Understanding artificial intelligence has become an essential skill for a media leader.

That isn’t simply because you need to determine whether to allow scraping of your website, whether to sue for copyright, or if you should do a deal with a company like OpenAI. It’s also because you need to figure out which aspects of AI you’ll use in the service of impactful journalism and audience engagement. AI will reshape the media landscape, and the organizations that use it creatively will thrive.

Reporting tools

A news organization is only as good as its stories, and AI offers opportunities for new reporting discoveries and alert systems. AI generally isn’t appropriate for reporting — especially in the physical world where new knowledge and information aren’t already on the internet, where AI lives and operates. But here are some great examples of AI tools that help human journalists write deeper, faster, more interesting stories.

First, to discuss an area near and dear to my heart — investigations. AI and machine learning models can be used to find things on the internet that would be unspottable by even the most diligent reporter. At The Wall Street Journal, we found this to be true in investigations we did about algorithms — like this one, which examined how TikTok’s feed works, or this one on Google. In addition, reporters are increasingly coming into possession of large data files, and using AI tools to query them is a no-brainer. In every case I’ve seen, there’s plenty of human-only reporting to do on top of this sort of AI investigative digging. AI is just part of the puzzle solving.

A different type of AI reporting tool is a story alert system. This can apply to all sorts of topics, but at The Wall Street Journal, we used it for business reporting. We built a stock movement alert tool. When our models noticed stocks of companies moving in ways that typically indicate news, our system pinged the relevant beat reporter in Slack so he or she could hit the phones and see what’s going on. It’s a great way to break news.

There will be many other AI-assisted reporting tools that get created. News organizations should have in-house experts who understand the level of care good journalists put into reporting and who under AI enough to ideate, build and evaluate new tools.

Content creation

This is perhaps the trickiest area, and also the area most people have focused on in recent months. Flubbed attempts by publishers like CNET and Gannett have gotten lots of attention. In general, people tend to react to new technologies by thinking of their most obvious use cases, especially ones that cut costs — but those obvious uses are often the ones where new tech falls down. In general, more success may be found by thinking more creatively about how to use new technologies like AI.

Content creation is one of the areas where creative uses of AI can have the most impact on improving what we provide to audiences. In my consulting work, I advise news companies to focus on using AI to create new content they wouldn’t have had otherwise. Rather than focusing on how to get AI to do all the things human reporters do, why not make new types of content that will engage readers, but that are beyond what your human teams can create?

One example: Personalized content. No news organization can have its best reporters manually create content that’s tailored to every reader. But there are occasions that readers want personalized content. When I was at The Wall Street Journal, a great example of this was the reader hedcuts we introduced in 2019. (The image illustrating this post shows reader photos in the process of being turned into art.) The Journal is famous for these hedcuts — pen-and-ink dot drawings of people’s faces — and runs them alongside many news stories.

Usually, only famous people get a WSJ hedcut. We liked the idea that any subscriber could have one, but of course, the Journal’s human artists couldn’t create that many. The solution? AI. We used a GANs model to create a tool that converted uploaded headshot photos into personalized drawings. Read more about the AI methods behind the hedcuts here. (GANs are now superseded by diffusion models like Midjourney and Stable Diffusion, and I’ll be doing a more technical post on some of these technologies next week.)

Plenty of types of written stories can be individualized to the audience. At the Journal, we experimented with the “Flexicle,” where articles expand out with personalized information.

User tools

For all the controversy in the industry about whether news companies will do deals with OpenAI and other tech giants, there’s already been a deal between a technology company and a news outlet that worked great.

In 2020, The Wall Street Journal partnered with Amazon Web Services’ machine learning lab to create a user-facing tool that would answer the public’s questions about political candidates. That effort grew out of a tool that our teams had created for the reporters in the DC bureau to fact-check politicians using the Factiva news database. Our consumer research showed that readers were interested in fact-checking candidates, too, so we created a tool where people could ask questions and get answers.

This tool, which we called Talk2020, was an example of something we couldn’t offer readers without AI. The models behind it were an early deployment of technology that we now see in language models like Claude and ChatGPT, and our audience engagement was fantastic. It gave our marketing team something unique to advertise during the presidential debates and led to more engagement with our stories, which we promoted alongside it.

Analytics tools

Newsrooms are increasingly paying attention to audience data to inform their coverage. That’s good in some cases, but this data can also be blunt and misleading. At the Journal, we used neural networks to create a topic model that allowed us to make more sophisticated recommendations about what the data showed. The idea was that an AI model could help us learn a lot more about what our stories were about, beyond the manual tags editors had put on them.

Only the beginning

It is unlikely that media companies will be the place of deep technological innovation with AI, in part due to cost constraints. (Media companies are unlikely to spend $150 million on GPUs to train large language models from scratch.) But news companies can benefit by hiring people who know how to use these technologies and can think creatively about how they improve news experiences. Next week, I’ll share a technical article on some of the AI open source systems that I think are most important for news product and engineering teams to be examining.

We are still in the early days of what the news industry will do with AI, but let me leave you with one big thought. AI will change user behavior too. The public will begin acting differently online and come to have different expectations online. News organizations need to look to AI not only as a way to improve what they are doing today but also as a way to adapt along with the changing public.