There has been a lot of talk about how RPA (robotic process automation) can be a major game-changer for businesses and these discussions have revolved around the concept of cognitive automation as well. Here’s learning more about these concepts in brief.
What is cognitive automation? How is it used with RPA?
Robotics and automation go hand in hand and the same may also be true for cognitive automation. Cognitive automation is usually defined and characterized as a subsidiary form of artificial intelligence (AI) based technologies which imitate the behavior of human beings. RPA in fusion with cognitive technologies like NLP (Natural Language Processing), speech recognition and others, enable automated processing of tasks based on judgment and perception, that were once kept aside for humans.
This is the definition given by Deloitte and it is right on mark. IBM has added its own two cents to this definition, stating that cognitive computing is basically a system which provides information for helping a human being decide on something. Now, we need to delve a little into cognitive RPA or robotic cognitive automation as it is often called. This is a term encapsulating RPA systems and solutions which tap AI based technologies like text analytics, OCR (optical character recognition) and machine learning for improving workforce and customer experiences alike.
Some of the processes include learning, reasoning and self-correction. Cognitive robotic process automation enables companies to comprehensively automate processes covering unstructured sources of data (scanned emails, documents, voice recordings, letters, etc) and also automate tasks which are more complex and not really based much on rules. Cognitive RPA can tackle exceptions without the need for intervention by human beings. It enables data improvements via text analytics and NLP along with enabling automated decision making.
Give some overview about RPA & AI
RPA (robotic process automation) revolves around automating business processes via robots for lowering intervention by humans. Robots can capture data and interpret applications of the same for transaction processing, manipulation, response triggering and even communication with other systems. Artificial intelligence (AI), on the other hand, equates to a task performed by a machine or program which mimics the same process that a human being would have employed for executing the same. This is a broader encapsulation of the whole concept.
Artificial intelligence (AI) based systems mostly showcase some of the behavioral patterns associated with the intelligence of humans, i.e. learning, planning, solving problems, reasoning, representation of knowledge, motion, perception and manipulation along with creativity or social intelligence to a lesser extent in most cases.
How can we embed AI into RPA?
A process followed by any enterprise will usually have a sequence like the following:
Data -> Judgment -> Action.
Most companies are tapping AI for leveraging available data through the addition of predictions as a sequence step. This creates the following structure then:
Data is the key aspect to be noted here. Prediction is the first enterprise automation step that successfully embeds AI into RPA. This means securing crucial data and converting the same into vital insights or information for processing. Enterprise data is usually classified into structured, unstructured and conversational information.
Here are a few key points worth noting:
- Structured information- The data can be neatly fitted into the SQL database for working with algorithms. Automating downstream processes will ensure an improved success rate.
- Unstructured Information- This data is like the language we speak, being tough to delineate through algorithms. This includes unstructured images, videos, audio, etc. which have their own challenges for extraction. Image conversion is one modern process via ICR/OCR technologies along with NLP (natural language processing) technologies which tackle interpretation of text that is unstructured.
- Judgment- This is another step in the process. This includes fusing information with trends and deciding on the next action to be taken. This step covers both trend based and rule based judgments.
- Final Action- The last stage of the process has actions being taken on the basis of the outcomes from the earlier steps. Actions may be automatic, i.e. processing and transfer of data or communication via emails or it may be conversational in nature as well.
Give some related AI & RPA information
Companies have to understand that they have to invest in other technologies as well including NLP (natural language processing) and also language generation since language is a key factor in the process. Business applicability in future years will be based lesser on form and will be based more heavily on interactions. This trend has already been seen and 20% of searches are now being done on the basis of voice based instructions/interactions.
As a result, cognitive technologies that fuse AI (artificial intelligence) and RPA (robotic process automation) for mimicking human behavior and processing exceptions without human intervention, will be playing a vital role in future years.
Game-changing technologies need early investments
More and more companies are waking up to the ocean of possibilities created by technologies like RPA, AI and cognitive RPA among others. However, futuristic adoption of these technologies requires early investments and patience in the same.
If cost savings, higher revenues and better decision making can be achieved seamlessly, most organizations will certainly look forward to adopting these game-changing technologies.