By Jeff Piazza, SVP, Experience Design, Orion Innovations
Designing exceptional user experiences involves research, strategy, conceptualisation, design, and testing. Given the speed digital products get released, it is critical to optimize the time teams have to execute each step in the pipeline. A flurry of AI tools has been introduced with the capability to automate tasks such as summarizing research findings, reviewing image suggestions, and executing variations of design ideas and tests. AI can free up the design team’s time and provide more freedom for them to focus on higher-value, strategic activities.
At Orion Innovation, our Experience Design team started an exercise to explore the use of AI tools for the entire UI/UX design process. Our goal was to deliver an end-to-end solution, using AI technology for everything from user research to final UI delivery. Read on to learn what we discovered in the process, our key takeaways, and our recommendations for integrating AI into user experience design workflows.
What we learned
Operational benefits cannot be ignored
As we set out to complete the full-cycle UX design process through AI, one benefit stood out to us: speed. For example, gathering and analyzing user data can be time-consuming, and it is easy to overlook crucial insights. Product and solution design often involves addressing complex issues with no clear solution. By leveraging AI, our team was able to navigate the complexities of user needs and quickly grasp what the target users are struggling with. On top of streamlining research, we were also able to cut down on lengthy prototyping cycles and analyze usability issues early on. This set us up for success and eventually helped us reduce the overall completion timeline by 30%.
Quality is more important than ever
Despite the drastic improvement in productivity, we encountered challenges in ensuring quality, accuracy, and consistency in the results. Our exercise required us to generate several design elements for a platform, and AI was often providing generic designs that weren’t relevant or useable. Not to mention the concerns surrounding AI in privacy, security, compliance, and ethics. AI is effective at speeding up manual processes, but activities should be complemented with strategic thinking—a designer to come up with the right prompts, double-check the accuracy and effectiveness of output, and constantly iterate to ensure the final solution aligns with requirements and key outcomes.
Good experiences are not guaranteed
When we think about what makes an experience truly exceptional, the answer is what it always was. Think about some of the best apps or websites that you use on a daily basis. These platforms excel because they understand and anticipate user needs, providing seamless, intuitive interactions that delight users—while they accomplish goals. At the core, these experiences are simple, intuitive, and familiar, which is not automatically guaranteed with AI technologies. Our exercise reiterated that good design doesn’t happen by chance. It requires a well-led team of skilled designers who can leverage the right AI capabilities to create user-friendly experiences. This reflects the Human-in-the-Loop (HITL) approach—or perhaps more appropriate in this case, Designer-in-the-Loop. This involves the designer orchestrating each activity within the process and ensuring it aligns with the strategic vision of the product.
Integrating AI: Where to start
Imagining the capabilities of AI to accelerate design workflows is difficult due to its vast and abstract potential. It can be hard to pinpoint where to start when it comes to getting started. In our exercise, we found AI to be most effective in certain phases and activities, such as summarizing user research findings, organizing solution features through card sorting, and brainstorming ideas. As the tools continue to advance, the focus will shift right and support production-oriented work to manufacture high-fidelity design and code at scale. Every team will have different needs and will benefit differently from AI tools. To start, it is important to understand and consider what’s possible with AI to accelerate design innovation:
⦁ Conversational UI: Create more intuitive and engaging user interactions through chatbots and voice assistants.
⦁ Computer Vision: Implement image recognition to improve the usability and functionality of visual elements.
⦁ Recommendation Systems: Personalize user experiences by suggesting features based on preferences and behavior.
⦁ Speech Recognition: Integrate hands-free interaction capabilities to enhance accessibility and convenience.
⦁ Reinforcement Learning: Optimize user interfaces and decision-making processes through adaptive learning models.
⦁ Expert Systems: Provide users with specialized, context-aware assistance in areas like healthcare or finance.
⦁ Autonomous Systems: Develop self-operating features that streamline user interactions and automate repetitive tasks.
In our work with Fortune 500 businesses, we found that clients often start integrating AI with non-core use cases as an experiment to boost confidence. Start with quick wins to set design teams up for success and help them scale more effectively with AI technologies.