Measuring ROI in Robotic Process Automation (RPA) is essential for providing concrete, data-based evidence to justify the investment in automation to business leaders. It’s also crucial for planning and garnering support for future investments and scaling the RPA initiative more effectively.
According to estimates from the Institute for RPA, RPA solutions can deliver immediate savings of 25% to 40% in labor costs alone.
Furthermore, studies conducted by McKinsey Digital suggest that automating business processes with RPA can result in an ROI of between 30 and 200 percent in the first year.
However, to achieve these results, ROI must be the focus during the planning, implementation, and governance phases of the project. ROI in RPA is a complex concept that is difficult to quantify precisely.
In this article, however, we will help you estimate the initial magnitude of ROI, without considering variables such as the internal rate of return, monthly cost variations, currency fluctuations, and other specifics in processes.
Learn more: RPA budgeting for 2024: a strategic approach for leaders
How to calculate ROI in RPA projects?
The costs to consider in calculating ROI include annual licenses, infrastructure, implementation, development, and maintenance of automations. The model and potential usage of the licenses should also be assessed, spreading the cost across multiple automations.
Of course, for a complete analysis of ROI in RPA initiatives, it is crucial not only to evaluate the financial costs and potential benefits but also to consider human and intangible aspects, such as team satisfaction from shedding low-value tasks and having more time for strategic activities.
License costs
The cost of RPA software can vary significantly depending on the chosen platform and licensing model. Low-code platforms, for example, typically operate with enterprise licenses charged per bot and per machine. In contrast, open-source RPA or high-code RPA platforms, such as those based on Python RPA, offer a cost structure that adjusts according to the scale of use, potentially resulting in savings of up to 80%.
It’s also important to highlight the differences in technological dependency. Low-code tools often present high lock-in due to their proprietary technologies, requiring the maintenance of licenses for the bots to function. On the other hand, high-code platforms offer greater flexibility and independence, without lock-in, and the freedom to run and use the robots as needed.
Team capabilities
The internal structure and IT team also significantly influence ROI. For companies starting with RPA, hiring specialized RPA implementation services, such as RPA as a service, can be a way to save initial costs by designing scalable RPA processes and systems.
For companies with technical development teams, a more effective approach to increase ROI might be to reduce licensing costs, using the technical staff to create and orchestrate scalable systems in code.
Example of ROI Calculation in RPA
Let’s look at a fictional example. Considering popular low-code platforms, suppose an initial license package costs approximately $80,000.00 annually, divided among 6 automations. This would result in a monthly cost associated with licenses of $1,111.00 per robot.
Of course, this cost could be considerably lower with high-code platforms, but here we are considering the more popular low-code platforms in the market.
Monthly cost per automated process
With the above perspective in mind, the simplified formula, with monthly values, would be:
Monthly cost/robot = License cost/robot + development cost + maintenance cost
Let’s assume that a company has a medium-complexity robot with a one-time development cost of $38,000.00 ($3,166/month) and a monthly maintenance cost of $900.00. This would imply an approximate cost of about $5,170.00 per month for this specific robot.
Calculating the FTE (full time equivalent)
Considering a full time equivalent (FTE) of an employee working 2080 annual hours, if a manual process consumes 7000 hours per year, this would be equivalent to approximately 3.3 FTEs in hours worked.
However, we also need to know the FTE in hours saved. Let’s assume that an automation can work 10,000 hours per year on this process, which is equivalent to a saving of 1.4 FTE.
Then, the potential savings or cost avoided monthly with the automation can be simplified with this formula:
Monthly savings = FTE of hours saved x gross salary.
In this fictional example, let’s assume that the salary of a mid-level analyst is $4,350.00 per month, including charges. Thus, we have:
- Monthly savings = 1.4 x 4,350
- Monthly savings = $6,090.00
RPA Return on Investment (ROI)
Using the classic ROI formula, considering the potential revenue as savings, we would have in the first year:
- ROI = (Revenue – Monthly cost per bot) / Monthly cost per bot)
- ROI = ([12 months x potential revenue] – [12 months x monthly cost of the robot]/ [12 months x monthly cost of the robot]
- ROI = 73,080 – 62,040 / 62,040
- ROI = 17%
However, if we look between the second and third year, with the reduced monthly cost, without considering the development returned in the first year, the ROI is already much more significant. Let’s see what the return would be in the following 24 months:
ROI = (146,160 – 48,097) / 48,097 = 200%
Of course, there are many other important metrics to measure besides ROI, and the ideal is to automate even this intelligence of insights to improve your operation.
Next, we will explore more about the importance of management reports to monitor ROI.
Step by step to calculate ROI in RPA initiatives
Monitoring ROI reflects the maturity level of your organization in RPA and should be done considering different moments in your automation journey.
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Pre-implementation: Establishing the Base
Before any implementation, determine a starting point that reflects the current state of processes before automation. This initial point is crucial for future ROI comparisons.
In this phase, it is important to involve not only the technical area but also the business areas, identifying the most critical processes. At this moment, you will likely be evaluating operational reality KPIs, before automation metrics, such as:
- Personnel costs;
- Average time to complete a process (lead time);
- Customer satisfaction;
- Employee satisfaction level in the processes to be automated.
2. Implementation: building a history
During and after the implementation, it is essential to evaluate and quantify the gains to build a knowledge base and establish internal benchmarks. In addition to monitoring the aforementioned metrics, it is important to consider:
- Hours saved and directed to other tasks;
- Pre and post-implementation downtime rate;
- Error rate pre and post-implementation;
- The total cost of the RPA solution, including licenses and subscriptions;
- Additional costs of the infrastructure needed to support the RPA solution;
- Development cost, including analysis, redesign, and deployment;
- Ongoing maintenance costs.
3. Creating orchestration dashboards
To present these gains to leadership and involved areas, it is interesting to create advanced orchestration reports, especially in large-scale operations, to assist in governance and accuracy in measuring ROI.
Modern RPA tools, such as BotCity, offer customizable dashboards to measure the ROI of CoE operations, the number of automations in production, hours saved, total cost savings, and more, transforming complex data into practical insights.
Have a Data-Based Automation Backlog
Classify and prioritize types of automation according to return potential. Contemplating automations of low, medium, and high complexity, as well as their potential return, is fundamental to direct efforts and investments.
In general, the ideal tasks for initial automation in a company include:
- Rule-based tasks with few exceptions;
- Labor-intensive tasks that require more than an hour to complete;
- Tasks with structured data, involving clearly defined data inputs and outputs.
Automating ROI Measurement with Centralized Intelligence
BotCity stands out with its code-based RPA orchestrator, BotCity Maestro. We go beyond deployment, scheduling, and task logging. Our platform features BotCity Insights, a management dashboard with fundamental information to inform stakeholders, provide critical insights, and effectively orchestrate large-scale operations.
Our tool has helped companies like Andrade Gutierrez save up to 100,000 hours of human work with RPA. The bots built with our tool are 3 to 15 times faster compared to low-code tools, thanks to the absence of an intermediate interpreter.
With elastic computing, ultra-parallelism, and affordable licensing, we can scale the results of your RPA initiative. Schedule a conversation with us to explore how BotCity can transform your RPA journey.
