In Journalism in the Age of AI, Rodrigo Zamith, Tomás Dodds, and I make the case that artificial intelligence is reshaping how we create, how we learn, and how we understand the world around us — and that journalism, the institution society depends on most to make sense of it all, is at the center of that transformation. But we also think the way journalism and AI are developing together right now isn’t working. It’s largely accelerating a broken system that has been grinding journalists and public trust down for decades.
Our book, published by Polity, argues it doesn’t have to be that way. AI can also offer journalism a rare chance to break that cycle and build something better. We draw on questions of creativity, democratic accountability, platform power, and human agency to offer readers a toolkit for reimagining journalism in the age of AI — not just as an industry, but as a profession dedicated to serving the public good.
The book engages with AI’s impacts on multiple levels, covering everything from the hands-on realities of AI-assisted news production and the shifting architecture of media power to AI’s implications for democratic life, journalism education, and the profession’s future. Below is a portion of Chapter 2, which examines how AI is already reshaping every stage of news production and asks whether these tools will reinvigorate journalism or merely accelerate its hamster wheel. This excerpt focuses on emerging applications of AI across five stages of the newsmaking process — story ideation, sourcing, verification, storytelling, and distribution — highlighting both their transformative potential and their limitations.
You can download a digital copy of Journalism in the Age of AI from our website for free starting August 6, and purchase paperback copies starting November 6 in the U.K. or January 19, 2027 in the U.S.
Smaller organizations have also stumbled. In November 2025, reporters at Suncoast Searchlight, a Florida-based nonprofit news organization, sent a letter to their board of directors after discovering their editor-in-chief’s undisclosed use of AI editing tools — which included instances where the AI inserted hallucinated quotes into reporters’ drafts. Some staff members were unaware that AI was being deployed on their work at all, raising important questions about transparency, editorial control, and ethical boundaries.
These uses of AI illuminate the emerging complications around authorship, accountability, and transparency — questions that grow more vexing as AI becomes so integrated into workflows that some people no longer consider it worth mentioning. While the need for disclosure is often underscored in debates about AI-generated news, the thresholds become murkier as AI permeates routine support activities and sometimes even directly modifies journalists’ work without their knowledge or consent. The line between tool and author therefore continues to blur, challenging foundational assumptions about who is responsible when storytelling goes wrong.
AI and distribution
Media distribution fundamentally shapes who can access journalism and under what conditions. For decades, news organizations had considerable direct control over their distribution pathways. However, the rise of digital intermediaries — search engines, social media platforms, news aggregators — has dramatically altered this landscape. Today, algorithmic systems increasingly determine which stories reach which audiences and provide the marketplace for monetizing them, forcing news organizations to adapt their editorial practices to accommodate the demands of algorithmic distribution. At the same time, AI empowers news organizations to rapidly remix their own content for multiple platforms and to expand accessibility.
Optimized distribution: Media publishers have traditionally owned large segments of their distribution channels. They often owned the printing presses and the delivery trucks. They owned the cameras and microphones to record the news, as well as the antennas to beam their stories across the country. With the advent of the internet, newsrooms also had significant control over their own websites. As social media platforms started to gain popularity in the 2000s, though, media publishers gradually became more dependent on platform distribution by tech companies like Google and Facebook, including their social media feeds, search engine results, and news aggregators.
Thus, news organizations have had to manage their own proprietary distribution channels while optimizing their content for algorithmic visibility. Some newsrooms even required reporters to build personal social media followings, effectively transforming individual journalists into distribution channels. Meanwhile, audiences increasingly encountered news through algorithmic feeds, where platform logic, not editorial judgment, dictated the final presentation.
Several news organizations have adapted by integrating AI into their distribution through two main strategies. Algorithmic optimization uses AI to decode platforms’ shifting preferences, advising journalists on ideal formats, optimal publishing times, and strategic keywords to maximize visibility and reach across platforms. Targeted delivery harnesses AI for precision audience segmentation, enabling organizations to serve personalized content to specific demographic or interest groups.
BBC News exemplifies this approach. In March 2025, the broadcaster announced a new department dedicated to AI-powered personalization. As then-CEO Deborah Turness wrote to staff: “We must become ruthlessly focused on understanding our audience needs, on delivering the kind of journalism and content they want, in the places they want it, designed and produced in the shape that they enjoy it.” The path forward, she argued, requires deploying “AI to support, enable and accelerate our innovation and growth.”
It isn’t just major organizations doing this, either. The Rural News Network, a collective of more than 500 local news nonprofits across the United States, created Text RURAL to solve a distribution challenge. With member organizations producing nearly 5,000 stories monthly, the AI-powered tool automatically selects, aggregates, and summarizes the most relevant content for individual users based on their location and interests. It then delivers personalized weekly news roundups via text message. It’s a practical solution that makes local journalism more accessible to rural communities underserved by traditional distribution channels. However, it is an automated solution that bears risk, as even major publishers have discovered in their experiments with AI summaries.
Automating content adaptation: News organizations are also using generative AI for automated adaptation, or the rapid generation of derivative products tailored to different platforms and accessibility needs. AI systems can automatically produce social posts, video summaries, audio versions, and translations from content originally created by human journalists. While these derivatives are typically reviewed before publication, they nevertheless reduce the time, cost, and labor required to meet audiences across multiple platforms while expanding accessibility.
Dow Jones Newswires launched a custom AI language service in 2024 targeting this exact opportunity. The system produces what it describes as “fluent” translations of hundreds of English-language stories daily into Japanese, Korean, and French. “It gives us an ability to very quickly, almost instantaneously, translate our very rich existing English-language content into a language that will attract other audiences,” observes Chip Cummins, The Wall Street Journal’s chief Newswires editor. The service reaches professionals “who prefer to read their business news in their native language or who are suddenly tapping into the US market as a place where they want to invest, or their clients are investing.”
Similarly, the U.S. television station WCPO 9 uses AI to automatically reformat content for different platforms. “AI helps us take horizontal video storytelling you might see on our broadcast or YouTube and convert it to vertical on Instagram or TikTok,” notes senior manager PJ O’Keefe, “so that, again, we can make as big of an impact as possible, on as many platforms as possible.”
Dow Jones’ approach includes transparency measures: each translated article carries a warning about AI translation and links to the original story. However, as WBEZ reporter Araceli Gómez-Aldana observes, current models still struggle with the “accuracy of the translation for news stories, cultural understanding, translating quotes for tone and understanding — not only a literal word-by-word translation — and adapting to various dialects and literacy levels.” In other words, there are still risks associated with this kind of functionality.
Despite these powerful capabilities, what AI cannot do is liberate news organizations from platform dependencies. No matter how sophisticated their AI tools become, newsrooms cannot alter the ethical, economic, or political decisions made by platform owners regarding content moderation, algorithmic demotion of news, or sudden rule changes. Platforms can unilaterally reshape their algorithms — as Meta has done repeatedly in prioritizing and then de-prioritizing news content on Facebook — leaving news organizations scrambling to adapt their carefully crafted AI-optimized strategies to new, often opaque, rules. This structural power imbalance constrains AI’s potential to transform news distribution in ways that truly benefit journalism.
