Process Discovery Uncovers the Invisible Enterprise

Process Discovery bots are a powerful tool and provide far deeper capabilities than the mere visibility of underlying operational structures.

Discovery bots run on employee machines in a non-intrusive way, without hampering daily work and collecting data on how the employees use various applications to perform their tasks. Advanced machine learning (ML) algorithms then analyze the data, generating automation workflows that can be moved to an automation tool for a seamless automation journey. In utilizing Artificial Intelligence (AI) capabilities, Discovery tools use neural nets and deep learning to recognise the tasks, applications, and human interactions that can be used to create the metamodel for future work.

In using these newer technologies such as computer vision, machine intelligence and deep learning, Process Discovery gives birth to an enterprise’s digital twin: the invisible enterprise.

Read also:

• How Process Discovery Works Towards Amplifying Operational Productivity
• Why Process Discovery Drives Significantly Optimized ROI for Automation
• Fueling Corporate Intelligent Automation with Data

The invisible enterprise is the digital representation of the enterprise created by the amalgamation of all the nuances and drifts that diverge from the actual business processes. It is so named because this enterprise is generally excluded from traditional forms of human-led process mapping. With Process Discovery, it is possible to extract the process footprint through user interaction with the systems. Both the visible and the invisible processes can be collected through this advanced approach, taking the invisible parts of the enterprise and making them visible.

Other tools developed and intended to support automation initiatives can often be entirely separate from a Robotic Process Automation (RPA) solution, and hence an enterprise would have to look at integration between these two tools to align vendors for an efficient output. This integration and the inefficiencies associated with it increases the time to value realization of an automation implementation.

On the other hand, the data collected by Process Discovery tools is empirical and captures the bits and bytes of a process more effectively. Thus, it ensures that the enterprise is not automating broken or inefficient processes. With such a complete map of processes, automation initiatives will not fail and can bypass bottlenecks caused by human biases and errors. This leads to faster value realization of RPA and better ROI.

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