Digital Transformation Vs Intelligent Automation
There is a bit of confusion in the market about the synergies and differences of two concepts whose adoption rate has increased considerably in the past five to ten years. These are Intelligent Automation “IA” and Digital Transformation “DT.”
This article provides my two cents for shedding some light on this topic by breaking the concepts into three areas. 1) differences between “IA” and “DT,” 2) an analogy of “DT” to a tree, 3) the rise of digital banks, and its recipe for success.
Differences between “IA” and “DT”
Intelligent automation “IA,” also known as hyper-automation, is a concept that leverages cutting-edge software-based technologies to mimic the capabilities that white-collar or knowledge worker use to perform daily activities.
As hyper-automation is a vast area. I want to share a framework created by Pascal Bornet, former E&Y and McKinsey, Ian Barkin, RPA expert, and Jochen Wirtz, in their book “intelligent automation.” Their framework turns this wide concept into a friendly and more structured way. Pascal, Ian, and Jochen broke the capabilities of intelligent automation “IA” into four pillars. Those are vision, execution, language, and think & learn. The last section of this article explains the key technologies per capability.
Digital transformation is a business art utilized by executives and innovators to create value using strategy at its core and intelligent automation “IA” technologies as a tool to meet its customer’s unmet needs.
As the picture enclosed shows, a successful digital transformation must be built on five pillars of equal importance. Those pillars are; 1) business digital strategy, 2) customer & staff engagement, 3) culture of innovation, 4) technology, and 5) data & analytics. Ionology www.ionology.com , a boutique consulting company, founded by my digital transformation mentor, Professor Niall McKeown, created this framework based on data collected in hundreds of implementations. I like to stress that companies who do not invest efforts in each of these five blocks tend to fail in their transformations.
The next section focuses on the first of these five blocks, the business digital strategy.
An analogy of “DT” to a tree
I find that the life of a tree and a company share similarities. That said, the picture at the heading of this article shows a tree half death and half alive for representation purposes.
On the one hand, let us start by describing the visible part of a tree. The visible area is made of a trunk, branches, and leaves. The branches and leaves are attached to the trunk. The trunk, or core, is an essential component of a tree architecture as having a business digital strategy is essential for every company to succeed in the digital economy.
The branches are the connections or paths, the core uses to reach the leaves. In “DT,” the branches are the tactics defined by the strategy execution delineated by management with the intent of reaching their target customers. There are times that several branches collide and merge before reaching the leaves. Coincidentally, companies often run into similar situations when using different tactics, such as running an online and offline marketing campaign to position a product or service for a specific customer segment.
A tree has multiple branches and hundreds of leaves. Still, only one trunk. The same way companies must have a unique and well-defined business digital strategy broken into multiple tactics if their intention is to reach out different customer segments.
Aside from the trunk, branches, and leaves, there is still one piece of the equation missing, sometimes is visible to the human eye, which is the resin. The resin is like the blood of the tree. It runs through the trunk and branches, bringing life to the tree. The resin is the equivalent of the structure and unstructured data generated by a company’s staff, customers, and collaborators concerning the company.
In the digital economy, companies must identify, capture, process, clean, and make sense of their data to implement data-driven decisions that deliver customer value. The stakes can not be higher as the data is the fuel of any successful digital transformation. Moreover, the amount of unstructured data such as documents, invoices, media, sensors data, chats, blogs, and emails keep growing at a stellar rate. IDC projects that 80% of the data will be unstructured by 2025. Therefore, C-level executives across industries are investing heavily in data analytics platforms.
The choices are numerous. You can go with data analytics vendors like Cloudera, Snowflake, and Teradata, who provide robust, scalable, and easy-to-connect platforms via application interface “API” or select the analytics tools from the top Cloud Service Providers “CSP” such as Microsoft, Azure, and Google.
On the other hand, let’s describe the non-visible part of a tree. Those are the soil, where the tree is planted, and its roots, which one of its main functionalities, aside from anchoring its trunk, is to suck in the water molecules.
The soil, along with the sun and water, are the resources a company needs to compete in the marketplace,which in this analogy are summarized into three: time, talent, and cash. The resources are one of the four components of the microanalysis every company must perform to determine its current market positioning in the digital economy. Being the other three; your customers, the market, and your company itself. These four components are part of my favorite digital transformation framework, known as the 7 principles of the business digital strategy described in a previous article in more detail.
Finally, the roots are equivalent to the current stage your company finds itself into and its unique value proposition. Going back to the article’s heading picture, there are times when weather conditions change so drastically that a tree becomes at risk of losing its life. A similar situation happens at a corporate level when a competitor displace its oponent, or a disruptive player takes over the market.
Does it mean such a company is doomed? Fortunately, the answer is no.
The same way a tree can be carefully plucked from the ground, transported to a richer soil with better exposure to sunlight and water, eventually bringing it back to life. A company can rethink, reassess, and rebuild its unique value proposition by having a clear strategic ambition, a deep knowledge of how the market place works and operates, a balanced amount of resources, and a clear understanding of its customer’s needs and unmet needs. Following this train of thought, the next section includes an example of how “DT” and “IA” played a key factor in reinventing the banking industry.
The rise of digital banks and its 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 numbers of digital banks. The main reason for this surge 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 like 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. These competitors are large banks with big pockets. Second, they understood the multiple pain points of these traditional banks’ clients, such as international high fees per transaction, long queues at the bank’s branches, and a mediocre customer support, amongst others. Third, they took a deep look at the resources needed to disrupt the banking industry. As discussed in the previous section, those resources are time, talent, and capital. Finally, these disruptors came up with their own strategic ambition and unique 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 did not vary much across countries or regions. The digital banks solved each of the traditional banks’ customers’ unmet needs mentioned above, launching a simple but effective Go-to-Market strategy.
At first, they eliminated the fees for purchases and withdrawals overseas, provided superior customer experience, and the need to visit your nearby branch regularly. The word spread out, and. As a result, these disruptors acquired customers at an excellent pace empowered by intelligent automation “IA”/ hyper-automation, with the banking industry’s lowest customer acquisition cost.
Initially, digital banks applied two out of the four capabilities of “IA.” Those are execution and vision. A clear example is the disrupting onboarding process of new accounts. Digital banks combined optical character recognition “OCR” plus image and video analysis for capturing the data from the new client’s national Id or passport, with robotics process automation “RPA.”
In a nutshell, the client can either take a picture or record a video of his/her identification. This info is shared with the digital bank via the app. The “OCR” software captured the data from the predesigned fields. The structured data is processed and stored in a database. After that, the “RPA” software pulls the data required to fill all the bank end processes, previously performed by several bank employees, ultimately sending the order to the logistics deparment for shipping a card to the new client.
As digital banks’ customer base grew further. They kept innovating and adopting additional hyper-automation technologies extending its portfolio of tools to the remaining two capabilities of “IA,” language, and think & learn. Regarding the former, digital banks include natural language processing “NLP,” used by Amazon Alexa or Google Assistant, embedded into their customers’ mobile app. This new functionality helps customers to perform day to day operations and provides financial assistant. Concerning the latter, digital banks use machine learning algorithms to capture insights to prevent fraud, set up credit limits, monitor accounts, and create models with the intent of launching new products and services.
I hope you enjoy reading this article. Please feel to share your comments or reach out if you want to discuss your “DT” or “IA” needs.
Chief Strategy & Digital Officer at www.digitalimpact.cloud