Artificial intelligence (AI) has gained sweeping recognition in the past few years. From design to manufacturing and marketing, companies are utilizing this technology in truly creative ways.

This AI frenzy isn't unfounded. AI technologies can solve complex problems at impressive speed, increase productivity and offer better experiences. Little wonder the industry is expected to grow by over 13x in the next eight years.

In many ways, AI is simplifying the work of UX professionals. From helping them automate rather mundane tasks to performing large-scale analysis. In this article, we look at various ways designers use AI in UX design. ‍

The widespread acceptance of AI has also raised ethical concerns, many people are wondering— will AI eventually replace UX designers? While what might come to mind with such a question is a robot sitting at a desk and etching away at designs, the case is far from it

The functions of AI in UX design

The UX and AI fields share certain similarities, both are geared towards achieving more beneficial results and personalized experiences for humans.  While UX struggles to make products more suitable for users, AI involves using machines to perform traditional human tasks— giving machines human-like abilities.

UX designers primarily research to collect data, analyze the data to generate insights and then use these insights to design solutions. You can read more on what a UX designer does here.

Nearly every aspect of the UX designer's tasks has room for the inclusion of AI technology.

The advantage of AI technology lies in leveraging the strengths of computers, e.g, computing power, storage, lack of bias/objectivity, etc, to enhance human operations.

However, that is not to say that AI can replace UX designers— not yet at least. Because AI has not developed to a point where it can easily provide the human touch that real designers bring.

At the core of UX design is empathy. This is a point where AI is deficient, and while a computer can deliver pretty impressive results, all its operation depends on the historical data you feed it. That said, here are some ways that AI can help UX designers:
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User research

UX designers spend a reasonable chunk of their time doing user research. This research is usually ongoing from the start of the product lifecycle to its finish.
UX researchers collect data from a target audience or users of a product. The goal is to analyse the data and develop insights based on user motivations, behaviour and needs.

AI can play a powerful role in user research. One of the areas computers excel over humans is the ability to work with and organise large heaps of data easily.‍

In UX, the more data we can access, the better our results. But humans have limitations— it is time and resource-consuming to conduct interviews for thousands of users, for instance.  

With AI in UX design, this is a different story. You can use software to conduct research reaching millions of users and interview them with deep, personalized questions. Already, UX designers are making use of several tools to automate repetitive UX research processes.

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Generating insights

After collecting user data, the next step of the design process is to discover insights —patterns in the data set. ‍

Insights are simply the result of data analysis.

And AI is excellent at data analysis. Spotting patterns in numerous rows of data could take significantly much less time using AI. Therefore, you can automate this role with little supervision.

An AI model can be trained to analyse all kinds of data and to predict user behavior based on that analysis. The UX and AI collaboration has very impressive output here.

Presentation is another area in which AI-driven UX design shines. There are several ways and tools to present the data obtained from research to other designers, stakeholders, etc. Popular visualization tools include Tableau and Sisense.

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Creating Visuals

While this is still a relatively new application of AI, much is already being done. AI image generators, for instance, can be used to create original images and other graphics.

And the creative industry is taking steps to make these commonplace. For instance, The Guardian reports that image library Shutterstock recently entered a partnership with OpenAI to use its AI image generator Dall-E 2. This development means designers may be able to utilize visuals in their projects generated entirely from AI technology.
There are also AI technologies that can generate icons, logos and even mockups. For instance, LogoAI.com can make decent logos in a few clicks.

However, it is still preferable for the UX designer to create the visual designs as these AI technologies still have a long way to go in terms of accuracy and usually need fine-tuning.
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Here some examples of what AI can do with one simple prompt, using Midjourney AI Tool:

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Usability test

This is another vital area to use AI in UX design. Usability tests enable designers to observe users as they interact with a prototype of their product. The goal, like all research, is to deduce patterns.

AI tools are up to the challenge here once again. Intelligent tools can "observe" user behaviour, such as eye movement, clicks, routes to task completion, etc, while they use your product and present this in an easy-to-understand format to the supervisors of the usability test.

These AI tools can monitor user interactions, store and process the data in real time reducing the time and resources that would otherwise have been wasted.‍

Maze.co, a popular tool for usability testing uses AI to power some of its features. With such a tool, the UX designer's involvement in a test is near absent.

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Automating UI tasks

Designing interfaces involves more than ideation and knowledge of UI principles. Many times, there are repetitive tasks— in need of no creative input —the designer has no choice but to perform.

Not only do these tasks affect productivity, but they can also be more expensive to do manually. Hence, It is beneficial to delegate them to AI.

Examples of UI tasks you can automate include removing the background of images, increasing the resolution of images, cropping images, etc. Website maintenance is another routine task some UX designers have to do. AI can be used to monitor important metrics and make recommendations for improvement.

