segment-pixel
For the best experience, try the new Microsoft Edge browser recommended by Microsoft (version 87 or above) or switch to another browser � Google Chrome / Firefox / Safari
OK
brand-elementsbrand-elementsbrand-elementsbrand-elementsbrand-elementsbrand-elements
brand-elementsbrand-elements

User Experience (UX) has been at the heart of creating new digital products, applications, and interfaces. As modern products continue to thrive on human-machine interaction, UX requires a deep understanding of the user’s needs, issues, and perspectives to create products that solve real problems and make it easy for the user to interact with them. With so much riding on seamless user journeys, businesses today need to deploy the necessary tools to create better experiences and develop smarter products at a faster pace.

But this doesn’t come without having significant dependencies on devices, systems, information technology, and data science.

There is no doubt that the constant research and growth in technology contribute to better products and experiences for the users. For instance, AI technology and its algorithms are developed using real-world data to recreate better solutions, more relevant search results, intuitive platform designs and interfaces, and much more.

Take Remove.BG for example, a powerful editing tool that uses AI to remove backgrounds from images in no time. Earlier, tools like Adobe Photoshop were used to edit and remove backgrounds manually and required having at least a basic understanding of the tool to be able to use It effectively. From hours to minutes, Remove.bg not only automated the process but also did away with the need for users to learn and access professional tools. This, however, raises a few questions that we must ponder.

  • How far do we want to depend on them?
  • What are the limits of using AI in designing modern experiences?
  • Will AI-powered-UX make human-powered UX redundant?

A core component of UX is design thinking. It is a multi-iteration process with a non-linear workflow that considers people’s reasoning for engaging with a design in a particular way and makes assumptions to develop a design strategy.

Is it possible for AI to meet designers at the intersection of feelings and emotions in design thinking?

The Power of Empathy in Design Thinking

A large part of being a UX professional involves having empathy. The ability to experience the world through other people’s eyes is at the heart of the design thinking process. A deep understanding of who your user is, where they come from, what their perceptions are, and how they interact with their surroundings.

Empathetic research, a core component of UX design, is impossible without the involvement of people, a connection with them, and an understanding of them. It is beyond the functional needs and analytics, as real-world problems hardly follow a specific script. From experiencing unfamiliar situations to identifying conspicuous problems to cultivating curiosity, a UX designer does much more than just be an onlooker in the design strategy. Innovation comes from the designer’s ability to question convention and design more intuitive experiences.

What About Cognitive Bias?

Now that we have established the significance of empathy, we must talk about cognitive bias, a staple of human judgement. As designers, we sometimes suffer from relying on the conventions we have acquired from our learning experiences and peers. Just as important is having the ability to break away from generalizations in order to empathize with each client’s perspective individually.

AI tools are pushing the envelope every day. They can create art and literature without intervention from authors or artists. They can translate languages and converse without human intervention. However, it hasn’t been successful in expressing emotions, and that could be both a good and a bad thing.

Emergence of Language Models

Since the launch of ChatGPT and its newest version, GPT-4, one thing is clear, the possibilities have become endless and are growing every day. For UX designers, AI tools using language models are advancing search results, contributing to brainstorming, and even generating user profiles—resources that are key in building intuitive user experiences. It is especially helpful for designers who work in isolation or in small teams. AI can be a gateway to eliminating designers’ block or the blank page syndrome, enabling designers to push their own limits. Here are some limitations of using ChatGPT in UX:

  • Limited number of solutions: Responses generated by ChatGPT are limited by the phrases or play of words one uses. Noting the requirements, and analyzing the requirements, processing solutions can have many combinations. GPT can give, let’s say, the top five or best three solutions to the “text” that is generated, but if the intent is not communicated, it needs further prompting. Even so, human intervention in understanding, deciphering, and taking the responses forward is still required to translate the answer into a successful experience.
  • Dependency: There is a large dependency on the availability of the internet, appropriate devices, bandwidth, and even a successful registration. This means at any given point, the progress made on interacting with ChatGPT could be lost.
  • Security: Despite its advanced problem-solving and accuracy, the question of security compliance is crucial, especially at organizations that deal with need-to-know data or an NDA structure. Collecting and storing data is the backbone of training the AI, but it’s also a major caveat that may expose sensitive business information.

