What next after RPA and AI?
Since its inception in early 2000, RPA has now spread globally, and is widely used by a wide variety of organizations. Indeed, most organizations have started scaling their growth by implementing RPA in their processes and benefitting from it with significant extrapolation.
All repetitive tasks are now done by the Robot, compensating for the need for faster execution & results. However, changes in the environment often constrict & hamper the execution cycle, thus failing the bot and limiting the reliability of automation.
“To enhance the reliability of the automation, work on different use cases of automation, ready for unexpected scenario”, suggests – Daniel Dines, Co-Founder and CEO, UiPath. He further announced an emerging advanced technology called SEMANTIC AUTOMATION in its event FORWARD – IV.
What is Semantic Automation?
Semantic Automation is an emerging technology designed to reduce the gap between human mind and robots. The Bot will now not only work on the repetitive task, but it will think and take decisions more like a human does in case of unexpected and disparate scenarios. This will increase the scope of automation, lowering the rate of exception occurring in business processes and applications in the future.
How does Semantic Automation work:
Combining the power of RPA and AI, semantic automation will not only read and capture the data but also decode the meaning of the input received. It will interact, try to learn and understand the connotation of task performed in real- time and execute perform the activity based on the learning and understanding.
- Understanding unstructured data – With a semantic understanding, bots can read and analyze unstructured information from documents of different formats or layouts and increase the accuracy of data extraction.
Example: The bot identifies a particular type of document from a diverse collection of documents, that is, if it is an invoice, receipt or an insurance document without the developer writing a set of code for the bot.
- Inconsistent user interfaces – Automation often fails when the UI changes or with the new updates in the application.
Example: location change of submit button, some spelling updates, tittle change, etc. This limits the automation and developers must invest time debugging and updating the changes. With this new technology, the bot can easily understand the updated changes and withstand the changes/updates that occurred.
- Chatbots/Search Engines – The irregularities of informal languages such as typos, missing punctuation, abbreviations, etc. often lead the bots to misunderstand the user’s requirements and ends up providing irrelevant answers, resulting in user dissatisfaction. Bots that have semantic understanding and knowledge of domain-specific language, in contrast, can understand the context of the conversations and use common sense and reasoning to provide answers to queries.
What change will semantic automation bring?
Semantic automation will completely transform the scope of RPA & AI and their benefits as we know today by far & large.
- Reliability – Can handle large numbers of exceptions and changes.
- Accuracy – Provide accurate output for different input types.
- Scalability – Expand the scope of automation by adding different business scenarios, and thereby aligning future growth.
- Adaptability – Adapt any minute or drastic changes in the process or application seamlessly.
- Less time consuming – Consume significantly less coding effort as the mapping of data and relations is already done by the system.
- Inconsistency – Deal with inconsistent data.