This talk addresses the distinct challenges of designing AI-first products when the foundational technology is advancing at an unprecedented pace. We will discuss the practical implications of this rapid evolution on the design process and share our insights into creating effective user experiences in such a dynamic environment.

AI is fast transforming the way companies operate and design products and services. New design practices like “vibe coding,” or “prompt engineering,” are accelerating product launches at speed and scale. But with the rush to market; how can teams maintain their commitment to human-centered design, minimize the risks to consumers and keep up with the increase in developer velocity? We showcase various tactics companies are using to manage the risk of implementing AI into their products while keeping their ever tightening GTM timelines.

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.

Learn how Accenture’s GrowthOS accelerator partners with WEVO to simulate user responses at scale—cutting research time from weeks to minutes. In this session, Accenture will show how AI-powered testing of early concepts, Figma prototypes, and full journeys helps their clients identify friction, de-risk launches, and refine designs before development begins.

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.

Our team at Gazzetta, a media research lab, is tackling a fundamental challenge in journalism: the disconnect between media output and community needs, particularly in restricted or distorted information environments of autocracies. We have learnt over the past years that traditional audience research has led to quant-heavy, superficial understanding, ineffective content and, ultimately, irrelevance.

To address this problem, we have developed a three-stage process using AI knowledge bases to build empathy, map information needs, and analyze information flows. We have used this process to systematically review multiple information sources to build deep community understanding before product development.

This methodology has helped us preserve nuance, identify knowledge gaps, and assign confidence levels to findings. Rather than treating AI as a black box solution, a thoughtful process-oriented approach can help us better understand and serve information needs, and gradually rebuild relevance.

Product Context Analyzer is an AI-first solution that automates the discovery and analysis of product usage context, instantly synthesizing insights into industry-standard requirements models. By transforming raw inputs like tasks, personas, user journeys, or interview transcripts into structured design inputs, this tool dramatically reduces reliance on time- and labor-intensive primary research. It empowers any team, at any time, to generate actionable user insights in minutes, enabling continuous learning about user needs and scaling research operations across an organization. In this session, we’ll demo how to go from raw input to clear, structured requirements and user stories and illustrate how AI is reshaping the way teams develop a more inclusive and deeper understanding of users to make the right strategic and tactical design decisions to deliver successful products.

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.

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Whether you’re looking to expand your network, meet your next client or connect with collaborators, this is your opportunity to make it happen.

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