After appearing on the top spot on Gartner’s Top 10 Strategic Technology Trends for 2020 list in October 2019, ‘hyperautomation’ became the next buzzword in the IT industry. According to Gartner, “Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.)”
Though Gartner coined the term, the idea is not new, and has been there in the industry for quite a while. Different organizations have been using varied terminologies to represent the same idea; for instance, ‘Digital Process Automation (DPA)’ used by Forrester, ‘Intelligent Process Automation (IPA)’ used by IDC, or ‘Hyper Intelligent Automation (HIA)’ – all point to the same concept. In layman’s terms, hyperautomation is a confluence of multiple forms of automation, all of which synergize seamlessly to amplify the ability to automate work. It’s an expansion of automation in both breadth and depth.
Pic Credit – Gartner (Source: https://community.nasscom.in/communities/emerging-tech/rpa/understanding-robotic-process-automation-rpa.html)
Components of Hyperautomation: As hyperautomation aims to augment human capabilities, it must combine different tools, as there is no single tool that can achieve the desired effect. The technologies that form the core components of hyperautomation are:
Robotic Process Automation: RPA refers to the technology that helps automate repetitive manual tasks. It generally applies to repetitive and rule-based processes having manual touchpoints and well-defined inputs and outputs. However, RPA has its limitations. It is only applicable for structured data and does not involve any cognition or ability to learn and understand the context.
Business Process Management: BPM refers to the set of activities performed to discover, model, analyze, measure, improve, optimize, and automate business processes. It forms the foundation of an automation strategy by providing clarity on the different workflows involved in the process. It further helps avoid process breakdowns in the future.
Artificial Intelligence/Machine learning: AI refers to the simulation of human intelligence or cognition in computers, whereas ML (a subset of AI) refers to the AI’s ability to improve its cognition over time. Advanced computer algorithms are capable of mimicking the workings of the human brain, thus enabling machine learning. ML is of two types – supervised and unsupervised. While supervised ML has well-defined inputs and outputs before the prediction, unsupervised ML uses structured data sets to develop insights through pattern recognition.
Advanced Analytics: Advanced analytics refers to the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. It helps overcome the limitations of RPA as it can handle both structured and unstructured data. It enables the analysis of unstructured data (such as images) to get actionable insights that were traditionally inaccessible.
Benefits of Hyperautomation: Hyperautomation comes with many benefits, but a few noteworthy advantages are:
According to Gartner, “hyperautomation is an unavoidable market state in which organizations must rapidly identify and automate all possible business processes.”. It has also predicted that by 2022, 65% of organizations that deployed robotic process automation will introduce artificial intelligence, including machine learning and natural language processing algorithms. In the present technology landscape, transformations such as hyperautomation have become increasingly inevitable; they not only help companies optimize current business processes but also to stay competitive.
Being a forerunner in the technology space, we at ValueLabs identified this trend at its inception and have developed advanced solutions that not only meet organizations’ needs for hyperautomation but are flexible enough to align to the specific business goals. ValueLabs’ DocSpot is one such solution. It can extract data from unstructured documents like scanned PDFs, spreadsheets, and images and output the data in a desired structured format. On top of this, DocSpot takes continuous feedback to learn and improve accuracy. We recently automated the claim filing for an Insurance ISV in the US using DocSpot, resulting in a 50% reduction of manual efforts. There are many more success stories like this, where we have helped our clients start and see the fruit of their automation journeys.
As hyperautomation involves a mix of tools, it becomes crucial to select the right set of tools customized to meet the company’s unique needs. Gartner calls this process of identifying the right toolset ‘architecting for hyperautomation.’ With a rich experience, proven expertise in automating critical business processes, and the right set of customizable automation solutions, we are ready to give your business the head start in the hyperautomation race. Are you ready to embark on a journey to the future with us?
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