Modern advancements in artificial intelligence (AI) and machine learning (ML) have allowed innovative methods and improvements in natural language technologies, allowing intelligent search capability to occur.
Enterprises are evolving to become more insight-driven, and Internet search engines are changing users’ expectations when seeking fast and correct results from query searches. As all forms of data grow, it will become essential for organisations to see beyond traditional searching in order to deliver beneficial insights that support successful business outcomes. This scenario is where enterprise intelligent search solutions can generate tremendous value.
Intelligent searching utilizes AI technologies, such as Natural Language Processing (NLP), semantic search criteria, and Machine Learning to provide a smart, insightful, and relevant search experience. It grants a single point-of-access to business content sources, enabling data, both structured and unstructured, to be enhanced, examined, and analysed.
By permitting real-time access to meaningful information that facilitates decision-making, intelligent enterprise searching can produce benefits across sectors, industries, and corporate business functions.
For example, in the recruiting industry, labour hiring companies have extensive and combined databases of potential candidates. Search engines use the job description and find a list of likely candidates that can be approached to commence the initial job recruitment process.
With modern recruiting, search-and-match intelligent applications use AI and ML to effectively and efficiently understand the right candidate type ideal for the role.
Another use for intelligent search is the ability to aggregate, combine, and index all relevant data about everything involving the design, planning, and construction of a new commercial building.
All building planning proposals, funding operations, and architectural drawing elements, including windows, doors, stairs, wires, fixtures, plumbing pipes, electrical panels, etc. are combined and indexed. A range of complex rules using AI is automatically formulated and expressed as complex search engine queries that can validate all planning and design components regarding the new building.
The outcome is an intensive database of pertinent information that can be readily scrutinized to provide accurate estimates for all of the building’s planned construction costs.
Intelligent search applications like these are converting our day-to-day lives. As end-users become more proficient in using these websites with built-in intelligent searching capabilities, they expect the same experience within their business enterprise. The building blocks for this capability can show organisations how to move forward on their journey to attain intelligent enterprise searching.
Enterprises wishing to adopt intelligent searching must internally commit to leveraging their own business statistical data and utilize practical AI technologies to understand much better what the end-user search intent actually is. This technical pathway is required to fulfil the AI-driven, intelligent search experience end-users will come to expect.
NLP tools can create and develop a customized dictionary for any enterprise, which promotes the search application to understand the language presented in search queries and searchable documents.
End-user query and click log files can be electronically mined, and statistical analysis will periodically measure and improve search relevance.
ML and NLP modelling can automatically extract and organize relevant information from the searchable content. This gives an alternative to strictly rules-based approaches, initiating extraction and classification processes that are more scalable and efficient when scrutinizing large volumes of data content.
The search “training” of these intelligent searching models will require an appropriate and relevant data set of all total searchable content. Progressing these datasets serves as a unique challenge for any enterprise during the implementation process.
Relevant information extracted from searchable content can populate information knowledge databases, which will deliver improved searching performances and result in suitable searching implementations.
There is no doubt that organizations will seek to increase the ROI of their enterprise searching investment, and the intelligent searching maturity process will provide practical guidelines for maximizing deployment success.
Aligning technologies with an organization’s business needs is a natural leadership function. Whether an enterprise is evaluating or planning within the initial phase or has identified an appropriate technology path, Sinequa can accommodate every step of the way. From evaluating and selecting the best-fit solution to implementing, optimizing, and managing the overall searching application.
