A recent study, Do Journalists’ Political Orientations Translate into Partisan News Reporting? The Limits of Bias and the Limits of Counter Mechanisms, examines how journalists’ views interact with newsroom structures and professional norms and how those dynamics shape coverage. 

The findings point to a more complex reality than public debate suggests. Personal ideology rarely appears directly in reporting. Instead, newsroom processes and professional expectations shape how political news reaches audiences. 

Measuring bias in news coverage 

The research, conducted in Austria, combines content analysis with a survey of journalists. This approach links what journalists say they believe with what they publish. Austria provides a useful test case for many western newsrooms because its media system includes multiple political parties and a strong tradition of separating news from opinion. These conditions shape how journalists work and how editors oversee coverage. 

The study examines three areas where bias could appear: 

1. Subjectivity, when reporting includes personal opinions 

2. Party visibility, or how often certain political actors appear 

3. Issue framing, or how stories present debates through perspectives such as economic impact or social policy 

Together, these measures show whether personal views shape political coverage. 

What shapes reporting beyond personal ideology 

The survey findings show that many journalists place themselves slightly left of the center, a perspective shaped by education, location, and career paths. However, journalists’ political views show little connection to which parties appear in stories or how issues are framed. Across the sample, coverage stays close to the political center and even shows a slight lean to the right overall. This gap between personal views and published coverage points to other forces that shape reporting beyond individual ideology. 

The study points to newsroom structures as a key part of the explanation. Journalists work within editorial systems that shape how stories develop through review processes, routines, and shared expectations. These factors limit overt bias and encourage more balanced coverage. 

Autonomy also matters. Here, autonomy refers to the level of editorial control journalists have over their work, including how they select sources, frame stories, and shape narratives. Greater autonomy gives journalists more room for individual judgment, which can strengthen independent reporting but also increase the influence of personal perspectives on framing. Journalists who report less autonomy show weaker links between their views and how they frame stories. This contrast highlights how editorial oversight helps maintain consistency in coverage.  

Professional norms and the limits of editorial control 

Newsroom structure is not the only force impacting political perspectives in news coverage. Professional norms also guide how journalists approach their work. Many define their role through principles such as observing events, presenting facts, and helping audiences understand public issues. Journalists who strongly embrace these norms show weaker links between personal views and subjective reporting. Professional identity acts as a check on how far personal views enter coverage. 

The findings also show how representation and framing influence the reporting process, with some elements easier for newsrooms to manage than others. Party representation falls more directly under editorial control, since editors can quickly assess whether a story includes multiple political actors and ensure a range of viewpoints. 

Framing works in a different way. Even with the same sources, stories can present issues through different perspectives based on emphasis and context. These choices rely more on individual judgment than editorial direction, which makes framing harder to monitor. The sources remain visible, but the perspective can shift more subtly. 

This study shows that personal ideology does not move directly into published reporting. Instead, it is filtered and shaped through editorial processes, professional norms, and newsroom culture. For media leaders, the implication is clear: the integrity of political coverage is less about individual viewpoints and more about the strength of the systems that govern how journalism is produced.  

Editorial standards, review structures, and shared professional norms are mechanisms that sustain trust. That discipline is a competitive advantage. It distinguishes professional journalism in an increasingly fragmented and unverified information environment. 

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In today’s digital-first newsrooms, journalists often report live, publish updates in real time, and interact directly with audiences as stories unfold. The result is tension between speed and accuracy. It is no longer just a professional challenge but, increasingly, an ethical one shaped by the systems and workflows that define real-time journalism.

Our latest research with student and early-career journalists, drawing on interviews and survey responses, highlights how strongly this concern is felt. Many young reporters say the expectation to publish quickly, correct later, and keep the feed moving can feel like pressure to take risks. When verification occurs after publication rather than before, accuracy becomes reactive instead of foundational.

For media executives, this shift raises an important question: how can news organizations deliver the speed audiences expect while protecting the credibility that sustains trust? Addressing that question requires more than reminding journalists to “be careful.” It requires rethinking the systems, workflows, and newsroom culture that shape real-time journalism.

The ethical pressure of real-time news

Live blogs, rolling coverage, push notifications, and social platforms mean that each new detail can reach audiences within seconds. This immediacy is powerful, enabling newsrooms to inform the public almost in real time. But once information is published, it spreads quickly across platforms and communities, often far beyond a newsroom’s control. Even when updates or corrections are issued later, there is no guarantee they will reach the same audiences. The original version can continue to circulate long after corrections have been made.

For younger journalists working inside these workflows, the ethical stakes feel high. They are often operating at the intersection of reporting, publishing, and audience interaction. In some cases, they are expected to monitor live feeds, write updates, verify information, and respond to audience questions simultaneously.

The intention behind these workflows is understandable. Audiences expect immediacy, competitors publish in real time, and the news cycle moves quickly. But when newsroom systems reward velocity above all else, they risk signaling that speed matters more than judgment.

That perception matters. Trust depends on the belief that news organizations prioritize accuracy even when it slows them down. If journalists feel pushed to publish unverified information, that trust becomes harder to sustain.

When technology accelerates publishing but not verification

Digital publishing tools have transformed how breaking news is reported. They allow reporters to update stories instantly, provide minute-by-minute coverage, and keep audiences informed as events unfold.

Used well, these tools strengthen journalism. They enable transparency, allow corrections to be made quickly, and give audiences a clearer view of what is known and what is still developing.

