“AI shines in problems where the goals are understood, but the means aren’t”
Designing products with Gen AI presents an opportunity for me to engage with AI technology, addressing both its potential and challenges to create user-centered applications and meaningful experiences.
As AI rapidly transforms industries, companies often rush to implement AI without considering the user experience, making design more crucial than ever.
To navigate this landscape effectively, we must quickly become knowledgeable about AI and its implications.
While AI presents significant potential, its adoption also brings complexities and ethical considerations. It is essential to design AI systems that prioritize user needs and uphold human values.
In my role, I strive to bridge the gap between design and AI disciplines, identifying opportunities to leverage Gen AI for user, business, and societal benefits.
I apply design thinking principles to AI concepts, ensuring alignment with user needs and ethical considerations. Additionally, I collaborate closely with data scientists and ML engineers to create AI systems that enhance user experiences and uphold human values.
Due to confidentiality agreements, several of my design case studies involving Gen AI products are not publicly available. However, I have developed a design process that outlines how I refine AI-related products. For more detailed information on these experiences, please feel free to reach out to me directly.
For the ideation stage:
Explore the potential of AI
How can we utilize it to address challenges, discover possibilities, and generate benefits for users, the community, and organizations? What's the optimal way to harness the potential of Gen AI? Which of its functionalities align with the project's requirements?
- User-centered problem solving
Initially, I ensure alignment with user needs and context, laying the foundation for research and identifying potential user challenges to address. This phase involves investigating how AI could enhance solutions for user needs.
- Tech-driven opportunity spotting
Spotting opportunities for Gen AI to create value involves integrating machine learning and Gen AI into the design of tech-driven innovation. Recognizing which problems align with AI capabilities can unveil new possibilities.
“AI shines in problems where the goals are understood, but the means aren't.”
Yonatan Zunger
- Data-driven opportunity spotting
Data forms the bedrock of machine learning and GenAI. By exploring various data sources, including both private and public datasets, we can uncover opportunities to enhance user experiences. It's essential to prioritize data security, ensure ethical data usage, and maintain transparency in algorithms and processes. These considerations are paramount and have been reinforced through my experience!
For the assessment stage:
Understand feasible, viable, and desirable of ideas
Some tools help me evaluate which direction to move forward with the team.
- according to the desirability and feasibility
The matrix maps help with idea selection by assessing the intersection of factors such as user value, desirability, and responsibility and the investment needed for feasibility and viability in machine learning.
- according to the value for users
Validate ideas through user testing or surveys to ensure we are addressing the correct problem and delivering value to users. Iterate and refine based on feedback and insights gathered from user testing.
- according to the feasibility
Assess the feasibility of ideas, determining whether it's more efficient and effective to utilize GenAI or rely on human intervention.
- according to the computational way
By creating a basic flowchart of the usage process, we can start outlining the possible inputs, outputs, and logical steps needed for the model to generate value. This process also helps identify any assumptions and uncertainties.
For the prototype and validation stages:
Evaluate the practical utility
Through the utilization of prototyping, user research, and testing methodologies, we can effectively assess whether the designed solutions meet user expectations, evaluate user comfort levels regarding adoption, and facilitate the secure sharing of personal data. My goal is to validate these experiences in real-world scenarios to ensure the efficacy of adaptive AI systems and establish comprehensive design and implementation specifications for users.
“Designing without understanding the user is like building a key without knowing which door it unlocks.”
Donna Spencer
For the design and implementation stages:
Transform the idea into the real world
How can we convert user needs and requirements into algorithmic solutions?
What factors and compromises should be taken into account during the design phase?
How do we synchronize requirements among teams and stakeholders while maintaining a user-centric approach?
“I think AI will necessarily have to embody human values because it will require human feedback. So I think it's impossible to avoid the human element when talking about AI.”
Fei-Fei Li, director of Human-Centered Artificial intelligence, Stanford Institute
I draw inspiration from a multitude of visionary designers, skilled engineers, knowledgeable scholars, and esteemed organizations, including Design with Google, Yonatan Zunger, and Nielsen Norman Group.
Check out my other related works
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SanDisk Memory Cards Showcase Project
Check out my Visual Design Works