Robotic Process Automation (RPA) promises huge benefits—saving time, reducing costs and boosting productivity. However, implementing RPA is not without its challenges. In fact, a study by Ernst & Young found that up to 50% of initial RPA projects fail. The root cause is often not the technology itself but how it's implemented.

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1. Performance Issues

A key frustration during RPA implementations is performance degradation. Processes that should be automated to enhance speed may instead slow down significantly, often requiring manual intervention to complete.

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Why It Happens

• Large Data Loops: RPA bots often iterate through large datasets or interact with many UI elements sequentially. When looping through thousands of items without optimisation, the execution speed drops drastically if one part of the process is holding up others unnecessarily.

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• Workstation Resource Constraints: RPA bots typically run on virtual machines (VMs) or dedicated desktops. These workstations can easily become overwhelmed by the memory and processing demands of large or complex automations, especially if multiple bots are running simultaneously. Limited CPU, RAM, or storage can cause delays or even system crashes.

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Solutions

• Optimise Loops: Instead of having bots iterating through every item one-by-one, switch to more efficient methods like batch processing or direct database queries, reducing the number of iterations the bot has to handle.

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• Go Asynchronous: Introduce asynchronous operations where possible. This allows multiple parts of the process to run simultaneously without waiting for other components to complete. For example, if a bot is generating reports, let that happen in the background while other tasks are executed in parallel.

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• Upgrade Resources: If performance bottlenecks stem from hardware limitations, consider moving the automation to a cloud-based infrastructure where resources can be dynamically scaled up as needed. Cloud-based VMs can provide flexible performance improvements, reducing the likelihood of slowdowns.

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Ntegra specialises in building cloud-based applications from the ground up, designed to fully leverage the power of the cloud. Whether you’re aiming to boost scalability, improve performance or accelerate deployment, Ntegra is here to help. To learn more about our ‘Cloud-Native Applications’ offering visit: www.ntegra.com/cloud-native-applications. 

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2. Incomplete Tasks

One of the more disruptive issues in RPA projects occurs when bots fail to complete their tasks or miss critical steps. This can happen inconsistently, making it difficult to troubleshoot and resolve.

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Why It Happens

• UI Changes: Many RPA bots interact directly with the user interface (UI) of applications. Even minor changes to the UI - such as a button being moved or renamed - can prevent the bot from finding the element it needs, leading to task failure.

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• Software Updates: Frequent updates to applications or systems can disrupt bots, especially if the bot’s logic relies on specific version-dependent functionalities. For example, if a button’s identifier changes in a system update, the bot may no longer be able to locate it.

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• Unreliable Selectors: RPA platforms often use screen-scraping techniques or UI selectors to identify elements in an application. If these selectors are poorly defined or too rigid (e.g. based on exact text or position), any change in the UI, even a small one, can break the automation.

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Solutions

• Retry Mechanisms: Build robust retry systems that handle transient errors such as missing UI elements due to interface changes. For example, if a bot cannot find a button, it can retry after a brief delay or look for alternative ways to complete the task.

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• UI Independence: Instead of relying on fragile selectors, implement resilient methods such as image-based recognition, anchor-based selectors, or dynamic selectors that are more adaptable to small changes in the UI.

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• Sandbox Testing: Before deploying bots in production, test them in a sandbox environment that mirrors your production systems. This allows you to identify any issues caused by UI or software updates without impacting live processes.

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3. Scalability Roadblocks

RPA can be a victim of its own success. As your business grows and you need to automate more processes, scalability issues can creep in. Suddenly, the bots that worked fine for smaller tasks can’t keep up with the increased workload.

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Why It Happens:

• Bottlenecks: Some RPA processes are not designed with scalability in mind. For instance, if the bot is built to process tasks sequentially, it can quickly become a bottleneck as the volume of tasks increases. A poorly designed process may not allow for parallel execution, making it difficult to handle a growing number of tasks.