The problem arises when technology rewards speed without supporting the editorial decisions behind it. Real-time publishing environments can encourage constant updates, even when information is incomplete. If newsroom dashboards or performance metrics emphasize update frequency or time-to-publish above all else, journalists may feel pressure to move forward before verification is complete.

Media executives should consider whether their tools and metrics reinforce the right priorities. Do workflows allow time for verification? Do editors have clear visibility on updates before they go live? Are journalists encouraged to label uncertain information clearly rather than present it as confirmed?

Technology cannot replace editorial judgment, but it can either strengthen or weaken it.

Credibility built through transparency

Accuracy is not only about getting facts right the first time. It is also about how news organizations respond when information changes.

In live coverage, new details often emerge that challenge earlier assumptions. Responsible reporting means correcting inaccuracies quickly and clearly. It also means explaining those corrections so audiences understand what changed and why.

This transparency is essential for maintaining credibility. Audiences are often more understanding of evolving information than silence or defensiveness when mistakes occur.

The same principle applies to audience engagement. Today’s journalists frequently interact directly with readers through comment sections and social platforms. These conversations can build trust when handled well, but they can also spread confusion or misinformation if inaccurate claims are left unaddressed. When false information appears in comment threads or audience discussions, correcting it promptly helps prevent those claims from spreading further.

Newsrooms should be prepared for this reality. That preparation includes setting clear community guidelines, assigning responsibility for monitoring conversations, and ensuring journalists are supported when responding in fast-moving environments.

Responding quickly matters, but so does responding carefully.

Building systems that support ethical speed

The core challenge facing digital newsrooms is not whether to move quickly. Speed is part of modern journalism, and audiences expect it. The challenge is ensuring it does not weaken the editorial standards that define the profession.

That preparation starts with clear expectations. Verification is not optional, even under pressure. When information is uncertain, the responsible approach is to say so.

It also requires practical support. Editors, producers, and audience teams should work together so reporters are not juggling every responsibility alone during live coverage. When someone is responsible for monitoring comments or verifying incoming information, the reporter covering the story can focus on accurate updates.

Training also matters, particularly for younger journalists who are starting their careers in live, digital news environments rather than traditional reporting structures. They need guidance not only on how to publish quickly but also on when to pause.

Finally, newsroom leaders must reinforce that credibility remains the industry’s real competitive advantage. Speed may capture attention in the moment, but trust determines whether audiences return tomorrow.

Accuracy sustains trust

The modern newsroom operates in an environment defined by constant updates and immediate audience response. That reality is unlikely to change. What can change is how organizations balance the demands of speed with the responsibility of accuracy.

Journalism has always required difficult judgment calls. In digital reporting, those decisions simply happen faster and in public view. The goal is not to slow down the news cycle, but to ensure that the systems behind it protect the principles journalism depends on.

Speed may capture attention. Trust depends on whether the systems behind the newsroom protect accuracy when the pressure to publish is highest.

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Recent research in Canadian newsrooms reveals a cautious reality. Amid the hype, media leaders are not hiring for AI expertise. Instead they’re doubling down on core journalism skills, treating AI as an efficiency tool rather than a replacement for human judgement. It is important to note that this research focused specifically on the editorial use of AI, where the priority is the preservation of audience trust, rather than on use of AI for business growth or commercial purposes.

Terra Tailleur and I conducted interviews with CEOs and editors-in-chief at 12 media organizations, ranging from public broadcasters, national news outlets and wire services, to regional dailies and independent digital startups, over the course of eight months to explore how Canadian newsrooms are using and adopting AI in their editorial practices. Three practical approaches to responsible AI adoption have emerged for media leaders:

Trust-first guardrails

Our findings suggest a growing divide based on the size of outlets. Larger ones, like The Canadian Broadcasting Corporation (CBC), The Globe and Mail, and the Canadian Press have robust guardrails and policies. Smaller outlets, constrained by time and resources, often rely on informal oversight. This relegates ethical boundaries to individual intuition rather than documented standards.

In a recent HEC Montreal study of over 400 journalists, 36% of those surveyed were unaware if their organization even had an AI policy. Thus, publishers face an operational challenge not in drafting policies, but in driving them clearly and consistently from the executive corner office to reporters’ desks.

Small newsrooms don’t have the budgets of national broadcasters or wire services. So, they are forced to create simpler, more practical models. At Cabin Radio in Yellowknife, for example, editor Ollie Williams says that AI experimentation “happens so far off the side of the desk that it’s like the movie Inception and it’s like the desk has folded back in on itself three times before I get to it.” Therefore, he doesn’t have time to research AI uses and meet with staff to develop formal policy because he’s too busy running day-to-day operations with his editorial team of four.

For resource-strapped newsrooms, a simple governance model can start with a clear approval process, requiring editor sign-off for all AI use. Newsrooms should actively disclose when and how they use AI, prioritize transparency with audiences, and train staff on verification rather than technical skills.

For 2026, the goal for smaller publishers isn’t to draft a 50-page manual. Rather, they should try to establish living documents that provide clear guidance for daily tasks. As DCN outlined, “staff need clarity about when and how to use AI and how to validate its output.”

Upskilling and in-house training

The media organizations we spoke with weren’t hiring engineers with a surface-level interest in news for editorial purposes. Instead, they are conducting in-house training to fill the tech gap. The focus has been on upskilling existing staff who already understand the brand’s voice, ethics and audiences is more effective than bringing in tech-first hires who may lack journalistic DNA.

Tools can be taught in a controlled environment. At the CBC, for example, they aimed to train every employee, from summer hires to 30‑year veterans, with a full‑day AI program adapted from Radio‑Canada. This approach keeps AI adoption grounded in newsroom culture, not vendor experimentation.

