There is a lot of conversation happening around Artificial Intelligence, Machine Learning, and using algorithms to shape the future of Design and the role of the designer. But how is that changing the way we work in the near future?
The end is near”, according to specialists in robotics and artificial intelligence. Not really the end of the world itself, but the fact robots will be taking over a portion of jobs currently occupied by humans.
Futurist Thomas Frey, as an example, predicted in a TEDx talk that 2 billion jobs will have disappeared by 2030. Just to put it into perspective, that number represents half of all the jobs in the world.
Yes. Because of robots.
Robots are not replacing designers Well, at least not in the near future.
When you look at Design, things are a bit more complex than that. Humans have this unique ability to set the context for our designs and create empathy for other users.
Instead of a problem, a series of opportunities.
Let’s talk about them.
Yes, a designer’s job sometimes includes legwork. Just by looking at what my team is working on at a certain point in time I can tell approximately 20% of their time is spent solving for problems that could be easily automated by a robot with artificial intelligence.
Cropping assets, resizing images, colour correcting photos — some tasks cannot be automated by a simple Action on Photoshop since they require human curation and human eyes that are able to make quick decisions as these tasks are happening.
But what if we could teach an AI to do that job for us?
Adobe has recently announced Sensei, it’s Artificial Intelligence that will help designers become more efficient at what they do.
Adobe Scene Stitch, for example, identifies patterns in the image to help designers patch, edit, or even completely reinvent certain scene.
Or the Context-Aware Crop, that ensures the subject of the photo never gets cropped out by accident, for example.
Or Netflix’ automated translation, that speeds up the process of content localization. When they need to create multiple banners for a show in different languages, all the designer has to do is to look at hundreds of layout options previously created by robots, to say whether they are approved or not.
Artificial intelligence can help make your design system even more robust. If you’re not familiar with the term, a Design System is a series of patterns, modules, and elements that, combined, build the design language of a brand or product.
From enterprise to startups, companies are trusting design systems more and more to keep their products consistent for users: Teams like Salesforce, GE, Airbnb, WeWork, Google, Atlassian, and IBM are redefining how design teams are working together on Design Systems — just to mention a few examples.
Now imagine adding an intelligence layer to these systems that can analyze metrics on how users interact with each of these elements, and immediately “understand” which one works best for each function. The more this AI learns about what’s working and what isn’t, the more it can start to optimize each of these modules to make sure they deliver better results.
You have probably seen tools like the Artisto or Prisma apps, that apply intelligent filters to photos and videos based on image recognition tech. The technology identifies whether a photo contains a human face of a lemon pie, for example, and defines the best visual effect to apply to either of these.There’s a whole generation of apps like these, powered by the tech that can create dynamic and generative visual styles.
Another example of auto-generated visual elements powered by AI is Auto Draw, one of Google’s AI experiments that auto-completes your sketches and turn them into more polished versions of what you are capable of sketching with a mouse in a few seconds. This is only possible because of machine learning: the more people engage with the tool and draw their own sketches, the more the artificial intelligence learns about what users are trying to draw.
Technologies like these make the design more accessible to more people. Designers (and non-designers) can increase the quality and polish of what they are trying to create, without spending a lot of energy doing it— another example of AI being used as an assistive tech, and not try to steal your job.
“There’s a whole generation of apps like these, powered by the tech that can create dynamic and generative visual styles to enhance the designer’s capabilities.”
Websites are getting smarter and taking multiple user data points into consideration to enable more personalized experiences for visitors: time of day, where users are coming from, type of device they are accessing from, the day of the week — and an ever-growing list of data points and signals users don’t even know about. Triangulating all these factors can give you creative insights into what users are more likely to be looking for when they land on your site.
But these decisions on what to look at / what to do with that information have always been done manually by a team of strategists, designers, and technologists who are thinking through possible use cases and scenarios.
“When machines start taking over that part of the process, the ability to scale use cases and make them hyper-personalized will become more viable and accessible for more companies.”
More personalization in the user experience usually means more relevance for users, which leads to better conversion rates.
There are more and more systems out there: sites, apps, digital services. And more users. Every time a user interacts with one of these systems, data is generated. Lots of data. The growth of business intelligence has only begun; data analysis processes will become increasingly complex, cross-referencing more refined and more valuable data sets that will help designers and product owners make more informed design decisions.
In the near future, a lot of the process of collecting and analyzing data will be done by artificial intelligence. That doesn’t mean we will need fewer analytics specialists, but instead that the same number of analysts will be able to make way more refined (and deeper) analysis about users’ interactions with a product or service.
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