Using AI to Improve Mobile User Experience

Pseudo AI technologies are on the rise and are affecting the way we go about our tasks and entertain ourselves. There are several examples of artificial intelligence in our day to day lives. It’s important to know that they are just machine learning software that runs on behavioral algorithms and adapt to things we like and don’t like.

A true AI learns on its own. Google’s deep mind aims to combine neuroscience and machine learning without relying on predefined algorithms to build an AI that we are used to seeing in science-fiction literature and films.

There is no doubt that a tiny glimpse of AI can be recognized in mobile technology. Personal assistants like Alexa and Siri have become part of our voice and non-voice based interactions. With their wide acceptance, the data is used to improve their skill set and usefulness.

AI In Design

AI will center around optimization and speed. Mobile app development efficiency will increase significantly and designers can craft designs faster and cheaper. Its primary function in the initial phase would revolve around

• Analyzing vast amounts of data and
• Suggesting design adjustments

AI design tools will scale the prototyping in design.  Once the basic sketches are scanned with few parameters, a library of established UI components would seamlessly render a prototype for a company’s product. An AI algorithm could generate millions of unique designs for products graphic identity.

AI would also assist designers to create 3D AR VR worlds with few parameters involved. The best bit about AI inclusion is that it would present designers with multiple options to choose from.

 Designers Will Boost User Experience

A subset of AI, machine learning renders self-learning ability to systems so that they can improve themselves without being programmed. It focuses on the development of programs that learn for themselves with the large chunk of data at their disposal. This next-gen mobile experience will raise the level of customer experience and have already given UX designers a free hand to channel their creativity. By delivering intuitive and automatic responses derived from user behaviors, machine learning driven technology can identify the design sketches, wireframes and prototypes in seconds to convert them into codes in real time. Likes of Airbnb and Nutella have already followed this suit and others will join the party soon.

Designers will boost by leveraging:

Shared Language

The product has to align with the shared vision to create a meaningful and intelligent user experience. Machine learning development with product design and goals have to be prioritized and understood by the whole team. Experts from both fields can be instrumental in optimizing the machine learning model that produces more accurate personalized content for the end user. Fine tuning a user experience design without a shared language or technical expertise will have its own drawbacks.

Use Case

Use case methodology is an effective way to analyze, identify, clarify and organize systems with their requirements. A high-end consumer product is that which prioritizes user experience. Mapping out a use case for app features requires UX designers and machine learning experts to draft multiple prototypes before finalizing the design. With the input from data engineers, designers and data scientists, features have to be tested and improved in every development phase. It also helps determine KPIs that are closely associated with the metrics of machine learning.

Data

User experience design and machine learning can be fully optimized when both, qualitative and quantitative data are used to full effect. Qualitative data such as questionnaires, user interviews, feedback and reviews on product features can shed light on how users feel and interact with your app. Any factors that affect machine learning development and user experience can be improved through quantitative data. For e.g., whether its a particular feature that is not being responsive to user behavior or its the feedback loop that is ineffective. Robust data renders a wider perspective on user behavior and is essential for designers and machine learning experts to create cutting edge apps for the future.

Conclusion

AI has been called by many names in the Mobile world. Wired magazine calls it AI-Driven design, while others Design Intelligence. Others have named it Algorithm- Driven Design. The state of progress that we are witnessing is more of “Augmented Intelligence” than “Artificial Intelligence”. It’s wiser to stick to the former for the short term. Irrespective of how it is coined, in the end, it is simply non-human intelligence capable of generating results that seem real to the human eye.

Many software companies have started offering end-to-end mobile app development services to leverage the potential of AI in healthcare, retail, and entertainment industries.

Designers co-creating with AI will present us with exciting opportunities. With the combination of art, engineering, science, and design, it will establish new relationships between customers and products. Users are demanding a more-in-depth personalized experience. With the power of mobile, designers will define the context for innovation with exciting new opportunities lying in front of us.