Priority on core journalism skills

Across the board, editors-in-chief emphasized that AI expertise comes second to strong reporting fundamentals. More than the ability to write a clever prompt, they were looking for curiosity, critical thinking, strong judgment. They also focused on an ability to interview people, build sources, and find good stories. All of which are fundamental skills that define quality journalism.

Toronto Star editor-in-chief Nicole MacIntyre told us that while the next generation will, “absolutely need to embrace the tools that can help them work smarter and more efficiently… their success will still depend on having the core skills to adapt in a fast-changing media environment: curiosity, critical thinking, strong judgment, and a commitment to truth.” Ultimately, AI fluency matters, but only on top of reporting fundamentals.

Considerations before rolling out ambitious AI products 

The test for managers and boards this year lies in day-to-day governance: Are newsrooms giving editors clear boundaries on generative AI, or leaving it to gut instinct? Before scaling AI deeper into editorial routines, here are pointed questions to ask:

• Do frontline editors know the exact off-limits line for generative AI, or are we relying on vibes?
• Are we allocating time and tools to verify AI-assisted content, or prioritizing speed over trust?
• Do budgets support upskilling current staff on AI literacy, or are we waiting for perfect new hires?
• With a third of the industry unaware of AI policies, what steps turn intranet PDFs into daily habits?

Publishers entering 2026 face practical choices about how AI fits into editorial workflows. The Canadian newsrooms we spoke with are moving cautiously, focusing on guardrails, staff training, and core reporting skills rather than rapid experimentation. Their approach suggests that AI in journalism will be shaped less by hype than by the daily realities of newsroom capacity, oversight, and editorial judgment.

The post What newsroom leaders say matters most in AI adoption appeared first on Digital Content Next.

AI moves into the center of the tech and media stack 

AI is focal to Deloitte’s 2026 outlook because it drives nearly every major trend in technology, media, and telecommunications. More than half of the predictions in the report link directly to AI. This level of influence matters because TMT now contributes close to 50% of global market capitalization. When a sector of this scale leans into AI, the impact moves quickly into media production, distribution, and monetization. As AI expands, expectations for accuracy, quality control and accountability grow right along with it. 

Search integrates AI summaries and changes discovery 

The report predicts a continued shift in discovery behavior. Deloitte estimates that daily use of search engines with integrated AI summaries will reach 29% in 2026, while standalone AI chat tools will reach about 10%. This divide shows that audiences adopt AI most quickly inside familiar interfaces, not through separate apps or standalone services. 

Embedded summaries reshape the top of the discovery funnel. Search results present short explanations, bullet points, and synthesized information before users click deeper. This pattern affects publishers because audiences may not feel the need to explore full articles. This shift creates more pressure on publishers to show clear and immediate value beyond the short explanation that AI generates. 

Computing demand rises and infrastructure strains 

AI demands will increase. Deloitte estimates that AI inference workloads, the work that occurs after a model is trained, will make up roughly two thirds of total AI computing in 2026. The report expects global spending on AI chips to surpass $50 billion, with supply concentrated among a few manufacturers. Data centers will face tighter energy and cooling constraints as demand grows. Cloud providers will respond with price increases that reflect limited capacity. 

Media organizations that rely on cloud-based video processing, encoding, personalization, or moderation will feel this pressure directly. A single high volume content initiative might require thousands of GPU hours. If capacity is tight, production timelines stretch and costs rise. Infrastructure moves from a back-end detail to a strategic factor that shapes product development and creative planning. 

Content formats shift toward shorter and more visual experiences 

Deloitte forecasts continued momentum in micro series, which often take the form of short dramas or social videos optimized for vertical screens. Revenue for micro series will reach an estimated $7.8 billion in 2026, which reflects growth of more than 100% over two years. These formats attract younger viewers who prefer snackable content and rapid storytelling loops. 

Generative video is part of the picture as well. AI tools create scenes, characters, and transitions at high speed. This supports experimentation but raises questions about authenticity, provenance, and brand trust. Regulators consider labeling requirements for AI-generated assets, while platforms explore detection tools. Media companies evaluate how to blend human and AI production in ways that keep audience expectations clear. 

Podcasts also shift toward video. The report notes that more than 60% of new podcasts now launch with video as a primary element. Vodcast advertising will approach $5 billion worldwide in 2026, an increase of almost 20% year over year. 

Navigating hidden tradeoffs in a complex environment 

The 2026 predictions reveal a landscape shaped by intertwined challenges rather than a single disruptive event. AI drives innovation, yet it also drives costs, regulatory complexity, and infrastructure strain. New content formats attract audiences, yet they also challenge legacy production economics. Cloud systems grow more intelligent, yet they adopt billing models that complicate financial planning. Digital sovereignty promotes resilience, yet it restricts global reach. 

For media executives, these concerns point to a year defined by structural tension. Every opportunity arrives with a corresponding constraint. Every technological advance creates new expectations around cloud storage availability, content authenticity, and financial transparency. Companies that understand how these forces interact gain a clearer view of the risks and realities that define the year ahead. 

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Except that it did increase workload, and the work was too manual for publishers to sustain. But the idea may have just been a decade ahead of its time.

