When you want to automate parts of your business, you are bound to come across the terms ‘rule-based automation’ and ‘cognitive automation’. However, do you know the difference between them?
Cognitive automation is a type of software that brings intelligence to information-intensive processes. It is commonly associated with Artificial Intelligence (AI) and Cognitive Computing, with the assistance of Robotic Process Automation (RPA).
By leveraging Artificial Intelligence technologies, cognitive automation extends and improves the range of actions beyond those that are automated with RPA. Cognitive automation can handle semi-structured and unstructured data inputs and has the ability to “learn” to improve itself.
Semi-structured information such as invoices and unstructured data such as customer interactions can be analyzed, processed, and classified into useful data fields for the next steps of automation.
Cognitive automation creates new efficiencies and improves the quality of business at the same time. It can mimic and learn from humans’ experience through machine-learning, natural language processing (English, Chinese, Vietnamese, Indonesian), image-recognition, and predictive analysis.
For better understanding, read the three main differences between rule-based automation and cognitive automation.
Rule-based automation is a system that applies man-made rules to store, sort and manipulate data.
To work, rule-based systems need a set of facts or sources of data and a set of rules for manipulating. We sometimes refer this rule to as an ‘If-else statement’ because it has to follow instructions.
Automation software like Gleematic is an example of how your can start from rules-based automation. (Gleematic can go beyond rules-based automation, but we will get to that later.) It automates the process by breaking it down into steps. First comes new business data or events, then comes analysis: the part where the system processes data against its rules, then automation.
So, what is a rules-based system? It is a logical program that uses pre-defined rules for making subtractions and options for performing automatic actions. However, it can be very rigid as the system will not be able to adapt to changing requirements easily.
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Rule-based systems always work by rules. These rules outline the triggers and actions to be followed (or triggered). For example, the trigger might be an email with the word “invoice”. The next action might be to forward the email to the finance team.
This rule most often takes the form of an ‘if statement’ as a way of describing a trigger, which then specify the action to be completed. So, if you want to create a rule-based system capable of handling 100 different actions, you’d have to write 100 different “if-else” rules. If you want to update the system and add actions, then you need to write a new rule.
All in all, you use rules to tell the machine what to do, and it will do exactly what you tell it to do. From there, the rules-based system will carry out the action until you tell it to stop.
The system does not work on its own or make intelligent decisions. Rules-based systems will also not change or update, and will not ‘learn’ from mistakes, so make sure you input the correct steps.
So, if you have a scenario of having to classify into categories such as “job application” or “advertisement”, there is no way of writing all “if-else” rules for matching the text.
We define cognitive automation as intelligent automation that includes the help of artificial intelligence, it is possible to automate more activities.
By leveraging Artificial Intelligence technology to expand automation and enhance the range of actions typically correlated with rules-based automation, you can provide benefits which cut costs and increase customer satisfaction.
Artificial Intelligence includes three broad areas of computer-vision/ image-recognition, natural-language processing (NLP) (understanding human words), and machine learning (to predict outcomes). With a combination of the capabilities, you can build automation that is more human-like.
To recognize images, it is impossible to have the exact duplicated images with exact resolution. Thus, the machine has to be trained with multiple examples.
An example of use of NLP could be recognizing the trigger for “approval”. For example, humans can write approval in different ways, such as “ok to proceed”, or “all good to go”, or “nothing else needed”. There is no way to pre-define all the various word combinations as rules to detect that.
Machine learning can be used to make predictions in areas where there could be thousands or hundreds of thousands of scenarios. For example, if you are looking to predict the likelihood of a person resigning from a job, you may have to consider close to a hundred variables and would need to program for all possible permutations which will be very time-consuming. Machine learning speeds that up and gives you the prediction in an efficient manner.
Companies today face several challenges: increasing efficiency, improving decision-making, staying competitive, ensuring customer loyalty and compliance is just one of many obstacles that businesses face.
We have proven that cognitive automation is effective in overcoming these key challenges by supporting companies in optimizing their day-to-day activities and their entire business.
Based on our experience, we believe that a company can expect savings of over 50% on its production activities and reduction in the relevant costs.
The benefits that result from cognitive automation also include improved compliance and overall business quality, greater operational scalability, reduced turnover, and lower error rates. All of this has a positive impact on business flexibility and employee efficiency.
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Written by Elsa Ajarwati