RPA adoption is on the rise. The pandemic-induced remote work scenario and rise in digital transactions have also nudged enterprises to buy into automation. It helps them reduce the pressure on their human employees while maximizing operational efficiency and accuracy of transaction processing.
Robotic Process Automation (RPA), which forms part of the hyper-automation set of technologies, is also one of the core technologies that Gartner has predicted to be one of the top 10 strategic technology trends for 2022.
Everyday scenarios where RPA can be used to automate processes include:
- Opening email and attachments
- Organizing files and folders
- Copy-pasting content
- Filling web forms
- Reading and writing files to databases
- Scraping data from the web
- Extracting structured data from documents
- Taking actions based on ‘if/then’ decisions/rules/scenarios
Despite having a tremendous opportunity for automation, according to Deloitte, only 3% of organizations have scaled their digital workforce with automation programs like RPA. Further, only 3% of these organizations have managed to scale RPA to a level of 50 or more robots (Source: Deloitte – The robots are ready, are you?).
Why is RPA difficult to scale?
For any automation process to work at scale, it must have an end goal. Without a specific end goal, the automation process will only go around in circles. For RPA to deliver maximum ROI it should work to attain specific organizational goals. Some tangible benefits include:
- Increase agility and IT resiliency
- Suppress increase in operational costs
- Fortify remote work operations
- Establish quality controls
- Implement touchless transaction processing
- Elevate customer experience
- Create a single true version of organization data
When these benefits are not clearly defined as an end goal, scaling RPA becomes an enormous challenge.
To scale RPA, first, get RPA adoption right
Getting RPA adoption right is the basic first step to scaling it. That said, there are three ingredients to maximizing RPA adoption.
- Executive buy-in
The org executive team consisting of C-suite and top-level managers must believe in the long-term positive impact that RPA can create. They should be willing to look beyond short-term cost savings and factor in other long-term benefits like improving core operational areas.
Improving core operational areas through automation can bring manifold returns than cutting corners at random places. The executive team should be willing to look at RPA as an investment for a better future and one that is inevitable. They should avoid the notion that RPA is yet another software or tool.
- Practical goals/targets
One of the reasons why RPA adoption staggers is because the organization usually sets for itself unrealistic goals that cannot be achieved even by progressive enterprises with the full might of talent and technology.
For example, 100% digitization of operations or virtual workforce is a goal that is impossible at the moment. To get RPA adoption right, it is necessary to set realistic targets that can be achieved within a specific time and can be measured with some certainty. The goals should then be broken down into time-bound action items with specific functions as owners. This will drive RPA adoption and give a big picture of what is working and what can be scaled.
- Plan for contingencies
There are things that RPA can do and those that cannot be done. In fact, certain tasks that look easy to automate hypothetically may not be easy to automate without the right mix of talent. It is necessary to arrive at a consensus with the relevant stakeholders and then plan for contingencies that may arise during the implementation. Setting such things right before scaling RPA will go a long way in maximizing its ROI.
Most organizations fail to get these basics in place. As a result, their RPA initiatives tend to lose steam and gets stuck with a handful of applications (mostly ten or less according to the Deloitte survey mentioned earlier).
Why do enterprises stagnate with less than 10 automated processes?
The lack of any of the above-mentioned ingredients along with a combination of any of the below-mentioned factors can slow down RPA automation. Most organizations are unable to scale RPA beyond a small number of processes due to these factors.
They choose the wrong process to automate
Automation allows turning any repetitive process into a smart process that is faster and more efficient. This irresistible benefit could allow organizations to utilize RPA to an excess without any prioritization. Prioritizing the right process to automate is one of the challenges in scaling RPA.
Choosing the right process to scale is essential because starting RPA automation on the wrong foot could cause the entire project to slow down or even come to a standstill. The ideal choice of a process would be one that has been successful as a pilot and shows promise for the long term.
Inadequate data or inconsistent process
RPA needs huge volumes of accurate data to prove itself useful. Without volume, the benefit of RPA will be limited to preventing errors and managing quality, which human agents can manage with some training. Large volumes of data will ensure that there is enough transaction volume to automate.
However, while supplying an RPA system with large volumes of data, care must be taken to ensure that such data is clean. Unclean data consisting of outdated or obsolete information will prove detrimental to the quality of RPA processing. As a result, it is necessary to ensure that occasional data sanity checks are done to ensure that the RPA system gets high-quality data to process at scale. Unfortunately, this is a challenge that most organizations are grappling with.
