Top 10 RPA Implementation Challenges to Be Aware of

According to a study by Ernst & Young, up to 50% of initial RPA (Robotic Process Automation) projects fail. A knee-jerk response to this metric would be to write off the technology as moderately effective and turn to other solutions.

But hold on for a minute…

…because the same study shows that RPA projects fail because of the technology and how RPA is implemented.

Handled by experts in automation deployment, RPA can significantly impact process efficiency, resulting in time and cost savings, productivity boosts, and operational flexibility. Just have a look at dozens of successful RPA implementations across industries.

But to reap those rewards, you need to know how to recognize and deal with common issues affecting RPA projects. To get you started, we’ll investigate the most notorious RPA challenges and the strategies to overcome or prevent them.

From our experience, the most common RPA challenges organizations meet fall into three categories: technical, business, and regulatory. Let’s see how they differ.

One of the first challenges businesses must consider when planning RPA implementation is compatibility with the currently used software. PwC reports that 45% of companies using AI and robotics have encountered difficulties with deployment or integration. A company’s existing systems often combine different technologies, user interfaces, and protocols that need to stay in sync. An inappropriate data type can also be an issue: RPA works best with structured data and may require additional technologies like AI or OCR (optical character recognition) to process unstructured datasets.

Moreover, organizations may implement RPA alongside other solutions like IoT devices, mobile apps, SaaS platforms, or other connected data sources. The role of RPA is to link and orchestrate them. However, RPA solutions can be limited by the number and types of endpoints to which they can connect.

Major RPA platforms such as UiPath offer out-of-the-box RPA integration with popular third-party enterprise systems like SAP, Salesforce, or Amazon Web Services. For custom integrations, it’s best to involve an experienced RPA implementation team.

As organizations grow, their data flow and workload volumes increase. Along the way, some automated processes change or must be adjusted for regulatory updates. At this point, RPA systems built to support smaller capacities may start underperforming.

One possible solution is to assign more robots to accommodate the growing workload, but this will quickly start eating up your resources. For this reason, scalability may be particularly challenging for smaller organizations that can’t afford to throw extra robots at the increasingly demanding processes infinitely.

RPA systems should be built with expansion in mind right from the start to prevent scalability issues. A report by McKinsey hints at communication between departments as an important enabler of RPA scaling. Planning for gradual growth will allow for a thoughtful and dynamic distribution of robots between tasks depending on the demand for processing power rather than complicating the processes. Another solution may be uncovering improvements with process mining tools.

RPA bots need a robust and reliable IT infrastructure to operate efficiently. It should ensure enough processing power and storage capacity to accommodate the additional stress placed on the system by RPA and keep all scripts running.

The infrastructure must also be stable to allow bots to work reliably around the clock. For that, consider limiting the influence of external factors or deploying a failover server.

Another requirement is security. RPA bots often process sensitive customer data, and organizations must implement adequate protection measures to keep these records safe. There are multiple ways to ensure that, from ensuring unique log credentials for each bot and avoiding using hard-coded access rights to regular review and validation of RPA scripts. All activities like that should be a part of a strategic, overarching security framework that requires expert analysis and planning.

It’s easy to assume that bots can be left to themselves once deployed. But in reality, the true work begins only after the RPA implementation is complete.

Any deviation from the pre-programmed sequence can confuse RPA bots and cause errors. Still, changes are inevitable as your RPA platform must be adjusted for regulation updates, changing business requirements, and new additions to your tech stack.

Even when the system operates properly, it will eventually degrade without any modifications. The RPA tool will accumulate bugs, or a database can reach its capacity, leading to memory overflow.

Monitoring and testing these changes will help you quickly find and fix errors and find issues that went unnoticed during RPA implementation. Observe the performance of your RPA system and regularly check for updates. Automate regular maintenance operations such as running stress tests, cleaning cache registers, or copying data from temporary storage to a larger unit, and appoint an RPA owner who’ll oversee the condition of your RPA bots.

New technologies give rise to new possibilities but also new challenges. RPA implementation is no different and will affect how your business operates. To pave the way for digital transformation, you must plan for it from a purely technological perspective and consider people and processes.

If you ignore the people relevant to the automated processes, you will likely face fear or even outright aversion to the new technology. Getting executive buy-in is crucial and a good place to start, as senior management can influence company culture and lead by example.

Regarding processes, treating RPA implementation as an opportunity to revisit your current operations is essential. Develop a clear vision of what you want to achieve by automating each workflow, and don’t overlook the improvements that can be made along the way without automation.