> Metrics look objective. They are not. Every metric carries the business interests of whoever designed it, so a newsroom running on off-the-shelf analytics is optimising for someone else's goals

> Platform analytics are built to maximise ad inventory and time on network. If you sell subscriptions, you are measuring success with a tool designed for a different business model

> The free analytics smaller outlets use are modelled on what the biggest outlets negotiated with platforms. The advantage compounds: winner-take-all monetisation, hierarchies that are hard to challenge, and incentives to consolidate

> Chasing volume shows up in the copy. Articles translated into several languages, wire reports pasted in, headlines bent towards trending keywords. In the AI era the suggestions are no longer keywords but whole sentences, which risks framing social issues more stereotypically

> Goodhart's law: once a metric becomes a target, it stops being a good metric. The argument is not to abandon analytics, but to ask of every number on your dashboard who designed it and what behaviour it rewards

Based on the PhD research of Sophie Chauvet.

What I have found, in a nutshell, is that the bigger the size, the bigger the wins. But to find out about this simplified insight, I first had to take a step back from newsrooms, find the right tools, and ask key questions in order to open the black box of analytics the best I could.  

First, where do metrics come from? A genealogical approach proved to be deeply informative.  

Second, who profits from metrics? Following the money, and identifying who defines them, helped identify the main technical and political dynamics at their core. That’s where the macro approach became fruitful. 

Third, how realistic are metrics? There, I investigated what happens when a quantified measurement captures a social process, and what gets erased.  

After analysing hundreds of white papers, events, and interviews with practitioners across the data monetisation ecosystem, including ad tech, platforms, measurement and analytics companies, there was a single metaphor that helped to understand the stakes of metrics, and why they may negatively re-shape media landscapes: confusing the map for the territory, or how metrics tend to be self-fulfilling prophecies, thus impacting reality based on biased premises. 

The crux of metrics: Self-fulfilling prophecies, or why we confuse the map for the territory

Confusing the map for the territory is an expression borrowed from Alfred Korzybski, which is often used in quantification studies to describe the nefarious impacts of ill-used measurement. It means thinking the measurement model associated with an object is as real as the object. In the context of metrics, this helps to understand why there might be some confusion as to whether clicks mainly represent the audience’s interest, when in fact, they also represent the interests of those who built the model of metrics.  

Let’s break this down further, by answering the first question: where do metrics come from? Here, the history of audience measurement, as told among others by Cécile Méadel, Philip Napoli, and Josiane Jouët, is particularly enlightening. To practitioners in companies such as Médiamétrie and Nielsen, who historically developed studies on audience measurement on television and radio thanks to their Audimat and Peoplemeter, the audience was understood to be more of a convention than a reality, a means to gauge advertising revenue, with measurements mostly agreed upon by the different media involved.  

As such, measurements hold a kernel of truth, but they also serve a function of coordination between players in order to synchronise, predict and project financial benefits, with measurement containing an added layer of filters based on the parties’ main interests, which are also audited in order to ensure some degree of fairness within the ecosystem.  

With the rise of the internet in the late 90s, the first analytics companies, such as Urchin in 1998 that was acquired by Google, emerged with the promise of more realistic measurement. The internet was sold at the time as a new medium where clicks served as the new proxies for direct and real-time feedback from the public, through personal computers.  

This promised to help bypass the often heavy, costly, and slow studies of more traditional audience measurement. This promise was then thought to be fulfilled exponentially with the rise of social media and their troves of personal data waiting to be collected at a scale never seen before.  

This helps explain why the public is sometimes conflated with analytics, or why the map is confused for the territory, as analytics tools entered the newsroom in the 2010s, a field traditional measurement companies hardly ever dared to enter so as to protect journalistic integrity from advertising’s interests.  

Who profits from confusing metrics? 

The rise of analytics in newsrooms coincided with the rise of platforms and real-time data collection at scale. Here, the answer to the second question, who profits from metrics, is answered. Indeed, one aspect that contributed to making platforms so powerful is that their models rely partially not only on providing real time data on website use, but also on collecting this data back to improve their own models, by maintaining a more global overview on all the websites they are implemented on.  

With this broader perspective on audience flows, they are able to optimise advertising models which are key to their financial monopoly over the internet. These models however are black-boxed, as platforms are rarely audited.  

This has severe consequences on the internet ecosystem. For example, recent trials have proved that Google have abused this position of power over data flows by choosing specific, opaque algorithms that prioritised them over news outlets in an undue fashion, leading the latter to lose millions in revenue.  

But beyond the potential for fraud, these black boxes, this network of metrics used by many newsrooms, also contain the different ties of coordination between a slew of internet players that monetise data across the ecosystem.  

