Today Technology trends make users excitable to deliver desirable data based on their interests. Automation booms and makes easier human tasks wherever complication comes to humans. Artificial Intelligence (AI) and Machine Learning (ML) play a major role in studying user interaction, synthesizing data, and delivering data as personalized.
The automation field was begun in the 18th century and advanced rapidly in the 20th. Artificial intelligence is a form of automation and this was coined in 1956. Artificial intelligence serves for different industries to analyse, compute and cater based user needs. Eventually, AI improves the user experience that is implemented in the product. AI and ML also contribute to analyze user research data and the design process to make it easier for UX designer’s task.
AI in UX Process
User research is encapsulated with qualitative and quantitative research. These two types of research approach have been utilized based on a need basis.
Qualitative Research
Structuring and organizing the user feedback is a challenging task when Qualitative research will have complex data gathered from User interviews which will become complicated to synthesize from different feedback and suggestions. AI can be involved to organize the data and planning predictions.
Quantitative Research
UX designers conduct surveys to understand existing product pitfalls and usability issues. Questionnaires will be created and sent to a large set audience pool. Once they collected data from the audience then that data will become more challenging to synthesize since the feedback and suggestion can be biased among with audience. Artificial Intelligence (AI) algorithms will be more useful to distill and correlate to our needs and intention.
Define User Problem
AI can take up further steps to define user problems once received and synthesized the data. AI algorithms can access huge amounts of user data from different channels. This will leverage to define user problems and help us to find customer experience funnel and opportunities. Ultimately, AI UX design enables highly personalized user experiences. This enhances the value of your product to a wider audience of users.
AI developers can be developed various solutions to manage human mental and physical tasks. Example, industry giant Adobe has developed a time-saving stitch feature to stitch images together.
Let’s recap, AI can analyze huge quantities of data to find patterns. This provides valuable UX research insights efficiently and effectively.
UI Design
Let’s dive into creating AI-powered design solutions for defined problems from user research.
Important things need to keep in mind when we have AI in product design. AI solutions should have a trustworthy solution for our technological needs.
Mikael Eriksson Björling and Ahmed H. Ali, senior designers at Ericsson, defined trust come under following 4 pillars:
- Competence
- Benevolence and openness
- Integrity
- Charisma
The above pillars are more influential to the success our design solutions. User interface design should satisfy usability metrics Effectiveness, Effectiveness and Satisfaction.
UX designers need to be more ethically aware about their design which needs have adhere of AI. Summing up, trust is an integral factor in designing AI products. This trust can be built through proper user education, clear communication, flexibility, and integrity.
AB Testing
UX designer can arrive various design solutions along with options, but these design solutions should be evaluated with the end user who is going to use the product. Design validation is a time-consuming task when we have a complicated problem that needs to be fixed to favor user experience. AB testing or variant testing can be used to evaluate design with a different set of audiences and can be arrived feedback to improve the design.
Artificial intelligence (AI) algorithms can learn user mental models and based on that AI can predicate design solutions and understand user reactions.
Eventually, UX design involves a plethora of repetitive tasks. AI helps to automate many of these tasks, speeding up projects and freeing time for more innovative, big-picture tasks.
Conclusion
AI algorithms can help us easily understand user patterns and their feedback. AI can power up to bring intuitive design solutions through a proper understanding of user problems and defining solutions.