We all know that connecting people to information is critical for learning and discovering new facts and details. Connecting people to information is a part of everyday life and a necessity for businesses, especially when implementing artificial intelligence systems.
For example, Google provides searchers with the most relevant sources for their queries. If searching “How old is Michael Jordan?” the age will come up at the top, not information regarding his career statistics. This happens because Google uses a relevancy model and recognizes what the query is asking.
The same idea applies to businesses and their departments. Finance teams searching for contract details or payroll information do not need insights from documents containing marketing material and vice versa.
The Mindbreeze InSpire relevancy model ensures this is the case across all functional areas. Even in implementing artificial intelligence systems.
Relevancy Models in a Nutshell
Going back to Google, the search engine prioritizes links clicked on for specific information. Knowing statistics on what people are searching for and what sources are helping them find it is key to their success.
Artificial intelligence solutions, like Mindbreeze InSpire, study the actions taken by users after searching for details within their enterprise – allowing for the best sources to be bumped up the chain of results. Statistics analyzed consist of click rates and action paths taken by customers, allowing them to give feedback on what documents were helpful to them.
Voting systems and user feedback are essential to help predict relevancy for other users in similar roles and departments, ensuring seamless interaction within their workflow. Endless hunting and scrolling are eliminated from their processes and research.
Look out for relevancy to be a top trend in 2023. What information is valuable and what is not?
A Prime Example of Implementing Relevancy in AI Solutions
Sometimes search results are not only valuable to a single department. Customer information could provide value to sales, customer support, marketing, finance, and research and development.
Breaking down departmental silos allows easy access to structured and unstructured data sources for all departments needing information on a customer and their use case. Sales can find critical details on their use case to present to other clients. Customer support may need access to the duplicate files for quick problem-solving. Marketing can utilize the same information to write case studies, press releases, and whitepapers. Finance and accounting may need the information to process invoices and review contracts with this specific customer.
If a solution is rolled out throughout an entire organization, the relevancy of certain documents may need to be tuned for multiple departments and not just one with the primary need.
The connection of data sources and an accessible entryway to information authorizes these actions and grants proper user access.
Highly Specific Low-Code Solutions for Your Business
Guaranteeing that employees are making valuable contributions to operations should not be assumed. Equipping the workforce with highly specific solutions will help them with ultimate efficiency and productivity while saving them loads of time. With highly specific solutions, employees can easily drag and drop the details they want to see and where they want to see them – creating their very own relevancy on top of the models already in place.
Conclusion: The Overall Goal of Relevancy and Specificity
Ease, ease, ease is what it all comes down to – ease of connecting people to information, ease of ensuring generated results fit their needs, and ease for the workforce to be more productive and saving hours on research. Machine learning and AI learning from actual use with the combination of user feedback permits quick accessibility to the extracted information. When looking for a solution, asking about the steps to ensure relevancy is much needed to evaluate correctly and make intelligent decisions for your business and workforce.