This, added to the complexity of the technical layers that sustain online consumption, and the un-audited incentives for inflating numbers, not to mention spam and bots, contribute to a phenomenon of entropy, which help explain why metrics do not always make sense. Disclosing this entropy is not in the interest of platforms. 

Overall in that regard, metrics, when provided by bigger, non-journalistic players, could be regarded not as direct public feedback, but rather as a reflection of bundled interests of the technical infrastructure of the internet, and the political economic negotiations that happen often behind closed doors. 

Metrics have therefore kept their function of coordinating the interests of the players involved, but without much transparency, and with an added layer of realism which is heavily advertised by many internet players, whether in online advertising, or via analytics companies.  

The benefits of scale: Supercharging the Goodhart’s law  

Now there is perhaps one factor that is the most significant when it comes to further understanding why metrics are confused for reality, and why they tend to become self-fulfilling prophecies with deep impacts on media landscapes: scale. This is due both to the fact that metrics are made of statistics, and because online audience flows are dominated by platforms.  

First, the statistical nature of online data flows highlights the importance of scale in monetising data online. This is caused by the fact that most predictions online are based on probabilities, and their associated deterministic patterns.  

This statistical technique helps to observe long term audience consumption patterns and how to optimise the most fine-tuned, cost-efficient audience trajectories according to a specific click architecture. In order to fulfil the promise that predictions become true, scale is a sine qua non condition in order to decipher what historical patterns are the most profitable.  

A corollary of this is that this deterministic parameter tends to cement historical repetitions, and to further benefit media players who existed first, as both benefits of scale tend to generate cumulative effects, and to provide a reputation for reliable profit which is highly valuable when it comes to organising and predicting budgets.  

This one main parameter is what allows platforms, whose main objective is to accumulate data, to maintain a stronghold on media landscapes, by adopting this meta-deterministic perspective on data patterns, and therefore, holding a monopolistic, black-boxed overview of what performs best online. 

By using probabilities and providing metrics as a map, platforms may supercharge the effect of self fulfilling prophecies online, through metrics that often tend to become objectives rather than simple indicators. 

This mechanism is called the Goodhart law. When metrics are used as objectives, they cease to be good metrics. In the case of metrics in news, it means that using scale in order to fulfil the deterministic parameter of finding truth through statistics tends to create self-fulfilling prophecies. By incentivising scale, volume, clicks, and page views, there are some negative macro effects that come as a consequence of mis understanding the power of metrics.  

Macro impact #1: The winner-take-all effect  

First, there is a winner-take all effect. The bigger the media, in terms of volume of clicks and data, the bigger the possibilities of monetising the data fruitfully. This winner-take-all effect is similar to the one observed in the business model of platforms, where scale advantages tend to become cemented and supercharged once a certain volume is reached. The bigger the media in terms of data volume, whether through subscriptions or through the accumulation of page views, the bigger the monetisation opportunities. The benefits of owning historical data tend to be cumulative as the predictions are more reliable and more precise.  

Furthermore, metrics models provided by platforms are often based on those bigger media outlets, as platforms are naturally more interested in those large pools of data that benefit their own meta-deterministic models of data analysis. In that sense, adopting the metrics provided by platforms tends to mean, that one’s strategy becomes symmetrical to platforms’ interests.  

Macro impact #2: Media hierarchies  

A second macro effect of this model is that it tends to also favour media hierarchies that are difficult to challenge. Bigger outlets remain at the top in terms of traffic and monetisation, because platforms tend to model their metrics and software according to those levels of scale and volume, which is what is of interest to them. Bigger outlets are also those who benefit more from in-person negotiations with platforms as to what metrics matter, and those models tend to be those that are then provided in free versions of analytics software that are more accessible to smaller outlets.  

Furthermore bigger outlets with more financial resources are more able to tweak analytics parameters, whereas smaller outlets tend to have more ready-made, black-boxed tools that may not be that adaptable to their own needs. Those outlets who have higher volumes of data also tend to be favoured in negotiations with other players of the data monetisation ecosystem, such as ad tech companies, as they benefit from a solid monetisation reputation.  

This model is not necessarily new, as traditional measurement companies also used similar hierarchical, rankings-based models, where media outlets with more resources and established historical deterministic patterns, have access to more precise and efficient measurement. 

In this digital analytics model therefore, new players have fewer chances to compete in terms of data monetisation because they lack the historical reputation and the reliability of accumulated historical patterns that unlock trust in predictions and funding opportunities. Therefore, the parameters of determinism, scale, and self-fulfilling prophecies are revealed by the simple fact that measurement and metrics are based on statistics and projections.  

Macro impact #3: Media concentration  

The data monetisation benefits provided by scale include a third significant macro impact on media landscape: media concentration incentives. In order to compete in this quantified, rankings-based system, some media outlets have adopted strategies to access those scale benefits. By creating alliances, accumulating media outlets, and re-joining databases, more volume can be reached. In that sense, media conglomerates can be incentivised to optimise online advertising and data monetisation opportunities, with a potential risk for media diversity.  

