RPA Success in the Enterprise Could Come Down to Having a Robust Performance Engineering Strategy
78% of business leaders agree that remote working is driving the adoption of automation technologies, as per Deloitte’s Global Intelligent Automation report.
According to Gartner, global spending on RPA tools reached $2.9 billion in 2022 – a 19.5% increase from 2021. Cathy Tornbohm of Gartner says that organizations are using RPA to “accelerate business process automation initiatives and digital transformation plans.” This way, they are “linking their legacy nightmares to their digital dreams to improve operational efficiency.”
With all its benefits, a successful RPA project comes down to an efficient IT infrastructure — precisely what this article sheds light on.
Why Is IT infrastructure Important for RPA implementation?
Through automation, RPA tools offer companies the pathway to a radical digital transformation. For instance, RPA-enabled solutions can automate business processes without any change to existing enterprise systems and infrastructure. RPA promises speed and agility, two ingredients that can improve the return on investment for the adopting company.
Ultimately in the final count, RPA implementation in any company is only “as good as” its underlying infrastructure. Without a firm foundation, 30-50% of RPA projects fail. Deloitte previously reported that of 53% of organizations that started an RPA project, only 3% achieved scaling their operations.
Among the critical factors, successful RPA implementation is dependent on the high availability of critical IT infrastructure, including servers, databases, enterprise systems, and network assets. For minimal downtime, it’s important to configure RPA tools and their underlying infrastructure to avoid any outages. For measuring availability, organizations must consider the impact of the failure of one (or more) of the RPA system components. Besides, availability is also about how quickly enterprise systems can recover from sudden failures and data losses.
Besides availability, there are concerns that RPA solutions cannot scale after automating most of the routine tasks. Taking infrastructure scalability into consideration is imperative when adopting RPA. That’s because core business processes can become bottlenecks when they can’t be scaled after the initial automation success. For effective growth, enterprises must consider scalable and well-managed RPA platforms with the IT infrastructure to support it.
Apart from availability and scalability, a high-performing infrastructure is also critical to a successful RPA project. As RPA bots interact with more business applications, organizations need to design their supporting infrastructure to enable the smooth performance of RPA-enabled bots.
Next, let’s discuss how performance engineering can build an RPA infrastructure with high availability, scalability, and performance.
How Performance Engineering Can Overcome RPA Challenges
At the outset, it’s important to clarify that this is not an attempt to establish a direct connection between RPA and performance engineering. This is a look at how performance engineering can impact the surrounding ecosystem of applications and tools to make RPA success more predictable and meaningful.
As the name suggests, performance engineering is an organized approach to developing software applications & tools that consistently meet performance expectations.
On its part, performance engineering techniques can measure the performance metrics of any application, such as:
- Usage
- Volume
- Response time
- Bottlenecks
Most organizations implementing RPA cannot initially assess the number of RPA bots they require for their needs. Too often, this task is the system administrator’s responsibility, who needs to install and configure the virtual machines. Here is how organizations can use the following performance engineering metrics to determine the availability, scalability, and performance of their RPA solution:
1. Traffic Volume
Volume is a key factor in performance engineering that determines the optimized number of RPA bots. Traffic volume is either regular or based on a schedule (or batch). The more the volume, the lesser the response time.
For instance, consider a business process with regular traffic and an end-to-end response time of 2 minutes. In this case, a single RPA bot is allocated to complete the assigned work in a single day.
Now, consider another scenario where a business process has lesser traffic volume – but each task requires 40-60 minutes to complete. In this case too, a single RPA bot is required to complete the work in a single day.
2. Capacity Utilization
This factor is related to memory and CPU utilization. Generally, 70% of CPU and memory utilization in VMs is optimal and stable. An RPA-powered bot can increase CPU and memory utilization to over 80%.
Consider the case of an RPA bot that:
- Processes 100-150 daily transactions
- Completes a transaction in 20 minutes on average
- Runs for 16 hours every day
On average, this robot utilizes capacity at around 52%. At peak levels, this utilization increases to 78%.
Conclusion
In sum, a successful RPA implementation is dependent on IT infrastructure that is available, scalable, and with high performance. Performance engineering is crucial for organizations to make their RPA implementation work for their benefit.
With its AI-powered software tools, appNeura is a product venture company of Avekshaa Technologies, which has pioneered solutions in application performance engineering. Our customized solutions (powered by AI and machine learning) have solved over 4,500 customer problems related to performance, scalability, and availability.
At appNeura, we understand the importance of application performance for business success and survival. Leverage our performance engineering expertise to solve complex infrastructure-related problems. Contact us today.