While ChatGPT has its limitations, and the newest version has addressed some of them, it is definitely proving to be a great tool in the UX designer’s toolkit. It is a time saver in the UX research process and can be used as a virtual assistant to discover missing features from an app, propose a questionnaire for interviews, provide statistics for a survey, and much more.

Leveraging Image Models in UX

Image models are another aspect of AI in UX. While UX leverages data analytics to develop ideal solutions, aspects like gathering data for customer feedback, performance, and sales numbers or understanding common patterns, trends, behaviors, and pain points during CJMs, though significant, take away from the creative process. This is where image models come in.

Applications like Midjourney and Dall.E help designers design prototypes and ideate so they can focus on the more creative aspects of the process. These applications also ensure that UX professionals don’t get lost in the necessary yet taxing process of analytics.

For instance, Uizard’s AI, though not free, helps one create mockups by converting hand-drawn sketches into digital screens. It also converts a screenshot into an editable design. This can be used to create a unique design that aligns with the specific needs of the client. Ideation or quick mockup timelines are reduced since designers don’t need to create each element of each screen from scratch.

User research or user testing can get tedious and time-consuming, and AI image models are now doing the heavy lifting. In Maze 2.0, the designer only needs to copy and paste the prototyping link to start creating real-life user test cases for their design. These tests are customizable to check for specific queries. The platform tests and gives actionable insights to the designer for further action.

Why Do AI Image Models Matter?

AI image models have the ability to combine text into attributes, styles, and mostly, a concept or imagination. The accuracy of the “artificial” translation over a human translation will still be a matter of discussion, but the variations, image generations, and almost immediate result output are where its biggest value proposition lies. Although it uses existing works of art to produce a remarkable output, unlike in text, the ethics of producing inspired work have been a point of discussion.

Since AI requires a large data set to be able to generate results, it is alleged that a large amount of artwork is used by Midjourney without the consent or compensation of artists. One can argue that inspiration from other artists’ works has been the backbone of art creation, and what Midjourney does is no different. It is the lack of tangible originality that sets human artwork apart from that created by AI. This speaks to the authenticity, originality, emotions, and ability of the creator to take elements of inspiration and infuse their unique experiences into their original creation.

Can AI tools replace UX or UI Designers?

As established, AI is delivering remarkable output in response to well-crafted prompts, but its greater potential lies in complementing and assisting designers rather than replacing them. As an added resource in the designer’s toolkit, it can help them breeze through the initial design phases and ideations with much more precision and a lot less trial and error.

For the final design decisions, human intervention will remain key since it requires that added touch of real-world experience and an understanding of emotions and motivations that AI cannot yet produce.

As UX designers, we are at an exciting intersection of technology and creativity and need to accept the value that AI will add to UX.

Conclusion

As Don Norman says, “artificial intelligence still has the word “artificial” in it and it is still designed, processed, and made by “human” minds.”

AI will become an integral part of the design process, and its machine learning abilities will be deployed across voice, gesture, text, image, sign, sound, vision, touch, thermal, motion, expression, and much more. As designers, it needs to be able to free up our time and mind space by taking over the more mundane and analytical aspects of design, leaving us to delve deeper into human creativity and the spirit of innovation. The goal is to push the envelope and craft harmonious machine-human interaction to provide efficient solutions in the best interest of the users and businesses.

Looking to build modern customer experiences using latest technologies?

We're Here to Help

Get Started

vector_white_1
Think Tomorrow
With Xoriant
triangle triangle triangle triangle triangle
Is your digital roadmap adaptive to Generative AI, Hyper cloud, and Intelligent Automation?
Are your people optimally leveraging AI, cloud apps, and analytics to drive enterprise future states?
Which legacy challenge worries you most when accelerating digital and adopting new products?

Your Information

3 + 16 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

Your Information

1 + 2 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

Your Information

3 + 0 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.