AI in Journalism

If you’re in PR and not paying attention to what AI is doing to search and news, you’re in for a rude awakening. AI-powered search isn’t just “tweaking the game”— it’s in the process of rewriting the rules. From how publishers decide whether to allow AI to crawl their content, to how your clients or company get discovered, this shift will change the way PR pros operate in 2025 and beyond. AI-powered search will reshape how companies and clients get visibility—and PR pros need to adapt quickly. Here’s what’s happening, why it matters, and how you can stay ahead: 1️⃣AI Search Engines Are the New Gatekeepers: Tools like Google’s Gemini and OpenAI’s SearchGPT prioritize aggregated content from trusted publications over individual websites. Your beautifully optimized website? Irrelevant if AI search decides it’s not worth surfacing. 2️⃣Publishers Deciding If They’re In or Out Big outlets like The New York Times and Wired are currently opting out of AI crawlers to protect their IP, while others allow it for traffic. This means PR pros need to strategically target outlets that feed AI models—because your story only gets told on the likes of SearchGPT if the outlet carrying it is in the AI ecosystem. 3️⃣PR Is Even More Crucial for the ‘New’ SEO: Placement in trusted media is no longer just about audience reach; it’s about ensuring AI search engines authentically and accurately pick up your client or company’s narrative. Strong media relationships will be the difference between AI surfacing your story—or perhaps leaving your brand out of the conversation, or even worse, misconstruing it. 4️⃣Crises Are on a New And Faster Clock: AI prioritizes recency and credibility, so your crisis response needs to be swift, transparent, and authoritative. A slow or ineffective reaction could leave misinformation embedded in AI models, compounding damage to your brand’s reputation. 5️⃣ What PR Pros Need to Do Right Now: Focus more on media outlets that AI trusts: Build deeper and non transactional relationships with publications and reporters already working with AI search to ensure your stories are seen. Closely Monitor AI trends: Stay ahead of updates in tools like Gemini, SearchGPT, and Perplexity so you can adjust strategies as more info emerges. Be proactive with publishers: Understand which outlets are allowing AI crawling and how that impacts your clients’ visibility. This is going to change rapidly in the coming months. The bottom line: AI search isn’t just changing how people find information—it’s going to force PR practitioners to immediately rethink how we interact with media, manage crises, and position brands for discovery. This is an underrated but important trend that will accelerate in 2025 and beyond. Anything I’m missing here? Please put in comments.

If you still had doubts that AI was going to play a major role in newsrooms, The New York Times just erased them. The paper is now officially allowing its journalists to use AI tools for specific tasks—things like generating SEO headlines, summarizing articles, brainstorming ideas, editing, and research, according to a report from SEMAFOR. But there’s a firm no-no list, too: 🚫 No drafting or major rewriting of articles 🚫 No uploading copyrighted third-party material 🚫 No paywall workarounds 🚫 No generative images or video (except to report on the tech itself) Reporters have a curated toolset to work with, including Google Vertex AI, GitHub Copilot, and a limited version of OpenAI’s API (only with legal approval). The Times also built its own summarization tool, Echo. The Times often gets knocked for being late to trends, but in reality, it’s been an early adopter of new storytelling tech—longform interactives, VR experiments, even an app for reading with gestures. So it’s not too surprising that it’s embracing AI, even while it’s suing OpenAI. AI isn’t just another platform—it’s a foundational shift in knowledge work. The Times sees the risks but also understands that banning AI doesn’t make it go away. Studies show that restrictions just lead to shadow AI—unregulated, unauthorized use. So it’s opening the door. The real test? Whether The Times can keep control of it when the first big AI-related error inevitably happens. But if any newsroom has the standards and policies to pull this off, it’s probably the Gray Lady.

It costs $211,900 to produce a news investigation. It costs $0.02 for an AI to extract its value. 10 million to one. That is the First-to-Know Tax. People debating whether AI can "do journalism" are missing the point: It doesn't need to. It just needs to wait 3 minutes for a journalist to do it first. Within minutes of publication, AI has crawled, indexed, summarized, and served the essential value, often before the reader even considers clicking through. News aggregation extracted attention. AI extracts comprehension. The user can now understand the story without ever visiting the source. The parasite needs the host. The host is losing money. But the host hasn't realized it still has the ultimate leverage. A handful of AI companies have started paying publishers. It won't matter. A licensing fee is a toll on a road that's being rerouted. The moment an article is published, its value can be extracted by anyone. No deal changes that. For 200 years, the business model of journalism was temporal advantage: knowing first. AI collapsed that advantage to a window of minutes. The monetization window after publication is 3 minutes. The monetization window before publication, the detection phase, is days to weeks. One window is collapsing. The other is where the entire value of the information economy is migrating. The only information AI can't extract is information that doesn't exist yet. The ratio for a daily news story is 158,000 : 1. What is it for a research report? A legal brief? An equity analysis?

