Over the past three years, almost all of the discussion on the use of AI in media has been about efficiency: how much time AI can save you when it helps with story ideas, research, production, headlines, social copy, and even, to some extent, writing. All of those use cases are about doing basically the same thing, only faster.
No shame in any of it—efficiency is a worthy goal, and artificial intelligence can help immensely in advancing it. But AI can also open up novel ways of achieving the ultimate goal: connecting and engaging with audiences who find your information valuable. This is “opportunity AI,” and it’s something I was inspired to write about after my former colleague NLW dedicated a recent episode of his AI Daily Brief podcast to it. He was speaking to a broad audience, but I’m interested in what opportunity AI means for the media specifically.
I also think this is an opportune time for the media to think about new ways of applying AI, because the efficiency gains haven’t exactly won over the goodwill of readers. While some newsrooms are seeing impressive numbers in terms of output (one African digital publisher told an industry conference that a single reporter’s weekly output jumped more than 150% after adopting its in-house AI tools), the experience for readers is ultimately the same as before: an article. Except now they’re looking for AI tells. Opportunity AI in media means treating the article as just the beginning.
Your archive is a product now
When generative AI was new, the novel opportunity that became the default was the chatbot. All kinds of publications experimented with chatbots—from The Washington Post to my own—but the ones that still exist point to the right way to think about the experience: not as a chatbot first, but as a way to turn the publication’s expertise into a product.
This obviously favors niche titles especially, and a great example is Nursing Times, a trade publication for nurses. It launched its answer engine, which draws only from its clinical and news archive, in February 2024. According to Robin Booth, the publication’s managing director, it had fielded more than 200,000 questions by mid-2025, with about 100 subscriptions directly attributable to the feature. A revealing stat: 80% to 90% of the usage came from suggested questions embedded in articles, not an open chat box. Readers don’t want to interrogate an archive; they want the next question answered right where they are.
Similarly, Skift’s “Ask Skift” feature has existed since 2023. It’s built to answer the questions of travel professionals, fueled by the publication’s more than 11 years of reporting, research reports, travel companies’ financial filings, and more. It’s meant to turn Skift’s most engaged readers into users, CEO Rafat Ali wrote.
But how would you put the site-based chatbot through a 2026 lens? That’s what the independent newsletter Lenny’s Newsletter has done with “Lenny’s Data,” offering its archive of 370 posts and 317 podcast transcripts through an MCP server that paid subscribers can connect to their personal AI, whether that’s Claude, ChatGPT, or something else. That way, the reader can invoke that expertise at any time, or the AI can reach for it when the context is relevant. Lenny’s Data is probably better described as a creator business instead of a newsroom, but it’s the cleanest illustration of an archive becoming a product rather than a destination.
In a similar vein, local outlets could turn a beat or an ongoing story into something readers can participate in rather than just subscribe to. The Texas Tribune turned its ongoing coverage of school vouchers in the state into something readers could ask about, with their questions sometimes influencing future coverage. Every time the chatbot was forced to answer “I don’t know,” the question was forwarded to the reporting team, which could turn it into a new story. The first question it couldn’t answer, about how vouchers would affect the state’s teacher retirement system, became exactly that.
For an international example, Agência Mural in São Paulo worked out a way to take public weather and flood data, add its editorial judgment, and deliver personalized WhatsApp alerts to five underserved communities. This is service journalism for the age of AI: a local publication creating a continuous decision service instead of a stream of stories.
The video desk you can build yourself
Creating a sustainable video strategy has been something of a holy grail in digital media organizations. Even a modest operation typically requires a team of highly skilled individuals to create, manage, and distribute the content. To make the reporter-narrated short-form videos that are becoming more common, publications like The New York Times and The Economist can hire staff. Publishers without dedicated resources usually need to settle for a clipping tool.
That is, until now. AI models and tools have become sophisticated enough to edit video on their own. In theory, a reporter could shoot a few minutes of crude video on a smartphone, and a custom pipeline will cut it to 60 seconds, clean up the audio, and add captions and branding. Once approved, the AI can publish and share it across social networks. In practice, NLW already does a version of this: The AI Daily Brief ’s clips run through a pipeline he built himself with Claude rather than a commercial tool, and he is not a coder.
Short videos are hardly a new idea, but this is still an opportunity rather than an efficiency. For most publications, nobody was making these videos before—so there was no workflow to speed up. With AI handling the bulk of production, reporters can become visible in a format audiences are moving toward while the newsroom retains ownership of the whole pipeline.
Many will fail, and that’s fine
A pipeline, however, isn’t the same as presence. And using AI to create new opportunities also means some of them will fail.
Case in point: A research center at the University of North Carolina at Chapel Hill partnered with four local outlets to build chatbots with software that cost about $40 a month. Over 45 days, the bots got just 185 queries total, and about a third of the conversations included a question the bot couldn’t answer. Three of the four outlets dropped theirs when the program ended.
Politico’s venture into opportunity AI was much more expensive. Its custom-report tool could build bespoke publications based on the detailed reporting of its Politico Pro library. However, it sometimes created material that included hallucinations, so the company agreed to shut it down in May as part of its arbitration with its staff’s union.
What separates the experiments that survive? For starters, the cost—and thus the risk—is relatively low. In almost every case, the tools are given a narrower job than simply dumping an “ask us anything” chatbot in front of readers. They’re placed where readers have a reason to ask specific questions (a WhatsApp group or an article page), and there’s a feedback loop back to the staff, so the tool can improve.
The Opportunity AI test
If you’re thinking about a use case and not sure if it’s opportunity AI, there are a few questions to ask:
- Do readers get something they could not get from you before, or the same thing faster? If the answer is faster, it’s not opportunity AI.
- Could you have afforded to try this a year ago? If yes, it isn’t opportunity AI—it’s a project you never prioritized.
- Does it make your reporters more visible or less? A new video pipeline for first-person short videos? Yes. A chatbot that brings new ideas to the staff? Yes, indirectly. A rewrite AI where the reporter shares the byline with a bot? No.
- Is the subscription still the unit of payment? Almost every example that works so far bundles the new product into an existing subscription or experience. Nobody has proved readers will buy “AI” on its own.
None of this requires abandoning the article. It requires admitting that the article was never the point. The point was always the reader who needed something, and for most of the media’s history, the article was the cheapest, easiest way to fulfill that need by a mile. That’s no longer true. A nurse can get an answer, a local resident can get a flood warning on a messaging app, a subscriber can put a decade of reporting to work inside a personal toolset, and a reporter can show up on a phone screen without a production crew.
The newsrooms that figure this out won’t be the ones with the biggest AI budgets. They’ll be the ones that stopped asking how much faster they could make the old thing, and started asking what the audience would do with the new one.