Liquid content—sometimes called structured content, modular content, or atomic content—is the practice of breaking stories into reusable components that can be reassembled for different formats, audiences, and platforms. The concept first gained traction a decade ago as publishers struggled to meet rising distribution demands across web, mobile, and social channels. Back then, the tools weren’t mature: versioning content for every platform created more work, not less. Today, generative AI, improved personalization, and cross-platform automation are reviving the strategy. With AI now able to help structure, maintain, and re-render content, publishers are revisiting liquid content not only to serve current channels more efficiently, but to future-proof their operations for distribution platforms and channels that don’t yet exist. 

For David Cohn, senior director, generative AI innovation for content and the newsroom at Advance Digital, the concept itself isn’t new, just newly practical. From 2021-2014, he ran Circa , which was a mobile news app that restructured news into atomic units like events, statistics, quotes, and images that could be resurfaced and reused. The problem was scale: even with a globally distributed team working 24/7, Circa could barely keep up with the biggest stories of the day because every atom of news had to be created by humans. It wasn’t sustainable. 

“Now, we just need more compute,” Cohn says. “And, while compute is expensive, it’s a lot cheaper and a lot more doable.” So, he says, it’s time for media execs to revisit “those ideas around structured content and see if they are more attainable and more worthy of our attention.”  

The data layer: from story to query 

Cohn’s current work at Advance Digital, while still in the thinking and testing phase, treats liquid content less as format differentiations, and more like infrastructure. Instead of a one‑off story, an article is framed as a query across a structured datastore of atomic units like facts, quotes, statistics, dates, and other fields extracted from reporting.  

An article then becomes more like a SQL query that joins pieces of verified information and uses an AI layer to render them as a traditional story, a video script, or another format altogether.​ In other words: it is rendered whatever way people prefer to have their content delivered . 

“But, what we’re in charge of is, I’ll use the word purity, of the data: how well it’s structured, how well it’s maintained, how much we add to it,” Cohn says. “That can be a proprietary data set. It is in our ability to create those proprietary data sets that we all of a sudden have value over large, giant technology corporations.” 

Cohn is quick to flag that structured, liquid systems excel when audiences want clarity and up-to-date facts but are a poor fit for narrative non-fiction like the classic Gay Talese’s Frank Sinatra Has a Cold. “This type of structured content would be terrible to apply to that story,” he explains. “It’s about knowing what type of news and information this is great for and what types it is not. There are certain types of stories that are best told as a narrative. It’s challenge to make sure it’s applied in the right way.” 

But, the upside, according to Cohn, is creating a massive system of highly adaptable content. “Once you start to do this at scale, you end up where the parts are more valuable,” he says. “There’s a number of ways that we can imagine that that kind of information becomes useful once it becomes a database that you can query.” 

Digital media companies’ vast archives may become useful datasets in this framework. At SXSW this year, Lee Enterprises CIO Virginia Fletcher suggested that media outlets get their archives in order, wrote Alex Mahadevan for Poynter.  With a structured database of articles, media companies can convert what they have into different mediums.  

This sentiment was echoed by Jane Barrett, head of Reuters AI strategy, this year at the Nordic AI in Media Summit, noting their vast archive has inconsistent metadata. “Generative AI gives us the possibility to classify and structure our archive more effectively,” she said in a panel discussion. This moment is pushing us to think about our content as data. As Barrett put it, “it’s kind of giving us a good challenge to think now about our content as data and what needs to be true to turn our content into data that then can flow into these liquid experiences.” 

The knowledge layer: encoding journalistic value 

While Cohn’s focus on liquid content is its data system, researcher Sannuta Raghu, who heads Scroll’s AI Lab, pushes the idea one layer deeper, to what she calls the knowledge layer of journalism.  

During her ICFJ Knight Fellowship in 2024, she mapped the structural layer of journalism, and compiled her findings in what she calls the Directory of Liquid Content. The directory is a scalable and modular taxonomy designed to map, describe and standardize how digital news is structured, styled and surfaced.  

“Liquid content is not necessarily moving something from one format to another,” she says. “It’s moving something, or it’s using information such that it can be contained in any container on demand.” 

Liquid content can move between various containers – from an article to a podcast, a calculator, or a decision tree, she says. Within these, it can also be formatted in various styles, as an inverted pyramid, an interview or an explainer for short TikTok videos.  

“So, it’s making sure that you are able to have information that is dynamic enough that can be adapted to various containers, with high fidelity to the source where it came from,” she says. 

 Months into that fellowship, she was granted another Fellowship at the Reuters Institute, where she developed the News Atom to codify journalism’s epistemic layer. Raghu explains when AI parses a news article, “what they’re essentially doing is they’re looking for statements which can be converted into causal relations and subject object predicate,” she says.  

When a news article is used as raw material to answer questions, AI will remove the intentionality of the signals a journalist has added. These can include the temporal aspect, words that describe when something happened, complex quotes and nested quotes.  

“There are many subtle examples,” Raghu says. “Words like likely, reportedly, allegedly, very intuitive to us as journalists, but are considered extra when it comes to form.”  

The News Atom provides a way for the news industry to structure, define and embed its own meaning rather than having that meaning imposed by external systems. Raghu believes if publishers don’t define and encode their own values, like trust, attribution, temporal precision, into the data they produce, those values won’t exist for AI models.  

Liquid content in 2025 and beyond, then, is about survival in an ecosystem and consumption experience of on-demand form. This on-demand form transformation is already happening around the world, Raghu points out. Take for example, Google’s NotebookLM, which converts PDFs into mindmaps and podcasts. 

“For us, it’s about making sure that you are codifying some of the practices that are intuitive to us. Like the inverted pyramid, for example, is a very journalism format. But, it needs to be codified and taught to a model in a very deterministic way so that it can be replicated and converted into multiple formats.” 

