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Hyperautomation is your moonshot to crystalize business growth and digital transformation

Gartner has identified Hyperautomation as one of the Top Strategic Technology Trends for 2022 [1] to accelerate growth and business resilience by rapidly identifying, vetting, and automating as many processes as possible. It is also forecast by Gartner that world-wide Hyperautomation enabling software market will reach $600 billion in 2022 [2] .

What do we observe from this? We need to elucidate what Hyperautomation means and the need to look beyond RPA for next-level digital transformation with high value outcomes.

Industry outlook

Enterprises and technology innovation leaders create a myopic view of tactical routine automation over long-term strategic goals. RPA may provide quick relief as non-invasive form of integration however processes are not always simple, routine, repetitive and stable. They may be long running, require human intervention, often involve intelligent automated decision-making and optimization. The real challenge to scale beyond the low-hanging fruits or low complexity, high return routine processes cannot be solved by a single tool or with siloed strategies.

There is a need for end-to-end automation beyond RPA by combining complementary technologies to augment business processes. This is “Hyperautomation” which refers to an effective combination of technologies and sets of tools that can integrate functional and process silos to automate and augment business processes.

Evolution from Robotic Automation to Hyperautomation

Robotic Automation is a deterministic integration technology used to automate routine and predictable tasks through orchestrated UI interactions that emulate human actions. This is of two types – assisted, which is triggered by humans and deployed on individual desktops and unassisted, which is deployed on centralized servers automating end-to-end tasks and workflow scheduling.

While RPA tends to focus on automating repetitive and many times rules-based processes, intelligent automation incorporates artificial intelligence (AI) technologies to simulate human intelligence processing higher-function tasks that require some level of reasoning, judgment, decision, and analysis.

Hyperautomation is a combination of automation tools, AI technologies (Machine Learning, Natural Language Processing, Speech, Vision and Deep Learning), integration platforms (iPaaS), decision management systems and intelligent business process management (iBPMS) tools. Hyperautomation drives higher-level functioning from task automation to orchestration to intelligence enabling predictive insights, guided recommendations, processing mining, and adaptive decision making. This is when machines take over humans for full stack automation.

Coforge’s differentiated capabilities to enable Hyperautomation for guaranteed outcomes

Embracing new technologies, change management and process optimizations in production come with its own challenges and learnings. Being a Global SI, our objective is to become strategic partners helping organizations in their digital transformation journey.

As part of our Digital Asset Store, we have developed a range of proprietary frameworks and solution accelerators based on open-source technologies. The objective is to help enterprises jump start their automation journey, leverage pre-built domain ontology models for optimized and high accuracy outputs, and cross pollinate best practices across implementations. Our point solutions have helped enterprises to reduce development time by up to 70% and ensured production grade quality from Day 1.

Following is a brief overview of some of the key accelerators which are all API based and integrate out of the box with any RPA, Low code or iBPM tool to achieve straight through processing:

IDP: extracts data from unstructured, semi-structured, structured documents (emails, pdf, word, excel, scanned images) and provides advanced analytical capabilities like cognitive search, summarization, document clustering, knowledge graphs, etc. ML Veins: open-source accelerator to automate complete ML Ops lifecycle. This includes data preparation, data wrangling, modelling and deployment. GraphX: uses Machine Learning and Deep Learning on knowledge graphs for connected feature extraction, adding explainability and graph analysis SpeechX: provides large scale Speech analytics supporting 20+ languages with advanced features like noise removal, cloning, speech to text conversion, language translation, speaker detection, etc Third Eye: uses open source and cloud provider services of Vision API for image and video analytics. This includes capabilities like object detection, facial analytics, video intelligence, etc Digital Assistant: conversation AI framework for text and voice-based messaging channels. It comprises of domain specific language model, test framework including simulation of voice and text bots, and best practices for bot development and certification Enabling enterprises with Hyperautomation to standardize and scale 

There needs to be a strategic approach to bring Hyperautomation at scale across IT and business operations, derive ROI and manage associated risks. Coforge enables enterprises by providing end to end advisory services which includes defining Automation strategy & roadmap, process discovery and mining by qualifying processes against multiple technologies, setting up COE, Bot factory model for agile based development and value realization. Critical to make this successful is cross-functional teams and development methodologies (like part time specialists in PODs) that cut across the Hyperautomation technologies and are not limited to RPA or any single automation technology.

What is right for your organization?

While the industry is advancing on different technology segments across the automation continuum, there is no single solution for all requirements. Organizations should look to use or leverage RPA, Intelligent Automation or Hyperautomation depending on the strategic imperatives, technology roadmap and requirements to meet business objectives. Many initiatives do not sustain or generate target ROI if business or IT qualifications fail. However, for larger enterprises there is a greater steer towards the hyperautomation fabric which forms the superstructure to rethink products, services, operations, and business model.

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