Can we Use Unstructured Data in Intelligent Automation Activities?

Can we Use Unstructured Data in Intelligent Automation Activities?

Unstructured data in intelligent automation helps organizations streamline various purposes

Structured and semi-structured data can be automated using Robotic Process Automation, whereas unstructured data can be automated using Artificial Intelligence. Artificial Intelligence can augment Robotic Process Automation as it can process unstructured data in intelligent automation and it can also learn/improve its performance. AI can read unstructured data with the help of OCR, Natural Language Processing, and Machine Learning or Deep Learning.

With the help of Artificial Intelligence, we can increase the use of automation in corporations and streamline various processes to reduce turnaround time and increase efficiency with reduced cost. AI can be trained through various methods such as Supervised Learning and Continuous Learning. With the goal to increase efficiency, decrease cycle times, and redeploy critical resources for higher-value while reducing the risk.

Automation in Effect

When we talk about automation it is mostly Robotic Process Automation where the robots mimic the user's activities. It can process structured and semi-structured data. Robotic Process Automation is rule-based automation without any scope to learn and is mostly used for standardized processes.

Need for Processing Unstructured Data

There is also a need for processing unstructured data, like processing receipts because there isn't much need for intelligence to process receipts, but a Robotic Process Automation is unable to perform it as there can be multiple different numbers of receipts from different vendors and clients. Since reading receipts can be tedious there is also a factor of human error and also a huge amount of work hours is tied to it leading to less productivity for the company which is very undesirable.

Another example is the queries received on the helpline mail. Where a human touch is required to understand the query of the user and then forward it to the department for correct resolution.

The points these examples have in common are:

  • Long Turnaround Time
  • Manual processes with large teams
  • Multiple document formats
  • High cost of operations
  • High error rates
  • Difficult to automate using rule-based automation technologies

Processing Unstructured Data with Artificial Intelligence

Artificial Intelligence can process unstructured data with the help of Machine learning and Deep learning.

It can mimic human thought processes using photo, audio, and pattern recognition. Artificial Intelligence can augment Robotic Process Automation as it can process unstructured data and it can also learn/improve its performance by the collected data without having to be explicitly programmed. Though Artificial Intelligence is probabilistic in nature, with safeguards in place, it can be made deterministic.

Benefits of Document Processing

  1. There are huge benefits for document processing namely
  2. Streamlining document tracking
  3. Ability to handle, multiple formats
  4. Increased workforce efficiency and productivity
  5. Faster turnaround time
  6. Improved governance and compliance
  7. Improved accuracy
  8. Reduction in cost for high volumes of data

Solutions

Unstructured data in the form of documents or live conversations can be augmented by Artificial Intelligence solutions like Chatbots used in live conversations and Intelligent Document Processing robots can be used for reading documents.

Intelligent Document Processing (IDP)

Intelligent Document Processing is defined by Everest Group as a software product or solution that captures data from documents (e.g., email, text, pdf, and scanned documents), categorizes and extracts relevant data for further processing using Artificial Intelligence technologies such as computer vision, OCR, Natural Language Processing (NLP) and Machine or Deep Learning.

The document is captured using Computer vision or OCR and then the output is generated in natural language format which is useful in automating replies to email, tweets, etc., or used for Natural Language generation capabilities. Artificial Intelligence may be used to classify the input data using Text mining and machine learning to generate summaries from the documents which are useful in providing insights based on descriptive, predictive, and analytic capabilities.

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