Top 5 Reasons Why RPA Implementation fails

RPA Implementation has already become a key component of digital transformation. The potential for handling huge amounts of transactional processes, while increasing productivity and reducing costs makes RPA a disruptive technology that can change strategic landscapes.  

However, many RPA projects end up failing for lack of the right process selection, deliverable strategy, governance, failed Return on Investment (ROI) as well as misunderstanding the true capability of RPA. It’s a result of poor RPA implementation.

RPA implementation comes with its share of challenges. As sometimes happens with innovative technologies, many factors in the design and execution of a solution can make a huge difference with the overall success. Unfortunately, RPA failure does occur. The good news is that it is largely avoidable if companies pay attention to a few crucial aspects of their solution.

RPA is inherently business-oriented. Unlike some costly IT solutions, where the business payoff is indirect or abstract, RPA is able to drive direct, tangible financial results without overloading the IT department. The benefits can come from improved workforce productivity, better customer service, and so on.


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Typical mistakes that lead to RPA implementation project failures include misjudging the groundwork needed before the projects begin. Management wants a faster, efficient, and often cost-effective approach to RPA. A failed RPA project is likely to occur due to one of the following five main areas stated below:


  1. RPA Process Selection
  2. RPA Governance
  3. RPA Solution Design 
  4. Business Case – RPA ROI
  5. Post RPA Process Go-Live and RPA Implementation


  1. RPA Process Selection

The first step to a successful RPA project is to pick the right processes to automate. Not all processes within a company can be automated and/or only portions of processes can be automated. Understanding the capability of RPA is key to this. For example, automating 30% of one process may add far more business value and return then automating 100% of another process.

Selecting the right process, which fall under the standard RPA “process criteria”, automatically translates into a quick Return on Investment. The process can be small, but the savings achieved can be hugely significant.

RPA is seen by many as a cost-effective solution and even as a ‘Magic Bullet’ to strip out cost. Companies need to be careful as automating a wasteful or ineffective process will mean you are still processing waste. Understanding the capability of RPA, whilst considering the entire process dependencies i.e. applications in scope, master-data, offline calculations, frequency, volumes, application downtimes, exceptions, and error handling is crucial to optimising your RPA solution.

  1. RPA Governance

RPA governance is one of the most critical factors in the success of RPA implementations, so building a team with a clear division of roles and responsibilities is of the utmost importance when establishing internal delivery or commonly known as RPA Centre of Excellence.

RPA projects must be delivered through a governance framework that will manage the process of developing, documenting, and implementing Automation best practices, as well as analysis and reporting of the implemented processes.

Implementing RPA governance framework will significantly improve an organisation’s ability to achieve its automation goals, ensuring the full value of the RPA solution is realised and thereby maximise ROI. A COE enables companies to better gather, assess, and manage the necessary knowledge and capabilities needed to effectively deploy an RPA solution and provides guidelines to overcome any challenges that surface along the RPA journey.

A competent RPA COE is the long-term strategic approach to ensure RPA is embedded into the organisation, and automation ingrained into the culture of the organisation.  It enables an organisation to scale RPA at the enterprise level through common technology, standard processes and procedures, and a governance model. It provides a scalable foundation to allow an organisation to redistribute process automation knowledge and resources across future deployments.

  1. RPA Solution Design 

Design failures occur where the RPA solution has missed vital component(s). Poor RPA design can be one of many reasons i.e. missed process requirements, failure to develop via RPA best practice principles, or not following key software development lifecycle milestones and/or absence of solution architecture. Despite growing experience within RPA, poor design practices are still prevalent in many organisations. 

There has been such an upsurge in RPA skills post-2017, many organisations have hired RPA individuals without the necessary skills and placed directly into automating high-value business operational processes with only rudimentary training.  Design failures can be very expensive particularly if the solution design has missed key process SLA’s or service disruption due to overlooked application downtimes. 

  1. Business Case – RPA ROI

Creating a business case for Robotic Process Automation (RPA) is often one of the most difficult, yet important step to embark on a successful RPA journey. RPA is an effective tool and should be treated with the same due diligence as any other investment. The vast majority of RPA failures are financial in that they have failed to deliver the expected savings and/or Return on Investment. 

To calculate an accurate Business case and ROI roadmap, it is essential to distinguish whether this relates to a specific process, or of an entire department. Many companies normally start with the first option where they will automate only a handful of processes. By starting with an initial selection of processes, companies quickly identify the staff-hours saved and thus assess the benefits relatively quickly.

As the organisation start to scale and the process pipeline increases, ROI calculations are no longer pinpointed to a specific process but rather to a portfolio of processes, teams and/or locations.  A wider selection of processes presents a different set of cost drivers and benefits for ROI calculation. On the cost side, there are four major categories to consider i.e. the cost of the actual automation tool, cost of infrastructure, cost of development and the cost of monitoring and maintenance.  It is crucial to clearly articulate the total cost of ownership (TCO) and potential benefits associated with implementing RPA to get buy-in from senior leadership.

  1. Post RPA Process Go-live and RPA Implementation

RPA failures often result in the mismanagement of Bots once deployed into production i.e. live mode whereby the Bot is executing the actual operational business process. On the surface, it may seem that once the automated process has been built, the work is complete and will run autonomously with no oversight.  In reality, once live, Bots need monitoring i.e. reporting, process changes, exceptions, and changes in work prioritisation.   

In reality, Automation is more than just “software” and should be managed just like a human worker. Post go live protocols with a comprehensive operating model to anticipate the handling of these scenarios create robust support model as all processes post go live will require a level of “fine tuning”  until it has operated long enough to have encountered most scenarios.

A well-run digital workforce post go-live support model will not only ensure Bots are optimised to deliver at their peak performance, but also monitor and report on the measurable value delivered by the Automations and the overall impact to the business process.


Not all RPA project failures are the same, and most, if not all, are avoidable. RPA was a new technology five years ago. Today, it is a mature, robust cost-effective business operational solution. The key to delivering successful RPA implementation is to have enough governance based on your current Bot footprint and to balance this cost and complexity to ensure RPA does not lose its value.


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