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• Limited Infrastructure: Many RPA implementations rely on a finite number of workstations or servers to run bots. When the demand increases beyond the capacity of these systems, it can lead to system slowdowns or crashes. Inadequate infrastructure planning can result in insufficient hardware or bandwidth to handle more bots or complex processes.

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Solutions:

• Modular Process Design: Redesign your RPA workflows to be modular, meaning that each component of the process can run independently. This allows you to scale only the parts of the system that are under heavy load, rather than the entire workflow.

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• Load Balancing: Utilise load balancing across multiple virtual machines or cloud resources to distribute the processing load. By spreading tasks across several machines, you can prevent one system from becoming overwhelmed and improve overall throughput.

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• Backend Integrations: Instead of relying solely on bots to interact with user interfaces, integrate bots directly with backend systems via APIs. This reduces the need for bots to interact with UI elements and increases the speed and scalability of the automation.

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4. Error Handling

Effective error handling is crucial for any RPA implementation, yet it’s often treated as an afterthought. Without proper exception management, even minor errors can cause the entire automation to fail, making it difficult to maintain reliability.

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Why It Happens:

• Lack of Proper Monitoring: Many RPA projects lack the monitoring tools needed to detect and track errors. Without real-time insights, errors go unnoticed, leading to widespread issues.

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• Weak Recovery Strategies: Bots that encounter errors often stop entirely, requiring manual intervention to restart. Without proper exception handling, the bot cannot recover from these errors and may leave processes incomplete.

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Solutions:

• Detailed Logging: Set up detailed logging mechanisms that track each step of the bot's execution. Include logs for successful actions and failures, allowing for easier identification of where the problem occurred.

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• Exception Handling: Develop recovery mechanisms that allow the bot to retry failed actions, skip tasks that are blocked, or roll back certain steps when necessary. This ensures that a single failure does not stop the entire automation.

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• RPA Dashboards: Deploy monitoring dashboards that provide real-time insights into bot performance. Dashboards can alert you to errors as they occur, allowing you to resolve them quickly before they impact other systems.

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5. Maintenance Overload

RPA systems are often expected to run with minimal intervention post-deployment. However, bots require regular maintenance, especially as systems evolve over time. The failure to plan for these ongoing updates can lead to hidden costs and operational disruptions.

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Why It Happens:

• Fragile Automation Scripts: Bots are frequently built to rely on specific configurations or UI layouts, which makes them highly susceptible to changes. A minor update to a third-party application, for example, can break the automation, requiring time-consuming updates.

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• Lack of Proper Documentation: Without thorough documentation, it can be challenging to maintain or update bots when processes change. When the original developers are no longer available, undocumented bots can become difficult to troubleshoot and modify.

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• Manual Testing and Updates: Many organisations do not invest in automated testing for RPA bots, which makes it harder to identify issues when systems change.

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Solutions:

• Modular, Reusable Bots: Build bots with reusable components that can be easily updated or modified without requiring changes to the entire system. This modular approach makes it easier to maintain the automation as systems evolve.

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• Automated Testing: Implement automated testing to ensure bots continue to function properly after system updates. This can catch issues before they impact production and minimise the need for manual intervention.

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• Version Control and Documentation: Use version control to track changes to your bots, and ensure all processes are thoroughly documented. This helps future teams maintain and update the bots efficiently.

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RPA is a powerful tool. While it can help transform business operations, successful implementation requires careful planning and proactive management of potential challenges. Whether addressing performance issues, task completion failures, or scalability - these challenges can be mitigated by leveraging best practices and robust technical solutions.

Get in touch with us at hello@ntegra.co.uk to learn how Ntegra can support your RPA journey, helping you transform your business and avoid common challenges with proactive solutions.

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_{Authored by Anusha Gurung (Technical Research Analyst)}

_{Fact-checked by Sean Bailey (Head of Research and Development)}