What metrics should you track for cloud-based RPA projects?

Metrics are good, but capturing them comes with its own set of challenges. Inconsistencies in data sources and integration bettlenecks can impede a clear financial picture for metrics such as revenue impact. Also, customer satisfaction metrics depend highly upon maintaining human touch amidst automation. Personalizing automated interactions and leveraging AI-driven sentiment analysis to understand customer emotions can help.

In my role, I work with UiPath customers and prospects to build sustainable and valuable programs. Measuring value or determining strategic goals are often overlooked in programs. A lot of prospects I meet have jumped into 'making a lot of bots'. I agree that metrics and understanding the value is critical, however, it can be really hard for the RPA teams to identify what metrics to track, what metrics are most valuable, ect - It's a tough task, which is unfortunately why it is skipped frequently. My suggestion is to visit local RPA chapters and ask how they are measuring value or what lessons they have learned. Or better yet, work with a trusted SI, like The Silicon Partners, and see how other customers are measuring success and value.

For cloud-based RPA projects, key metrics to track include execution time, bot performance, error rates, scalability, resource utilization, cost efficiency, and security compliance. Monitoring these metrics ensures optimal process automation, identifies areas for improvement, and helps in managing resources effectively. Regularly assessing these factors enables organizations to enhance the efficiency and reliability of their RPA implementations in the cloud.

Evaluate process automation by analyzing rates, completion times, and stability. Monitor resource utilization, costs, and operational efficiency. Prioritize compliance and security through an audit trail and access controls. Assess RPA scalability, user experience, and cost metrics for financial insights. Implement real-time monitoring for prompt issue resolution. Regular analysis of tailored metrics provides valuable insights into efficiency, compliance, and project success.

For business value metrics, track metrics based on their relevance to the initial business goals. e.g. if your only goal is to cut costs, then measure something like VTG. Employee satisfaction, although important, wouldn't be relevant. But note that, you can still measure many other technical and business metrics (in fact it's a good idea to look for other potential added value) but at least it's not in your top priorities when reporting to decision-makers. It's a second thought.

For one of the automation projects involving, Azure cloud and Blueprism RPA, Azure applications insights was a game changes in capturing metrics. It provided us with user flows, which were instrumental in having a comprehensive view into how users navigate through automated workflows. This allowed us to optimize it further.

- one of the important metrics to consider is "value realization". How much ROI is being realized when an automated process is launched in production. - From Operational aspects, "Optimum utilization of Runner Machines". How can we run more and more bots on lesser machines.. - Ideation to success ratio: How many ideas submitted were finally automated and successfully deployed - Resource utilization: No. of processes being Automated per FTE per month or say per quarter - Process Stabilization. No of changes implemented to a process over a period of time after it went live. Many product companies provides tools to enable capturing metrics e.g. CoE Manager, Bot Insights from Automation Anywhere which could be leveraged.

Monitor bot performance, processing time, resource use, error rates, compliance, scalability, cost, user satisfaction, throughput, and maintenance downtime for efficient cloud-based RPA projects, ensuring optimal functionality and addressing issues promptly.