UX In The Age Of AI
There’s no denying that two of the hottest topics at the moment are Artificial Intelligence (AI) and User Experience (UX).
At the time of writing this article there were 66.684 AI and 58.769 UX related job listings on Linkedin. On Glassdoor, there were 1.012 AI and 16.686 UX job listing in the U.S. alone.
I think the data speaks for itself, but how well do the two disciplines work alongside each other. Considering that one is based on logic, focused on autonomy and possible “de-humanization” of the solutions, whereas the other is based on emotion, cognition and completely focused on the human experience? It might be logical to consider them incompatible.
This article will explore the relationship I believe will develop between AI and UX in the coming years to create better and more valuable solutions.
To AI or not to AI
Much like any new technological trend, your first instinct might be to start experimenting right away or even implementing an Ai strategy as soon as possible to stay ahead of your competitors.
Before moving ahead with anything, you need to understand if it really makes sense to move forward with something or forget the idea for a later time. The truth is that AI might not be the right solution for your brand or product. It might not even be ready to solve the problems you want it to prepare.
I recently asked the opinion of a fellow designer I admire who is active in the field and was pointed in the direction of this great article written by Kathryn Hume in the Harvard Business Review. Kathryn shares great advice and a formula on how one might analyze whether there exists an opportunity or not to invest in AI.
Another important aspect when considering AI is at what point in development does the discipline find itself and how it might evolve in the following years. The Axion Zen team recently posted a great article that explains in very simple and straightforward terms where AI finds itself and some aspects of it’s base structure, which is definitely worth a read. Article
The bottom line, think before you leap.
Humans communicating with machines
Moving on to the actual practical applications of AI and how UX can be the perfect complement, let’s first look at the communication between humans and machines.
The title above might seem to imply that we humans need to learn how to communicate with machines in order for them to respond in the expected manner, however I wish to explore the aspect of how machines are able to understand humans.
Currently machines have a long way to go before they can understand the way humans speak as easily as we understand each other. Natural Language Processing (NLP) as it’s known, is still baffled by tone, similar sounding words with different meaning, changes in subject and so much more. In this article in the economist, the author explores the evolution of NLP over the years, it’s current a capabilities and how far machines need to evolve.
The role of the UX professional will be to work hand in hand with engineers to help “educate” the machines about the intricacies and nuances of how humans communicate. Trying to empart empathy and “human” traits to systems that are based on logic and make sure that the human factor is never forgotten, rather than forcing humans to speak like machines.
Machines communicating with humans
The reverse side of communication between humans and machines is the way machine communicate with us.
For humans, the way machines communicate with us can make all the difference in whether we are delighted, frustrated, uncomfortable or simply creeped out. The subtle differences are so small that one can easily drift into one of these domains and create the most incredible experience or irreparable damage to a brand.
As referred in the same article of the economist, many of us might have recollection of machines having a mechanical voice as depicted in many blockbuster movies (2001: A Space Odyssey, The Terminator etc.), but we’ve also seen depictions of the flipside of when machines communicate in a way that is too similar to humans (Her, Ex Machina etc.). Truth be told, these are fanciful sci-fi movie that explore the worst possible outcome for dramatic purposes and have helped in feeding many myths around the dangers of AI.
Looking at the basic analogy these movies make, if machines seem to cold to understand humans, or are too similar to humans, we have difficulty trusting the machines.
Once again, to create the best experience, we’ll need to bring the humanity and the empathy to the machines, but in a way that will maintain the user comfortable and secure, while bringing actual value.
We’ve seen some experimentation with bots and personal assistant, where machines try to communicate naturally with users. In the most successful examples, the machines inform the user that they are in fact machine. If done well, the user loves the options that a machine offers while still being understanding that it is still a not human.
Where it can and often does go wrong, is when the machine is unable to help or understand the question, just because it isn’t phrased correctly or doesn’t contain recognisable keywords.
Here the UX professionals will be paramount to designing the way these situations are handled so as to reduce the discomfort of the user.
UX Augmentation
So far we’ve explored the general impact AI is having, how UX can help AI to evolve and lastly I thought it pertinent to discuss how the possible ways that UX will evolve with the help of UX.
Reading Kathryn Hume’s article, the obvious impact will be taking some of the mundane tasks that UX professionals have to trudge through in order to get some of the insights that will help them in creating the solution. Repetitive tasks such as competitor/baseline analysis or analytics analysis might become automated to an extent.
Going further though, it might be worth exploring how AI is going to take UX to new levels by Augmenting our current capabilities. I believe we’re moving towards a future where the UX professional has more time to focus on the aspects that only a human can do and reaps the rewards of having incredible computational power to sift through and objectively analyze data.
One interesting example is the solution created by iMotions, that uses AI to aggregate biometric research for easier comparison and analysis. The research data is presented in a way that is easier to extract insights, thus being able to focus on various research resources at the same time.
Solutions such as the one presented by iMotions, will help us extract quantitative data from qualitative research, something which hasn’t always been easy in the past. With objectivity and logic we will be able identify more complex patterns and user needs. It will then be up to the UX professionals to apply empathy and human traits to the all the insights obtained.
Done well, UXers will be able to do more with less effort, focus more on the emotional, thus being free to create even more innovative solutions.
Conclusion
It would be great to be able to tell a machine to “design a new app to organize my schedule” for example, but the truth is much more complex than that. Despite the incredible strides we’re seeing, there still doesn’t seem to be a universal solution that “does anything and everything” at the click of a button.
UX should take an active part in helping AI in understanding humans and making the interactions between AI and humans more comfortable for the users. But more than that UX is also poised to reap the rewards of ever evolving systems that present more and more complex solutions that become themselves a living system, while maintaining an intuitive and simplified user interaction.
I suggest UX professionals begin to understand where machines can help and where they as UXers need to accept AI as part of their bag of tricks and not focus only on the negative, as something that will turn systems into cold, monotonous and calculated monsters.
I believe that if we want to defend the user’s needs, we have to step up and make sure that AI evolves in the right direction towards a mutualistic symbiosis.
