Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

Industrial Automation

Progress in Robotic Process Automation, Machine Learning, and Analytics have ushered in a new age of Intelligent Automation (IA), transforming the way businesses work. Enterprises ranging from Financial Services, Healthcare, Manufacturing, Energy, and Telecommunications are witnessing the profound effects of IA. IA is successfully providing productivity gains in the back office and enhanced customer experiences in the front office. However, with a significant increase in Digital transformation adoption, industries across the spectrum are witnessing critical challenges in this journey.


Mining of Dark Data


Businesses are creating data at a very high rate today; however, most of them remain in an unstructured or semi-structured format locked in emails, text, PDFs, and scanned documents, most referred to as ‘Dark data’. The prevalence of ‘Dark data’ in the system can cause significant hindrance to the automation process workflow. Artificial Intelligence (AI) technologies such as Machine Learning (ML) and Natural Language Processing (NLP) helps to capture, classify, and extract the most of the ‘dark data’.


The capability of AI to learn from human actions over time enables these solutions to improve and provide higher extraction accuracy and increase Straight-Through Processing (STP) rates. This has led to increased intelligent automation adoption across banking, financial services, and insurance (BFSI) and healthcare industries.


Automate – anywhere – anytime


Earlier, automation systems were built for on-premise deployment, since a majority of the applications were desktop-based Digital Automation(think traditional – Microsoft Excel). However, as those applications moved to the cloud (e.g.: Office 365), the need for having a strong unified, fully cloud-native, web-based intelligent RPA solution was strongly felt in the industry. Having a cloud-native platform offers customers both RPA-as-a-Service from the cloud, as well as an on-premise deployment with enterprise-class data privacy, security, and encryption in each.


The software-as-a-service model allows users to gain the inherent benefits of RPA, but with reduced cost of ownership, near-infinite scalability, and dramatically decreased time to value. Customers can choose to deploy on-premise or provision it from the cloud. Users will now also have the ability to easily integrate with any third-party solutions by the built-in AI capabilities in a cloud-native IA solution through drag and drop features into their automation workflows.


Identifying processes to automate


Nearly, 80% of automation opportunities remain undiscovered in organizations, even those that have deployed the first phase of their automation projects struggle with what new use cases they should automate next. 65% of time and effort is still spent on understanding processes before implementing automation.


According to Gartner, “50% of RPA scripts will be generated automatically by 2023”, which means process discovery will be critical in enabling technology for digital transformation. Smart process discovery solutions will help in detecting automation opportunities by visualizing process flows. Such solutions will also enable quick RoI assessment and assist in prioritizing opportunities, creating a model bot, and deploying automatically. Automatic process discovery will also allow recording of user interactions with enterprise solutions (ERP, CRM, business process management (BPM), ECM, etc.), personal productivity applications (Microsoft Excel, Outlook, etc.), and terminal and virtual environments.


Democratizing Bots


In order to achieve peak efficiencies in digital transformation, it is best to have a combination of humans and bots working alongside, thus, drastically improving productivity at a much lower cost. Pre-designed, downloadable, plug-and-play digital personas called ‘Digital Workers’ that possess built-in cognitive and analytical abilities to emulate and connect with human workers are available online.


Over time, these Digital Workers can learn to prioritize specific business and IT processes, allowing them to operate in roles such as accounts payable or IT operations specialists with little to no supervision. They are virtual employees that enhance and augment human work by combining AI, machine learning, RPA and analytics to automate business functions from end to end. This fast-evolving technology and human collaboration has the ability to impact healthcare, insurance, transportation, logistics, education, manufacturing, and many more industries.


Overcoming common RPA deployment obstacles


One way to overcome these challenges is to enable shared vision and ownership between IT and business teams and ensure strategic IA alignment to the business. Finding the right balance between setting up IA infrastructure and then measuring its success can determine the overall impact of IA. Many enterprises make the mistake of assigning IA solely to business teams or IT teams, and this leaves a significant gap between expectations and reality of the impact created with automation.


The competitive benefits of IA far outstrip its implementation pain points, and convincing enterprises on potential benefits with an integrated approach is what solution providers should focus on in the coming years. Choosing what processes to automate and the type of solutions to deploy is a finely poised balancing act, and IA deployment teams across industries are learning every day along with people and systems to master the art and science behind successful transformation journeys. 

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

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