AI-powered audience listening for better product-market fit
OUR VISIONThe rise of AI is a pivotal moment for the news industry to lead, not follow. We’re not waiting for AI tools that meet our journalistic standards. We’re building them.
The News Product AI Collaboration Lab is a community-led initiative, stewarded by the News Product Alliance in partnership with the Patrick J. McGovern Foundation. Together, we’re developing open-source AI infrastructure to solve one of journalism’s most urgent challenges: understanding and engaging audiences to build sustainable news organizations.
Newsrooms increasingly rely on audience data to guide editorial and business decisions. Yet for many — especially small and local outlets — that data remains fragmented and unusable, locked across platforms without the infrastructure to connect it. Fixing this foundation is key to unlocking AI’s full potential.
That’s why the Co-Lab launched the Audience Data Commons — a shared, open schema for organizing first-party audience data. It gives newsrooms a common language to use their data more strategically, ethically, and effectively with AI.
This is just the beginning. By aligning around shared infrastructure, co-creating open tools, and embedding journalism’s values into the systems we build, the NPAI Co-Lab is ensuring the next era of AI in news is powered by collaboration — not dependency.
Let’s build a future where AI serves journalism — not the other way around.
THE CO-LAB APPROACHWe catalyze collaboration and innovation across the news industry through three key activities:
Developing Tools
& Standards
We steward the development of the Audience Data Commons — an open-source schema developed by Co-Lab members and Newsroom Robots.
Partnering
on Pilots
We collaborate with newsrooms and vendors to test real-world applications of the Audience Data Commons and open source AI tools.
Sharing
Solutions
We publish case studies, guides, and open-source resources to help all news outlets improve their audience data infrastructure.
Guides & Resources
Explore our growing library of resources designed to help newsrooms build stronger audience data foundations and prepare for responsible AI use.
START HERE
PRACTICAL GUIDES
CASE STUDIES
The Audience Data Commons
News organizations rely on audience data to make smarter editorial, engagement, and revenue decisions. But most newsrooms—especially small and local outlets—are held back by fragmented, siloed, or poorly structured first-party data.
That’s where the Audience Data Commons (ADC) comes in.
The ADC is a shared, open-source standard that helps organize first-party audience data like survey responses, newsletter signups, comments, and donations. It allows newsrooms to:
Connect data across systems using a common structure.
Experiment safely with AI, using your own data with any LLM or local setup.
Protect privacy, with clear practices for handling personally identifiable information (PII).
Collaborate across the industry, using interoperable formats everyone can build on.
The Audience Data Commons is an industry catalyst: by aligning on a shared foundation, we can unlock new opportunities, reduce duplication, and ensure that AI in journalism is built with our values at the center.
Explore the open source repository of the pilot version.
PARTNER WITH US
We’re inviting newsrooms and vendors to pilot or adopt the Audience Data Commons — a shared framework for structuring first-party audience data to power ethical, AI-informed decisions.
Partner activities may include:
Data Mapping – Align your existing data (e.g. newsletters, donations, surveys) to the ADC.
Workflow Testing – Use schema-aligned data to power real editorial or product decisions.
Tool Integration – Connect your platform to the ADC through APIs or command-line tools.
Implementation Feedback – Help shape the schema’s evolution with real-world insights.
As a community-led initiative, we helped kickstart the Audience Data Commons, and our goal is to transition stewardship to partners who can own and maintain the tool for long-term industry benefit.
With the support of the Patrick J. McGovern Foundation, we’re able to offer support to partners, including technical help, financial incentives, and co-creation of resources based on your use case, while crediting your team for contributions that support the wider field.
Reach out to us directly at hello@newsproduct.org to learn more. NPA Slack members can also find updates and join us in conversation in the #ai-tools channel.