This entry is part 1 of 4 in the series Hyperautomation

From Digital Transformation to Hyper Automation

It’s no longer good enough to just be digital or even phygital – it’s now time to hyper automate!

By VICTOR ANJOS

The world of digital transformation and generally management consulting is very vast and increasingly saturated with people promising to deliver on massive organizational change who often miss the mark. The main issue is not that these consultants are delivering bad work, it’s often that the organization itself lacks what’s necessary to continue with the momentum that these highly paid snipers command.

At the organizational level, going to a DIGITAL FIRST strategy, through to Robotic Process Automation (RPA), getting into real machine intelligence (read AI) wields a great path toward hyper automation. Savvy CIOs will tell you: Digital transformation doesn’t come in a box — or a cloud. “It’s like most tech trends that get hyped up and overused,” as it’s become more mainstream, says Genpact CEO Tiger Tyagarajan, who advises enterprises on digital strategy.

So mainstream, in fact, that 40 percent of all technology spending will go toward digital transformations, with enterprises spending in excess of $2 trillion through 2019, according to IDC. But what, exactly, is that money funding? Replatforming on new technologies is critical. Forrester Research, for instance, recommends a disruptive approach to transformation that includes everything from incremental improvements to moonshot approaches.

But what does it all mean?

"Customer expectations are far exceeding what you can really do. That means a fundamental rethinking about what we do with technology in organizations."

Digital Transformation Defined

Digital transformation marks a radical rethinking of how an organization uses technology, people and processes to fundamentally change business performance, says George Westerman, MIT principal research scientist and author of Leading Digital: Turning Technology Into Business Transformation. Digital transformation, which Westerman says should be led by the CEO, requires cross-departmental collaboration in pairing business-focused philosophies with rapid application development models.

Such sweeping changes are typically undertaken in pursuit of new business models and new revenue streams, driven by changes in customer expectations around products and services.

“Customer expectations are far exceeding what you can really do,” says Westerman. “That means a fundamental rethinking about what we do with technology in organizations.”

"Retail and consumer goods need to continually reinvent themselves to meet ever-changing customer needs."

RPA and its journey so far

“Robotic process automation” is currently one of the fastest-growing industries and likely to reach 3.11 billion by 2025. It started emerging in the 2000s, 2003 is when Blue prism, Automation Anywhere, and UI Path introduced consolidated libraries in an RPA product like offering for companies to manage their end to end bot building life cycle within a single scalable platform. RPA found its first way through reducing costs in Business Process Outsourcing in the early days and then spreading to Shared Services, IT Outsourcing, and other Business areas within 4–5 years.

Just like most other technologies in its new fancy, RPA found its sweet spot with early adopters. They started using RPA products for quickly configuring business rules to automate simple tasks and processes which involved a high volume of data handling or/and application cross overs to earn quick wins by saving FTE times. Though these early adopter enterprises were able to build up to 10 robots to show quick wins, yet a high-value business impact still felt like a dream. These enterprises experienced two key challenges: first was about scaling RPA to a human-like digital workforce setting (say 50+ autonomous bots). The second was about building intelligent processes wherein business rules weren’t straight forward or binary.

"Though these early adopter enterprises were able to build up to 10 robots to show quick wins, yet a high-value business impact still felt like a dream."

Roadblocks and Challenges

RPA products in their early releases offered speed-to-market promises by offering user-friendly and straightforward features, including intuitive Visio style workflow design GUIs, pre-built automation libraries, and drag/drop script generation capabilities. Leaders who started betting early on RPA were to put together an enterprise automation program that can encompass a holistic view of their tech stacks, build cross-functional synergies, and motivates their workforce to see robots as friends, not enemies. RPA champions committed to make RPA happen within their organization discovered that to unlock value from RPA, their sponsors needed to look into RPA technology as one of the three core pillars of a three-legged stool necessary to hold enterprise automation steady. The other two pillars being Artificial Intelligence (machine learning, including OCR, Predictive models) and existing Packaged software (CRM, ERP, BPM).
In the first wave of RPA in an industry built aspirations among organizational leaders to accelerate their ROI’s in no time and enhance their customer experiences to step up in a digital game. Soon enterprises adopting RPA started to feel challenged with not being able to scale RPA and make it live up to a daily hassle-free stable. Scalable operational settings only to discover the next zen, a newer approach to automation, which Gartner named as “Hyper Automation” in 2020.

Hyper Automation

Hyper automation, in my opinion, refers to a holistic approach that an organization needs to undertake across the enterprise value chain to digitize its business processes and optimize its return of investments using technologies, methodologies, concepts, and trends of relevance and value in the digital economy.
An organization’s ability to architect and leverage Enterprise-wide Hyper automation approach is crucial to success in this decade because digitization of processes isn’t a ‘good to have’ but rather a ‘must to thrive’ in rapidly maturing digital economy. In the digital world, organizations cannot afford to sit on a response time and need to change or respond to market or customer expectations if not real-time reasonably quickly. In such a digital agility pursuit, a continuous improvement initiative that targets both reduction of operational costs and transformation of customer experience can’t be a siloed initiative. Instead, it needs to be a core theme that governs and overlays the rest of business processes downstream to upstream, back office to middle office, and front office. The question then arises — how do an organization on its digital transformation journey build both agility and efficiency in a proportion that is determined, governed, and navigated by strategy and changing market needs? Hyper Automation is the ‘Nirvana’; a secret sauce for building such as digital enterprise and success lies not just in understanding how Hyper Automation will help but also in what it will take to implement it right.
Hyper automation can be achieved by integrating, augmenting, or overlaying automation with existing enterprise applications, including ERP, CRM, DBMS, and others, while also considering drawing intelligence either from machines, humans, or people as relevant in the hyper-automated, semi-automated or autonomous processes. One can envision it as having electric, hybrid, and autonomous cars as automotive technologies have evolved.
"By 2024, organizations will lower operational costs by 30% by combining hyper automation technologies with redesigned operational processes"

What does success look like?

Digitalization and automated processes, intelligent software applications, and smarter machines are fundamental tenets to building an enterprise hyper-automation framework. Though Hyper automation may sound smooth and manageable in theory, its execution is severe and complicated, and no wonder one can find statistics on how 50% of the Automation project fails.

May it be a ground-up or top-down initiative Hyper automation requires consideration of a set of technologies across digitization, artificial intelligence, and connectivity. Hyper automation requires envisioning a complete end to end automation wherein processes are intelligent, machines are assistants, and procedures are capable of self-optimizing. However, this may sound like a far fetched scenario companies such as Amazon.

Many others have achieved some degree of success in several business areas, including merchandise returns, order fulfillment, and procure to cash. Other examples are companies like rocket mortgage which have capitalized early-stage hyper-automation to build an on-demand pull style adjudication and risk management processes, which potentially save consumers several hours from otherwise working with cumbersome mortgage application. The legacy applications required to pull together documents and data from various systems both at consumer and enterprise end and decisions had to travel across desks and hands by the time a final approval informed to the customer. By this time, the consumer may have already decided, lost interest, or even chosen a competitor who might be just a little faster than the incumbent.

TL;DR

Accelerated process agility and optimization

Reduce capital RPA and Other Automation deployment related costs

Avoid automating broken processes and redundant silo’s efforts and thus project failures

Uncover hidden digital value of enterprises with compounding returns

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