Art of Automation (eBook)

Chapter 1
Introduction

Many companies are declaring this year as “The Year of Automation,” seeing automation as an essential part of how we address our new normal.

Chapter Author: Jerry Cuomo

Covered in this chapter

• Automation definition
• Process: Discover, decide, act, and optimize
• Examples of business and IT automations
• Debunking automation myths
• IBM Automation overview

Here we go…

This introductory chapter to the Art of Automation provides the fuel that powers the chapters to come. Starting by establishing AI-powered automation as a key aspect to the new normal emerging from the worldwide pandemic, you will learn about the process of automation and how it is structured around a feedback loop involving phases of discover, decide, act, and optimize. Applying artificial intelligence (AI) during each of these phases propels automation to go beyond what was doable before, including understanding opportunities to predict versus react and see actionable patterns in noisy unstructured data.

This chapter outlines the styles of tasks that can be automated across an enterprise while also clearing the air by debunking popular myths about AI and automation that readers need not be burdened with while reading the book.

“The Year of Automation”

Many companies are declaring this year as “The Year of Automation,” seeing automation as an essential part of how we address our new normal.6 The COVID-19 pandemic has changed the dynamics of business and how we work.

For example, YouTube recently said in a blog post that with fewer people in its offices around the world, automation software is doing more of their video content moderation. “We have started relying more on technology to help with some of the work normally done by [content] reviewers,” the company said. “This means automated systems will start [curating] content without human review.”

The ideas around automation are simple, are “tried and true,” and have been applied to business since the very beginning of the industrial era. So, why do we feel the time is right now for automation?

Businesses today are all-in on being digital. The pandemic has accelerated digital transformation, with digital means to do just about everything from ordering a pizza to telemedicine to our Zoom-enabled workplaces—all digital-powered. As every aspect of a business becomes digital, the doors open for automation. In a sense, businesses, like cars, have become computers that can be programmed, and automation is the software that can propel a business to have “autopilot” or “auto-assist” modes. Digital-powered then sets the table for AI-powered.

Today, we have technology that can greatly change the “state of the art” of automation, with artificial intelligence (AI), machine learning, computer vision, and natural language processing. AI has given birth to new ways to automate things that were difficult to automate before. AI, in the form of machine learning models, applied to IT automation with AIOps can predict the risk associated with making a change to your application, avoiding costly outages. Similarly, on the business side, natural language understanding with robotic process automation can put automation in the hands of every business user to automate time-consuming and error-prone data entry tasks.

So, the Year of Automation is ushered in by a pandemic-accelerated, digital-powered revolution and is being paired with AI-powered technology, setting a big stage and placing intelligent automation at its very center. As such, many industry analysts have declared automation—or extreme automation, hyper-automation, etc.—as the most critical technology trend to act on now.7

Art of Automation Fundamentals

Automation as an “art form” for business could be simply viewed as a two-step dance. As discussed in the previous section, our new normal is driving client demand to automate processes that eliminate repetitive and monotonous tasks. This is the first step in the dance and enables the second step, which is to augment humans to produce super-human results more rapidly.

Here is a closer look at these two simple yet fundamental aspects of the Art of Automation:

Task elimination targets simple, repetitive tasks across business and IT. Automating these tasks will free employees up to do more thoughtful work. For example, with closed offices keeping many of its workers away, PayPal has turned to chatbots, using them for a record 65% of message-based customer inquiries in recent weeks. “The resources we are able to deploy through AI are allowing us to be more flexible with our staff and prioritize their safety and well-being,” PayPal said in a statement. 8

Task augmentation supports, speeds up, and increases employee efficiency. For example, with the increased use of online services during the coronavirus pandemic, AI-powered customer service agents can allow a single agent to help more users, decrease service queues, and increase customer advocacy. AI is used to gauge user intent and capture information and the nature of the problem the customer is asking the company to solve. An automation workflow can then examine possible resolutions with, or possibly without, engaging a human. That being said, the most powerful form of task augmentation is when humans and AI systems work hand in hand in achieving the desired outcome.

It is tempting to some businesses to stop after the first step and declare victory because of cost savings associated with task elimination. However, the true Art of Automation crescendos with augmenting humans with automation software. On Episode 3 of The Art of Automation podcast, Claus Jensen, chief digital officer at Memorial Sloan Kettering Cancer Center, commented that automation software won’t replace doctors. Instead, it’s really the combination of artificial intelligence and human doctors which, he stated, “outperforms either in isolation.” Claus further exemplified his comment with how doctors are using AI and computer imaging to enhance X-ray photos, highlighting anomalous structures, which then allows a doctor to discern and make a diagnosis, which would not have happened if the doctor used his eyes alone. A great example of the Art of Automation in action!

Autonomous vehicles and the process of automation

Automation has graduated to hyper-automation. We see it on the streets, with self-driving autonomous vehicles. This is truly a marvel of modern ingenuity. Leveraging the best of artificial intelligence, internet of things, and cloud computing, the automotive industry has set the “bar high” with this accomplishment.

Test driving a Tesla Model 3 with its autopilot features is a fun way to witness the process of automation at work while gaining an appreciation for how Tesla has taken automation to an extreme with advanced technology. To keep with the autopilot theme, we will use the following figure as a guide to examine the process of automation with an eye towards how we might apply this same process to create an autonomous business.

Chapter 1 - Figure 1. Autonomous vehicle automation process

Think about the “wow” moment when the car changes lanes by itself. This outcome is achieved through a process described as a set of tasks. This process of automation involves discovering data from sensors, including radar. As data is discovered, it is analyzed and correlated, such that another car might be recognized ahead. Then decisions are made. Is the car ahead too close? Based on a tolerance threshold, a proactive decision to trigger an action to slow down or switch lanes occurs. The discovery, decisions, and actions occur simultaneously and are continually optimized against past and present data to predict future actions, forming an AI-fueled closed-loop automation system.

While there are dozens of variations of closed-loop automation (remember MAPE loops?9), we prefer to use this simple 3-plus-1 methodology described in the above example: Discover, Decide, Act, and Optimize. 

Discover involves collecting, organizing, and classifying structured and unstructured data that flow through your enterprise. Structured data comprises clearly defined data types whose pattern makes them easily searchable. Unstructured data—“everything else”—comprises data that is usually not as easily searchable, including formats like audio, video, and social media postings.10

AI is used to understand relationships and correlation, derive deep insights, find gaps, and establish baseline Key Performance...