The more we dive into artificial intelligence (AI) and automation, the more terms we need to learn. Different AI capabilities, different ways to train a machine, and different types of automation all make for a complex glossary of terms to navigate. One new kind of automation – and a newer term for navigation – is called cognitive automation. It combines the worlds of automation, artificial intelligence, and cognitive computing.
What is cognitive automation?
Cognitive automation describes various ways to combine the power of artificial intelligence (AI) and process automation to improve business outcomes.
It is a range of approaches that improve how we automate data collection or decision-making and scale automation. It also offers a method of packaging AI and automation capabilities to capture best practices, facilitate reuse, or as an element of an AI service app store.
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Cognitive automation is based on algorithms and technological approaches such as natural language processing, text analytics, data mining, semantic technologies, and machine learning.
Cognitive automation extends and enhances a range of activities typically associated with robotic process automation, delivering cost savings and customer satisfaction benefits and additional accuracy advantages in complex business processes involving unstructured information using artificial intelligence technologies.
Advantages and challenges of cognitive automation
The main benefit of cognitive automation is that it integrates unstructured data from documents, customer interactions, voice, and machine vision into business processes. The list of other benefits:
Optimize information technology (IT) management tasks to identify issues and automate incident response.
Automate the value of existing automation by bridging the gaps between existing robotic process automation (RPA) bots, low-code applications, and interface integration tools.
Automate the decision-making process to reduce manual bias, and speed up business processes that human decision-makers may have slowed down.
Improve the customer experience through RPA bots, conversational AI chatbots, and virtual assistants.
The main difficulty lies in the fact that cognitive automation requires customization and integration specific to each enterprise. It’s less critical when cognitive automation services are only used for simple tasks, such as using OCR and machine vision to interpret text and invoice structure automatically. More complex cognitive automation, which automates decision-making processes, requires more planning, tweaking, and constant iteration to see the best results.
Examples of using cognitive automation
There are many areas in business where cognitive automation can be helpful:
Finding inconsistencies between contracts and invoices: intelligent bots with natural language processing capability can be used to detect any discrepancies between contracts and invoices. When they are found, the company will be alerted to the problem so that you can make the necessary corrections.
Banking: Public records, document scans, and customer handwriting can be used to perform required KYC checks. Cognitive automation can be helpful when dealing with trade finance transactions. The processing of international trade transactions requires paperwork and regulatory inspections, including sanctions control and proper buyer and seller apportioning.
Insurance: It can be used to serve policies using data mining and NLP algorithms to extract policy data and the impact of policy changes to make automated decisions about policy variations.
Most businesses are just starting to work with cognitive automation technologies and have not fully realized their potential. A cognitive automation solution may be just what you need to revitalize resources and take operational productivity to the next level.