must have additional complexity to ensure it can continue working in an ever-changing environment and deliver the right results.

Again, this is hitting the target. If the process requires decisions on a case-by-case basis, you still want people to be actively involved.

Tasks that involve creative thinking, brainstorming, or interacting with the physical world (such as pulling papers from a filing cabinet) are better performed by humans. This does not mean you cannot automate any part of these processes – a workflow automation tool can help you deal with repetitive steps in a process that requires decision-making and human skills. Workflow automation tools and RPA can work together.

The idea is to leave human work to be done by humans. Nobody likes to work on low-value, thoughtless, or repetitive work.

RPA follows instructions well. It cannot learn on its own or react to unexpected events.

Some people don’t make that distinction as they combine RPA with AI (RPA is generally dumb as it stands, although we will likely see an increase in use cases when deployed alongside cognitive technology).

We’ve seen an RPA bot break down when encountering scenarios it has not been trained or instructed to operate. One of the most common examples is changes to the target user interface. To put this in perspective, if the RPA bot has been configured to go to a web page and click in the upper left corner to go to the registration page, it can do so until the bot can find that specific button in the upper left corner.

The problem arises if you move this registration button to the middle of the page – or anywhere other than the top left corner, for that matter. The RPA sequence will stop because the bot will keep looking for it in the upper left corner. From a technical point of view, we are wrong to call it an RPA failure; this is indeed a human error.

The bot works exactly as it was told. I didn’t expect such an obstacle. This is generally a more significant issue than some think when first deploying RPA.

A common problem with RPA is process rigidity, dependency, and sensitivity of applications or systems that are being automated. This is because RPA typically uses screen-cleaning technologies with obvious problems that arise when changing user interface screens.

Here, we want to point out another consideration that has nothing to do with the process itself: the political landscape of your organization. As with many technology investments, your chances of success will be higher if senior executives can help you overcome obstacles when they arise.

This can be especially important in organizations where “automation” is a dirty word, often because it makes people paranoid about the safety of their jobs.

Areas of the business that do not attract the attention of key executives can also be poorly suited for RPA, as RPA requires political capital in the form of management support to establish corporate governance and provide annual financial costs to sustain operations.

For those who buy automation and RPA, it doesn’t seem like there is any reason not to, but for some people in the organization, the sale may not be that easy. Without the support of the right people, RPA projects can stop quickly. While participating in these projects, it is recommended that you think well about potential blockers and work with them to overcome any fears or reasons not to use RPA.

As with any technology initiative, you need metrics to measure results and ensure RPA meets its intended goals. Ensure these goals are