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WSJ News Bytes
WSJ News Bytes is an AI-powered news intelligence product built on top of The Wall Street Journal’s reporting ecosystem. By combining summarization, sentiment analysis, conversational AI, and trend detection, it transforms traditional news consumption into a faster, more intelligent experience.
Role
Product Designer
Industry
Media & Publishing · AI
PLATFORM
Web & Mobile
TIMELINE
6 weeks
Challenge
Modern professionals rely on trusted journalism to stay informed about markets, technology, policy, and global events. However, the volume of daily news makes it increasingly difficult to keep up with the topics that matter most.
Readers often face several challenges:
Limited time to read multiple full-length articles
Difficulty connecting related stories across industries
Lack of visibility into broader trends and sentiment shifts
No easy way to explore deeper insights across multiple articles
The challenge was to design a system that could surface the most important signals in the shortest amount of time, without sacrificing the depth and credibility WSJ readers expect.
Process
I began the project by working closely with product leadership and editorial stakeholders to understand how AI could enhance the news experience without compromising journalistic integrity. Through collaborative workshops, we explored how readers discover information, what signals they rely on to understand emerging trends, and how AI could accelerate insight discovery.
During discovery, I facilitated affinity mapping sessions to organize user needs and product opportunities. These sessions surfaced recurring themes around speed, personalization, and insight discovery. From there, I ran feature prioritization and value-vs-effort mapping exercises to help the team identify which ideas would provide the most value to readers while remaining technically feasible.
That process shaped a product roadmap built around six core capabilities: AI-generated article summaries, sentiment analysis, cross-article trend detection, personalized topic alerts, a conversational Deep Dive interface, and source-linked transparency throughout.
To validate the experience with stakeholders, I used this project as an opportunity to integrate AI tooling directly into my design workflow. Rather than building a static click-through prototype, I used Figma Make to produce a fully interactive React app that simulated the complete product workflow across web and mobile, letting stakeholders and test participants experience the product as a live, responsive application.
Outcome
Concept validation sessions with 8 knowledge workers confirmed the core design decisions. AI-generated summaries quickly became the primary entry point for understanding news, with participants using them to decide which full articles were worth reading. Source transparency proved critical for trust, with participants regularly opening linked WSJ articles to verify information behind AI-generated insights. The Deep Dive conversational interface allowed users to explore topics and ask follow-up questions more efficiently than traditional search.
The live React app built with Figma Make is available to explore via the link at the top of this page, and played a key role in aligning product, engineering, and editorial stakeholders around the vision before development began.
Back
WSJ News Bytes
WSJ News Bytes is an AI-powered news intelligence product built on top of The Wall Street Journal’s reporting ecosystem. By combining summarization, sentiment analysis, conversational AI, and trend detection, it transforms traditional news consumption into a faster, more intelligent experience.
Role
Product Designer
Industry
Media & Publishing · AI
PLATFORM
Web & Mobile
TIMELINE
6 weeks
Challenge
Modern professionals rely on trusted journalism to stay informed about markets, technology, policy, and global events. However, the volume of daily news makes it increasingly difficult to keep up with the topics that matter most.
Readers often face several challenges:
Limited time to read multiple full-length articles
Difficulty connecting related stories across industries
Lack of visibility into broader trends and sentiment shifts
No easy way to explore deeper insights across multiple articles
The challenge was to design a system that could surface the most important signals in the shortest amount of time, without sacrificing the depth and credibility WSJ readers expect.
Process
I began the project by working closely with product leadership and editorial stakeholders to understand how AI could enhance the news experience without compromising journalistic integrity. Through collaborative workshops, we explored how readers discover information, what signals they rely on to understand emerging trends, and how AI could accelerate insight discovery.
During discovery, I facilitated affinity mapping sessions to organize user needs and product opportunities. These sessions surfaced recurring themes around speed, personalization, and insight discovery. From there, I ran feature prioritization and value-vs-effort mapping exercises to help the team identify which ideas would provide the most value to readers while remaining technically feasible.
That process shaped a product roadmap built around six core capabilities: AI-generated article summaries, sentiment analysis, cross-article trend detection, personalized topic alerts, a conversational Deep Dive interface, and source-linked transparency throughout.
To validate the experience with stakeholders, I used this project as an opportunity to integrate AI tooling directly into my design workflow. Rather than building a static click-through prototype, I used Figma Make to produce a fully interactive React app that simulated the complete product workflow across web and mobile, letting stakeholders and test participants experience the product as a live, responsive application.
Outcome
Concept validation sessions with 8 knowledge workers confirmed the core design decisions. AI-generated summaries quickly became the primary entry point for understanding news, with participants using them to decide which full articles were worth reading. Source transparency proved critical for trust, with participants regularly opening linked WSJ articles to verify information behind AI-generated insights. The Deep Dive conversational interface allowed users to explore topics and ask follow-up questions more efficiently than traditional search.
The live React app built with Figma Make is available to explore via the link at the top of this page, and played a key role in aligning product, engineering, and editorial stakeholders around the vision before development began.