Quick Wins

This is the perfect gateway exercise for AI-curious journalists. Five minutes and you've already saved yourself 20 minutes of highlighter work. I love using this as a first step before I even decide if a press release is worth covering.

A useful efficiency tool, but remember: press releases are crafted to spin a narrative. The AI summary is only as good as the source material. Always cross-reference claims with independent sources before writing a single word.

I use this to quickly figure out what angle will resonate with our audience. If the AI summary doesn't surface anything my readers care about, that tells me something important about whether to cover it at all.

Headline writing is one of those tasks where AI genuinely sparks creativity. I've found angles I never would have considered. The trick is to use these as creative fuel, not final copy.

Headlines carry enormous responsibility — they're often all a reader sees. Use AI to brainstorm, yes, but every headline must pass the accuracy test. A clever headline that distorts the story is worse than a boring one that's truthful.

I love generating social-media-friendly headlines separately from print headlines. Different platforms need different hooks, and AI helps me think about each audience segment quickly.

I use this before every major interview now. Not because I can't think of questions, but because the AI often suggests angles I haven't considered. It's like having a brainstorming partner available at 2 AM.

Good interview preparation has always been the hallmark of good journalism. AI can help you prepare more thoroughly, but never walk into an interview reading questions off a screen. Internalize them, adapt them, and always be ready to follow unexpected threads.

I especially love asking AI to suggest questions from the audience's perspective. What would our readers want to know? That reframing often produces the most engaging moments in an interview.

This is where AI really shines as a research accelerator. Breaking a complex claim into verifiable pieces used to take me an hour. Now I get a roadmap in minutes, and I spend my time actually checking the facts instead of figuring out what to check.

Let me be absolutely clear: AI is not a fact-checker. It is a fact-check planning tool. The moment we start treating AI output as verified information, we've failed our readers. Use it to organize your verification process, then do the real work.

Our readers share claims on social media constantly and want to know what's true. This workflow helps me respond faster to trending misinformation while still maintaining rigorous verification standards.

This workflow has completely changed how I handle interviews. I used to spend three hours on a one-hour interview. Now I get the transcript analyzed in minutes and spend my time on the creative work of story construction.

The quote extraction feature is useful, but I cannot stress enough: every single quote must be verified against the original audio. Transcription errors become attribution errors, and attribution errors destroy credibility.

I love using the theme identification to figure out which parts of the interview will resonate most with our audience. It helps me structure my story around what readers actually care about.

Data analysis used to be a bottleneck for journalists without stats training. AI democratizes this. You can explore a dataset conversationally and find stories hiding in the numbers. It's like having a data editor on demand.

Data journalism demands precision. AI can help you explore, but every finding must be verified using proper statistical methods. A pattern spotted by AI is a hypothesis, not a conclusion. Do the rigorous analysis before publishing.

The story angle suggestions are gold. AI helps me see the human stories behind the numbers — the angles that will actually matter to our readers, not just the statistically interesting ones.

Every article deserves a great social media rollout, but most newsrooms don't have time to craft them. AI makes it possible to create platform-specific content in minutes. This is audience development on autopilot.

Social media is where misinformation spreads fastest. Every post in a thread must be accurate in isolation, because most people won't click through to the full article. Review each post as carefully as you'd review a headline.

This is one of my favorite AI use cases. A well-crafted thread can drive 10x the traffic of a simple link share. I always generate multiple versions and A/B test the hooks to see what resonates.

This is a game-changer for local newsrooms with skeleton crews. When a big national story breaks, you can immediately start mapping its local implications instead of scrambling to figure out your angle.

Localization is where journalism meets community service. AI can suggest angles, but only a journalist embedded in the community can tell which ones truly matter. Use AI to brainstorm, but report on the ground.

Readers always want to know: how does this affect me? AI helps me answer that question faster, which means our local coverage goes up while national outlets are still focused on the big picture.

Explainers are the future of news. As stories get more complex, audiences need help understanding them. AI lets you create these high-value pieces in hours instead of days. I use this workflow for every major explainer we publish.

Explainer journalism is one of our most important functions, and getting it wrong can misinform thousands. Use AI to structure your thinking, but every fact, every analogy, every simplification must be vetted by someone who deeply understands the subject matter.

Our analytics consistently show that explainer content has the longest shelf life and highest engagement. AI helps us produce more of it without sacrificing quality. The 'why it matters now' hook is what gets readers in the door.