There’s a common pattern in high-minded essays about how AI tools will alter journalism, like this one recently published at the Reuters Institute. After doing the requisite spelling-out of the transformative power of AI and comparisons to previous technological change, there’s usually a point where the piece generally takes a swipe at the supposedly low-hanging fruit of creating efficiencies producing stories.
This is understandable, given the problems we saw in 2023 with publications trying to use AI to write full articles, which mostly ended in disaster. But criticizing this practice is really more a condemnation of the current media landscape and less about the AI tools themselves, or the efficiencies they bring to the table. Underlying the criticism is an assumption that many, if not most publications are primarily interested in using AI to push out more “low-quality” content (think rewritten press releases or pure SEO fodder) with the same or even fewer editorial staff.
While that’s certainly true to a large extent, the fact that many websites base their strategy on pumping out dreck is a symptom of a problematic information ecosystem, one that’s warped incentives over the years to the point where the substance of the information being created matters less than it being “optimized” for whatever platform.
Usually the author of one of these AI strategy essays then offers up a sort of counterweight to the “let’s do more dreck” idea: that AI-minded publications can and should focus their efforts on developing “entirely new” processes, formats, and tools, positioning themselves to unlock the most fruitful rewards of an AI-driven future. That sounds good on paper, but these recommendations are almost always entirely vague. What are these incredible AI-driven experiences that will enhance both reporting and the reader experience? No one knows.
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My view is that AI newsroom strategy today is best applied with the pragmatic spirit of unlocking efficiency, but not at creating more dreck. Instead, media AI tools should be selected and tailored to what journalists actually do. While disposable, optimized-beyond-all-recognition content is a tragically large part of the media landscape today, real reporting is still the entire basis of the news ecosystem. It can benefit from AI as well, and not in a purely theoretical way. Today’s generative AI tools can aid reporters in news gathering, production, and distribution of stories, all without relying on it to write actual stories.
Applying AI to Real Journalism
At the news-gathering stage, reporters have been assisted by data-mining tools for a while, but now generative AI can complement them with speedy document analysis and summarization. Some newsrooms have created AI-based tools that can analyze emails, sort them, and prioritize the important ones. And many public databases now have front-end chatbots that can surface relevant information quickly, without needing advanced knowledge of all the nuances of what the database contains or how it’s organized.
For news production, AI can speed up many parts of story creation without doing the actual writing. With the right prompting and notes, generative tools are very useful at suggesting story ideas and specific angles. Similarly, once a draft is written, AI can highlight the weak spots and gaps in your coverage. And while copy editing mileage may vary depending on the tool, proofreading to a style guide is child’s play for AI. Fact-checking can also be a great use of AI, which may sound counterintuitive, but newer tools are starting to show they may be more reliable than human fact-checkers.
Finally, optimizing a story for various platforms is a reality of today’s ecosystem but something most reporters treat as an afterthought and aren’t necessarily skilled at. AI is well-suited to coming up with headlines and summaries for search and social, reducing this thankless task to merely selecting an option from what the AI comes up with and tweaking it.
All of this can be done today, not in some imagined AI ecosystem of the future. With a pragmatic view of AI tools, reporters can use them to create better stories faster, and in a way that doesn’t lean on chatbots to write articles for them. Certainly, as AI becomes more intertwined with all the ways people get information, we’re going to need new definitions and relationships and even do high-minded things like unpacking what the “atoms” of journalism are. But even with today’s legacy platforms and problematic incentives, there’s a clear path to implementing generative AI to not just unlock efficiencies in producing “content,” but enhance both the quality and quantity of real reporting.
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