The Growing Role Of AI And Machine Learning In Hyperautomation

15Jun - by aiuniverse - 0 - In Machine Learning

Source – https://www.forbes.com/

Gary Fowler is a serial AI entrepreneur with 17 startups and an IPO. He is CEO and co-founder of GSDVS.com and Yva.ai.

The long-standing question of whether technology will replace humans is a very popular topic for discussion.

And yet, the resolution to this debate is not that simple or black and white; there are a lot of factors playing into the relationship between technological process and human engagement.

Among the biggest trends in technological advancement that have been raising such questions are automation and hyperautomation. The two are different yet interrelated. And while the goal of each is, ultimately, improving and standardizing processes in the most efficient way possible, they represent two types of approaches to deploying machines to streamline processes.

Before explaining the relationship between automation and hyperautomation, let’s explore the meaning of hyperautomation first.

Hyperautomation

In its annual report on Top 10 Strategic Technology Trends for 2020, Gartner named hyperautomation the first trend that would transform the world. Accordion to UIPath, hyperautomation is the process of applying advanced technologies, such as artificial intelligence, machine learning and robotic process automation, to automate and templatize tasks that used to be humans’ responsibility. The integration of such advanced tech tools and technologies naturally amplifies our ability to automate work. 

Hyperautomation doesn’t stop there, though; it also includes the level and sophistication of automation. This process begins with robotic process automation (RPA) at the very base — which is the core of automation at its simplest — and broadens the horizons of automation through AI and machine learning.

In other words, hyperautomation builds on automation and broadens the meaning, goals and capabilities of automation, turning it into an ever-improving, AI-driven process that feeds on data. This leads to more accurate, faster and more efficient results.

The Difference

Going back to the question about the difference between automation and hyperautomation, the answer is very simple: at the core of hyperautomation is automation. But hyperautomation makes the end-to-end automation process more sophisticated, smarter and driven by AI-powered robotics. It becomes not just about the execution of tasks, but the optimization of the best ways to complete them.

The enhanced intelligence aspect of hyperautomation comes in many shapes and forms that seem very natural and ubiquitous nowadays. It can be an NLP algorithm that understands speech and writing and allows it to interpret communication. It can be the process of transforming images into text through optical character recognition — or OCR. Or it can be a machine learning algorithm that continuously analyzes data and identifies patterns to make more accurate predictions. At the end of the day, all these types of advanced technologies join forces to significantly increase the scope of automation possibilities.

Will Automation Replace Humans?

Going back to the highly debated question of whether automation will eventually replace humans, the answer is the following: the mission of automation has never been to replace humans. In fact, the goal of this trend is the exact opposite: it is to enable humans to fulfill their potential by focusing on high-involvement tasks and augment human capabilities to produce high-quality and highly specialized work. Automation ensures that mundane, repetitive tasks are relegated to machines to cut costs and increase productivity, all while continuously ensuring a superior quality of work in terms of the output that humans create when they get the opportunity to focus their efforts on high-risk tasks.

The same applies to hyperautomation — but in this case, the goal of technology is to augment human capabilities by working side-by-side with them to deliver maximum efficiency. With hyperautomation, the answer to the debatable question becomes more ambiguous and requires more analysis and thought. While automation might be a natural fit for a company from the operational perspective and is far less complicated to implement with human employees, hyperautomation brings the question of whether the business is ready to adopt a smart, AI-driven automation process that will operate in assistance to — and on par with — the human counterparts. It will become increasingly important to truly understand where hyperautomation fits in the larger business and how ready the employees are to have it operate with them synergistically.

What is also important is the transition from automation to hyperautomation through the introduction of AI and a higher level of robotic intelligence. It is key to establishing a network of multiple isolated instances of automation that will work together to continuously smooth out and streamline processes across multiple tasks and devices at the same time. To achieve this, it’s crucial to first assess the business’s capabilities and level of digitization before starting to build this network of AI-driven automation across multiple technologies.

At the core of hyperautomation is the right combination of a variety of tools connected by superior AI intelligence. For any business, it’s a good idea to identify a number of initial tools to invest in to start building the hyperautomated processes internally. As a rule of thumb, these tools need to belong to a single system and communicate with each other with ease to ensure maximum efficiency and smooth operations from day one.

But what perhaps matters more is understanding where the employees of a business stand with automation and how they view this introduction of new AI-powered processes. Before taking the leap to start hyperautomating, it’s crucial to ensure a welcoming environment internally and educate employees of all skills and backgrounds about how these new technologies will augment their abilities and introduce more weight to the work they do day to day. 

One way or another, AI-driven hyperautomation is not in the distant future anymore. It’s here and now, and the best way to embrace it is to kick off internal assessments and begin the adoption process one step at a time.

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