AI-driven UX design is one of the hottest trends for 2019. That said, product designers are not among the jobs and specializations expected to be replaced by robots soon.After all, coming up with the right product idea and executing it is a complex process that combines creativity and advanced analytics.
Some tedious product design tasks that can be automated quite easily. On the other hand, deep learning methods are introducing new possibilities of human-machine interaction, as well as open new opportunities for creating autonomous, self-learning products. It's only a question of improving the effectiveness to start a revolution now. Introducing AI can cut product development costs and time to market. Additionally, it can improve the quality of the design substantially.
AI solutions are already disrupting many markets – health and legal services, logistics or telecommunications are but a few examples. If a machine learning algorithm can give a better diagnosis than experienced doctors, maybe it can design products?
Let's take a look at some of the AI uses in product design that we here at Netguru think can make an impact.
Speeding up and improving the design with automation
Product and UX designers do a very creative job. Still, much of their work time is tedious data analysis. Digital products, such as websites, web and mobile apps generate lots of user data that are very useful.
UX teams perform A/B tests, use advanced heat maps, conduct surveys, and usability tests. All this data can be cross-analyzed with the use of machine learning methods, which opens up amazing possibilities.
Data analysis will help best product redesign projects where you have lots of data available, and often it's difficult to optimize the processes more. This usually happens with e-commerce UX design. In many cases, too much data hinders the design project, since it's difficult to digest these amounts of data. Sometimes it's better to forget about it. AI can change it, as designers will be able to collect more data and make decisions based on analyses made by machines. When it happens, the UX design team will finally focus on the most exciting assignments and increase their productivity.
Minimizing the interface thanks to a better understanding
The most efficient machine learning algorithms and frameworks are based on human brain anatomy. They are called neural networks for a reason. Products using AI can feel more familiar and friendly to people, creating trust and seamless relationships.
Interfaces and human-machine communication are important issues for digital product designers. Although the technology has evolved from type-in text-only interfaces to mouse-based visual ones to tapping and swiping, the real revolution is yet to come, as, in a way, we are still "operating" our laptops and smartphones rather than talking with them and speaking out commands. This can finally change with the development of AI.
Neural networks are getting better at understanding natural language – both written and spoken. Designers developing products for Alexa, Siri or Google Assistant ecosystems already work in an utterly new environment. This will grow further as voice interfaces will become more accurate and begin to understand our natural language – not the voice commands they taught us. Voice interfaces will lead to frictionless UX – a trend that seems unstoppable as voice search is already more popular than typing among the youngest generation.
Apart from voice, modern digital products can communicate with the environment through image analysis. The investments in self-driving cars stimulate the development of this technology. Millions of visual searches are performed on Amazon every day already.
Opening new features for digital products
Neural algorithms can perform some of the most challenging tasks that hardcoded algorithms were not able to crack. They cover the usual problems – such as spam filters – but also let the machines reason from images and videos and make predictions based on data.
These achievements can be beneficial to product designers. Some AI-based apps are not only able to see – they cab also understand the surroundings, can recognize objects, use them or navigate through the city. Algorithms are also very efficient in reading emotions from people's faces.
By feeding machine learning algorithms enough data you can teach them to make predictions. This is the main Google Assistant feature that has been promoted for years, but finally is approaching the stage it is useful. Machine learning advisors can plan your day for you – arrange the calendar, or prepare the optimal route home.
Accurate predictions create entirely new opportunities and challenges for the designers as they can not only automate many user tasks but also come up with new solutions and products based on the prediction modules.
Including product's self-improvement in the design process
Just like your spellcheck, keyboard autocomplete or Tesla Autopilot, new products using machine learning algorithms can learn on their own. Most contemporary products do it, which is an additional challenge for UX designers.
If the idea is for the product to start working correctly after it's fed enough amount of user's data, how to provide a quality experience in the meantime?
Auto-learning, in the long run, reduces the development time; however, since at some point, the program is evolving autonomously, it has to reduce the number of manual updates by developers.
This trend will also lead to strengthening the personalization of digital products. After being launched or sold, they will evolve and develop themselves based on user data.
Agile design-centered product development is the key
The challenges and opportunities brought in by AI to the product design industry are unique and sophisticated. That is why the design teams need to get ready for a very dynamic environment in 2019. That is why they need to be more agile than ever before. It is the key to involve all the stakeholders in the design process as soon as possible. Everyone needs to understand the possibilities that machine learning solutions bring to the table.
Machine learning is based mostly on open frameworks provided by Google, Facebook, or Amazon. That is why designers should focus on collaboration rather than competition with each other. Each product should be designed for interaction.
Future digital products will grow and evolve on their own. They will learn based on user data; that is why you should respect your user data privacy, and be very careful to control your product's evolution. AI brings an entirely new product design, and that's why working in the industry has never been more exciting.