Why RPA Projects Fail and How Yours Can Succeed

Automation rules the modern world. Almost every industry (no matter how close it is to technology) has a number of routine, repetitive tasks that take up a huge amount of time. Filling out documents, confirming signatures, processing invoices, writing emails, onboarding new employees. This is only a small part of the tasks where automation can come in handy. To be more specific, robotic process automation (RPA) will be especially useful in such conditions. With the help of bots, you can unload your team and redirect their resources to more important business tasks and goals.

However, the statistics are still not the best: About half of the RPA projects fail. Why? Why, with such benefits, is it not always possible to successfully integrate RPA into processes? And how can your business avoid these difficulties? Read on and find out.

What is RPA?

As usual, let’s start with defining this technology. Robotic process automation (RPA) uses software "robots" to automate repetitive rule-based tasks that are usually done by humans. RPA bots can mimic human actions and interact with software (clicking, typing, copying and pasting, moving files) without changing it. This technology is most useful for simple yet high-volume and time-consuming tasks where it’s easy for humans to make mistakes.

Robotic process automation can be: 

• Attended: Bots work alongside humans and help with specific tasks within a larger workflow. 

• Unattended: Bots work without human intervention from start to finish. 

• Hybrid: Bots can work both attended and unattended for greater flexibility. 

With the help of RPA, you will get increased productivity, efficiency, and speed. Your team will get more time for more meaningful tasks that truly matter for your business goals. Also, your operational costs will be lower.

We already mentioned that there’s a high chance your PRA project will face some troubles. But, as they say, forewarned is forearmed. If you decide to handle challenges unprepared, you will join the unlucky 50%. So here’s what you should know about the possible struggles you may encounter.

Technical challenges of RPA implementation

The first part that may let you down is the technical one. Not everything can go smoothly, especially if it’s your first time working with RPA and you don’t have enough technical experience. You can meet the following challenges:

Poor process standardization

RPA thrives on predictable, rule-based tasks. The more standards and instructions the process you want to automate has, the more efficiency and accuracy you can expect from your bots. However, a lot of companies apply automation to processes that vary too much or have a lot of exceptions. Such a strategy is the quickest way for automation to become fragile and ultimately fail.

Lack of integration with legacy systems

If it seems like your software has existed from the beginning of time and barely had any updates to its codebase, design, and/or architecture, applying RPA is doomed to collapse. Legacy systems can combine a lot of technologies, with some of them already considered outdated. It can stall the integration of RPA since your team will have to build workarounds like screen scraping, which are fragile and hard to maintain. Also, your RPA can struggle with the limited number of endpoints it can connect with, so if your system doesn’t have them, you’re in trouble.

Scalability

Businesses don’t always stay the same. They change, grow, and evolve. And so does the amount of data they use and the number of processes they implement. These changes will inevitably affect automation. RPA bots can work well for a single task, but they may not scale to handle higher volume or parallel processing without proper orchestration. It can be even harder for businesses that don’t have enough resources and infrastructure to support broader automation coverage. That’s why when planning for automation, you should pay attention to scalability from the very beginning.

Bad exception handling

When companies implement RPA, they often assume that it will run smoothly across all tasks and that all input will always be correct. The reality is, unfortunately, different: Your team can’t control everything, and errors will occur. Some examples include network outages, timeouts, UI changes, poor-quality data, and wrong formats. Your automation should be able to handle all of them properly.

However, many implementations don’t plan for edge cases, and they cause bots to fail “unexpectedly” when something slightly unusual happens. These failures accumulate, manual interventions increase, and, as a result, users lose trust in the reliability of automation.

Security and access control

In RPA, bots often need access to a lot of systems (just like humans do) to complete their tasks properly, especially if your business manages multiple applications. When bots perform multiple tasks across departments, access permissions can become complex and inconsistent. Sometimes, bots are even given admin-level access “just to make it easier for them.” These permissions can open sensitive data to bots, and if they can’t handle it securely, it creates significant risks of data leaks and compliance violations. And if any of those risks come true, the trust will be lost, as well as your reputation and money.

Organizational challenges of RPA implementation

The next level is the challenges you can face inside your business. They are not related to revenue or workflows, they are related to your team and their mindset. Not every employee is ready to accept automation, and it can easily lead to the project’s failure. Here’s what you need to be aware of:

Lack of clear ownership

RPA projects often suffer when it's unclear who owns what. Is it IT? Operations? Delivery? Marketing? Without a department that clearly owns the RPA project, no one can take responsibility if things go wrong or get rewarded if things go right. This confusion can sometimes cause different departments to run their own RPA initiatives independently. With little to no coordination across the organization, bots developed by different departments may vary in quality, so there will be no reliable automation ecosystem. Also, such independence can lead to departments automating similar processes twice and wasting time and resources.

