Generative AI won’t take your UCD job, but it might change it

🌈 Abstract

The article discusses the implications of generative AI for UCD (User-Centered Design) professionals and how they can adapt their practices to accommodate this new technology. It explores the potential benefits of using generative AI in public services, as well as the challenges and considerations that need to be addressed.

🙋 Q&A

[01] The Buzz Around Generative AI

1. What are the key points the author makes about the current state of generative AI projects in the public sector?

• Most generative AI projects in the government context are still in the proof of concept stage
• User feedback primarily relates to attitudes rather than real-life behavior
• Trust in generative AI and AI literacy remain low
• The public sector is still figuring out the risk appetite and tolerance for error when using generative AI, as well as when and how to integrate a 'human in the loop'
• The author suggests we need more time to understand generative AI's place in our world before confidently declaring it as the solution to user problems

2. What risks does the author identify with the "solutionising" around generative AI?

• There is a risk that stakeholders may see the experimentation with generative AI as evidence that it is the de facto solution, even if there is no evidence that it will meet user needs
• This affirms the continued need for UCD skills to ensure user needs are not lost to misplaced excitement over new technology

[02] Adapting UCD Practices for Generative AI

1. How does the author suggest UCD professionals should adapt their practices to accommodate generative AI?

• Continuing to prioritize user research to ensure a solid understanding of users and their needs
• Keeping space for solution-agnostic ideation to avoid falling into the 'innovation trap'
• Rethinking how to prototype and test services with users, as the unpredictability of generative AI responses makes it difficult to define fixed scenarios and tasks
• Spending more time trying to understand AI behavior as well as user behavior during synthesis
• Potentially blending the traditional double diamond design approach with more hypothesis-driven, rapid experimentation to accommodate the need to learn about both user problems and generative AI capabilities simultaneously

2. Why does the author suggest UCD professionals should increase their AI literacy?

• The most effective experiments have been when UCD teams are embedded with the data scientists and developers building the AI models
• This increases their pace of learning about generative AI and ensures they better understand its two-way relationship with users, allowing them to harness it appropriately to meet user needs

[03] The Continued Importance of UCD

1. What is the author's view on the future of UCD in the "age of AI"?

• The author is not concerned that generative AI is developed enough to be taking UCD professionals' jobs
• Instead, the author becomes more convinced that UCD is a critical part of holding innovation to account and ensuring new solutions solve real problems for users
• While UCD practices may need to adapt, the author does not believe the profession is at risk