AI tools open a scenario of change and bring numerous opportunities to design teams. The importance of continuing to focus on iterations and quality controls to achieve user-centered products.

It was not long ago that AI was installed massively and has not stopped generating debates, especially in sectors that see these tools as a threat that can make some roles obsolete. Since the popularization of ChatGPT and Midjourney, different technological giants have launched the race for innovation in AI, and professionals from all areas continue to be surprised by the increasingly complex tasks that these tools can solve in a matter of seconds, automating processes that previously depended on specialists.

But there is much more on the horizon and it is still early to measure the true impact of this technology, without falling into conjectures. What cannot be denied is that with new technological changes, resignifications in roles inevitably arise.

To be clear from the start, AI is not going to replace designers, but it is certainly going to define what skills are valuable in the future.

AI as we know it today is still taking its first successful steps, and we must not forget that new technologies have limitations and opportunities for improvement, which evolve with use and feedback from users. Rather than focusing on whether AI can replace designers, we should identify the potential for AI to be integrated into design processes, and what tasks design teams can automate by applying it.

What we know with data

We still do not have data to know with certainty the effects of AI in replacing some professions, and it is difficult for this discussion to have a real impact on market decisions. What we do know is that generative AI improves employee productivity by 66% based on an average across 3 studies. The most complex tasks are the ones that benefit the most from AI tools, and teams can find their greatest impact in activities such as content generation, information collection, user data for research or first drafts at the time. to design.

A big caveat to this number is that it only applies to those tasks in which AI can provide support today, so in areas such as UI UX Design agency, where there are still processes that AI has not covered, the benefit may be less. . But the future sounds so promising that it can increase productivity in AI-assisted design tasks by 100%.

Of course, quantity is not relevant information without quality. In that sense, the results of the studies cited above show quality indicators that are on par in tasks with and without AI. An interesting fact is that the use of AI reduces the gap between designers with more and less skills and experience. This becomes relevant if we think about what skills will end up making the difference when hiring a designer that stands out.

Although these are initial data, the first conclusions already announce a great impact of this technology.

Current AI use cases

As we mentioned before, AI is not going to replace designers, but those designers who know how to use AI correctly (and ethically) will probably be the ones who take advantage, as they will have a series of tools that will give them the flexibility in the face of any changes to adapt quickly. That is why it is important to know what uses already exist for AI tools and how we can integrate it into design processes.

AI has the potential to improve user experiences through automation and hyper-personalization. What are the current contributions that give us that indication? Let's look at some examples of how AI is impacting UX design:

Content generation: AI can help designers create text, images, videos or audio automatically or semi-automatically, from data or instructions. This can save time and resources, as well as make it easier to adapt to different languages, formats or audiences. Some tools that use AI to generate content are GPT-3 or Lobe.

Interface optimization: AI can analyze user behavior and preferences to propose improvements in the design of interfaces, such as color, typography, layout or navigation. This can increase usability, accessibility, and conversion, as well as allow for more efficient A/B testing. Some tools that use AI to optimize interfaces are Adobe Sensei, Google Optimize or Optimizely.

Personalization of experiences: AI can use user data and context to offer more relevant and satisfying experiences, adapting content, functionalities or recommendations to their needs, tastes or emotions. This can improve user loyalty, trust and loyalty, as well as generate added value. Some giants that personalize experiences are Netflix, Spotify or Amazon.

Creating virtual assistants: AI can create systems that interact with users via voice or text, providing information, assistance or entertainment. This can improve the accessibility, efficiency and empathy of services, as well as create new forms of communication and relationships with users.

A new paradigm in UI

The impact of AI will not only modify UI/UX Design processes, it will also transform the way we interact with interfaces in general. This at least is pointed out by Jakob Nielsen, who sees in Generative AI a new paradigm in UI, the first in 60 years.

Nielsen points out that there are three paradigms marked in user interfaces, since the birth of computing. The first is batch processing, which appeared around 1945, when there was no exchange between the user and the computer, limiting itself to sending batches of information through punched cards to data centers, which returned printed sheets as a response. to the request.

The second appears around 1964, and is command-based interaction. Here the user executes one command and takes turns with the computer, which executes another in response. This paradigm has dominated the last 60 years, evolving through graphical interfaces that were massively installed since 1984 with the Macintosh.

But the third paradigm, the one that would be emerging with Generative AI, is that of the specification of results based on intention. With one caveat, Nielsen does not believe that the current tools (ChatGPT, Bard, etc.) represent the UI we will use in the future, as they still have major usability problems.

In these systems, the user does not tell the computer what to do, he tells it what result he wants. In these prompts, the user does not specify how to achieve the result, reversing the locus of control from command-based interfaces. The problem is that the lack of information about the process, the lack of transparency and the biases of current AI make it difficult to identify and correct errors in the results.

For Nielsen, it is not clear whether these Generative AI systems can achieve high levels of usability, so they may end up coexisting with command-based systems, or mutate towards hybrid interfaces.

Regardless of this still blurry future, we cannot deny that this new form of interaction with the systems that AI brings comes to propose new ways of approaching technology, and without a doubt it will generate new dynamics in the processes as we currently know them.

The human factor

The big obstacle for today's AI is that, to trust its systems as a replacement for people, these tools need to show human values, ethical regulations, transparency, privacy controls, information security, traceability, equity and inclusion, context, and a host of characteristics that define the human experience.

In UX Design, informed decisions require experience, common sense and knowledge of real-life contexts to achieve in the different iterations connected with what the user is looking for in our products. In that, AI still cannot compete with humans.

To create truly intuitive interfaces, human vision will continue to be essential. Surely, the bulk of the work of the design teams will move from the first iteration to the second or third, since the processes will accelerate with the emergence of generative AI that can create the first sketches with higher quality and in less time than expected. was done until now.

But if there is one constant in the field of UI UX Design services, it is that there will always be a need to iterate, test, collect feedback and more, to understand what works and what doesn't. In that sense, Generative AIs are good for generating ideas; but they cannot refine the product to take it further.

So while the visual skills of designers will continue to be important, the distribution of value that organizations place on different skills will surely change.