Cognitive automation


Using artificial intelligence to better automate critical processes that use unstructured and semi-structured data

An incremental approach in the pursuit of intelligent automation

According to IDC, 80% of worldwide data will be unstructured by 2025. While data has been regarded as the new oil, using unstructured data for business purposes poses challenges.

Robotic process automation (RPA) has helped companies in automating simple, straightforward, rule-based, structured processes targeting low-hanging fruit. However, a more intelligent, incremental approach is required to target processes with unstructured or semi-structured data.

Cognitive automation is the process of identifying and structuring data to enable intelligent automation for the business. Cognitive automation brings much needed intelligence into rule-based RPA. Here, machine learning (ML), natural language processing (NLP), and other tools may be used to make sense of the data. It augments RPA with AI capabilities to tackle more challenging processes.

HGS cognitive agents or digital workforce can perform the following functions:

  • Identify unstructured and semi-structured data
  • Classify and index the data
  • Interpret or contextualize the data
  • Enable the subsequent tasks for automation

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Types of cognitive automation

ML-led image content processing

This type of content processing lets you extract data from images or scanned documents. This could be applied to invoice processing, onboarding, enrolment, check processing, etc. Here the bot extracts the information from unstructured sources and presents the data in a structured format for processing.

ML-led NLP based automation

By interpreting text-heavy rules to translate observations from unstructured data into process-ready content, you can create seamless interactions between humans and technology. This can be applied across processes like request intake, financial anomaly detection, claims processing, and more.

Cognitive agents

Using ML and NLP, you can build a virtual workforce capable of executing tasks and learning from data sets, including decisions based on emotion detection. Cognitive agents support customers and employees for customer service, email reading, call notes, feedback, and any other free text extraction.

Use case for cognitive automation showing how cognitive bots improve the invoicing process

Cognitive automation: A use case

Invoice processing becomes a lot easier and simpler to handle with cognitive automation and RPA. A few challenges faced by organizations in invoice processing are:

  • There are multiple invoice requests in a day with stringent timelines
  • The range varies from a single line to multiple lines to multiple pages
  • The input is an image, making the data difficult to extract
  • Some companies have different types of documentation and processes

Cognitive bots are used to extract the relevant information from these unstructured sources and then process the invoice and post payments on SAP. This results in a reduction of manual effort and response time.

Intelligent content processing

Organizations are looking to transform their workforce by combining optical character recognition (OCR), RPA, AI, and analytics to automate their business processes.

HGS has developed an in-house solution to address this need – intelligent content processing (ICP). ICP uses a combination of ML, NLP, and other such competencies to extract data from semi-structured and unstructured sources, derive insights, and take action for further processing.

ICP analyzes images, detects whether the image contains text, and extracts the text into a machine-readable format using ML techniques. Extracted content is presented in a consumable format for bots and/or human agents according to process requirements. ICP utilizes ML models to identify the type of document and extract content to make the process less dependent on document templates.

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Innovate and improve with HGS automation services

We provide end-to-end automation.

Initially, we used RPA solutions only for clients but quickly institutionalized automation within our own business. Today, we use ML and natural language processing (NLP) as a virtual workforce that is capable of executing tasks and learning from data sets, including decisions based on emotion detection.

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