Transform RPA with AI and ML
IBM Robotic Process Automation (RPA) is a business process automation technology that uses software bots to automate back-end office tasks that are repetitive, tedious, and time-consuming, allowing you to focus on high-value tasks.
You can create bots to perform a wide range of tasks, as well as manage and schedule the bots based on their nature.
Hyper automation is the use of advanced technologies such as machine learning, AI, and process mining in conjunction with RPA to identify and automate significant processes while also improving existing automation.
We empower RPA systems with decision-making, problem-solving, persuasion, and even creativity by incorporating AI, ML, and NLP into robotic process automation.Organizations are reducing processing time, increasing productivity, and providing the highest level of client satisfaction.
ML in Robotic Process Automation
Machine learning models can be created and used with IBM Robotic Process Automation.
Because of the abstraction design of IBM Robotic Process Automation, the RPA developer only needs to worry about the training data and the model's input and output data.
RPA currently supports the following algorithms: BagofWords, N-Gram, Knowledgebase, and TextClassifier.
IBM RPA offers
- Knowledge base
- In IBM RPA, you can create knowledge bases for your robots to answer questions by searching answers in a knowledge base.The knowledge base is a machine learning model that stores data in the form of questions and answers.
- We can use the RPA tools Machine Learning Model builder to select the knowledgebaseV2 algorithm and provide the knowledgebase file.
- This knowledgebase file is made up of text organised into three tables, each in its own spreadsheet.The inner structure of the file must include three spreadsheets named KB, Word, and Synonyms.
- Insert the knowledge base's set of questions and answers, context label, and tags associated with the question into the KB spreadsheet.
- Once the Knowledgebase has been successfully created, we can select the newly created kb and look for sample questions and answers that have been trained.During this phase, we can also train the KB with additional related questions and answers.
- When the training is finished, publish it.We can now begin using published KB directly from the RPA bot script, using the AnswerQuestion function under NLP.
- Text classification
- IBM RPA allows you to create text classification models for document labelling.
- The Text Classifier model represents a collection of text documents organised and classified in tagged directories.As a result, each text document is assigned one tag.To train the model, the text classifier algorithm combines several algorithms.
- We can choose Train Classifier function from RPA tools Machine Learning and provide folder path of tagged directories.
- We can now use the published model directly from the RPA bot script, using the ClassifyText function under NLP.
NLP in Robotic Process Automation
Using IBM Robotic Process Automation, parse text using a natural language processing (NLP) engine. It enables tasks such as text entity extraction, spell checking, and question answering.
Natural language processing (NLP) is a subfield of artificial intelligence concerned with teaching computers to understand text and spoken words in the same way that humans do. It can interpret text from a variety of sources, analysing and categorising it in order to extract meaningful data and make decisions.
Speech recognition, part of speech tagging, word sense disambiguation, named entity recognition (NEM), co-reference resolution, sentiment analysis, and natural language generation are among the tasks it performs.
IBM RPA offers
- Watson® NLP
- Legacy NLP
- Customised NLP Model
They provide a collection of NLP models that you can use by specifying a default NLP provider in your RPA control canter tenant or during the runtime of your bot. Use NLP commands to perform tasks such as entity extraction, spell checking, text classification, and text summarization.
How it is Being Utilized
Applications and processes that are automated run more quickly. Automation of applications and processes, as well as real-time decision-making, forecasting, and prediction from multiple sources of structured and unstructured data, enables organisations to increase productivity and accuracy in their planning cycles.
Reduce operational costs significantly by using AI to automate actions based on automatic decisions.
The use of structured and unstructured data, as well as the automation of repetitive processes, results in better decision-making and more precise results.
- Enrich the customer experience
In the asset management industry, for example, bots were used to provide first-line customer support and pricing quotations. This optimization significantly reduced the time it took to respond to customer questions, thus improving the customer experience and streamlining the purchasing process.
I hope this was informative, and I thank everyone who read it.#RoboticProcessAutomation(RPA)#MachineLearning#NaturalLanguageProcessing
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