A CXO’s Guide To Digital Transformation With RPA: Part 1

We have been hearing terms like Digital Transformation and Digital Disruption quite often, particularly after the onset of COVID-19. The hero of these talks inarguably is Robotic Process Automation (RPA), which helps bring massive savings and productivity gains to businesses.

But, how does one demonstrate the potential of RPA? It starts with identifying the precise task that needs automation. This should be followed by setting up a metric to analyze the performance of the deployment and to predict its potential for scaling. Without proper performance metrics established at the outset itself, it's difficult to demonstrate the benefits of RPA to stakeholders. Although it is impossible to create a one-size-fits-all checklist to measure RPA performance, it is imperative to have a list of factors that indicate growth in the right direction for any organization’s RPA efforts.

The benefits of RPA implementation can be easily divided into bottom-line and topline. The former refers to the technology metrics whereas the latter refers to the value offered to the business. In this article, we will be discussing the bottom-line impacts, and in the second part of this article (to be published later), we will touch upon the topline impacts. So, let’s see the five performance metrics that can be used by any organization or individual to measure the growth and scale of RPA, at the project/process level.

1. Automation suitability

Although RPA has a wide range of automation abilities, some business operations are more suitable for automation than others. For example, it can be argued that simpler processes should be considered first and more complex activities should only be automated once companies are more familiar with RPA. Also, automating rule-based activities is simpler as compared to non-standardized, variable ones which are often difficult, if not impossible, to define within the limits of RPA software. Thus, identifying the right tasks to automate and analyzing their success, based on their impact on the bottom line and topline of the business, matters.

2. Efficiency through bot usage

Unlike humans, robots do not need as much downtime such as rest, food breaks or leaves. However, there will be rare occasions where they would need to go offline for patches, upgrades, and development. In this case, we can analyze bot utilization by isolating a process that has to be scrutinized.

This will help realize how it has changed before and after RPA. Track how much downtime would be required if it were to be completed by an employee. And after RPA, track how much downtime, if any, the robot requires to complete the same process. An example of this would be the work TCS does with Tata Capital. Today, over 25 percent of Tata Capital’s customers utilize a bot for loan assistance instead of the traditional approach, thereby freeing up the employees’ time to do more value-based work.

3. Feasibility to track and map automation for scaling prediction

Automation of mundane tasks requires in-depth understanding of which tasks can be automated, and how many resources are working on it presently. Also, we can look at are tasks that can be partially automated, and study how much human intelligence is required for them. Once they get automated, it is essential to track the progress of each task and do the troubleshooting as required. Learnings from this will be the key to understanding the scaling possibilities of that task, as well as scaling of RPA efforts across horizontals and verticals of the organization. Accurate mapping of the progress of automation, no matter how small the task, will be the cornerstone to building the digital transformation masterplan for the organization.

4. Increase in process velocity

Process velocity is a measurement of how long it takes to complete a process. With a digital workforce - the bots - complementing the human workforce, process velocity improves significantly. Like most elements mentioned before, this can also be measured before and after the RPA deployment. Average velocities across time intervals, such as seasons and deadlines, should be monitored to get near- accurate estimates. The work that robots are doing “off hours” that humans aren't able to do should also be accounted for.

5. Accuracy improvement

Inaccuracy arising from human factors was considered as a forgivable error up till a few years ago, before RPA became a commonplace technology. However, now, any operational cost incurred due to human errors is considered with much gravitas, because there is technology available which can completely eliminate error. To track the impact of RPA in eliminating errors, measure the amount of work that normally needs to be redone due to human error. Then, measure the amount of work that needs to be redone after RPA is implemented. This comparison will give a clear picture about the efficiency benefits of RPA, because each occasion where work needs to be redone, costs time and money. Nevertheless, it is important to keep in mind that automation doesn’t make the bad processes good.

Mapping these metrics to the success of the RPA implementation will help the developer teams understand the user feedback easily, helping them devise a plan of action to improve product quality. In the second part of the article, we shall see how certain business-facing factors can be analyzed to evaluate how well the RPA adoption is catching up. Stay tuned to know more about measuring  business-level and strategic level impacts of RPA!