The dearth of in-house talent
RPA implementation requires leadership. It also requires in-house talent that can optimize the RPA system and keep it running in top shape. Without these two critical resources, it is not possible to scale RPA at an org-wide level. Unfortunately, for most organizations that are testing waters with RPA, these resources are not readily available and have to be built from the ground up.
Whether you are choosing to engage an outside RPA partner or build an in-house RPA team, make sure that the below-mentioned resources are included:
Automation Infrastructure Engineer
Responsible for server installations and allied technical functions necessary to have an RPA infrastructure in place.
Automation Solution Architect
The person who is responsible for designing and defining the architecture of the RPA solution.
The person who is responsible for developing the RPA solution in accordance with the architecture laid out by the solution architect.
Automation Business Analyst
Responsible for defining processes and process maps which will be automated with the help of the RPA solution.
The person who uses the RPA solution at ground zero and optimizes the bots for better performance.
Automation Service Support
The user-facing personnel who provide technical support and troubleshooting when bugs or errors arise.
Taking the scale of RPA operations to the next level
For all the challenges that you could be facing while trying to scale RPA, there are viable solutions as well.
Optimize the processes before automating them
Most organizations dive into the automation race before optimizing their processes for automation. For an enterprise, a typical process would have become longer, complicated, or even obsolete at several stages. Identifying such scenarios and pruning the process for maximum efficiency and productivity should be a precursor to RPA implementation. The process should be streamlined and made compatible for scaling with automation.
Create a Center of Excellence (CoE) for RPA
It is good to have a dedicated team and org for RPA implementation. However, such an org should not become siloed from the rest of the organizational functions. There should be collaboration and interdependence so that more opportunities can be identified for RPA automation.
The ideal way to solve such a disconnection is by establishing a Center of Excellence (CoE) for RPA. The CoE will create shared ownership between the RPA org, IT team, and other stakeholders who can benefit from the automation. Further, it will also help the CoE to easily reach out to specific orgs and find information or access data that can further their objective of implementing org-wide automation.
Engage an RPA partner
There are times when RPA implementation and expansion cannot be done entirely in-house. In such scenarios, it is recommended to engage an external RPA partner or vendor who has the necessary talent and expertise to drive RPA upscaling.
Alternatively, digital transformation consultants with acumen in automation can also be looped in to make the RPA implementation and scaling process smoother.
However, the best option would be to use a combination of in-house talent alongside an external RPA partner. It would enable the organization to build capabilities while banking on the partner’s capabilities to scale fast.
There are several benefits of engaging an RPA partner. They can handle end-to-end implementing and scaling operations while you can focus on priming internal operations to suit RPA. In the long run, the cost incurred for engaging an RPA partner would yield higher RoI compared to building an internal team.
Implement proactive change management
One of the impediments to scaling RPA is that even the smallest change in the user interface of a system can disrupt the way RPA processes information. Change management is one of the critical pieces to plan for while upscaling RPA. Before making a change to existing processes that have RPA dependencies, it is necessary to conduct an audit of the impact that it could create. This could be referred to as proactive change management and it would help in reducing errors and disruptions en route to RPA scaling.
Ensure maximum stakeholder involvement
To leverage maximum value out of RPA, it is necessary to involve stakeholders from across the organization. It is necessary to win support and buy-in from all concerned stakeholders including admin ops, finance, and operational heads. The rationale behind the involvement is that, although each individual team is responsible for their outcomes, RPA still comes under the purview of IT operations. Their involvement will help spot blindspots early on that could disrupt the RPA implementation process. Hence, it is necessary to maximize stakeholder involvement while trying to scale RPA.
From x to 10X: Scaling RPA effectively
At present, there are automation approaches that are as effective and RoI-yielding as RPA. However, for RPA to give maximum dividends, it is necessary to scale it successfully. Successful upscaling refers to avoiding pitfalls, preparing for change management, and raking in stakeholder buy-in beforehand. It is also necessary to prime existing processes to suit the RPA workflows. Care must be taken to avoid any form of disruptions that could arise by changing a process that has an RPA dependency. The in-house RPA org or the external RPA partner must be well aware of how existing processes work and how they can be adapted to the RPA environment. Such a planned and prudent approach should help in driving RPA at scale and making it a key contributor to organizational goals.