Getting hyperautomation right | VentureBeat

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A lot has changed in the last two years. As the epidemic has thrown all enterprise operations out of gear, many trends, including distributed (remote) work, have pushed themselves into the limelight. However, in the comprehensive plan for digital transformation, hyper automation is becoming a topic of discussion in 2022, after Gartner Research made its first appearance in the pre-epidemic era as the top strategic trend of 2020.

And for good reason.

Hyperotomation is not just about technologies but about combining them to achieve the strategic objectives defined by the organization. Gartner redefined hyperotomation as a “business-driven, disciplined approach that organizations use to quickly identify, veterinarianize and automate as many business and IT processes as possible.” Furthermore, according to Gartner, hyperotomation involves the systematic use of multiple techniques, tools, or platforms to achieve their goals.

That’s where it differs from other technological trends. For example, unlike certain technologies such as robotic process automation (RPA), targets for hyperotomation can vary significantly from enterprise to enterprise. The way an enterprise implements hyperotomation can also differ widely from one another.

It works

Since hyperotomation is a comprehensive approach, it comes with its own challenges. And most of these challenges involve establishing clarity on multiple fronts:

  • Clear identification and description of strategic goals
  • Identification of use cases and their priorities
  • Evaluation of the roles of different technologies
  • Establishment of roadmap and implementation method

These challenges are intertwined. A clear vision of the ultimate goal helps.

Let’s take the example of a financial institution that wants to transform its account opening process into all products and services.

The vision for the conversion process varies, depending on the main driving factors or the objectives chosen. These goals can be any of the following or a combination thereof:

  • X% increase in number of requests to open an account
  • Reduce y% in abandonment throughout the process
  • Measure prospects and employee experience
  • Decrease cycle time m%
  • N% reduction in costs per closure
  • p Experience opening a 100% touch-free / human-less account every month

After identifying these goals, it is important to establish a roadmap, which includes identifying and achieving different technologies with good fairness and defining a long-term architectural stack. After all, opening an account is just the starting point in this case, and the real value of hyperotomation lies in taking advantage of the stack for multiple processes and applications with speed across the enterprise.

This brings us to the various technological capabilities that combine to make hyperotomation powerful. In this case it is important to define how they come together to open a digital account. Here’s an effective way to put them together:

  • Prospects apply for any account, for any product or service, from the device of their choice, using AI-supported chatbots.
  • The Natural Language Processing (NLP) engine analyzes all incoming requests and categorizes them by potential status (new / existing / premium), product / service, range, geography, etc. and triggers the related process.
  • Intelligent Image and Document Processing retrieves all information based on uploaded documents and launches a fully automated digital customer identification program (CIP) to establish ID authentication / verification, security credentials, financial status and reliability.
  • Intelligent process automation enables real-time end-to-end processing with straight-through processing (and the flexibility to intervene or root it if any). It also triggers RPA bots for automatic real-time execution of routine (traditionally manual) steps throughout the process.
  • At various points in the process, other major decisions, including AI / ML-powered rules-engines and RPA automatic approvals and routing, are traditionally taken by knowledge activists. This frees up time for their other value-added tasks that require human judgment, such as complex credit analysis for high value deals.
  • All relevant documents (or media) are auto-processed with content analytics and embedded in the context of the process, enabling contextual engagement with customers with authorized access throughout the cycle.
  • Throughout the process, prospects are engaged in the channels of their choice through omnichannel customer communication.
  • Upon final approval, the reception kit is automatically generated and delivered to the future digitally, while backend integration takes care of account set-up and funding, whenever applicable.
  • At the right time (at the application stage for existing customers or at closing for new prospects), the AI ​​/ ML algorithm presents cross-cell options tailored to the preferences and profile of potentials and triggers the relevant automated process if the potential is accepted. Offer

Achieving hyperotomation on an enterprise scale

From the example above, it is easy to see how the combination of hyperotomation technology can have a real impact. However, this is just one example. The enterprise is full of thousands of applications and processes ranging from small ancillary applications to large and deep mission-critical processes.

That’s why Gartner emphasizes the “approach” bit. It’s not just about doing it all at once but about achieving it over and over again with speed, for different processes and applications.

That’s where the digital transformation platform comes in. Let’s consider the following:

  • A set of key techniques form the basis of the hyperotomation strategy. These include low code process automation (combining what is traditionally known as business process management – or BPM – with rapid development through low code capacity), RPA, business rules management, case management and decision management.
  • Another key component in hyperotomation is reference content services that enable end-to-end lifecycle management of all types of content (media in documents and formats) to provide context to transactions and processes.
  • All applications and processes involve some form of collaboration and communication, requiring omnichannel customer engagement capabilities.
  • These technologies are advanced by AI, Machine Learning (ML) and Content Analytics to increase speed and intelligence.
  • Hyperotomation is effective only on an enterprise scale with end-to-end automation that is omnipresent in nature and can be achieved with speed and repetition. For example, once an account opening is digitized, are you able to extend it to a business’s credit line and allow your current customers to experience a similar digital interface for their loan needs?

While it is possible to do all of this by building architectural stacks or incorporating technologies such as RPA into existing processes, it is time- and risk-intensive, not to mention all the opportunity costs associated with any delay. Often, it may not even deliver the desired results just to implement AI or RPA with additional improvements over existing processes as the broad silo still continues.

Platform approaches can reduce not only kickstarts but also the long-term risks of technical debt. In addition, a digital transformation platform with low code capability, as promised, helps to realize the true potential of hyperotomation with speed and in the business enterprise-wide line.

Anurag Shah is the head of American products and solutions at Newgen Software,


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