Cleveland.com editor Chris Quinn is defending his newsroom’s use of AI to write news stories, saying it frees reporters to spend more time on the street gathering information.

“By removing writing from reporters’ workloads, we’ve effectively freed up an extra workday for them each week,” Quinn wrote in a February editorial responding to a job candidate who dropped out after learning about the practice.

The approach has drawn sharp criticism from journalism educators and practitioners who argue writing is integral to the reporting process, not a separate task that can be automated.

How the system works

Quinn describes a workflow where reporters gather information in outlying counties, then hand off their material to what he calls an “AI rewrite specialist” that turns it into story drafts. Human editors supervise the final drafts, fact-checking and editing before publication. Quinn says the extra time allows journalists to have more coffee meetings with sources and conduct more interviews. This reflects a broader trend of newsrooms trying to figure out how to use AI as a newsroom assistant while keeping journalists in control.

The newsroom initially used AI to identify potential stories in distant counties, a use case Quinn expanded to more coverage areas.

Journalism schools push back

Quinn blamed journalism schools for the candidate’s decision to withdraw, saying they teach students “AI is bad” and create unrealistic expectations about “long-form magazine storytelling.”

Missouri journalism professor emeritus Stacey Woelfel pushed back in a Substack post, writing that “reporting is not just the act of gathering facts.”

“Writing is an integral part of the reporting process,” Woelfel wrote. “Not only is writing necessary to put all the facts we gather into a form audiences can easily digest, but the concept of what form the story will take starts even before we leave the newsroom to report.”

This highlights the importance of teaching journalists to use AI without losing critical thinking.

The broader context

Quinn noted the difficult job market for journalists, citing widespread layoffs across the newspaper industry. But Woelfel countered that “many of the job losses he cites are the result of media owners looking to have human workers do less and automation—including AI—do more.”

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Public broadcasters across the country have been slashing staff following Congress’s decision to defund them. When South Dakota Public Broadcasting announced it was laying off eight people, Mike Janssen faced a question that would have stumped him a year earlier: Is this a story worth covering? The station isn’t one of the nation’s largest networks, and bigger outlets have cut more people from a single program.

But Janssen, digital editor at Current, didn’t have to guess. His analytics told him the answer was yes. Every layoff story gets traction with Current’s readers, no matter how small the station. “Without that information, I would have thought, ‘Oh well, we should only cover the layoffs at the biggest stations, or we should only cover layoffs if it’s a large number of people,'” he says. Instead, the data showed him where his readers’ attention actually goes.

Current is a trade publication covering U.S. public broadcasting, founded in 1980 by the National Association of Educational Broadcasters—the precursor to NPR and PBS. Housed at American University’s School of Communication since 2011, the outlet publishes daily online stories and a quarterly print edition, drawing around 43,000 page views per week. A mix of foundation support, advertising, donations and subscriptions keeps it running. Janssen oversees three full-time reporters, an intern and a handful of freelancers. He’s also, by default, the newsroom’s analytics point person.

For a small operation like Current, finding the right analytics tool meant finding one that didn’t require a data science background to use. Parse.ly, with its emphasis on historical trends over real-time dashboards, turned out to be exactly what they needed—a way to understand audience behavior on timescales that actually matter for a publication that posts a few stories per day, not hundreds.

Starting with the right questions

Before touching any dashboard, Janssen approached analytics with a specific frame: What decisions will this data actually inform? Too many newsrooms, he says, “just gather data for the sake of having the data.” That’s a waste of time and money. “If you understand what your goals are with it, then it helps you narrow what data is actually going to be useful to you.”

For Current, the primary goal was clear: grow subscription revenue. That meant understanding what resonates with repeat visitors—the readers most likely to convert into paying subscribers. Single-visit traffic matters less than building a loyal audience that returns week after week. With that goal defined, Janssen could focus on the metrics that actually connected to outcomes rather than drowning in vanity numbers.

This clarity also helped justify the investment. At $2,000 per month for sites with up to 5 million monthly unique visitors, Parse.ly isn’t free. But compared to flying blind—or wrestling with tools that require coding skills to extract basic insights—the cost made sense for a newsroom trying to make smarter decisions about limited resources.

Getting set up without a developer

Current runs on WordPress, and since WordPress owns Parse.ly, integration was straightforward. “If you can install a plugin and insert some information into boxes in your CMS, you’ll be fine,” Janssen says. No custom code, no developer hours, no multi-week implementation project.

The plugin handles the technical work: installing tracking, extracting metadata, connecting the dashboard to the site’s content. Parse.ly began collecting data immediately after activation. For newsrooms running other content management systems, the setup is only slightly more involved—adding a line of JavaScript and following formatting instructions that Parse.ly’s integration team provides.

The onboarding process also helped Current identify which metrics to prioritize from the start. Parse.ly’s team walked them through the dashboard options, helping translate editorial goals into specific data views. This handholding matters for small teams without dedicated analytics staff.

