5 Reasons Why RPA Implementations Fail
Original post from https://www.cigen.com.au/cigenblog/
Robotic process automation is currently a focal part of the digital transformation that takes place across a wide range of industry sectors. We’ve written in-depth about real world use cases for RPA in finance, banking, healthcare, public sector, etc.
“A 2019 global Deloitte report shows that 8% of the businesses have started to use automation extensively (i.e., more than 50 instances of automation), which is double the percentage from the year before.”
Because of its easily noticeable financial benefits (i.e., cost reductions and productivity boosts) and also owing to the more intricate, qualitative ones like employees’ increased job satisfaction, robotic process automation has become the perfect illustration of a disruptive technology.
However, as it is usually the case with innovative technologies, implementing RPA can be rather challenging. Despite the hype, it is crucial to keep your feet on the ground and acknowledge the fact that, without strong platforms capable to facilitate the ease of use, monitor, and administration of software robots, RPA implementations sometimes fail.
In fact, according to Ernst & Young, up to half the RPA implementations initially fail due to some common mistakes. Read more about the top 6 RPA mistakes we’ve encountered so far.
A very brief recap of the basics facilitates a better understanding of the mistakes. RPA automates rules-based, routine, tedious, high-frequency and high-volume processes – those processes that everybody in your company dreads.
Operational activities in sales, like data replication, invoice preparation and delivery, smooth update of Customer Relationship Management, etc., portal queries, data extraction and management, or simple customer service operations, like customer profile updates, are among the typical RPA application areas.
Software robots can perform those tasks faster and more accurately than humans can. Nonetheless, robotic process automation failures do occur. But not all is lost, since those are avoidable with the required degree of care.
Why do robotic process automation (RPA) implementations fail?
Once we accept that RPA implementations fail, we should start asking the why question in order to optimise both preventive and corrective measures. The list below will break down the overarching reason for failure, namely a deficit of tooling, process and knowledge.
1. Embarking on the automation journey with unrealistic expectations
This means assuming that the RPA solution will be ready to run from the very beginning, with little (if any) adjustments and customization. To put it bluntly, this is simply false, because professional services are required along the way, for deployment, onboarding, and further scaling.
Starting with a crystal-clear set of the objectives that you aim to attain is one of the primary helpful hints for a successful RPA implementation. In addition to a straightforward means – goals hierarchy, SMART goals are among the initial steps for success that you must take. Having realistic expectations thus amounts to specific, measurable, attainable, realistic and time-bound business aims.
2. Moving too fast
When it comes to RPA, it is definitely not the case that “everything goes”. When you are excited and can’t wait to see the benefits that people praise RPA for, it is rather tempting to rush into running a proof of concept before proper evaluation of how automation can assist you in dealing with your current business challenges.
Our recommendation is that, prior to starting to do things, you should thoroughly consider your business priorities, the areas that are most likely to benefit from RPA implementation, and how you can operationalise these benefits. By doing this, you will be able to assess more realistically the extent to which RPA is currently appropriate for your company.
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3. Inappropriately choosing what processes to automate
The process of choosing the business processes to automate starting the implementation is among the top five factors that ought to be examined when designing an intelligent automation plan.
Inadvertent picks are excessively complex, non-standardized, unstable (i.e., involving over 40% exceptions) processes, which typically require human intervention and have irregular labour requirements.
In order to outrival this potential implementation pitfall, both the business analysts and the IT staff should actively take part in the process selection and optimisation. It is crucial to establish a common understanding of the nuts and bolts of the candidate processes, particularly of what completion means.
4. Leaving the IT team on the outside
This amounts to placing the automation project wholly in the realm of business initiatives, and this is an excessive simplification. No one can deny the crucial roles of business operations teams, e.g., their longer-term vision of the automation journey, analytics monitoring in order to secure meeting the cost reduction, accuracy, and customer satisfaction standards.
However, efficiently meeting the very same standards requires participation of the IT team. Their technical expertise in general and data security competence in particular, the procedural knowledge of the current IT infrastructure, or the testing and maintenance capabilities, are just some of the reasons why they ought to be part and parcel of the implementation process.
Afterall, RPA implementation is a team work process between business and IT departments, each with its own vital contribution. Their above-mentioned cooperative involvement in the selection of candidate processes is a good example.
5. Lacking a coherent long-term plan, supported by strong governance
The phrase ‘automation journey’ should be taken seriously, and the adoption of RPA should not be seen as a one-time event. Hence, there is a need for a clear-cut strategy that takes into account the unfolding of the process over time, and has a wide perspective of its effects at company level.
In the direction of avoiding robotic process automation failures, you should plan realistically for months (not merely days), and share the plans with all those affected within your company. This calls for establishing an RPA Centre of Excellence once the system is launched, which can ensure the requisite level of oversight.
Conclusion
Once you acknowledge that sometimes RPA implementations fail, the best you can do is to actually learn from these past mistakes, either yours or those of other companies. As the list above shows, most reasons for failure are due to human error, so becoming aware of those errors is the first step towards trying different, more successful approaches in the future. Guidance from subject matter experts throughout the way increases the likelihood of profitable outcomes.
All in all, since our endeavours are fallible, indulgence in surreal expectations about a smooth way towards automation success should be at the top of your “not to do” list. We wrote an article about some aspects that you might want to consider and/or avoid in order to stay safe from robotic process automation pitfalls, which we encourage you to read.
Planning wisely (i.e., realistically), testing thoroughly, maintaining an adequate pace, involving all the right people in the project, are some of the recommended ways to go beyond RPA failures. Our ‘take home’ message is that neither past failures, nor anticipation of future ones should hinder your progress towards the gains of automation.
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