As an organization grows, the amount of newly generated data also keeps growing. According to Forbes, 15 Exabytes of data was generated worldwide in 2005 and almost 1200 Exabytes in 2010. As of today, this figure has reached 2.5 quintillion bytes. At this pace, the global data volume is expected to grow up to 44 zettabytes by the year 2020. Traditional data management systems cannot cope with the rising volume of data. Several leading enterprises that generate large chunks of data cannot rely on traditional document management systems (DMS). Over recent years, artificial intelligence (AI) has transformed conventional document management systems. The advent of AI has streamlined the way they store and archive important documents and extract useful information.
This blog post highlights five ways in which AI is transforming DMS.
The Power of AI in Document Management
If you search a document on a computer, you either need to know its exact location or perform a keyword search. AI enables human-like interactions with machines to accelerate the task of locating documents in a more engaging way. It not only reduces time and effort but also increases productivity. With AI, document management becomes smarter, simpler, and easier. Moreover, it delivers real-time contextual information that significantly improves workflow efficiency. Artificial intelligence services can significantly improve document management systems, making them smarter and less time-consuming.
Five AI Applications That Are Transforming the DMS Industry
Data Analytics: One of the most interesting prospects of AI in DMS is the support for analytics and the value it provides for decision making. An AI-based DMS uses machine learning, predictive analysis, and data visualization to derive insights from data contained in documents. For example, several cognitive platforms like OpenText Magellan and IBM Watson provide data analytics to transform document management.
Data Extraction: An AI-powered DMS takes data extraction to a new level by analyzing information to identify the context. The given data is analyzed and crawled to retrieve relevant information from a DMS in a specific pattern. All small-to-medium businesses produce massive chunks of data on a daily basis. Therefore, it becomes critical to extract relevant information to accomplish a particular task. Integrating DMS with machine learning algorithms enables users to easily extract essential information.
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