Focusing on historical data, not real-time noise

The main Parse.ly dashboard shows a graph of the day’s traffic overlaid on an average of previous comparable days—Monday versus previous Mondays, for example. It displays page views, unique visitors and engagement time at a glance. Below the graph, two columns show top stories over customizable time periods.

But what makes Parse.ly work for Current is its handling of historical data. A publication posting a few stories per day doesn’t need minute-by-minute traffic updates. That granularity is noise, not signal. Janssen needed to see patterns across days, weeks and months—time frames that reveal what content actually matters to his audience.

“Month to month, if you look at our top 10 stories in terms of page views or any metric, it’s largely layoffs,” he says. “It just confirms, yeah, this is where we need to be focusing our attention because this is what people want to be reading about.” That kind of insight only emerges from looking at data over meaningful periods, not watching numbers tick up in real time.

Tracking what loyal readers actually want

Subscriptions require loyalty. One-time visitors rarely convert. So Janssen configured Parse.ly to surface what returning readers engage with, filtering out the noise of drive-by traffic. “I don’t care so much where the one-time visitors are coming from, but we do want to know where the folks are who keep coming back,” he says.

Parse.ly’s engagement time metric proved especially valuable for longer feature pieces—the kind of journalism that takes significant investment but may not rack up huge page view numbers. If readers who do find those stories spend real time with them, the investment was worthwhile. “I want to know that someone is at least engaging with it significantly, even if it’s not our top story that month,” Janssen says.

The platform also tracks conversions—what content drives specific audience behaviors like subscribing to a newsletter or becoming a paying member. Newsrooms can define which actions to track, connecting content performance directly to business outcomes rather than treating all page views as equal.

Setting up alerts and automated reports

With a fully remote team communicating primarily through Slack, Current integrated Parse.ly’s alert system directly into their workflow. When a story gets significant traffic, the platform sends a notification to a Slack channel called “Wins.” The team sees momentum building without having to constantly check dashboards.

Automated reports handle the routine performance reviews. Current receives regular summaries—configurable for daily, weekly, monthly or quarterly delivery—covering site-wide metrics, section-specific performance and individual author impact. These reports arrive in inboxes without anyone having to run queries or export data.

The combination of push alerts for spikes and scheduled reports for trends means the analytics work happens in the background. Janssen doesn’t have to carve out time to investigate the data; the data comes to him in digestible form.

Discovering where the readers actually are

Referral data revealed surprises. Janssen knew Current’s newsletter drove traffic, but Parse.ly quantified just how important that channel was. More unexpectedly, the data showed LinkedIn was a significant source of readers—a platform he hadn’t prioritized for distribution.

“If you didn’t have some kind of window into how all that’s working for you, I don’t really know how you would even figure out what to care about,” he says. Without analytics, social media strategy becomes guesswork. With it, you can see where readers actually come from and focus energy on the platforms that matter.

This insight changed how Current thinks about distribution. Rather than spreading effort evenly across every social platform, they could concentrate on the channels where their audience actually discovers content.

What didn’t work—and how they adapted

Current previously used Chartbeat for content analytics, but Janssen found that its focus on real-time data didn’t suit a low-volume publication. The real-time focus was solving a problem Current didn’t have.

Google Analytics, meanwhile, remained an option—it’s free, after all. But Janssen found it “bewildering” and hard to navigate. “I don’t want to go through that trouble,” he says. Big media companies can afford professional data analysts to wrangle Google Analytics; small newsrooms need tools that work for journalists wearing multiple hats.

• Real-time overload: Chartbeat’s strength became a weakness for Current’s publishing pace. Switching to Parse.ly’s historical focus provided data at useful timescales.
• Complexity barriers: Google Analytics’ depth requires expertise Current didn’t have. Parse.ly’s simpler interface meant Janssen could get insights without coding knowledge.

The results

The shift to Parse.ly gave Current something it hadn’t had before: confidence in editorial decisions. Janssen no longer has to rely on instinct about what readers want. “I don’t know how I would judge what to focus on if I didn’t have Parse.ly showing me, ‘This is what the audience cares about,'” he says. “Otherwise, it’s just me with my gut saying, ‘Oh, I think that people care about this.'”

The data validated some assumptions—yes, readers care about layoffs—and challenged others, like the importance of LinkedIn for distribution. It connected content decisions to subscription goals, helping a small team focus limited resources on journalism that builds loyal audiences rather than chasing clicks.

What’s next for Current

With the analytics foundation in place, Current can continue refining its understanding of what drives subscriber behavior. The data infrastructure now exists to test hypotheses about content strategy, measure results and adjust—a cycle that compounds over time as the newsroom learns what works.

For publications with similar profiles—small teams, niche audiences, limited technical resources—the lesson is less about Parse.ly specifically than about matching analytics tools to actual editorial needs. Real-time dashboards solve real-time problems. Historical trend analysis solves the questions that matter for publications building audiences over months and years.

Newsrooms evaluating content analytics platforms can request a demo at parse.ly. Entry-level plans start at $2,000 per month for sites with up to 5 million monthly unique visitors, with conversion tracking and additional features available at higher tiers.

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