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Making more personalized products

AI excels at using historical data to predict user behavior. This can be channeled into making products that better "understand" and engage the user. ‍

Personalization is about tailoring content to match the user's preferences and needs. Of course, this can also be utilized in marketing endeavors by brands.

A personalized feel to a product can create a lasting impression on users. Such products make the users feel important and improve the chances of their retained interaction with it.
Digital products can be personalized using information made available by the users, e.g. through a profile, user location and other forms of historical data.

An engaging product is one of the goals of UX design. With the rise of AI, you can easily achieve this goal. AI models can deliver prompts, recommendations, responses and features that the users of a product could find enthralling.

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Making more intelligent and sophisticated software

The utility of AI in UX goes beyond the product development phase. In fact, some of the most popular uses of AI are features in consumer products or stand-alone software packages, e.g virtual assistants. Think Apple's Siri and Amazon's Alexa.

Other uses of AI include face recognition software prominently used in several security systems, speech recognition used in a plethora of applications as well, chatbots, face recognition, self-driving cars, etc.

These AI features all give a rich and sophisticated experience to the users.

AI is powering the most innovative products in the market today. Product designers have to consider the competition and offer similar or better alternatives.

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Will AI replace UX designers?

No. Not yet by a mile.

AI has caused major disruptions in the entire tech space. Different industries have long seen its benefits and are readily integrating them into their services. Broadly speaking, AI in UX design involves using AI to aid in the development of products and using AI as features in finished products.

This widespread success and notable advantages of AI have raised several concerns for both professionals in different fields and enthusiasts alike. ‍

A major question everyone seems to be asking is—just how far can AI go?

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Can AI replace humans in their Jobs?

Yes and No.

This largely depends on the industry. In the creative industry, for instance, AI is at a disadvantage because there are simply skills better suited to humans. AI cannot compete favorably in a creative industry like UX design because several subjective qualities can come into play.

To fully understand why then you need to consider the weaknesses in the current AI models available today.
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What are the challenges facing AI?

AI systems fully depend on the data they are trained on. This process is known as machine learning. While they are good at using this data, there are some limitations on accessing data and the technical skills needed to build such systems.  

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Empathy

AI can simply not compete with humans when it comes to empathy.‍

Empathy means understanding the user's motivations by trying to think and feel like the user does. This is a hard challenge for machines.

So, in a field such as UX design, AI is best used as a tool to aid the designers in performing tedious operations and making improved decisions, the human touch is still irreplaceable.

Other limitations of AI include: ‍

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Creativity

Creativity is an inherent skill in humans. The ability to come up with entirely new ideas seemingly from nothing requires more than just access to data.

With the recent developments in AI technology, particularly Generative AI, however, we are beginning to see machines that try to emulate the creativity of humans.

Image generators such as OpenAI's Dal E 2 and Stability AI's Stable Diffusion have caused a lot of fuss lately. These tools can generate "original'' images on request.

They imitate human creativity by depending on billions of images used as training data. Using technical computations these tools can generate images similar to the data set but different.

But this isn't much to go on just yet. As these technologies are still in the early stage of development, experts insist that they cannot replace human creativity.

Creativity is a vital skill for UX designers, from ideation at the interaction design stage to the creation of the visual elements of the finished product. Good designers have minds that can readily birth ideas.

Data access and bias

As mentioned earlier, data is the most important commodity in AI. If AI was a vehicle then data can be seen as the fuel on which it runs.

The more the quantity and quality of data fed into a machine learning model, the more sophisticated and accurate its predictions become.

But a major challenge is accessing the right amounts of data, of the right quality.

For instance, several countries impose strict laws on the topic of data privacy. And a lot of top players in the field, e.g Google and Facebook have to be very cautious in the way they handle user data or they come under legal scrutiny.

According to this Guardian article, AI company Stability AI used about 5.6 billion images to train their model for Stable Diffusion. These images are by living artists, many of whom are, as you may have guessed, outrightly displeased by what appears as their copyright infringement.

These situations can limit the AI and machine learning industries that need tons of data to create software that can compete with the human brain thereby affecting AI-driven UX design.

Restriction of data access and lack of infrastructure to access and process enough data can lead to training models with poor data quality. Hence, making the AI systems biased.

Limited knowledge and access

Asides from the inherent challenges facing AI technology, there are challenges on the part of humans too.

AI and machine learning are both technical fields requiring expertise, and the amount of professionals skilled in this area is limited. Hence, the reason AI developers are in high demand.

Also, most people can get along without this knowledge, and some might not see the point in an app that speaks like a human for instance.

Finally, some AI technologies are on the high side in terms of development and maintenance costs, requiring complex systems with high computing power and storage. These limitations make it difficult for just anyone to become a top player in the field.

Conclusion

AI already has an undeniable influence on so many aspects of our lives and the future of the industry is also very promising. AI-driven UX design is becoming commonplace as more people realize its potential. However, AI is not at a level where it can completely replace people, and might never be.

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