In this post, we will explain Cognitive Automation, its use cases, steps for cognitive automation success, and a few other related concepts. Sometimes, people also refer to Cognitive Automation as Cognitive RPA.
Post Contents
Cognitive Automation
Cognitive automation can be taught by humans to uncover and transform the hidden or “Dark Data” to automate business processes end to end faster and more efficiently while simultaneously eliminating human error.
Knowledge workers are no longer needed to extract information from unstructured content – documents, Images, emails, they are free to do what they do best – make decisions and handle the exception.
Cognitive Bot keeps learning from corrections made by your knowledge workers, getting smarter and more accurate over time. Cognitive automation implemented to achieve end-end process automation.
Cognitive Automation Use Cases
Most organizations have already begun with baby steps to cognitive automation, here are some areas to explore without a second thought.
Technical Understanding
A typical cognitive tool should consist of the below aspects:
- Document Analysis
- Preprocessing
- ICR/OCR Usage
- Enrich the Data
- Training the Document
- Validation
Let’s have a look at them one by one in detail.
Document Analysis
When multiple files with different structures & different variants are uploaded for an instance, distinguishes and categorizes document types and formats. Regulates the recognition and classification of content and layout with unsupervised learning algorithms
- Computer Vision
- Geometric Hashing
- Clustering
- Unsupervised Learning
- Convolutional Neural Network (CNN)
Preprocessing
Preprocessing is an imaging technique for enhancing the chances of successful recognition. Typical aspects are as shown below:
- De-skew
- DE speckle
- Binarization
- Line removal
- Layout analysis or Zoning
- Line and word detection
- Script recognition
- Segmentation
- Normalize aspect ratio and scale
OCR Usage
A typical part of cognitive automation is to convert the scanned document/Image and handwritten text into the machine-readable format and form the system identifiable reasons.
OCR is a field of research in pattern recognition, artificial intelligence, and computer vision.
- Available OCRs: Google – Tesseract, Abby – Fine Reader, Microsoft – Azure
- Long Short-term Memory (LSTM)
- Convolutional Neural Network (CNN)
- Segmentation color-coding – SIR, Form Fields, Table fields
- Key-Value Mapping, Co-ordinate Mapping (Native OCR)
Enrich the Data
Data enrichment brands to enhance and make the raw data more useful. The resulting enriched data is richer and more detailed, which enables brands to more easily personalize their messaging. Below are algorithms used to enrich the data in a typical automation phase.
- Fuzzy Logic
- Phonetic Algorithm match
- Semantic Analysis
- Demographic data enrichment
- Geographic data enrichment
Bot Training
Bots training is a process to map Fields labels, values, and table headers, these are automatically mapped based on the results of the classification process thus reducing time and effort to train a bot and increasing productivity.
- Auto-mapping
- Point and Click
- Code-free
- Key-value Mapping + Co-ordinate Mapping
Validation
With validation, the validator (human) uses a visual interface to “eyeball” changes and update text
extracted from the digital document. Once fixed and saved, the updated document moves back to the successful queue where it can be picked up by an upstream automation task.
Corrections made by a user teach the learning system that helps avoid repeating the same errors in the future. Uses computer vision and machine learning techniques to recognize patterns. Learns with every validation to improve continuously.
Typical validation process contains the below aspects:
- Machine Learning (Supervised Learning)
- Logistic Regression
- Naive Bayes
- Support Vector Machines
6 Steps for Cognitive Automation Success
- Select the right process to automate: Don’t be too ambitious when you start cognitive automation, Initial cognitive RPA deployment fails due to common mistakes that could be easily avoided. So, pick a process that is less risky if it fails also without bringing down the important enterprise operations.
- Build a business case: Automate a process that will give you a decent return of investment (ROI) within a reasonable time. Leading areas of high ROI are HR, Finance, Operations & IT.
- Get Buy-in from Internal Management and IT: Many cognitive automation initiatives start with internal business functions such as finance or HR, Leaders within the business units recognize the potential value of RPA, and eagerly jump in to reap the benefits.
- Limit Initial Scope: This is the major step where many organizations failed to implement RPA. Use the 80/20 rule, 80% of success comes from just 20% of effort, don’t jump in to start with across all domains, target specific area to show a good success. Slow and Steady win the race.
- Select Sample Documents: Cognitive automation is nothing but data, it requires a good amount of sample set to achieve better ROI. The more data you give it, the more accurate results will be.
- Scale and Expand: After you succeeded with the initial implementation, gradually introduce even more complex processes (Semi & unstructured) data.
Conclusion
Concluding the article with the hope that you were able to understand and follow the context around Cognitive Automation.
If you have any concerns or doubts related to this article, please comment below. Also, do share this information among your friends and colleagues by hitting any of the below social networks.
Happy Automation!