For publishers, the question is whether to codify their practices and values into these systems, or whether those definitions will be written by others. As Cohn puts it: “We’re in a world where generative AI feels like the beginnings of the internet again, where we can fundamentally rethink assumptions.” 

“We want to both understand it and have a solid foothold in it, rather than just be steamrolled by it,” Cohn says. “Has this fundamentally changed everything for Advance? No, but what we’re doing is to prepare if that does become the case, it’s on our terms.” 

The shift toward liquid content is not simply a tactical experiment. As AI systems increasingly generate fast, unverified content in every format, publishers’ defensible advantage is the integrity and structure of their own data. Pursuing liquid content at scale may require rethinking stories as more than finished containers and embedding core journalistic values — attribution, verification, temporality, precision — into the underlying layers where content is created, stored, and reused. 

This approach also raises the stakes for content architecture. Unstructured archives function as low-value files that can be scraped or summarized by AI. Structured, verified, and queryable archives become high-value assets, built from components that cannot be easily replicated with the same fidelity. Liquid content, in this context, is not a mandate but a strategic shift: a way for publishers to strengthen the foundations of their work and ensure it can adapt to the formats and distribution environments still to come. 

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AI use grows across core newsroom tasks 

UK newsrooms now treat AI as standard equipment. More than half of UK journalists use AI at least weekly, and more than a quarter of them use it daily. Language-processing tasks dominate AI usage. Forty-nine percent of journalists use AI for transcription or captioning at least monthly, and many rely on it weekly or daily. Translation follows at 33%, and grammar checking or copy-editing at 30%. 

At the same time, journalists are starting to turn to AI for core reporting work. Twenty-two percent use AI for story research at least monthly. Sixteen percent use it for idea generation or headline drafting. Twelve percent use it for fact-checking or source assessment, and 10% say they generate first drafts of articles with AI tools. This data reveals AI’s shift from the margins of newsroom work into its editorial center. 

AI habits vary by newsroom role and reporting focus 

The report also highlights important differences in adoption patterns. Journalists under 30 lead weekly use at 42%, compared with 29% among those aged 50 and older. Men use AI more often than women, though the gap narrows at lower frequency levels. Seniority matters as well. Journalists with strategic authority use AI at significantly higher rates than those with limited editorial responsibility. 

Beat assignments also influence use. Business reporters show the highest levels of weekly use at 43%, compared with only 21% among lifestyle reporters. Data-heavy and time-sensitive beats push more experimentation. 

Format demands also drive adoption. Journalists who work across many formats turn to AI more often. Journalists who work in at least five formats use AI monthly or more at 62%, compared with 48% of journalists who work in only one format. As newsrooms stretch reporters across text, audio, video, social posts, and visuals, journalists increasingly use AI to fill gaps to create efficiency. 

Journalists hold deep concerns about ethics and quality 

Even as their usage grows, journalists express concern and unease about AI’s impact on journalism. Sixty percent say they are extremely concerned about negative effects on public trust. Fifty-seven percent express the same level of concern about harm to accuracy, and 54% worry about the impact on originality. 

Concern spans almost all demographics. Journalists with higher AI knowledge tend to express stronger concern. Daily AI users express less anxiety, likely because familiarity breeds confidence. Still, the level of concern across the profession underscores the need for clearer standards and newsroom transparency. 

Responsible AI adoption in newsrooms 

The report points to several considerations for responsible and effective AI adoption: 

• Create clear protocols. Many newsrooms still lack guidance on transparency, bias, fairness, and appropriate use. Staff need clarity about when and how to use AI and how to validate its output. 

• Invest in training. Only 32% of journalists say their newsroom provides AI training. As more reporters adopt AI tools, they may need structured support and instruction in verification, accountability, and oversight. 

• Keep humans in control. Many journalists say AI increases low-level work such as data cleaning and output checking. Newsrooms should examine workflows carefully to ensure AI reduces, rather than adds to, workload. 

• Protect trust. Audience trust remains the biggest concern. Newsrooms need clear transparency practices and should explain how and why they use AI in reporting. 

• Match tools to newsroom goals. Different organizations have different missions, capacities, and audiences. A large broadcaster may build in-house tools, while a small digital outlet may rely on third-party services. Responsible adoption means aligning these choices with editorial values and available resources. 

AI adoption continues to expand across UK newsrooms, with journalists relying on it for tasks ranging from transcription and research to headline drafting. Yet many organizations are still in the early stages of true integration. Moving forward in the most positive direction will require clear guidance, sustained training, and a commitment to transparency. Newsrooms that prioritize ethics and trust as they adopt AI will be best positioned to capture its benefits while minimizing its risks as the technology evolves. 

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However, despite the undisputed appeal for audiences, it presents media businesses with a conundrum: produce video content cheaply to maximize ROI, or invest in quality video leveraging the brand, with the risk of a lower return. So how do media businesses choose which topics or subjects are right to build a video strategy around. And when do revenue opportunities like sponsors enter into those discussions?

Commercial viability

For most outlets, video has to pay its way. Documentary-style video is expensive to produce. Therefore, it is often simply not worth the investment for primarily text-based commodity news publishers. 

To offset that issue (i.e., the high cost of video production and the risk of spending money on content that doesn’t pay off), The Independent created Independent TV with a clear rule: No video series gets made unless it already has a sponsor or advertiser committed to funding it.

