Today, we will look at the top seven applications of 🧠 Artificial Intelligence (AI) within Digital Automation 🤖.

Since the use of AI is on the rise, it is prudent to understand and add these AI scenarios to your Digital Automation.

Unfortunately, AI is so hyped that it is difficult to understand its practical use.

• It is sold as magic that can do anything and everything
• It is amorphous and consists of many technologies
• Everyone claims to have “AI” but it is not clear what that means

So, let’s go beyond the hype and look at some practical and tangible ways you can apply AI to your workflows.

To give you a sense of how ubiquitous AI is becoming in Digital Automation, here is a typical customer onboarding workflow. You can see the use of AI technologies like Machine Learning (ML), Conversation AI, Computer vision, and Advanced Analytics in the workflow. 

Across workflows, here are seven key areas where AI is being applied:

1. Intelligent Document Processing (IDP): This is probably one of the most used applications in Digital Automation today. It uses AI technologies such as Computer vision, Natural Language Processing(NLP), and machine/deep learning to capture, categorize, and extract data from documents (e.g., email, text, PDF, and scanned documents) for further processing. IDP is used for Invoice Processing, Claims processing, Reading statement, Receipts, any type of forms, logs, and more.
2. Virtual Agents/Chatbots: These interactive programs that simulate real-time human conversations use Natural Language Processing (NLP) to understand your written content and Natural Language Generation (NLG) to generate a response. Chatbots can deal with common Employee, Customer, or Supplier queries. Digital Automation workflows can use this to guide the end user to carry out actions specific to the workflow.
3. Task Mining: This process interpretation technique captures the desktop-level event data - screen recordings, keystrokes, mouse clicks, and data entries. This data is then fed to Machine learning models to cleanse and contextualize the captured data. AI technologies such as natural language processing and optical character recognition interpret the data and generate process maps and insights.
4. Process mining: While traditional process mining tools did not use AI, modern Process Mining uses machine learning and deep learning to automate the collection, discovery, visualization, and monitoring of process data. AI-enabled process mining enhances applications such as digital twins and predictive analytics which rely on regular process mining capabilities.
5. Decision Intelligence: This is an emerging set of technologies that helps organizations turn data insights into automated actions. It can use event information, and enterprise customer transactional data, enriched with third-party data, superimposed with ML predictive models to automate or augment next-best actions.
6. Speech Recognition: Speech recognition transcribes the human voice into text and applies NLP to extract understanding and meaning from the text. You can use speech recognition integrated with automation to perform live customer analysis such as caller categorization, NPS (Net Promoter Scoring), Fraud detection, and complaints analysis.
7. Sentiment Analysis: You can analyze opinions, sentiments, evaluations, attitudes, and emotions in textual data. This uses NLP to deconstruct a sentence into constituent parts and score them on whether the sentiment is positive or negative and how strong it is. Companies are using sentiment analysis to monitor the tone behind online conversations and gain a true understanding of the public’s feelings toward their brand.

Those were the key areas we are seeing AI being applied in Digital Automation.

We just completed discussing AI in our Digital automation Club for our second cohort and thought I will share a summary with you.

That's all for today.

See you again soon! 

Nandan

Ps. If you are a leader in Intelligent Automation and looking for a place to find like-minded go-getters, join us in the club.

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