AI for UX: Creating better user experiences using artificial intelligence

Understanding the possibilities that artificial intelligence (AI) brings with it and keeping up with the most recent happenings in the AI field is a great way to anticipate future possibilities for better UX.

In simple terms, AI (Artificial Intelligence) is the simulation of intelligent behavior in computers which does not necessarily personify a human entity. It has been a widely discussed subject matter for the last couple of years, a mix of facts and myths with wild predictions ranging from a jobless future for the common human to the rosiest of pictures of robots working hand in hand with us to make the world a better place. As designers, we need to look closely at this evolution and ask – how can we leverage this tool to create better user experiences? 

Recently the UX research platform UserZoom conducted a survey where they found out that 80% of UX executives believe that UX design and AI are the future of user experience. This figure was only 66% last year. A lot of organisations are already participating in this competition. They want to make the UX of their products more intuitive with the help of AI technology.

Capital One’s gender-neutral chatbot is so human that people asked to marry it.

This is happening mainly for a very simple reason: soon, users will be satisfied only by this degree of effortless user experience. AI is the key to leveraging personalised behavioural data to increase conversions, resulting in better brand to customer relationships and boosted engagement levels.

What makes it possible for AI to enhance the UX?

From a goal perspective, AI and UX are both designed to interpret users’ behavior and anticipate their actions. Predictive analytics forms the basis of both areas, and great options for both business and consumer lie at the intersection.

Creating a better UX through artificial intelligence fully depends on AI algorithms. These smart-systems can process tons of information about the product or website users, and optimise the model as they go along. This continuous information gathering, application and adjustment of the algorithm is what drives a more personalised and engaging experience.

Here are some examples of applied categories of AI:

Analytic — Risk assessment, sentiment analysis, retroactive analysis

Functional— IoT solutions, robots, mechanical apparati

Interactive — Personal assistants, chatbots, Google Home, Alexa

Text— Natural language processing (NLP), text recognition, speech-to-text conversion

Visual — Computer vision, augmented reality

Jakob Nielsen from NN Group talking about the effect of AI and machine learning on UX research and methodologies

The Many ways that AI can improve UX

Quantitative Usability Testing & Bias

Due to its incredible data processing power, AI simplifies many forms of testing, such as tracking and evaluating a various crucial UX metrics, including: devices used to visit the website, user location, session time, session length, pages visited, categories and products viewed, bounce rates, exit pages and overall user journey.

This is all indispensable data for analysts, helping them build a better understanding of user behaviour and needs, identify any patterns which show gaps and pain points in the experience and decide what elements should be tested.

Even though researchers and designers are trained to hold back their biases during A/B testing, we are only human – unlike AI – and our reasoning can inadvertently influence our results. AI is completely impartial, relying purely on hard data which reflects the actual results.

When AI goes wrong: The Grid offered websites that build themselves. But after crowdfunding millions of dollars, things started going downhill… Read more here

Offering Datasets that were not available before

As UX designers and researchers, now we will have new types of behavioural data that we might not have had the opportunity to tap into before and need to combine and take into consideration to better understand user personality and communication:

Gestural Data: Identifying conversational tone via face and hand motions.

Physiological Data: Physical data such as heart rate, blood pressure, and skin temperature.

Facial Recognition. The way we would verify a person, and interpret their emotions and expressions.

Deep Learning: The way we would understand speech, and how language is used.

In the future we might also be able to read the energy – or aura – of humans, by capturing thermal dynamics using specialised hardware and the AI will be able to tell if the person is crying with happiness, or smiling with frustration.

In the future, the conflicting expression of complex emotions which make us truly human will no longer be impossible to decode by artificial intelligence

The algorithm and the UX designer

Designing an AI-driven experience means building calibrated trust with your user. The degree and accuracy with which AI helps drive the experience propagates trust between the user and the experience, which in turn drives habit forming behavior whereby people are intrinsically more motivated.

Delightful AI can predict user behaviour and needs, raising their expectations to a point where they depend on that experience to make their lives better. As a UX designer, always lead with: How can we cater to this user’s need? Then follow up with: Can we solve it better by leveraging AI capabilities? Unfortunately, it is easy to fall into the trap of trying to develop a feature that caters to an AI capability, instead of the other way round.

Ultimately, both the UX designer and the AI’s main purpose is very similar: serving human interests by building more accurately personalised experiences. As designers, it is our role to preserve the human element when crafting experiences that leverage AI. Keeping the user in mind while following the iterative agile process of building products is a tried and tested methodology that will continue proving itself crucial and indispensable for many more years to come.