The first news organisations that adopted AI all did so for a business reason and this could be why actual #newsroom adoption has been low or inconsistent. Most editors look at #AI with suspicion... as something imposed on them and not something they can trust or use to their advantage. Of course, AI can create content, but a newsroom is the last place it should do so. News organisations should protect their status as creators of credible primary knowledge and not outsource that job to machines. The smartest newsrooms use AI as a research assistant, data gleaner, and distribution agent. AI tools can also be used to translate with accuracy, summarise with a level of audience-based customisation, generate representational images with full disclosure, create video elements and audio where footage or clips are not available, as well as for marketing mailers, social posts, visualisations… but always with a human in the loop. If used wisely, AI can be a great force multiplier for news organisations, giving them an edge and speed that keeps them ahead in a competitive landscape. But using AI to create #content is like buying your death on a quick commerce site. 

Another thing I read recently: The AI and Journalism Research Working Group (of which my colleague Amy A. Ross Arguedas and I are a part), convened by the Center for News, Technology & Innovation has synthesised findings from 55 research studies across computer science, linguistics, and social science to evaluate how AI transcription and translation tools are currently impacting journalism. We found that AI use for transcription and translation is very common in news work and has arguably made a big difference to many journalists’ work, because these tools offer significant time savings compared to manual processes. For example the Houston Chronicle uses AI to summarise local public government meetings, A European Perspective at the EBU is enabling content exchange across 10 broadcasters, and Dubawa uses AI to help fact-check radio broadcasts in Ghana and Nigeria At the same time, we still see inequalities in the performance of systems based on the language spoken. Most tools are optimised for high-resource languages, primarily English, with significant performance gaps for low-resource languages (those with less available online textual data) which include languages spoken by hundreds of millions of people. Then there are issues depending on the accent and dialect of speakers, let alone when it comes sign language. Nuance can get lost as AI translation still often focuses on literal (referential) meaning while missing social functions (the indexical meaning) and cultural context (so e.g. ‘street food’ translated as ‘food of the road’), and bias can creep in, too. A good overview for anyone who wants a better understanding of the pros and cons and what can be – and should be – done to address them. Source: https://lnkd.in/eqdWbduS

As we look ahead to 2025, the landscape of journalism and technology is set for significant transformation. Here are key insights from the latest Journalism and Technology Trends and Predictions report by the Reuters Institute for the Study of Journalism and the University of Oxford: 1. AI Integration: 87% of newsrooms report being transformed by generative AI, focusing on back-end automation, personalization, and content creation with human oversight. 2. Shifting Platform Dynamics: Publishers are reevaluating their platform strategies. Sentiment towards X/Twitter has plummeted (-68 net score) while emerging platforms like Bluesky (+38 net score) are gaining traction. 3. Revenue Diversification: While subscriptions remain crucial, 36% of publishers expect significant income from licensing to tech/AI companies - double last year's figure. 4. Innovation Focus: News organizations are exploring new products, including games, educational content, and youth-focused offerings. 5. Talent Challenges: While 81% are confident in retaining editorial talent, there are concerns about attracting and retaining tech talent, especially data scientists and software engineers. 6. Audience Engagement: Combating news fatigue and adapting to conversational AI interfaces are key priorities. The rise of Bluesky in the social media landscape is notably interesting. This emerging platform has captured the attention of major news organisations, including The New York Times, The Guardian, and The Financial Times, which have already established a presence on Bluesky. These outlets frequently report higher engagement levels on Bluesky than on their accounts on X or Threads. This trend suggests a potential shift in how news is distributed and consumed on social media. Bluesky's growth key factors: 1. User migration: Dissatisfied users from other platforms, especially X/Twitter. 2. Robust moderation: The platform offers stronger content moderation policies. 3. Decentralized architecture: This feature gives users greater control and customization options. As we navigate these changes, the ability to balance technological innovation with core journalistic values will be crucial for the future of news media. This trend indicates a possible shift in how news is distributed and consumed on social media. What do you think about these insights? How do you perceive their impact on the way businesses communicate? Dinis Guarda Thomas Power Neil Milliken Sally Eaves Paidi O Reilly Tony Moroney Azita Esmaili Lynette Jackson 🚀 Ellen Schramke Ophelie Janus Dr. Ralph-Christian Ohr Kevin O'Donovan Paul Hobcraft João Morais Dalila Carvalho Marcus Borba Neville Gaunt 💡⚡️ Timothy "Tim" Hughes 提姆·休斯 L.ISP Efi Pylarinou Tamara McCleary Audrey DeSisto ARLENE NEWBIGGING GRADY Mei Lin Fung David Bray, PhD Frances West Eveline Ruehlin Jorge Cunha Luis Lancos Peter Torres Fremlin Shruti Pushkarna #Journalism #Media #Innovation #ArtificialInteligence

