Manish Rai

Anyone who has been following the Robotic Process Automation (RPA) revolution that is transforming enterprises worldwide has also been hearing about how artificial intelligence (AI) can augment traditional RPA tools to do more than just RPA alone can achieve.

You might even have noticed that some RPA software vendors — Automation Anywhere is one of them — are attempting to be more precise with their language. Rather than call our intelligent software robot (bot) product an AI-based solution, we say it is built around cognitive computing theories.

But is that any clearer? What is cognitive automation? Confusion abounds. Let’s try and dispel some of it.

Deloitte defines cognitive automation as a subset of AI technologies that mimic human behavior: “RPA together with cognitive technologies such as speech recognition and natural language processing automate perceptual and judgment-based tasks once reserved for humans.”

IBM takes that definition and adds to it, defining cognitive computing as differing from AI in how it is used: “In an artificial intelligence system, the system tells a doctor which course of action to take based on its analysis. In cognitive computing, the system provides information to help the doctor decide.”

Combine these two definitions together, you see that cognitive automation is a subset of artificial intelligence — using specific AI techniques that mimic that way the human brain works — to assist humans in making decisions, completing tasks, or meeting goals.

Cognitive automation: AI techniques applied to automating specific business processes

Unlike other types of AI, such as machine learning, or deep learning, cognitive automation solutions imitate the way humans think. This means using technologies such as natural language processing, image processing, pattern recognition, and — most importantly — contextual analyses to make more intuitive leaps, perceptions, and judgments.

Cognitive automation is gaining steam. According to IDC, in 2017, the largest area of AI spending was cognitive applications. This includes applications that automate processes which automatically learn, discover, and make recommendations or predictions. Overall, cognitive software platforms will see investments of nearly $2.5 billion this year. Spending on cognitive-related IT and business services will be more than $3.5 billion, and will enjoy a five-year CAGR of nearly 70%.

Also, according to IDC, the cognitive applications that will see the most traction in the coming year are quality management investigation and recommendation systems; diagnosis and treatment systems; automated customer service agents; automated threat intelligence and prevention systems; and fraud analysis and investigation. These five areas will capture nearly 50% of all cognitive spending.

Another way to think about cognitive automation is that it learns at least in part by association. It takes unstructured data and uses that to build relationships and create indices, tags, annotations, and other metadata. It tries to find similarities between items pertaining to specific business processes — invoices, purchase order numbers, shipping addresses, assets, liabilities, etc. Some of the questions that it uses to build these relationships include:

• Have I seen this before?
• What was done in the similar instance?
• Is it connected to something I have seen before?
• What is the strength of that connection?
• Who/what is involved?

There are a number of advantages to cognitive automation over other types of AI. Among them are the facts that cognitive automation solutions are pretrained to automate specific business processes and hence need less data before they can make impact; they don’t require help from data scientists and/or IT to build elaborate models. They are designed to be used by business users and be operational in just a few weeks.

The coolest thing is that as new data is added to a cognitive system, the system can make more and more connections. This allows cognitive automation systems to keep learning unsupervised, and constantly adjusting to the new information they are being fed.

What is Cognitive Automation?

Cognitive automation is based on software bringing intelligence to information-intensive processes. It is commonly associated with Robotic Process Automation (RPA) as the conjunction between Artificial Intelligence (AI) and Cognitive Computing. By leveraging Artificial Intelligence technologies, cognitive automation extends and improves the range of actions that are typically correlated with RPA, providing advantages for cost savings and customer satisfaction as well as more benefits in terms of accuracy in complex business processes that involve the use of unstructured information.

What is Cognitive automation and what it is not

Cognitive automation is not machine learning. Cognitive automation leverages different algorithms and technology approaches such as natural language processing, text analytics and data mining, semantic technology and machine learning.

What does cognitive automation mean for the enterprise?

The integration of different AI features with RPA helps organizations extend automation to more processes, making the most of not only structured data, but especially the growing volumes of unstructured information. Unstructured information such as customer interactions can be easily analyzed, processed and structured into data useful for the next steps of the process, such as predictive analytics, for example.

Cognitive automation creates new efficiencies and improves the quality of business at the same time. As organizations in every industry are putting cognitive automation at the core of their digital and business transformation strategies, there has been an increasing interest in even more advanced capabilities and smart tools.

Enterprise Challenges and Cognitive Automation Benefits

Today’s enterprise faces a number of challenges: increasing efficiency, enhancing decision making, staying competitive, ensuring customer loyalty, compliance are just some of the hurdles that businesses are facing.

Cognitive automation has proven to be effective in addressing those key challenges by supporting companies in optimizing their day-to-day activities as well as their entire business.

Based on our experience, we believe that companies can expect more than 50% in savings for FTE activities and relevant cost reductions (from 30% to 60% for email management, quote processing, etc.)

Advantages resulting from cognitive automation also include improvement in compliance and overall business quality, greater operational scalability, reduced turnaround, and lower error rates. All of these have a positive impact on business flexibility and employee efficiency.

Applying cognitive technologies to content is not an all-or-nothing thing. Each increased level of content intelligence providers greater value to the organization, but also requires increasingly more advanced technology to realize those goals. In this infographic from Cognilytica we explore the four levels of cognitive automation.