How Digital Transformation and Intelligent Automation Create Harmony | HCLTech

Creating harmony between digital transformation and intelligent automation

Creating harmony between digital transformation and intelligent automation
February 03, 2022

There appears to be some confusion about the synergies and differences between two concepts – with a tremendous rate of adoption in the past decade: intelligent automation (IA) and . The article sheds light on this topic, by further dividing the concept into three threads:

Harmony between Intelligent Automation (IA) and Digital Transformation (DT) can elevate the digital transformation journey for enterprises

  • Differences between IA and DT
  • An analogy for DT  
  • The rise of digital banks and their recipe for success

Differences between IA and DT

Intelligent automation (IA), also known as hyper-automation, leverages cutting-edge software-based technologies to mimic the capabilities that a white-collar or knowledge worker uses to perform daily activities.

Digital transformation is akin to a specialized art that executives and innovators create immense value with. DT has strategy at its core and utilizes intelligent automation “IA” technologies as a tool to enhance customer experience and address unmet needs.

Successful digital transformation journeys must be built on five pillars of equal importance:

  • The digital strategy of the business
  • Customer and staff engagement
  • A culture of innovation
  • Technology
  • Data and analytics

Ionology (www.ionology.com), a boutique consulting company, founded by Professor Niall McKeown, created this framework based on the data collected during hundreds of implementations. Companies that do not invest significant efforts in each of these five blocks tend to fail in their transformation journey. 

Building blocks

This approach is similar to the analogy of a tree. A tree can be carefully plucked out from the ground, transported to richer soil with better exposure to sunlight and water, eventually bringing it back to life. Similarly, a company can rethink, reassess, and rebuild its unique value proposition by having a clear ambition, a deep knowledge of the marketplace, a balanced amount of resources, and a clear understanding of its customer’s unmet needs.

The next section includes an example of how DT and IA have played a key role in reforming the banking industry.

The rise of digital banks and their recipe for success

The banking industry is a good example of how digital transformation and intelligent automation come together. The second decade of the XXI century has witnessed the emergence of digital banks, worldwide. Although, the European Union (EU) is leading the global ranking in terms of the number of digital banks. The main reason for the rapid surge of digitalization in the banking industry is related to the subprime crisis of 2008. The disenchantment of bank users grew exponentially, causing innovators to rethink the established banking business models. Consequently, new digital banks emerged mainly in the largest EU economies such as Evo Banco (Spain, 2012), N26 (Germany, 2013), and Revolut and Monzo (the U.K, 2015).

These newcomers had something in common. They flawlessly applied digital transformation and intelligent automation in unison. First, the digital banks had a clear picture of who they were competing against and their weaknesses. Their main competitors were large banks with deep pockets. Secondly, they understood the multiple pain points of the clients of traditional banks such as: High fees for international transactions, long queues at the bank’s branches, and mediocre customer experience and support, among others. Thirdly, they took a close look at the resources needed to disrupt the banking industry, which were: Time, talent, and capital. Finally, these disruptors came up with a unique strategy and value proposition that enabled them to compete in their respective markets, against the large traditional banks that invested billions of dollars in real estate, IT infrastructure, marketing, and staff.

The newcomers’ unique value proposition was quite uniform across countries or regions. The digital banks resolved the issues of the customers of traditional banks by curating a simple but effective Go-To-Market strategy. They eliminated the fees for overseas purchase and withdrawals, provided a superior customer experience, and removed the need to visit the bank branch. As a result, these disruptors acquired customers at an accelerated pace empowered by intelligent automation/hyper-automation, with the banking industry’s lowest customer acquisition cost.

Initially, the digital banks applied for two out of the four capabilities of IA. Those were execution and vision. A clear example is the disruption of the onboarding process of new accounts using advanced technologies and robotics process automation (RPA). Digital banks combined optical character recognition (OCR) along with video and image analysis for capturing data from the customer’s national Id or passport, with RPA software.

In a nutshell, the client could either take a picture or record a video of his/her identification. This information was then shared back with the digital bank via an app. The OCR software captured the data from the predesigned fields. The structured data was processed and stored in a database. The RPA software then pulsed out the data required to complete all the back-end processes - previously performed by several bank employees - ultimately sending the order to the logistics department for shipping a card to the new customer.

As the digital banks’ customer base grew further, they kept innovating and adopting additional hyper-automation technologies, extending their portfolio of tools to the remaining two capabilities of IA, language, and think and learn. Regarding the former, digital banks included natural language processing (NLP) used by Microsoft’s Cortana, Google Assistant or Amazon’s Alexa, embedded into their customers’ mobile apps. This new functionality helped customers perform day-to-day operations and provided financial assistance. With the latter, digital banks used machine learning (ML) algorithms to capture insights and prevent fraud, set up credit limits, monitor accounts, and create models with the intent of launching new products and services.

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