Artificial Intelligence, UX and the Future of Findability

We seem to be doing amazing things with chatbots and data mining that can expand the manual information architecture and generally improve the user experience. Through automatic processing of information, we can identify research models and recommend information structures to improve the content discovery.

As AI + UX = better search options

For example, suppose you have a service that contains a search component. Currently, users can search and use manual filters to sort results as usual with search users.

How do you know if they find what they need? You can rely on Analytics to see if they have access to what you want and search for users to see if they are able to complete their tasks. You can evaluate the user’s overall satisfaction when analyzing the use and timing of the service and consulting them directly in the interviews and comments.

For example, suppose some users have trouble finding or not going where they are waiting in the search results. Your overall experience may be satisfactory, but if you look at the keywords and click on the search results, your search results may improve, or it may be clearer routes to get information about your data.

How can you help them look better? If you use more specific terms or more complete terms, you can find more relevant information for your questions. But these are theories.

You will review the service, including research, with a user-centered design method. Cool! And we should add some artificial intelligence while we are there.

When you participate in the UX design process, you can use an artificial intelligence system to analyze large amounts of seemingly unrelated data to help you with your design decisions. For example, you can configure your mining tools to collect structured and unstructured data (analysis, search, and other usage information). When you discover the problems you want to solve for your users, you include an IA (like IBM Watson) to begin analyzing unstructured data.

Artificial Intelligence Training

But how does the artificial intelligence system know what to do? This is the fun part: First, analyze the nominal value and then train. Artificial intelligence systems can analyze large amounts of data in much less time than manually and learn in real time. They understand the context so you can help them understand the data by providing additional information in the form of business rules, metadata, and issues.

During the user experience research and design phase, the questions are constantly changed and the phases of analyzed data are changed. You can ask simple questions like: How many people are looking for X? What is the frequency for displaying Y in response? What information do we have about Z? The system responds as well as possible to issues based on data analysis. The best part is, however, that he is not limited by his ability to ask questions. The system takes your questions and data and really learns. Start asking your own questions. With time, the greater the number of searches made in the search engine, the greater the number of user analyzes, connections, trends, proposals and assumed assumptions collected.

How does it help the users with the search? If users search for information, you can increase the quality of the search results with this information. Think of more meaningful keywords, more relevant search results, and cross-references like Amazon. These provide the opportunity for a more immersed user experience, as content users need to be delivered directly via an engine that has learned from all previous users.

AI for AI And how can you help design better information architectures? One of the most challenging aspects of the information architecture is to create appropriate content packages with labels that are understandable to the user. Artificial intelligence can help identify and propose relationships.

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