Intelligent automation (IA), or hyper-automation, is a set of technologies that allow white-collar knowledge work processes to be automated across a variety of industries, such as health, banking, engineering, law, and retail. It aims to achieve business outcomes through automated processes with minimal human intervention, augmenting knowledge workers and freeing them up for more creative and relational tasks.
Intelligent automation increases efficiency (speed, cost-effectiveness, and process resilience) and effectiveness (quality, compliance, and ultimately customer and employee satisfaction). It is reliable and available 24/7, it’s scalable and universal to almost any industry and business function, and it’s accessible and user-friendly.
While artificial intelligence has become a buzzword used in many contexts, intelligent automation is very pragmatic and practical. As brilliantly put by Dr. Mary Lacity, professor at the University of Arkansas, “While AI is ensconced with Hollywood-levels of fear and hype, IA is a realistic Wall Street-to-MainStreet business strategy supported by a collection of tools to redesign knowledge work”.
Intelligent automation is not just another term for artificial intelligence (AI), although the two concepts do overlap.
It's a challenge to differentiate between IA, artificial intelligence (AI), robotics, and other business process management (BPM) platforms, as the boundaries between them are blurred and continually evolving.
However, based on my own experience and my survey of over 200 other IA experts, I offer the following framework.
Robotics is the automation of simple, specific, predictable, repetitive tasks, and includes both physical robots and software robots.
BPM platforms allow business processes to be streamlined, analyzed, optimized and automated. They are usually tools operated directly by human workers.
Artificial intelligence uses technologies such as machine learning and deep learning to reproduce human behavior and intelligence. It is more complex than a simple algorithm or set of instructions: it can learn from its environment or from its own past behavior. It can make decisions, but does not inherently have the ability to execute tasks based on them.
Robotics, BPM and AI all overlap with each other, and intelligent automation sits at the intersection between them.
Intelligent automation includes software robots, but not physical robots; industrial artificial intelligence, but not AI used in gaming or the arts; and BPM platforms that demonstrate some form of intelligence, but not those that lack the ability to support end-to-end processes. IA can act on top of existing BPM systems, not replacing the tools but leveraging AI to interact intelligently with them as a human knowledge worker would.
Intelligent automation is being rapidly adopted and will dramatically change the knowledge-work landscape.
In 2017, a survey by Avanade found that 86% of global business leaders believed they needed to deploy intelligent automation in the next five years to stay ahead in their industries—and those five years are nearly up. Another survey by Gartner found that 42% of CEOs had already begun theirIA transformation journeys, and 56% of those were already realizing gains from the transformation. Finally, a 2019 Deloitte survey reported that IA had an adoption rate of over 50%, and predicted that it would increase to 70% by the end of this year.
Meanwhile, IA capabilities are developing extremely quickly, and the IA and AI industry is experiencing explosive growth, with companies such as UiPath growing from a 40-person startup to a $7billion company in just five years, and the number of AI startups increasing 14-fold in the last decade.
According to my research, the potential scope of the impact of IA represents 84% of the US workforce: those employed in trade, transportation, finance, business, engineering, education, health, leisure, and government.
The adoption of intelligent automation has been compared to the Industrial Revolution in agriculture and manufacturing. However, while the Industrial Revolution took place gradually over a couple of centuries, the digital nature of IA means that the technology can advance much faster, unencumbered by the limitations of having to manufacture and ship large physical machines. This means it could make similarly dramatic changes to work and society in a much shorter timeframe.
Like tractors did for farmers, IA tools will significantly increase the productivity of knowledge workers, and so will reduce the number of workers needed to achieve the same or greater output. This will be disruptive for employment in the short term, but beneficial for the whole of society in the long term.
Agricultural automation reduced the number of farmers needed to feed America from nearly 100% of the population to only 3%, increasing total output enough to practically eliminate hunger and malnutrition, and freeing the other 97% to pursue other occupations, so that our society can enjoy modern medicine, universal education to age 18, household labor-saving devices, the internet, a vast array of leisure activities, and soon.
Imagine what a similar revolution in knowledge work could release us to achieve.