What are the most common mistakes when implementing RPA?

Very often, in order to catch-up with the new industry trends, organizations fail to understand the importance of long term vision when venturing into Transformation journey. Before developing business case and roadmap for RPA, the organizations must form a clear vision that would answer to the following questions and must communicate to its employees to show them benefits and impact of automation. - How does the automation fit in our existing ways of working? - What are the main objectives and drivers of automation initiative? - How will we measure and monitor the performance and value of automation? - How will we manage the change and risks associated with automation? - How will we scale and maintain automation solutions?

There are many lessons learned from recovering and scaling dysfunctional CoEs, relating to engagement, structure,service management, tooling, governance etc, but I think this is the most common error: Measuring success by FTE savings. Not only does this ignore other measures such as customer experience, accuracy, responsiveness etc, but it fundamentally drives the wrong behavior. CoE leads are forced to automate anything that moves to hit their target, irrespective of whether RPA is actually the most appropriate solution for the problem. This leads to low value, unreliable bots, or worse, RPA acting as a crutch for critical strategic processes.

Exaggerating RPAs implementation value. Companies who have implemented RPA should come out with ROI data on how much was invested on RPA and how much was the returns post implementation. That will shed light on what could be a problem statement.

Implementing Robotic Process Automation (RPA) can be highly beneficial for businesses, but there are several common mistakes that organizations often make during the implementation process. Here are some of the most common ones: - Poor Process Selection - Lack of Planning and Strategy - Neglecting Change Management - Overlooking Governance and Compliance - Not Considering Scalability - Ignoring Maintenance and Support - Relying Solely on RPA - Failing to Measure RO By avoiding these common mistakes and taking a strategic approach to RPA implementation, organizations can maximize the benefits of automation and drive efficiency across their operations.

The lack of a clear vision is one of the most common mistakes when implementing Robotic Process Automation (RPA) for several reasons: Undefined Objectives: Without a clear vision, it's difficult to define specific objectives for the RPA implementation. Objectives are essential for guiding the project and measuring its success. Misalignment with Business Goals: RPA initiatives should align with broader business goals and strategies. A lack of vision means there's a risk of misalignment, leading to RPA projects that don't contribute to the organization's success.

Poor Process Selection: RPA is most suited for high-volume, repetitive, and rule-based tasks. Avoid complex or low-volume processes for low ROI. Lack of Analysis: Rushing into automation without thorough analysis can result in automation of an inefficient process, furthering inefficiencies rather than solving them. Process Optimization: Before automating a process, it's vital to optimize it by eliminating non-value-adding steps, reducing errors, or improving process flow. Strategic Selection: Prioritize processes that align with organizational goals and save costs, reduce errors, and improve customer service. Understanding Complexity: Consider process complexity, change frequency, and automation ease to maximize RPA benefits and ROI.

The inadequate selection of processes often arises from flawed process analysis methodologies, including the use of manual or traditional approaches. Additionally, there are instances where automation preferences favor processes closely associated with decision-makers, presenting them as additional achievements. Recognizing that not every process is fully automatable, it is crucial to understand the potential for partial automation to significantly enhance legacy processes. Business leaders should also appreciate that as RPA becomes more widespread within an organization, prioritizing simple, low-complexity, high-ROI, and easily automatable processes can boost developers' confidence and prepare them for tackling more complex challenges.

Selecting the wrong processes for RPA is a common mistake, as not all processes are suitable or worth automating, factors such as complexity, volume, and the need for human judgment should be carefully considered to ensure that only the most suitable and impactful processes are automated, thus maximizing the benefits of automation.

In my experience process selection is important but equally important factors are: -Availability of enough data for vigorous testing - Implementation of Change Management system -Operational support in Production -Governance control and security -Log Management -Alerting and monitoring

Poor process selection can actually manifest from the demands put on the COE. Without a clear strategy, and solely an expectation of basic output metrics like FTE or cases processed COE will lower their quality standards to survive. A COE governance board is beyond critical here; an independent body focused on ensuring RPA is used only where truly valuable. This doesn’t mean that the COE will be irrelevant, in reality it pushes the COE to expand its automation stack and be able to tackle wider challenges. Not all your eggs in one basket.

Primary reason of failure - Unaligned road map with organization IT and IS policy as one time governance to ensure RPA is governed properly and has no road block in roll out

Inadequate governance is one of the most common mistakes when implementing Robotic Process Automation (RPA) for several significant reasons: Lack of Direction: Inadequate governance can result in a lack of clear direction and coordination for RPA initiatives. Without strong governance, it's unclear who is responsible for decision-making, oversight, and strategic alignment. Missed Opportunities: Effective governance helps organizations identify opportunities for automation and prioritize processes for automation. Without it, valuable automation opportunities may be overlooked.

Governance is the critical mistake of most RPA programs, it manifests in a few critical ways: integrity, data, security, and delivery. Integrity refers to the fact RPA is business-led therefore the resources, mainly developers, are not appropriately protected. Data refers to the too often overlooked idea that when you combine all the data from all the processes, you may have an issue. I knew everything about my company when I ran automation - that was a problem. Security refers to the patching and maintenance, and Digital Worker credentials. Virtual Machines are end-points and should be treated the same as any other end-point. Digital Worker credentials should only be know to Digital Workers. Delivery = process > requirements.