Poor change management

Implementing RPA is a change your team should be prepared for. Just throwing everyone under the bus and saying something like “Now we are using bots for this and this” is not enough. However, a lot of businesses do exactly that. And teams won’t take it lightly.

Without proper communication and prior training, people can fear job loss or perceive automation as a threat. The resentment will build up and will lead to pushback or even full disengagement from the project. As a result, RPA solutions get sabotaged, teams revert to manual processes, and innovation efforts die out.

Insufficient skillset

RPA requires a lot of skills from different areas. Besides the understanding of AI and the process you are going to automate, the team responsible for automation should have enough analytical and operational skills to complete such a project successfully. This includes not just developers, but also business analysts, process owners, and change managers. And if your team is not flexible enough, RPA will stall.

It becomes especially risky if your team is overdependent on a single RPA champion—someone who knows the tools, gets things done, and becomes the unofficial “go-to” for everything RPA-related. While this person is usually key to early success, relying solely on them becomes a bottleneck and a long-term risk, because the moment this person leaves, everything will crumble.

Business challenges of RPA implementation

These challenges will affect your business directly. They can disrupt your workflows, slow down processes, and ultimately affect your revenue. You need to pay attention to the following points:

Automating the wrong processes

One thing you need to know about automation is that not every task is worth automating. And one of the most common and costly mistakes organizations make is choosing the wrong processes to automate. Tasks that are too complex, too unstable, too underused, or have too many variables should not be your candidates for automation. Businesses usually choose them if the team prioritizes speed over strategy, and there isn't enough research and analysis. Incorrectly applied automation will result in higher maintenance costs and disappointed teams and stakeholders.

Unclear ROI expectations

Automation in general can easily fall into the “overpromise, underdeliver” trap. For example, stakeholders may expect RPA to cut costs by 80% or get rid of entire departments. When the reality is more modest, they become disappointed and resentful. Also, it’s easy for a business to underestimate the total cost of ownership (licenses, maintenance, support) and overestimate time or cost savings. And when true results come in, automation becomes viewed as a sunk cost, not a powerful investment.

Lack of documentation

Many RPA implementations move quickly from idea to development and skip one of the most critical steps: documentation. This includes process documentation, bot logic, exception handling rules, system dependencies, credentials usage, and many more. Without these, RPA becomes an unmaintainable, unscalable, and fragile Frankenstein monster. Onboarding will be hell, dealing with errors will become painful, and you can forget about scaling. If no one knows what to do, the progress will go nowhere. Solid documentation is an investment in automation that lasts.

RPA best practices: What to do to avoid struggles?

Now that you know what possible challenges can bring your RPA initiative to a stop, you need to know how you can combat them. Besides obvious things like clean code, prioritization, and employee training, there are plenty of things you can do to protect your automation efforts.

• Build in strong error handling logic: Define clear workflows for common failure scenarios. Include retry attempts, failovers, or alternate paths.

• Trigger alerts or escalations: Set up real-time alerts via email, messaging apps, or dashboards when something fails.

• Use a secure credential vault: Store and manage bot credentials using an encrypted vault (like CyberArk, Azure Key Vault). Never (!) hardcode them in scripts.

• Implement least privilege access: Give each bot the minimum level of access required for its task.

• Establish an RPA Center of Excellence (CoE): A CoE defines standards, provides support, manages vendor relationships, and ensures best practices.

• Communicate early and often: Share the why, what, and how of RPA across all levels.

• Use an evaluation framework: Score processes based on volume, stability, standardization, rule-based logic, and ROI potential.

• Start small: Focus on a “low-hanging fruit” that is repetitive, high-volume, and rules-based, like invoice data entry or report generation.

• Version control everything: Store documentation and scripts in one version-controlled repository like Git or Confluence.

Bottom line

Robotic process automation may sound easy and fast, but it’s actually a complicated process that starts way before your development team writes any code. You need to be prepared for possible challenges and address them quickly and efficiently.

If you want to implement RPA into your workflows without any trouble, drop us a line! Yellow’s team is ready to help you bring automation to your business and boost it to the next level.