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.

Usually, any change to the tried and true ways of doing things is met with strong resistance from employees, even if the change will eventually be for the better. RPA is no different—resistance to change is the third most common barrier for companies launching robotic automation.

Before the benefits of RPA implementation kick in, the daily schedules of some of your employees will likely be disrupted. Also, using RPA in daily work needs constant communication and training in new, often technical skills. You can address these RPA challenges by educating your staff about the benefits of working alongside bots and financing employee training.

As with any automated solutions, some of your employees will fear that RPA bots will replace them. Focus on dispelling these fears early by presenting bots as tools rather than competitors.

Just because a process can be automated does not mean it should be. This is for several reasons.

Firstly, not all steps in the sequence can be suitable for automation. Finding that out halfway through the RPA implementation process can derail the entire initiative or even make it completely pointless.

Then, it’s important to remember that despite its flexibility, RPA isn’t suitable for automating all tasks. The technology performs at its best, automating repetitive, unvarying, lower-value work, but may prove inefficient with highly complex or inconsistent tasks.

The final reason is the lack of a clear objective. What is the goal of the automation project? What tasks to automate? Could you prioritize automating this particular task over others? Knowing the answers to these questions is paramount for the success of any RPA project.

The most essential factor to consider when picking candidates for RPA implementation is finding whether automation will bring profits.

Tempted by the promises of massive savings, companies often assign RPA to tasks with the highest headcount. Unfortunately, these processes usually involve heavy workloads and lots of exception handling, ultimately leading to their failure.

Another common mistake is to undervalue the costs of RPA implementation relative to the benefits. Depending on license costs, the scope of the project, its complexity, and any additional infrastructure needed, expenses can ramp up quickly. It’s also easy to forget about long-term maintenance costs, which is crucial for RPA success.

These early RPA failures have a lasting impact on future automation adoption, as decision-makers within the company will be less willing to invest in solutions that previously didn’t deliver the expected results.

All that said, RPA implementation can bring quick ROI, given enough planning and careful selection of automated tasks. Auditing your processes and working with an RPA consultant are great starting points for achieving a quick ROI.

Legal requirements pose another major challenge for organizations considering RPA. Depending on the automated process, bots may process sensitive data such as customer records, financial data, and personal information that you’re legally obligated to keep secure.

From the RPA implementation perspective, regulatory requirements have several implications. You must establish RPA governance rules defining responsibilities and ownership at all automation and data processing stages.

Companies must place authorization measures like securely stored passwords or two-factor authentication to prevent accidental leaks or insider threats.

Lastly, protecting personal data, in the long run, will require data usage monitoring and running regular, security-oriented audits of your automated processes.

Like any new addition to your tech stack, RPA implementation adds another potential surface for cyber threats. Falling victim to any attack puts your business, its customers, and reputation at risk. Even worse, these attacks can incur non-compliance fines in privacy-sensitive industries like healthcare, finance, or insurance.

Keeping yourself safe from cyber threats will require additional security measures:

• Monitor the use of safety data to identify threats early.
• Minimize the risk by limiting third-party services, connections, and excessive data input.
• Encrypt sensitive data to ensure it stays secure even if a leak happens.
• Store activity logs on another system to ensure their integrity.

As you can see, there’s a lot to wrap your head around before starting your RPA implementation. We’ve already given you some tips when discussing these RPA challenges, but here are more strategic ways to approach them.

• Assess business processes: Define the expected benefits of automating the target process. Prioritize low- or medium-complexity processes for your first RPA implementation. You don’t need to automate the entire workflow: focus on critical steps within the process with the highest relative impact.
• Create a detailed RPA implementation roadmap: How will you measure the results of RPA implementation? What are your plans for scaling? Are there any other technologies you could leverage? Defining an RPA implementation strategy and answering these questions in advance will go a long way in the future.
• Focus on ROI and savings: Generally, automating high-effort, standardized, customer-facing tasks stands a much higher chance of bringing ROI than more complex ones. Remember that a full RPA implementation in a more minor task is often much more beneficial than taking a long time and excessive resources trying to automate an extensive process end-to-end.
• Get everyone involved: With your eyes set on the tangible benefits, it’s easy to forget that the primary goal of RPA is to aid employees in their daily tasks. To get everyone on board, you must educate your employees, ask for feedback, show them the benefits of RPA implementation, and promote cross-department alignment. To effectively automate a process, you’ll need the involvement of different parts of your organization, such as IT, marketing, customer service, or finance.
• Prioritize security and compliance: Warranting safe operations of your RPA bots is something you must consider from the beginning. Implement auditing and monitoring systems, and move sensitive data from insecure locations to encrypted, central ones. Develop a risk management framework for all your RPA operations, covering access rights, data governance, and activity logging.
• Build a strong relationship with your RPA vendor: Find an RPA vendor with technical automation experience and a track record of working with diverse clients and projects of all scales. Establish efficient communication with your RPA developer and be clear about your expectations while staying open to their suggestions. Don’t be afraid to rely on your vendor’s expertise to identify the optimal automation candidates and guide you through the implementation process.

Whether it’s linking RPA with existing software or promoting an automation-friendly company culture, Flobotics has helped dozens of clients overcome RPA implementation challenges. Contact us to see what we can do for your business.