This commissioning bottleneck of sorts serves as a test of whether the subject was of worth to a potential sponsor in video form. The approach does present potential frustration lin that that advertising priorities dictate what gets made, meaning creative or editorially strong concepts could stall if they didn’t appeal to sponsors. However, this approach prevented the paper from spending a significant amount of money on a video project that might never have been commercially viable.

A similar approach is taken by The Sun, which has seen its video share of digital revenue increased to 18% in the latest reported results. Jon Lloyd, Director of Video for the newspaper says that, “Making video in isolation doesn’t make sense at The Sun; when commissioning Sun Originals the commercial considerations are right at the beginning. That was the genesis for Sun Originals: high quality shows which advertisers like to sponsor. 

“It must always be editorially driven and work for our audiences. But editorial teams will work with the commercial teams in order to launch the show and build the commercial aspect in, tailoring it to the client. It can’t just be their name on the show anymore.”

As a result of that strategy The Sun is working with clients who “have never used us in a digital capacity before, like M&S and Card Factory”. Recognizing the synergy between its consumer-focused news approach and the commercial opportunity, it has been focused on two specific content verticals for Originals commissioning: sports and Fabulous, which encompasses women’s fashion, beauty and lifestyle.

At the same time, commercial considerations do not factor into discussions around video creation for news organizations whose commercial proposition is more supporter-based.

The Guardian confirms that they do not create videos with sponsors in mind. Those discussions are not part of the overall decision about when and where to launch a video series. 

Nevertheless, the company is not ignoring the audience growth opportunities of digital video  and is set to invest further in using it for storytelling. As its editor-in-chief recently told Press Gazette, “I mean, at the moment, [when] we get a big story, it usually will have a podcast attached for example. It will usually have a video explainer attached. It will usually have all sorts of stuff attached already, but I think it’s the next stage of that.”

Cost vs return

Some major titles are increasingly looking to video as an audience growth opportunity first. The idea is that audiences attracted by video are likely to convert, even if that form of content does not exactly match the majority of the paper’s content.

Juliet Riddell is head of new formats at the Financial Times, which has been experimenting with news-led short films. Its latest, ‘Recall Me Maybe’, is a short fictional film that examines dementia, AI, and the unreliability of memory and artificial intelligence. That’s quite the departure for the title. However, its position in front of the paywall speaks to its belief that video of this sort is worth investing in as an audience acquisition tool.

Speaking to Media Voices, Riddell explained: “All the films are trying to connect an audience with something that’s happening now and that we feel we need to communicate now.”  

We also see a number of publications investing in digital video to diversify the audiences that are exposed to the brand, albeit with a far lower cost base. That is particularly true for those media businesses looking to convert more audiences by creating more touchpoints.

Chris Stone is executive producer of podcasts and video at the New Statesman. He explains: “We’re already producing podcasts, so adding video to that production workflow doubles up on the content that we’re making, so we’re getting more out of a single record. That video then extends our audience reach on YouTube, [and] also on social platforms. And the purpose of that is to grow the top of our funnel.”

He also notes that the ROI of a piece of content – in any form – is based on how widely it can be repurposed: “If I was starting something from scratch, I wouldn’t start with original video. I would start with multimedia content that can be repurposed on lots of different platforms.”

Beyond digital video

Given the scale of investment in video podcasts over the past few years – with a recently announced collaboration between Spotify and Netflix acting as the cherry on top – it is unsurprising that newsbrands recognize the value of delivering its star audio content in another broadly-accessible format. Vox, for instance, has just poached the NYT’s Astead Herndon, appointing him as a host and editorial director with a remit to launch and lead a new multiplatform video podcast. 

This approach bears fruit for publishers that have been investing in multimedia content for years: the podcasts have already proven to be commercially successful in audio, and the additional costs of filming and editing them are relatively small. In commissioning these relatively small, these media businesses take advantage of advertisers’ hunger for video content, and video’s ability to open up new audiences. 

Whether it is a high-quality audience play like that of the Financial Times, or the more commercial-led commissioning approach, video is increasingly seen as a must-have for news and magazine brands. Finding the sweet spot between commercial growth and audience development is paramount, but dependent on the wider commercial strategy of the title.

The post Publishers reveal their ROI equation for digital video appeared first on Digital Content Next.

While the public generally feels comfortable with how they believe GenAI is currently used in journalism, concerns persist around its application to certain news-related tasks. Optimism about GenAI’s future varies by sector, with news and politics standing out as areas of skepticism.

The study also shows a growing adoption of GenAI tools, with trust in these technologies rising alongside user familiarity. As this trend continues, the advantage currently held by human news professionals may diminish, especially if news leaders fail to actively reinforce public confidence in the value of human-driven journalism.

GenAI in news and journalism

The percentage of those surveyed who are more comfortable with news produced entirely or mostly by human journalists has risen slightly compared to data from the previous year, while trust in news generated primarily or entirely by GenAI has fallen slightly. This indicates a growing preference for human leadership in news, presenting an opening for news executives to bolster public confidence. Across all six countries studied, a strong preference for human oversight in news prevails.

• 62% are comfortable with news made entirely by humans.
• 43% are comfortable with news produced mostly by humans with help from AI.
• 21% are comfortable with news produced mostly by AI with some human oversight.
• Only 12% are comfortable with content generated entirely by AI.

Comfort levels vary according to how AI is being used. Most participants are fine with GenAI use in checking grammar, spelling, and providing translation. They are less approving of use for research, writing, and data analysis. The public is decidedly disapproving of GenAI tools being used to rewrite content for different audiences, generate a realistic image when a photograph isn’t available, or create an artificial presenter or author.

