Getting hyperautomation right
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A lot has changed in the last two years. As the pandemic threw operations across enterprises out of gear, a slew of trends, including distributed (remote) working, have thrust themselves into the limelight. However, in the broader scheme of digital transformation, hyperautomation, after making the first appearance in the pre-pandemic era as the top 2020 strategic trend by Gartner Research, continues to be a hot topic in 2022.
And it is so for a good reason.
Hyperautomation is not just about technologies but about combining them to achieve the strategic objectives as defined by the organization. Gartner has even redefined hyperautomation as “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible.” Moreover, as per Gartner, hyperautomation involves the orchestrated use of multiple technologies, tools, or platforms to achieve their goals.
That’s where it differs from other technological trends. Unlike specific technologies, such as robotic process automation (RPA), for instance, the goals for hyperautomation can vary significantly from enterprise to enterprise. The manner in which an enterprise goes about implementing hyperautomation can also diverge widely from another.
Making it work
Since hyperautomation is a broader approach, it comes with its own challenges. And most of these challenges involve establishing clarity on multiple fronts:
- Explicit identification and delineation of strategic goals
- Identification of use cases and their priorities
- Assessment of roles of various technologies
- Establishing a roadmap and an implementation methodology
These challenges are intertwined. A clear vision of the end goal helps.
Let’s take the example of a financial institution that intends to transform its account opening across products and services.
Depending on the key driving factors or the chosen objectives, the vision for the transformed process varies. These goals may be any of the following or a combination thereof:
- Increase the number of account opening applications by x%
- Reduce abandonments throughout the process by y%
- Improve the prospect and employee experience measurably
- Reduce the cycle time by m%
- Reduce the cost per closure by n%
- Launch a 100% touch-free/human-less account opening experience in p months
Having identified these goals, it is critical to establish a roadmap, which includes identifying and acquiring various technologies with good justification and defining a long-term architectural stack. After all, account opening in this case is only the starting point, and the real value of hyperautomation lies in leveraging the stack for multiple processes and applications across the enterprise with speed.
This brings us to various technological capabilities that combine to make hyperautomation powerful. It is critical to define how they come together to deliver digital account opening in this case. Here is one effective way to piece them together:
- Prospects apply for any account, for any product or service, from a device of their preference, with help from an AI-supported chatbot
- A natural language processing (NLP) engine processes all incoming requests to analyze and classify them based on prospect status (new/existing/premium), product/service, category, geography, et al., and triggers the relevant process
- Intelligent image and document processing captures all the information based on uploaded documents and kicks off a fully automated digital customer identification program (CIP) to establish id authentication/verification, security credentials, financial status and creditability
- Intelligent process automation enables the end-to-end process in real-time with straight-through processing (and flexibility to intervene or route it for exceptions, if any). It also triggers RPA bots for automated real-time execution of routine (traditionally manual) steps across the process
- At various points in the process, AI/ML-driven rules-engine and RPA automate approvals and other key decisions, including routing, that are traditionally taken by knowledge workers. This frees up their time for other value-add tasks that require human judgment, such as complex credit analysis for high-value deals
- All the relevant documents (or media) are auto-processed with content analytics and are embedded in the context of the process, with authenticated access across the cycle enabling contextual engagement with customers
- Throughout the process, prospects are kept engaged across channels of their preferences through omnichannel customer communication
- Upon final approval, the welcome kit is generated in an automated manner and delivered to the prospect digitally, while backend integration takes care of account set-up and funding, whenever applicable
- At appropriate times (at the application stage for existing customers or at closure for new prospects), AI/ML algorithm presents the cross-sell options relevant to the prospects’ preferences and profile and triggers the respective automated process if the prospect takes up the offer
Getting hyperautomation right at the enterprise scale
Through the example above, it is easy to see how hyperautomation can make a real impact by leveraging a combination of technologies. However, this is only one example. Enterprises are replete with thousands of applications and processes ranging from small supporting applications to large and deep mission-critical processes.
That’s why Gartner emphasizes on the “approach” bit. It’s not only about doing it once but achieving this over and over again, for various processes and applications, with speed.
That’s where a digital transformation platform comes in. Let’s consider the following:
- A set of key technologies form the fulcrum of hyperautomation strategy. This includes low code process automation (combining what is traditionally referred to as business process management – or BPM – with rapid development through low code capability), RPA, business rules management, case management and decision management
- Another key ingredient in hyperautomation is contextual content services that enable the end-to-end lifecycle management of all forms of content (documents and media across formats) to supply context to transactions and processes
- All applications and processes involve collaboration and communication in some form, requiring omnichannel customer engagement capability
- These technologies are further augmented by AI, machine learning (ML) and content analytics to boost speed and intelligence
- Hyperautomation is only impactful at the enterprise scale with end-to-end automation that is holistic in nature and can be achieved with speed and repeatability. For example, after the account opening is digitalized, are you able to extend it to lending line of business and let your existing customers experience a similar digital interface for their loan needs?
While it is possible to do all this by building an architectural stack or appending technologies such as RPA to existing processes, it is time- and risk-intensive, not to mention all the opportunity costs associated with any delays. A lot of times, it may not even yield the desired results to only implement AI or RPA with incremental improvement over existing processes because the broader silos still persist.
A platform approach not only provides a kickstart but also mitigates the long-term risks of technical debt. Additionally, a digital transformation platform with low code capability helps realize the true potential of hyperautomation with speed and across lines of business enterprise-wide, as promised.
Anurag Shah is head of products and solutions for Americas at Newgen Software.
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