Today search in conjunction with Artificial Intelligence (AI) technologies have extended enterprise search capabilities. Consequently, it is expected that there will be more AI-powered search and search-based analytics applications supporting enterprises.
In the near future, the number of enterprises employing neural networks, a key technology that assists innovative enterprise AI systems would grow. Neural networks can create a semantic space — an abstract interpretation of enterprise content. Semantic space can be used to detect sentences having the same meaning, classify material to enhance navigation, and extract data from documents. As a result, more enterprises will introduce neural networks to understand their document content and user queries, offering highly relevant, context-based responses.
In the coming years, it is expected that there would be an increasing dependence on semantic search supported by factors such as growth in data warehousing, data lakes, and technology for content ingestion. Furthermore, new tools designed to apply semantic search and ML approaches have emerged. These advances suggest that enterprises can implement semantic search to manage a wide variety of queries and requests, and can respond directly from business systems with real-time responses.
AI allows information to be extracted from unstructured content by analyzing presentational elements— semantic knowledge that cannot be conveyed by text only. Therefore, intelligent document processing engines can be designed to read presentational information and provide instructions to end-users while improving the advantages of enhanced documented comprehension capabilities.
Powered by AI technologies, voice assistants are already making it easier for customers and employees to interact with enterprise data. A natural synergy exists between voice assistants and semantic search. For example, voice assistants bring a deeper understanding of the natural language to boost search and provide a completely new way of finding information. In practice, the synergy could mean that an efficient and robust semantic search engine could handle the back-end of company websites in the future.
Following last year's prediction, developments in knowledge graphs will continue to fuel smarter search interactions across the enterprise. Natural Language Understanding (NLU) algorithms can create an interconnected information network from the fragmented data records obtained from various enterprise functions. It will indicate how data records are linked to each other, creating the enterprise knowledge graph. Search engines and Question/Answer systems can then instantaneously pull a snapshot of connected information from the knowledge graph and deliver relevant insight when the user asks a question.
Looking ahead to 2020 and the coming years, these five technologies are expected to grow further within the enterprise and leveraged more broadly. Furthermore, as AI technologies and approaches are refined, enterprises can use them to solve technical as well as organizational challenges at lower costs and with powerful results. This will enable enterprises to create endless possibilities for innovation through strategic planning, domain expertise, and expert execution.
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