Fortunately, people’s comfort level with journalists using GenAI for certain tasks is aligned with how often they think journalists are already doing so. It appears that most of those studied believe journalists are using AI in ways that they find acceptable, and few believe it is commonly used in the ways they would find most unacceptable.

While there are differences among the countries surveyed,news is trusted significantly more than the most widely used and trusted GenAI system, ChatGPT, in almost every country studied.

• In Denmark, 72% report trusting news while only 32% report trusting Chat GPT.
• In Japan, 60% trust news; only 31% trust ChatGPT.
• In the UK, 45% trust news; 20% trust ChatGPT.
• The USA and France have lower margins, with 36% of respondents from both countries reporting trust in news while 27% trust ChatGPT.
• Only Argentina reversed the trend – with 37% trusting ChatGPT- more than the 31% who reported trusting news.

It’s worth noting that ChatGPT was found to be the most trusted GenAI tool among survey participants. This means that the differences in trust levels would be even more stark if comparing news with lesser known GenAI tools.

Caution: Trust in GenAI grows with familiarity

Increase in regular use of GenAI tools is leaping rapidly. The proportion of survey participants who reported having used a standalone GenAI system such as ChatGPT rose from 40% in 2024 to 61% in 2025. Those reporting weekly usage nearly doubled in a year, jumping from 18% to 34%. So, if trust rises with use and familiarity, traditional news media could soon lose their edge in public trust when compared to GenAI tools.

Not surprisingly, younger generations were found more likely to both use and trust GenAI tools. 59% of people in the 18–24 age range reported having used any GenAI tool in the last week, compared to 20% of those aged 55 and up. However, that age gap is driven mostly by ChatGPT. Other GenAI tools, including Google’s Gemini, Microsoft’s Copilot, Meta AI, and Grok, had narrower use differences across age groups. This is probably because the later tools are embedded within widely used products.

Optimism about GenAI varies by use case

The public generally leans optimistic about the future of GenAI. Across all six countries studied, on average 29% are optimistic and 22% pessimistic about the impact of such technologies. However, the share of optimism versus pessimism varies by several factors. Pessimism outweighs optimism when it comes to GenAI use in news media, government, and politics. Optimism outweighs pessimism when it comes to GenAI use in health care, science, retail, and search engine efficiency.

• Only 18% think GenAI will improve their experience with political parties or politicians.
• 27% believe GenAI will enhance their interactions with news media.
• 37% believe GenAI will improve their interactions with health care professionals and scientists.
• 43% believe that GenAI will enhance their experience with search engines.

The lower levels of confidence in GenAI’s impact on politics and news suggests a perceived link between those areas. It also aligns with the stated preference for human-produced news.

The future of Gen AI and news

While this data provides some reassurance for news media leaders that the public values human news professionals, it also points to some areas of concern. As use of GenAI rises, so do comfort levels and trust in the utility of the tools, potentially eroding the advantage currently held by human journalists and producers.

To stay ahead and remain better trusted by audiences, news organizations should prioritize original reporting. They must differentiate their offerings from GenAI content and actively communicate these unique values to their audiences.

The post AI and news: Humans have the edge (for now) appeared first on Digital Content Next.

To be clear, this was not a story written by AI. However, it was a story made possible by it. And it’s a perfect example of how we’re deliberately approaching AI at The Wall Street Journal – always to enhance journalism, not replace it. We spent the summer honing that deliberate approach with the goal of building knowledge, solutions, and a vision for AI’s newsroom role. 

⇒ Takeaway: Given generative AI’s transformative impact and the industry’s rapid change, newsroom leaders must address short-, mid- and long-term strategies all at once. 

NOW: Building a strong AI foundation

In the short term, we’re focused on ensuring our journalists know how to leverage AI and can do so in alignment with our newsroom AI guidelines. 

We rolled out enterprise AI tools and provided the standard company-wide “AI 101” training earlier this year. Yet even after months of effort, many reporters remained unsure how to use these tools effectively. I frequently heard questions like “How should I apply this in my job?” and “Where do I even start?” 

To address those concerns, we launched a summer series of lunch and learn sessions with the Newsroom AI task force to demonstrate how reporters and editors are already using AI in their work. Reporters walked away with role-specific tip sheets and more visibility on who is the AI evangelist in each department. Better yet, with the editor in chief in attendance, the sessions delivered a clear endorsement that the newsroom can and should be using these tools. 

 ⇒ Takeaway: We found that reporters need clear examples that demonstrate the return on investment for learning this new technology, especially as caution and uncertainty remain high. 

NEXT: Identify opportunities and embrace transformation

We scheduled brainstorming sessions with teams to identify elements of typical workflows that are rote and repetitive, more chore than creative. The key question we wanted to address was how can we free up time for journalists to focus on stuff only they can do? 

Concrete takeaways from these meetings are a prioritized list of workflow challenges. We identify places where new tooling or AI agents might help. We’ve now got a list of projects to prototype in the newsroom and to collaborate on with our tech and engineering colleagues.

Zooming even further out, we hosted a series of sessions with each coverage area that focused on how our newsroom can evolve to anticipate changes in reader behavior and expectations. The goal was to think big: Given what ChatGPT, Gemini and Claude can already do, how do our workflows evolve? How should our coverage change?

The industry is at a massive inflection point in the way audiences discover, digest and manage information – and that’s our business. While we won’t always get it exactly right, we do need to anticipate audience behavioral changes to stay ahead. Those conversations have never felt more urgent. Our future-proofing sessions clarified the need to focus on direct relationships with readers and creating unique, human-centered storytelling experiences that AI can’t replicate. 

