How to Achieve Successful Automation Outcomes?

Automation has the potential to improve organizational efficiency and reduce costs. However, data from Deloitte’s Global Intelligent Automation survey shows that realizing the ROI is still a big challenge. The survey finds that the average payback period from intelligent automation pilots jumped from 16 months in 2020 to 22 months in 2021 and 2022.

The question is – why do enterprises fail to achieve the desired automation outcomes despite significant investments? The key aspect of automation is knowing what to automate with technologies such as robotics process automation and intelligent document processing, among others. In order to understand the ‘what’ — enterprises tap into discovery methods such as process discovery, process mining, or hybrid process mining.

However, process mining techniques give visibility into structured interactions with records systems such as ERPs and CRMs. Process Mining techniques require event logs which, it is obtained from system logs and databases.

This blog discusses how to drive better automation outcomes by building credible automation pipelines through data-based process discovery techniques.

Learn More: What is Intelligent Automation? Examples, Challenges, and Solutions

The Importance of Process Discovery

To overcome these challenges, it’s essential to have a data-backed process discovery approach. Process discovery involves analyzing business processes to identify automation opportunities and build credible, scalable automation pipelines. By identifying the right processes to automate, businesses can achieve a significant ROI while also ensuring scalability. However, process discovery is not a one-time exercise, nor should it be based on point-in-time data. It needs to be an ongoing process that adapts to the changing needs of the business.

Learn More: What Is Process Discovery?

Building Credible Automation Pipelines

To achieve better automation outcomes, it’s crucial to build credible automation pipelines through data-backed process discovery.

The first step involves analyzing the workflows of a business process and identifying areas that can be automated. Doing so ensures that the automation pipeline is dependable and trustworthy.

The next step is to evaluate the identified processes against specific criteria to determine their suitability for automation. The criteria include factors such as the complexity of the process, the frequency of occurrence, the volume of transactions, the level of human intervention required, and the potential ROI.

Once the suitable processes have been identified and evaluated, the automation pipeline is built to deliver reliable, consistent, and accurate results while ensuring scalability, flexibility, and adaptability.

Learn More: Business Process vs. Robotic Process Automation: Key Differences

Accelerate Automation Opportunities with Scout^TM

Traditional solutions that are used for identifying and building automation pipelines have significant privacy, accuracy, and scalability issues. They fail to capture the work done outside the systems of record, such as ERPs and CRMs. 

Today, over 60% of the teams’ workday is spent on unstructured interactions in documents, emails, communications, custom applications, and websites – outside of ERP, CRM, and other systems of record.

This massive unstructured and undocumented interaction dataset between people and software is untapped and contains a goldmine of insights that could give a significant competitive edge to enterprises.

Through Soroco Scout^TM, powered by the world’s first work graph platform, organizations can unlock this data source to discover areas where inefficiencies occur and build credible, scalable automation pipelines. Scout work graph can enable enterprises to understand the exact processes that should be automated — and what should not be automated, thus enabling enterprises to realize the promised value from their automation initiatives.

To get started, request a demo of Soroco Scout^TM!