AI is reshaping digital news publishing, automation is cheap, content is commoditised, and search is being redefined, forcing publishers to rethink strategies for content creation and monetisation. At the final session of WordPress Publisher Success Week, I hosted a discussion on AI’s growing impact on digital news media. Pete Pachal (The Media Copilot) and Matthew Karolian (The Boston Globe) joined me, sharing insights on AI’s influence on content creation, revenue models, and search. AI-driven content is now widely accessible, reducing the need for mass-produced, low-value articles. Pete highlighted that while AI-generated “slop” exists, publishers are also leveraging AI for niche tasks. ESPN covers less prominent sports with AI-generated reports, and Quartz extracts financial data for quick news updates. While AI can automate routine tasks, publishers need to determine where it adds value beyond mass content production. Matthew underscored a critical shift: traditional content distribution models are failing. AI-generated search results reduce clicks, pushing publishers to seek alternative revenue streams. Future monetisation could involve original reporting that AI cannot replicate, subscription-driven models over ad-based revenue, and direct audience engagement via AI-powered tools. New AI-powered search tools like Perplexity challenge Google’s long-standing grip on information discovery. Unlike traditional search, AI-based tools provide direct answers rather than links, raising key questions. Will Google maintain its dominance? Can publishers optimise content for AI search engines? How will legal battles around AI training data shape the future? The truth is that AI is here to stay. To integrate AI effectively, publishers should: ·      Develop clear AI guidelines for content creation. ·      Experiment with AI chatbots for user engagement. ·      Leverage AI for internal workflows (e.g., summarisation, metadata tagging). ·      Test off-the-shelf AI tools before investing in proprietary models. Matthew shared how The Boston Globe uses AI-driven social media tools and WordPress plugins to streamline content distribution, proving AI can enhance, not replace editorial workflows. Here are key takeaways: 1.    AI accelerates commoditisation, making unique, high-value journalism more crucial than ever. 2.    Publishers must rethink revenue streams, with subscriptions and direct engagement replacing ad dependency. 3.    AI-powered search engines are reshaping traffic patterns, posing a challenge to Google’s dominance. 4.    Practical AI adoption requires experimentation and clear policies to balance automation with editorial quality. AI’s influence on news media is evolving rapidly. The time to act is now. How is your newsroom integrating AI? Share your thoughts in the comments section. #DigitalPublishing #AIinMedia #ContentStrategy #NewsMedia #ArtificialIntelligence

Alex Hardiman runs product at The New York Times. Its journalists got 500 hours of Trump campaign footage 10 days before the election. Impossible to watch in time. So they used #AI to transcribe 5 million words and surface key moments for their reporting. Now 90% of Times articles have AI-generated audio. International readers get recipes auto-converted from cups to grams. AI is everywhere in their products. But Alex is clear. "Journalists are out in the world finding information that does not want to be found. This is information that no LLM has access to." The Times owns the full stack. The journalism. The software. 12 million direct subscriber relationships. They chose subscription-first when everyone else was chasing clicks. Technology transforming media isn't new. How we use it matters. Watch the latest Pioneers of AI episode here: https://lnkd.in/edQizyH7 #AI #Journalism #PioneersOfAI

From Print-First to AI-First Without Losing the Soul. For decades, newsrooms were structured in silos, print over here, digital over there, social media somewhere in the corner. But the AI shift is forcing integration. Some Indian publishers are showing how it’s done: AI pre-writes predictable content — election results, cricket scorecards, budget outlines. A “multi-version desk” produces platform-ready content for print, web, and social at the same time. 100% AI-led experimental brands explore risky formats without affecting the main brand. It’s not about replacing editorial judgment — it’s about removing inefficiencies. The future newsroom will be fluid, with content created for multiple platforms from the very first draft. What’s exciting is that AI isn’t just helping speed things up — it’s allowing entirely new formats of storytelling to emerge. Interactive graphics, AI-assisted local language coverage, and on-demand explainers are just the start. Biggest takeaway: The future newsroom won’t be “print” or “digital” — it’ll be fluid, where stories are platform-ready from the start. *Part of a series based on sessions from a recent Google News Initiative conference, distilling key ideas, case studies, and takeaways for those who couldn’t attend.  Follow Kumar Manish for the next post in the series #AI #Newsroom #MediaTransformation #Storytelling #DigitalFirst #GoogleNewsInitiative #JournalismInnovation

Fascinating to see how this year's Pulitzer winners actually used AI. Spoiler: zero ChatGPT, zero article-writing bots. Four winners/finalists disclosed AI usage, but almost none used LLMs. Instead: - Wall Street Journal: used embedding models to visualize how Musk's 41,000+ X posts shifted from business content to divisive politics - "40 Acres and a Lie": a custom image recognition to read 1800s handwritten records and find 500+ additional land grant recipients - Washington Post Gaza investigation: geospatial AI to analyze satellite imagery and disprove Israeli military's narrative about journalist killings - AP "Lethal Restraint": OCR and speech recognition to process 200k+ documents and build national database of police killings These aren't parlor tricks - they're solving real problems that would be impossible to tackle manually. The future of AI in journalism isn't going to be ChatGPT writing articles. It's going to be expert journalists using specialized AI tools to uncover stories that were previously impossible to tell. h/t Andrew Deck for what could become an annual classic read ;)