⇒ Takeaway: Executive-led sessions reinforced that AI is a top priority – and every journalist should treat it with the same level of urgency. 

Our AI roadmap

As we shape our strategy and roadmap, we’re actively harnessing AI to deliver impactful journalism. AI continues to be a force multiplier for our newsroom, increasing speed and discoverability in areas and formats that have historically been hard to cover by a single reporter. 

Our data journalism team built a tool that does just that. Nicknamed “Orca,” this tool helps reporters digest and summarize conversations happening in podcasts. Where previously a reporter would have to listen to every episode, tallying hundreds of hours on a particular topic or show, now Orca leverages AI to transcribe conversations into text that can be searched, summarized or quantified. Orca was the engine behind this piece, which analyzed how conservative podcasters have discussed the Jeffrey Epstein case. Orca listened to more than 22,100 episodes from 148 podcasts, obviously a feat previously unfathomable by even a large team of reporters. 

⇒ Takeaway: Roadmaps and cross team alignment can take time. Let’s not keep that from holding back areas where AI can move at speed and make a real impact— our journalism.  

Embracing AI wisely means moving with both urgency and care – deploying it where it can supercharge reporting today, while building the culture and strategy to guide its long-term role. At The Wall Street Journal, our guiding principle is clear: using AI to enhance our journalism, not to replace it. By investing in training, rethinking workflows and pushing ourselves to imagine what’s next, we’re ensuring that our distinctive, exclusive and human-led journalism remains at the heart of every story we publish.

The post WSJ’s AI strategy: urgency, clarity, and human-centered reporting appeared first on Digital Content Next.

AI in media has matured beyond experimental pilots and workflow quick wins. The question is no longer if you will use AI. It’s how you’ll use it to stand out in a marketplace where speed, volume, and even exclusivity are no longer differentiators.

Arc XP’s latest report with Digiday, surveying 108 publishers and broadcasters to understand how media organizations are incorporating AI into their newsrooms, confirms the stakes:

• 97% of publishers are investing in AI.
• 88% are prioritizing AI to elevate content quality, not just efficiency.
• Yet only 1% have scaled AI across their organizations.

For the rest, the gap between AI experimentation and AI transformation is the opportunity and the risk.

Beyond automation: the rise of agentic workflows

The first wave of AI in the newsroom focused on automation by reducing labor time, accelerating production, and streamlining repetitive tasks. Those gains are real: 86% of publishers report reduced labor time and 85% report faster creation.

But the next wave, already underway, is about agentic workflows. Here, AI-powered assistants work in the background or are embedded in storytelling. They are designed to support journalists, not replace them.

Arc XP’s vision, as described by VP of Arc Intelligence Joey Marburger, is clear: “AI is like a junior reporter. Train it, guide it, but never expect it to replace your newsroom.”

In practice, this means agents that:

• Check style guides automatically.
• Suggest metadata and keywords in real time.
• Surface relevant video, photo, and archival content instantly.
• Summarize research while keeping humans in the editorial driver’s seat.

This “human-in-the-loop” approach is essential to maintaining trust, brand integrity, and editorial judgment, qualities AI cannot replicate.

Content has been commoditized. Insight has not.

Generative AI can repackage, summarize, and distribute stories faster than any newsroom. That means the old competitive advantages—speed, volume, and exclusivity—no longer guarantee audience loyalty.

What AI can’t replicate are journalistic instincts, mission, and audience. That’s why leading publishers are using AI to free up time for original reporting, local engagement, and audience-driven experimentation.

By pairing AI tools with secure, flexible, media-focused workflows, news organizations can focus on what machines can’t create: voice, context, originality, and trust.

Multi-modal storytelling for audience 3.0

Our State of AI in the newsroom report revealed AI is accelerating multimedia production—82% of publishers use it for video editing, 87% for image optimization. However, many remain dissatisfied with quality and consistency.

Arc XP’s AI roadmap tackles this head-on with a vector-based asset database, enabling instant retrieval of relevant photos, video clips, and audio. This makes it possible to reformat content across modalities, turning a text story into a video or podcast, or transforming audio interviews into publish-ready text, without sacrificing editorial quality.

This is critical for “Audience 3.0,” where engagement is immersive, adaptive, and co-created. In this environment, text-only strategies aren’t enough; audiences expect stories that move, speak, and adapt to their preferences.

An AI business imperative

AI isn’t just an editorial tool. It’s a business weapon. Our research shows forward-looking publishers are using AI to:

• Personalize content delivery
• Improve accessibility
• Unlock new revenue streams by protecting and monetizing premium content from AI crawlers and bots

These capabilities are also part of a broader shift toward strengthening direct audience relationships. This is particularly important at a time when referrals are declining and chat-based AI platforms are changing how people discover content.

The clock is ticking

In 2025, the divide is clear: publishers who treat AI as a strategic advantage are redefining their markets. Those who don’t risk irrelevance.

The winning formula is already visible:

• Treat AI as a platform, not a plug-in.
• Align editorial, product, and engineering around shared goals.
• Protect and monetize your intellectual property.
• Prioritize originality, insight, and human voice over scale alone.

The transformation window is open now. But it will not stay open forever. The choice is stark: lead the market by building intelligent, human-centered AI systems that multiply your newsroom’s impact, or watch from the sidelines as competitors redefine the media business without you.

The post The year of now: an AI turning point for publishers appeared first on Digital Content Next.