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How Can AI Help Design a Better User Experience

Contrary to popular belief, Artificial Intelligence (AI) is not the dystopian villain you see in the movie – “Terminator”. In reality, AI has revolutionized technology by mimicking human behavior to enable a better handle on data management and mining and provide results that may have generally been missed by humans. AI is not just simple algorithms but the combination of a variety of nascent technologies including deep learning, machine learning, augmented reality, robots, chatbots, virtual reality, etc. Some examples of successful implementation and evolving AI used by the masses include Apple’s Siri, Windows’ Cortana, Google’s Google Assistant, and Amazon’s Echo. While some of these are context-based AI chatbots that provide timely guidance and reminders, some provide navigational intelligence for driving, and other’s help with AI information processing that can study human behavior and patterns in various streams.

Combining AI and UX for better findability:

AIs evolve rapidly through the analysis of data, by forming patterns, and providing result choices that help with problem-solving and decision making. While humans can analyze structured data only in a single spectrum, AIs have the ability to compile and collaborate data from various sources, irrespective of structured or unstructured data platforms, through its coded algorithms and start adapting better models to represent data that is at a much faster pace than humans. The bandwidth of data that an AI can mine through is higher since the base system codes are designed for swift adaptability. This allows for any form of business to focus on specific data without wasting time, solve the problem, target the right customer, and cater to their users at a better rate than those relying solely on humans to study user behavior.

For example, multiple users using a search engine would click on different results based on their specific needs, and some of them even tend to stray away from their path by moving on to other related or unrelated scenarios. For a user to get the required data that they need, an AI would be able to study their browsing history and the phrasing of the keyword to ensure that the user gets only the most relevant results to their search. This way, the user experience is steered onto a clearer path without the user having to perform multiple searches or use manual filters by going through every result shown without AI integration.

AI customizes its learning for industry specifics and ensures that it allows for a collaborative effort wherein its algorithms evolve periodically to suit each individual user’s needs. Getting optimum results always betters the user experience rather than being stuck in a rut that needs one to comb through loads and loads of data manually.

Training the AI:

AIs are built with a basic code system that allows for data analysis, mining, solving efficiency, result generation, etc. But the real training for an AI to churn out better results depends on its constant usage. When an AI is posed with a series of queries, it delivers an output that is specific to those queries. When more queries are fed in, the AI evolves itself one step further by matching the second set of queries with the first one, and then provide an output that encompasses a better answer. With the progress of time, an AI will start picking up the traits like a little child being trained and keep refining its results while building its own query modules to analyze better and one step ahead of the last one. Its inherent ability to find connections between trends, analytics, etc, provides a better solution, thereby empowering the end user with the experience of optimum answers.

Integrating Information Architecture and AI for UX

The successful progression of AI depends on the designing better and appropriate information architecture (IA) as well. While IA is crucial for the suitable tagging of content based on relevance to the user, AI can generate the relationship between data through identification of trends, existing grouping, etc., by studying the user’s behavior history. This cross-linking of data becomes a smooth function when AI is integrated into the existent IA platforms. This will help businesses to focus on the end user’s needs and thereby generate optimum results by enabling an easier interface to navigate through large chunks of data. Through this, the user also has the advantage of getting more information out of the relevant content prompts rather than having to manually run searches for each link that they have thought off.

Today, UX depends on AI as the gap between various levels of data has been bridged successfully through this automated technology. While the AI technologies are gaining steady momentum in the real world, the gradual growth paves the way for AI and UX to become a synonymous future technology that would enhance both content reachability and findability. The best start for this practice would be to start with data collection and move on to technically integrating AI into your business systems for futuristic growth.

Author: Sigma Infosolutions

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