Robotic Process Automation or RPA is the harbinger of all the new changes taking place in the business realm, following the immediate need for enterprise-wide digital transformation. As RPA matured, adoption started spreading, but with a more limited scope than we hoped for.
Enterprises today are looking for ways to be more strategic with automation. Technologies are evolving to bring more business use cases under Automation; coupled with the right capabilities and best expertise, the scope for RPA has broadened over the years to Intelligent Automation, often referred to as Connected Automation.
Connected Automation Overview and Its Relevancy in the Current Business Scenario
The concept of Connected Automation was born out of a need for companies to solve specific automation challenges or opportunities emerging in certain parts of a large operation. As a result, businesses are bringing intelligence to automate processes to foster connected services, enabling end-to-end Automation.
In order to meet the urgency for rapid digital transformation, businesses are shifting Intelligent Automation to core company metrics like Sales and Customer Experience. Such a transition in organizations’ approach to accepting Automation enterprise-wide stemmed from the never-ending tech potential of digitally processing work in a more general sense.
Today, RPA has evolved into Intelligent Automation. Comprehensive models for complete end-to-end processes are being developed to provide a treasure map for automating almost any process running in a large enterprise. This enables customers to measure the business value from various business use-cases in real-time.
Unfortunately, most business use-cases are neither logically connected nor make up a significant end-to-end process. Yet, the ability to automate more of the e-e process remains the top priority for customers. Hence, it is no big surprise that many customers are still focusing on deploying Automation to a single department with finite use cases.
In the absence of a clear path to becoming a strategic enabler, the energy behind the program can begin to drop.
Top Barriers to Scaling Intelligent Automation
Below are a few barriers to scaling automation:
Lack of IT readiness: Choosing the right process to automate is a top challenge in scaling automation. And the lack of IT readiness is cited as the number one reason by 37% of organizations surveyed. The legacy approach to identifying new processes for automation is time and labor-intensive and often leaves behind a trail of human errors. Also, granular variations in how individuals handle each process remain invisible to the naked eye. Adding to that is the attitude of managers, leaders, and other team members who consider automation a direct threat to their positions. They steer clear of any new change or cooperate half-heartedly. They lack the skills and expertise needed to understand automation. They are not IT-ready, which disrupts the automation adoption at scale.
Absence of desired skills-sets: There’s a considerable talent gap in enterprises today, hindering a full-scale deployment of enterprise automation. But, to accommodate the same, companies need not shell out the existing human capital. Instead, with the right amount of training, companies can pivot people in ways to develop and leverage existing human capital for Automation.
Biased approach to change: Change is often not seen in a good light. There’s bound to be resistance in employees who mostly fear the loss of jobs to Automation. Also, a tussle exists between 21st-century ways vs. 20th-century methods. Hence, the best way forward is to revolutionize the way organizations are led/managed as part of the transformation.
Absence of structured data for analytics: This stems from the lack of knowledge about Automation and a biased approach to accepting the change in the existing processes. And in the absence of data, meaningful use of data and analytics is not possible. Without data, understanding the nuances and roadblocks existing in old legacy systems is tricky, and human-manned data analytics fail to capture the accurate picture. So, the focus should be on getting leaders to ask the “right questions” vs. using data to support the wrong decisions.
Lack of clear vision: Surprisingly, 17% of organizations surveyed cited the lack of clear vision as one of the barriers to scaling automation, and quite rightfully so. The desire to change doesn’t align with their vision, especially when companies have no accurate data to work on and implement changes. Most are unaware of how processes work in their organizations. Hence, they are left feeling perplexed when faced with the urgency to bring automation to their organization.
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Scaling the Automation Program: Importance and Elements
Connected Automation is the foundation for future Intelligent Automation. When smart automation resources are united in an integrated platform that brings them together with a core of robust executional capabilities, the size and complexity of the use-cases grow two to ten times what RPA is capable of alone.
How can enterprises automate more of the end-to-end processes? How can enterprises scale their automation program?
This is where the need for an end-to-end automation strategy that connects People, Processes, and Data together comes into play.
Process: Discovery, Complexity and Exception Paths
In the legacy approach to process discovery, business rules and judgments made along the way determined the precise flow of a piece of work to conclude. To properly route the work when automated, it is critical to know the business rules. Unfortunately, business rules come from Compliance requirements, the design of the systems of record, requirements from other departments, and suppliers & customers, among others.
Even though manual process discovery is still the most prevalent approach, new technology solutions like Task and Process Mining are emerging to address the complexities of manual process discovery.
With the help of process mining capabilities, the complete process map with all its exception paths is laid down with rules that should be used to guide the flow of work. Process discovery tools also provide the data to create Next Best Action Machine Learning (NBAML) models for making dynamic decisions regarding real-time workflows. When connecting task mining to machine learning models, the transaction data moving through the process determines the best route for the work to take.
People: Voice and Human Machine Workflow
A variety of technologies across several related fields are developed to deal with a voice as a formal input into enterprise processes, such as:
Data: Documents, Unstructured and Semi-structured Data
Unstructured or semi-structured documents in volumes enter the enterprise system every day. Creating an omnichannel for input sources regardless of channel is important. But the main challenge faced by IA lies in digitizing the inputs needed for optimal automation.
With technology advancements, automation platforms have OCR tools as a web service, and by using capabilities similar to NLU, the unstructured and semi-structured are reduced to their essence and fed to the automation system. This determines the correct workflow that should follow the initial interaction.
Today’s platforms bring the core of task automation along with machine vision, NLU, and human-bot interfaces.
Powering Connected Automation for Enterprises with AssistEdge 19.0
Intelligent Automation, or as we commonly name it, Connected Automation, is gradually becoming core to enterprises’ business strategies.
Many bottlenecks keep enterprises from adopting automation at scale realizing the full potential of their automation initiatives. AssistEdge 19.0 is a cohesive automation platform that empowers enterprises to deliver a Connected Automation experience. It addresses disconnects by forging deeper connections between Processes, Data, and People.
The automation journey for most enterprises began as a mere tool to robotize basic, manual processes without involving humans. Enterprises now realize the need for platform solutions that can seamlessly combine multiple capabilities and pool their heads together to optimize the benefits of Connected Automation to its full power.