AI enables the automation of medium to complex processes and takes Robotic Process Automation to the next level. It powers predictive maintenance of heavy machines thus reducing operational costs. AI enables pattern identification with vast data volumes thus augmenting human intelligence and decision making. It improves the analytics domain to the nth extent. While Natural Language Programing (NLP) algorithms read and augment data to produce analytical reports, Natural Language Generation (NLG) algorithms write sentences.

AI joins the dots from disparate data sources and discovers interesting patterns that are less visible to the human eye and intellect. Consistent variations, clustering of data points, dependencies, etc., bring forth interesting stories from the stagnant data resources. Using data, AI-enabled information mining generates hypotheses, verifies them, and deduces information. Information Mining forms the basis of Predictive Analytics and Machine Learning.

AI-powered Data Mining enables users to perform different levels of tasks –

• Anomaly detection, where unusual patterns or outliers are thrown up, which may be errors or require deeper investigation
• Associations, where the relationship between two or more variables are brought forth, and is frequently used in forensic investigations
• Clustering, where data points that are similar in more than one ways are discovered
• Classification, where known structures are generalized in order to apply to new data points; for example: Records or File Classification
• Regression, where a function is found, which models the data and estimates the relationship between different data points
• Summarization, where a concise summary is generated based on weighted keywords. It is popularly used for generating audit reports and executive summaries.

AI simplifies Document Management to a great extent. It enables automatic file classification or categorization as well as summarization. The files may include spreadsheets, documents, emails, PDFs, video files, audio files, social media, news, and other data types.

AI algorithms generate intelligence from high data volumes by correlating disparate data points. This data may comprise structured, unstructured, and multi-structured data.

A recent feature is AI-enabled Topic Modelling. It allows screening of huge data sets, such as reviews, emails, social media snippets, etc., and segregating them as per the predominant sentiment.

AI algorithms use weighted keywords and correlate them to generate concise summaries of lengthy documents, news articles, research papers, agreements, books, tweets, etc.

It uses two methods – Extractive and Abstractive. Extractive Summarization extracts several portions of the text and stacks them to create a summary. Abstractive Summarization uses NLP algorithms and generates a new summary.

AI-powered document summarization is used in audits, research study scenarios, social media listening, government services, etc.