In the times ahead, this aspect will become immensely significant. Without a strong emphasis on governance, customers and their existing automations will encounter challenges in support, maintenance, managing change requests, modifications, & upgrades. Therefore, governance is indispensable and an essential component of every RPA COE. No choices to offer. Governance should not only be implemented technically through tools and policies but also ingrained in the mindset of developers. Without the commitment of the dev team to create resilient, reliable, high-performing, efficient, and scalable automation, technical governance measures cannot achieve their full potential. Leads bear the responsibility for ensuring this mindset shift occurs.

RPA projects often involve multiple stakeholders, including business leaders, IT teams, and process owners. As part of governance, the process roadmap serves as a communication tool to align all stakeholders on the project's scope, timeline, and expected outcomes. Also, daily\weekly\Monthly calls between all Stakeholders prove to be helpful in aligning all stakeholders with the progress.

I believe testing is a continual process rather than a one-time exercise. Testing should begin immediately, whether in a proof-of-concept or a full-fledged project, with appropriate test data and test cases. Furthermore, it is critical for businesses to provide a robust test environment for their automation teams. At the individual level, I believe that testing an automation or solution should begin in the mind rather than on machinery. While designing or architecting solutions, I see each step that automation will take and mimic the testing realistically. This would aid in the development of automations that fail less frequently and are adaptable to any modifications that may be introduced.

Testing fails 99% as you do not involve the subject matter experts. Simple as that, often the development process is so far from away from the people doing the process, due to most likely cultural issues - perhaps developer superiority, perhaps lack of proper sponsorship from the business owner. The focus should be to enable the SME as much as possible to be able to test as rigorously as possible. Simple things, like when my most critical automation went down as a person did a normal thing and pasted a customers name to avoid misspelling. A normal behaviour we overlooked because we kept the automation away from the SMEs.

Insufficient testing often occurs because the automation team do not have a clear strategy for addressing the poorly understood manual process. Therefore step one in definition is not to produce enough information to automate. The process should begin with defining the scope and assessing the application. The next step is to build the assets to interact with the applications while defining the general logic. The next step is to deploy assets capable of routing and recording work to understand case variety and volumes. The final step is to deploy the solution with an agreed in-scope case list, and clear operational procedures for the target state. You can only automate as much as the business knows, so help them discover.

RPA testing is not just about validating the functionality of the RPA solution itself. It is also about testing the impact of the RPA solution on the overall business process. When organizations skip or rush the testing phase of RPA, they risk deploying RPA solutions that have unintended consequences. For example, an RPA solution may automate a task perfectly, but it may also introduce new bottlenecks or errors into the overall business process. This is because the RPA solution may not be integrated properly with the existing systems and processes, or it may not take into account the full range of scenarios that can occur.

Proper change management for the program and the process 1. Communication by the business/operations and RPA team on “What and Why” if the program and ensure change is understood. 2. Communication of new process post automation and ensuring it is adhered and adopted

Change Management is easily resolved if: - client satisfaction becomes a performance metric for the automation team. - business ownership of the process and its supporting systems is clearly agreed. - the role of the business and the SME's is clearly defined and communicated regularly. - the service offered by the automation team is transparent and documented. - the service offered by the automation team focuses on teaching the business how to consume their services. - there is a clear change enablement process you can borrow from IT.

This is the most important part of the effort and it is often overlooked. Do not underestimate the need for education, training and consensus building. A single person who is not on board with automation can derail the entire effort. We often talk about how automation can free up time for humans. For some, this is a negative, not a positive. If I have 8-10 or even 20 free hours a week, what will I do? Organizations must be thinking beyond the automation effort and redefining roles to fully optimize the liberated hours and share this information with those affected.

Overlooking Scalability can result in an ineffective system that needs constant adjustments. RPA should be scalable to adapt to growth and continuous improvement.

Es importante tener claras las características del RPA y concienciarse de que un proyecto de automatización, a pesar de estar programado a la perfección, suele estar sujeto a un contexto cambiante. Es muy probable, que en el futuro requiera de peque?as adaptaciones por cambios en las webs o en los aplicativos que utiliza. Si tenemos esto en cuenta podemos de igual manera reconocer que son proyectos muy rentables economicamente si se gestionan correctamente.

Most common mistakes are 1. Unrealistic expectations & deadlines. 2. Lack of testing. 3. Incorrect process selection for RPA. 4. Underestimating production support part. 5. Failing to scale up the team etc.

Production support is almost always underestimated. Generally 1 support FTE is required for every 20 bots. Failure to properly address this as part of the CoE will result is poor reliability and eroded confidence

Measurable Progress: It provides a means to measure and track progress. Milestones and key performance indicators (KPIs) can be integrated into the roadmap, making it easier to gauge how well the project is progressing.

Make RPA Testing a Transformative Stage RPA testing is not just about ensuring the technical aspects work correctly; it's an opportunity for organizations to reimagine their processes. In the testing phase, businesses can uncover hidden inefficiencies and redundancies within their existing workflows. It's a chance to challenge the status quo and question whether certain processes are even necessary. By actively involving end-users and process owners in the testing process, organizations can tap into their valuable insights and potentially redesign operations for greater efficiency, beyond the scope of RPA implementation.

When implementing RPA, common mistakes include poor process selection, insufficient planning, lack of stakeholder involvement, ignoring security concerns, overlooking scalability, neglecting governance and compliance, inadequate training and change management, over-reliance on RPA, failure to maintain and monitor automated processes, and not measuring ROI effectively.