The rise of hyper-automation, and its implications
Hyper-automation is the smart packaging of automation tools with embedded cognitive abilities and intelligence. Will it replace humans in jobs?
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One of the hottest trends in enterprise software over the past few years has been Robotic Process Automation (RPA). In layperson’s terms, RPA is simply the ability of a computer program to carry out repetitious tasks following a set of rules. RPA’s market size was already north of US$ 1 billion in 2019, and is projected to grow at 33 per cent compound annual growth rate (CAGR) until at least 2027. The ease of adoption for RPA and the ubiquity of issues which RPA can solve have correspondingly led to sizeable investments in the nascent sector. While the actual investment figures for 2019 are still to be definitively stated, the year 2018 witnessed investments over US$ 2 billion.
The RPA adoption has been swift and is projected to be at near-universal adoption over the next five years, according to Deloitte. However, scaling technology has proven to be evasive thus far. One of the principal reasons for that is the reliance on human judgment and cognition. This is not only for arcane decision-making, but also for the discernment of interplay between disparate enterprise applications.
With the rapid advancement in Artificial Intelligence (AI) technologies like Speech Analysis, Natural Language Processing, Image Recognition and Machine Learning, etc., it is now possible to reliably allow the software to enable both — decision-making, as well as task execution. Gartner actually coined the term “hyper-automation” to describe this marriage of RPA and AI. It has predicted hyper-automation as one of the top 10 technology trends for 2020. Simply put, hyper-automation refers to smart packaging of automation tools with embedded cognitive abilities and intelligence.
The adoption of Robotic Process Automation has been swift and it is projected to be at near-universal adoption over the next five years. (Representational Image: Reuters)
Although the usage of hyper-automation is a relatively new term, the notion of Intelligent Automation (IA) has been around for a while now. Deloitte had described it as the new era of innovation. Nevertheless, its phenomenal growth and adoption is a recent phenomenon. By 2024, Gartner projects a 30 per cent or more lowering of operational costs by organisations using hyper-automation technologies. A similar assessment was made by Forrester Research in their recent report — Automation Predictions 2020. This trend is likely to grow more expeditiously than predicted earlier, because of the current Covid-19 induced crisis, primarily due to the confluence of mutually reinforcing nature of two factors, scalable technology (hyper-automation) solutions and the dire need for organisations to aggressively cut costs.
Some of the sectors most likely to see a disruptive impact of such technologies are healthcare, insurance, travel and tourism, and arguably the largest single employer in most countries — the government. All these sectors have a preponderance of disparate legacy systems, myriad intermediate players and processes and some form of an intelligent cognitive input required in decision-making and delivery, making these sectors quite attractive for hyper-automation.
This poses a very interesting dilemma. On the one hand, the speed of delivery for goods and services will be drastically improved with consistently reliable results by the adoption of such technologies. However, on the other hand, the disintermediation of humans in routine tasks and non-critical decisions intelligence will have a real and sustainable negative impact on employment numbers, given that these sectors collectively represent more than 35 per cent share of the total workforce. Some jobs, and perhaps some supporting functions, will simply cease to exist.
It is therefore important that organisations prepare for this new reality by focusing on two broad areas. First, upskilling their employee base so that they can be engaged in higher-value delivery functions. Second, invest in a structured innovation program. Adopting some version of 3M’s 15 per cent rule might be a good starting point.