NASA faced increasing complexity in managing data, meeting customer needs, and streamlining processes across its diverse missions and operations. With fragmented data sources, manual workflows, and a lack of integrated AI tools, teams struggled to maintain efficiency, consistency, and innovation.

Through a cross-organizational AI-readiness initiative led by the OCIO, NASA engaged stakeholders across Service Lines, Centers, and Mission Support Offices to identify challenges, articulate use cases, and prioritize AI opportunities. Workshops, assessments, and stakeholder collaboration provided a roadmap for integrating AI solutions, enabling secure data usage, and automating repetitive tasks. Focused efforts on training, governance, and iterative implementation ensured alignment with organizational goals.

The Challenge
Users engaging with the AI shopping assistant often felt constrained by limited options, excessive follow-up questions, and a lack of personalization. These shortcomings led to user fatigue, misunderstandings, and a subpar shopping experience. Insights from user research (UXR) and transcripts revealed that users wanted more intuitive, human-like interactions that catered to their unique needs.

The Solution
A robust, adaptable framework was designed to transform AI conversations into sales-like consultations. By breaking user queries into three core components—use-case, constraints, and preferences—the framework enabled the bot to understand intent and deliver relevant, personalized results. Key enhancements included:

Allowing users to skip questions and navigate freely.
Providing contextual help for technical queries.
Transitioning to open-ended interactions after gathering essential details to prevent over-questioning.
Displaying diverse and curated results aligned with user preferences.

LLMs are known for generating ‘original’ content by predicting the next most likely word. But their real power lies in generating thoughtful, nuanced feedback. Drawing from my background in writing and technology, I discovered that LLMs can be remarkably insightful critics when prompted with the right questions.

Using composite examples inspired by Hear Me Out’s culture assessments with leadership teams and employees, I’ll demonstrate how extended conversations with LLMs like Anthropic’s Claude help us pressure-test our research from multiple angles: anticipating participant concerns, surfacing potential misinterpretations, and identifying gaps in our analysis that could undermine stakeholder trust.

We built a novel math tutoring app for 11-year-olds in the UK. Since this was our first AI project, we expected lots of technical issues. Those happened and on top of it, we ended up questioning the value of designers in AI-driven products.

AI moves fast. In the time since we began programming DwAI25 in December, we’ve already seen important new developments like DeepSeek R1, Deep Research from OpenAI and Perplexity, and Mira Murati’s Thinking Machines Lab. By June, the terrain will have transformed even further. In this panel, we’ll learn about the latest developments in AI from representatives from major AI players, and what they mean for UX professionals.