RPA, as it has been widely publicized, is a very useful tool due to its ability to boost innovation, the increase of productivity indicators, the satisfaction level of teams, the optimization of intrinsic costs, the improvement of the final customers’ experience, among other clear benefits it offers. In addition to these, based on the very careful and well-considered identification of candidate processes for RPA, it also guarantees a very high potential for generating a rapid and significant return on the value invested (ROI) in their automation.
According to McKinsey Digital, business process automation using RPA can generate ROI of between 30% and 200% in the first year and, according to a Gartner study, can deliver immediate savings of 25% to 40% when only labor costs are taken into account.
That said, it begs the question: How can an organization put together its RPA + IA implementation strategy taking into account the estimated ROI?
First, you need to understand what elements are important to evaluate and measure in your journey to process automation, and also understand the importance of calculating ROI in order to better justify driving the implementation of this technology within business units and/or functional departments.
Why is estimated/verified ROI so important for the adoption of RPA?
Pre-calculating ROI is part of the critical path to first estimating, and subsequently gauging, the return on investment in process automation for each business. Based on this metric it is perfectly possible to develop a set of relevant actions for the implementation of RPA + AI, such as:
- Justify to managers with decision-making power, with real and concrete data, the initial investment required;
- Take the best strategic decisions, from the point of view of RPA + AI technologies to be adopted, during the planning phase;
- Plan and build the foundation for subsequent and successive investments to expand and scale RPA within the organization;
- Improve and scale the solutions that use RPA more quickly and effectively for the best results;
- Raise the adoption of RPA + IA to higher levels, both in terms of complexity and the required intelligence, adding more value to processes.
In summary, it is essential to understand that ROI is much more than a mere requirement for both the validation of a list of automation pipeline processes and the best decision-making about the necessary investment during the RPA + IA implementation journey.
It is a fundamental tool for positioning, justifying, planning, governing, and managing the appropriate return on investment expectations throughout the process automation and transformation cycle.
Therefore, the calculation of ROI, whether estimated or verified, should always assume a relevant role, with great prominence and protagonism, among the various weighting and decision criteria regarding the RPA + AI implementation strategy.
And how should ROI be estimated?
In a simple way, it is possible to calculate the estimated ROI by dividing the benefits derived from the necessary investments by the associated costs; however, as RPA + IA projects comprise countless variables and nuances, we should consider the following “inputs” as being the most relevant:
1. Full-Time Equivalent (FTE) – Represents the number of work hours that a single employee performs in a month, year, or another specified period of time, from there we can perceive and quantify how much time in FTE’s will be saved when automating a process that was previously performed manually.
2. Employee Satisfaction – Usually the processes with the highest potential for automation are those that are slower, repetitive, and that nobody wants to do. Therefore, after being automated, it provides satisfaction, fulfillment, and higher productivity. Considering this nuance when validating the generated ROI may even seem like a mission impossible, however, it can be estimated based on the reduction of the employee turnover rate, satisfaction surveys, increased productive capacity, among other ways of obtaining positive feedback.
3. Process Lead Time – The process lead time is the time from the beginning of the work to its conclusion. Many processes involve some form of waiting, which is wasted time, however, when you automate a process with RPA, it is perfectly possible to “burn” these inefficiencies. Reducing your process lead time can also allow for a higher volume of work, increasing productivity and potential revenue too.
4. Savings from Increased Accuracy – When there are fewer errors to correct, there is greater productivity in other functions or areas.
What was previously time spent on preventing, identifying, and resolving errors will be interpreted as costs associated with quality. It is crucial to measure the cost of any process-related errors in order to account for these savings.
5. Availability and Quality of Service to the End Customer – The modern customer does not wait for anything, hence this consumption profile serves as inspiration and motivation for many organizations, to the extent that they started to offer a service, via RPA + AI, during the 24 hours of the day x 7 days/week x 365 days/year, such as chatbots. Therefore, it is in this framework that service availability comes into play since it takes into account both the stoppage of the process or the time in which a service is unavailable and the agreed service level (SLA), which is the time in which it is available and should provide answers to what is requested. It is also very important to consider the value of greater customer service availability, through the metrics available regarding the best customer experience. It is also worth tracking changes in the quality of response times, either through customer satisfaction measurements or other data relevant to the issue.
In fact, it is undeniable that RPA+ AI solutions offer companies advantages in terms of more efficient processes and significant cost savings. However, first of all, it is necessary to understand the key performance metrics, in order to estimate and measure, as accurately as possible, the ROI generated by the automation of each process, as well as quantify the various costs, direct/indirect, tangible/intangible, associated with the process, in order to better consider and decide about the implementation of RPA+IA.
By fully understanding and comprehending the expected ROI calculation, when implementing each automation project, organizations will be able to make more informed, conscious, and accurate decisions regarding future automation, as well as justify and prove all necessary and effective investments.
It is a fundamental tool to position, justify, plan, govern and manage the appropriate expectations regarding return on investment throughout the transformation and process automation cycle.
