Pascal Bornet is a recognized expert and pioneer in the field of intelligent automation (IA). He founded and led the IA practices for Mckinsey & Company and Ernst & Young (EY), where he drove hundreds of IA transformations across industries. He is a member of the Forbes Technology Council and he was awarded Global Top Voice in Technology 2019.
What is Intelligent Automation (IA)? How do you define it?
It is one of the most recent and impactful trends in the broad field of artificial intelligence. In our new book “Intelligent Automation”, my co-authors Ian Barkin and Jochen Wirtz have defined Intelligent Automation (also called Hyperautomation or Cognitive Automation) as combination of methods and technologies, involving people, organizations, machine learning, low-code platforms, robotic process automation (RPA), and more. It is aimed at automating end-to-end business processes in a computerized environment. It delivers business outcomes on behalf of the employees. It works hands in hands with them to deliver faster, better and cheaper services, improving significantly employee and customer experiences.
For example, IA supports the automation of most work activities in “Procure to Pay”: from the selection of vendors (using machine learning), sending of orders (leveraging workflow platforms), reception and processing of their invoices (with natural language processing), up to the payment of these vendors (with RPA).
How is Intelligent Automation related to Industry 4.0?
It is in the middle of it. While I4.0 involves all types of automation, both material and information based, Intelligent Automation focuses only on the information based one, which we also call the automation of knowledge work.
IA focuses on automating the work done by knowledge workers, whose principal capital is knowledge. Examples include programmers, physicians, pharmacists, architects, engineers, scientists, designers, public accountants, lawyers, and any other workers whose line of work requires them to “think for a living.” As opposed to manual labor, which is material-based (common in manufacturing industries), knowledge work is information-based and commonly found in service industries. Simply put, IA is the “white-collar” version of the “blue-collar” industrial automation, which started in the 19th century.
We demonstrate in the book that, today, knowledge work represents more than 80% of the global workforce. Hence we expect the impact of IA on our world to be as significant as the one of the previous industrial revolutions.
In which industries and fields has IA been used so far? And what have been the impacts?
Key business impacts include the improvement of business efficiency by 20 to 60%, the increase of client satisfaction by 50%, while improving significantly employee’s experience by automating or avoiding about 60% of the mundane tasks.
In terms of applicability, it is universal. IA provides strong benefits across functions and across industries. To demonstrate this, in the book, we have built the largest publicly available library of 500+ IA use case. It covers 8 functions and 5 industries.
While service industries (i.e. banking, insurance, telecommunications) have been leading in the adoption of IA, all industries are concerned, as all companies have at least support functions which can benefit a lot from IA (e.g. finance, HR, procurement).
In what stage of Intelligent Automation’s adoption companies currently are?
86% of global business leaders recently surveyed believe that to stay ahead in their given domains, their organizations must deploy IA in the next five years. Because of its unique characteristics, we estimate that IA will be able to reach a level of adoption and sophistication in the next five years which took industrial automation over 200 years. According to a Deloitte survey, IA already has an adoption rate of over 50%. This rate is expected to increase to more than 70% in the next two years. If this continues, IA will have achieved near-universal adoption within the next five years.
What are the most challenging things when adopting Intelligent Automation?
SCALING! It is easy to succeed at implementing a proof of concept or a pilot. But when it is about scaling the transformation within an organization across several departments, functions or divisions, it becomes very hard. While 50% have started their journey in IA (according to Deloitte), only 15% have been able to implement it in more than 3 functions or divisions (according to McKinsey)
How can companies succeed in scaling IA?
According to our experience and research, companies which were successful had all implemented 5 initiatives:
To start with, they have implemented two fundamentals:
- Always put people in the center of an IA transformation: IA is built by people, for people. Without people there is no IA. Without IA there are still people.
- Start with a strong and healthy foundation: management support and sponsorship, capability building and change management
Then, they have implemented the below initiatives:
- Thirdly, combine the IA capabilities to create synergies, and be able to automate complex end to end processes
- Democratize IA by using technologies, such as low code platforms, that require limited skills to build IA applications. They make IA accessible to most business users. As a result, they accelerate the speed of the transformation, drive higher ownership and acceptance of IA, and allow the shift of the company culture to more digitalization and automation.
- Accelerate IA implementations by leveraging technology. For example, process discovery and process mining, data discovery, AutoML, and automated maintenance.
How have IA transformations been impacted by COVID-19?
Intelligent automation used to be a factor of competitiveness, where the companies which had implemented it were able to gain market share by selling cheaper and better products.
With the current COVID 19 crisis, IA has become a factor of survival. Indeed, companies which are not digitalized and automated enough can’t survive in our new world. Companies that can’t sell products and services online, collect the cash online, motivate their employees remotely, and manage their operations remotely with minimal human intervention are only alive thanks to government subsidies.
Despite the devastating aspects of Covid-19, the pandemics has helped the world understand the importance of digitalizing processes enabling remote performance, and automating them to rely less on the human workforce and improve them.
How does IA improve employee’s experience?
According to Gallup research, 85% of employees worldwide are not fulfilled by their work, because it is too manual, repetitive, and tedious. IA solves a large part of this issue by freeing up employees from repetitive and transactional tasks (e.g., keying in invoices in an accounting software). And it refocuses them on more value-added and exciting tasks (e.g., the ones involving insights, creativity). It also augments them, transforming them into superhumans able to generate insights from millions of data in a few seconds (e.g., identifying a tumor on an X-Ray in a few seconds). According to our research presented in the book, 30% of the current scope of work tasks can be augmented, while 70% can be either automated or eliminated (e.g. unproductive meetings or emails).
Can IA improve customer experience as well?
Building trust, satisfying and retaining customers is critical for businesses. 96% of unhappy customers don’t bother complaining, and 91% of them will simply leave and never return. IA helps to create innovative and customized products, and highly responsive, omnichannel customer services available 24/7. Based on my experience with IA, companies can increase the level of their customer satisfaction by over 50%, while reducing the contact center workload by over 50%.
You say in your book that IA can save lives. How?
IA has the potential to save millions of lives every year by supporting clinical trials and disease diagnosis, and avoiding medical errors. In developing countries, it can help reduce deaths from preventable causes (e.g., 1.6 million people died from diseases related to diarrhea in 2017) and compensate for the shortage of 4.3 million physicians globally, by enabling remote diagnosis. For example, IA application Tissue Analytics instantly diagnoses chronic wounds, burns, or skin conditions just by taking a photo from a smartphone.
How can IA save money and how much?
In addition, IA could have the potential to realize a $10 trillion of cost savings yearly, by reducing frauds, errors, and accidents. Indeed, IA not only makes transaction processes more efficient and reliable, but it also generates log files for every action, creating transparency and ease of compliance. Such a vast amount of money would allow us to double our global budget for education, help restore our planet from pollution, or even eliminate hunger!
You have just published a new book you co-authored called Intelligent Automation, why did you write that book?
First, it is about sharing a passion, a conviction that we can make our world more human with intelligent automation. Then it was a necessity. A need to inform and educate. Finally, while many books have been published in the fields of AI, machine learning, or robotics, a comprehensive reference guidebook had never yet been written on the topic of IA. Hence, it seemed critical to us to objectively document and build the latest insights on this field in a first reference document.