Intelligent Process Automation Analytics – How to Make Process Automation Programs Efficient and Scalable

Intelligent Process Automation Analytics – How to Make Process Automation Programs Efficient and Scalable

An organization looking to adopt process automation technologies, such as RPA (Robotic Process Automation) or others, faces two essential questions: what is the potential savings (time, money, and errors) when automating a process, and what is the real potential after the implementation is in production? This is a critical question that partners and clients constantly grapple with in RPA projects, often not evident or easy to measure. Additionally, there are other important questions, the answers to which are not easily obtained, such as the effectiveness of platform licenses or which automations may fail next.

Process automation, especially for repetitive tasks, brings a set of efficiency improvements that are evident and widely proven. However, translating this efficiency increase into metrics is not always straightforward. If we think of RPA as a fleet of virtual workers who must perform their tasks daily, with the potential for maximum occupancy to make the most of their cost. The interruption of these robots implies significant losses, which can be paralleled with other industries, such as aviation. Airlines profit when their planes fly at maximum capacity, but they incur losses due to unplanned maintenance and breakdowns. Efforts have been made in various industries to compile key indicators, predictive analysis, and standardization to minimize unplanned downtime.

Today, RPAs play a crucial role in the operation of companies, making their readiness a critical factor. Simply relying on orchestrators and KPI collection is not enough to understand their true performance. Continuous real-time monitoring tools are required, based on Artificial Intelligence and Machine Learning, to comprehend their installed potential and predictability. These platforms shed light on data and evidence that were previously scattered.

Recently, platforms with this morphology have emerged, which we call Intelligent Process Automation Analytics (IPAA), aiming to address a set of challenging questions in RPA Centers of Excellence. In our view, these platforms are built and should commit to the following verticals:

Consolidated View of the Automation Ecosystem

These platforms connect to RPA platforms from various providers, creating a global view of all automations. This eliminates the need to consult multiple platforms and machines to understand the overall landscape, resulting in greater efficiency and the ability to export results considering the global scope of RPA technologies.

Predictive Analysis

Using AI models, these platforms analyze records and execution evidence, searching for behaviors and deviant patterns that may indicate potential future failures. This allows first-line support teams to take action before the problem occurs, which is especially important in critical business automations, avoiding future downtimes or interruptions.

Centralization of Alarms

Aggregating alarms in a single platform provides a single source of information for support teams, avoiding the need to access multiple platforms and machines. Maintaining a history of alerts allows the definition of future strategies regarding automation programs, either for consolidation or expansion.

Automation Program Efficiency Analysis

Based on heuristics and AI, it is possible to assess the savings generated by automating each process, taking into account licensing and maintenance costs. This allows mapping the most expensive processes to operate, enabling the definition of improvement actions to reduce maintenance costs. It is also possible to trace a license allocation map to ensure that allocated resources are fully utilized.

At a time when Process and Communications Mining is a hot topic in the RPA market, with major vendors strongly investing in incorporating these capabilities into their platforms to identify points of improvement in various organization software, IPAA platforms act as Process Mining focused on RPAs, filling a gap that was previously insufficiently covered by each vendor’s orchestration platforms.

Adding to this, the growing multi-RPA software strategy comes into play. We have witnessed various clients with various software in their RPA Centers of Excellence, often motivated by the value associated with licensing scalability and structuring. This strategy allows significant cost savings but raises additional governance and operational consolidation issues.

In conclusion, Intelligent Process Automation Analytics (IPAA) platforms play a fundamental role in maximizing the benefits of process automation, offering visibility, predictive analysis, and operational efficiency. They empower organizations to proactively address the challenges of automation, ensuring smoother and more effective operations while making the most of RPA technologies and adapting to an ever-evolving business landscape.