Macro impact #4: Devaluing journalism 

The final macro consequence that derives from the volume incentive, which is perhaps the most significant of all, is the devaluation of journalistic standards. From a strictly volume-oriented data monetisation perspective, it makes sense to produce more content which translates into more clicks, at the expense of journalistic standards. While clicks and webpages can easily be inflated from a quantitative perspective, the labour that tends to come with quality, original reporting, which often takes time, tends to be disregarded, from the perspective of this quantified financial model.  

In that sense, favouring financial value to access scale benefits tends to empty journalism from its values, and the value of its labour. Some strategies adopted by some media outlets include quickly producing articles at scale, by translating them into different languages, copy-pasting news wire reports, and coordinating with trending online keywords that do not necessarily stem from organic audience behaviours or from journalists’ flair.  

Journalists have sometimes conceded, when interviewed for this research, that they felt they had to twist their instincts in order to ensure content production that may have felt industrial rather than the byproduct of careful listening of their public’s needs. On the brighter side, another strategy also includes the attempt to quantify the value of journalism’s democratic mission, by promoting the quality of the involvement with the public as a selling point.  

Other media employees, involved more in advertising monetisation rather than direct editorial output, have also mentioned how adopting these keyword-tweaking strategies, informed by listening to more global internet trends provided by platforms, tend to be fruitful. Small tweaks, multiplied by thousands of webpages, thanks to benefits of scale, result in interesting profits.  

However, these strategies also tend to benefit platforms’ meta-deterministic needs, with consequences on how social issues covered by journalists are phrased online. With certain keywords being chosen over others, in order to fulfil the internet’s echo-chamber of what gets attention or not, meaning, what is observed statistically as echoing what was said in the past or by others, platforms mechanically end up having quite a say as to how social issues might be framed. 

In the era of AI, this editing power based on historical patterns might be supercharged, as suggestions not only contain keywords but sometimes entire sentences. Mechanically, this also runs the risk of framing issues in a more stereotypical way.  

Re-seizing the power of metrics 

Metrics are rarely neutral tools. They benefit some and devalue others, according to decisions that are black boxed behind their deceptive simplicity. They might contain a kernel of truth, but they often reflect the interests of those who manufactured them. In the context of news, they might be one reflection of the public’s clicks, but this reflection is also heavily mediated by the infrastructure of those who are able to have an overview of the internet’s secret flows and complex behind-the-door negotiations.  

Theoretically, metrics should be seen as indicators, not as objectives. In practice however, they tend to re shape media landscapes in a way that is detrimental to media diversity, journalistic autonomy, and may contribute to journalists’ loss of meaning.  

Following a map provided by non-journalistic players may lead journalism into platforms’ projected territory, one that is biased and mostly driven by scale, where truth is defined according to statistical parameters and their own interests. That’s the price to pay when meaningful, values-oriented work is translated into simplified quantified measurements.  

But I would argue that the potential of metrics to really benefit news remains unexplored. Metrics, when seized with the awareness of what they can and cannot do, can be powerful tools to visualise injustice and quantify the reality of invisible social issues.  

When the self-fulfilling prophecy aspect of measurement is mitigated, and when metrics are seen as simple indicators, they can be used to jolt actions that favour values rather than value. Knowing the limits of metrics, and being aware of what territory is being mapped when choosing them, is therefore key to secure journalism’s interests and social mission.  

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765140 – based at Samsa and with the University of Toulouse.

> Journalism has to be right the first time. Product work learns more from what doesn't work. Frank Bi, AI Strategy and Product Advisor at Hacks/Hackers, argues that when products are held to the journalistic bar, nothing half-baked ever launches, so nothing ever teaches you anything

> Build a public sandbox. A labelled subdomain (beta, labs) resets the expectations readers bring to the homepage while still producing real behaviour data. Bi launched the Minnesota Star Tribune's AI restaurant guide Culinary Compass on the main domain instead, where readers judged a four-week experiment as a Star Tribune guide

> Set the end date before launch, ideally by picking something with a natural start & end. Bi's Fair Bot ran only the 12 days of the Minnesota State Fair, so nobody ever had to make the call to shut it down

> Show your work. Newsrooms already have a practice for issuing corrections, so extend a version of it to experimental products and publish what the team believed and why, before that thinking disappears at launch

> The Age of News Experience argues that for social platforms and games, experience is not a feature added to the product. It is the product, and that is what holds attention

> Strong products do not add up to a strong outlet. Readers still meet a fragmented set of things unless you design how they move between them, an approach the authors call Experience Islands

> The AXR framework runs audience to experience to revenue. Skipping the middle turns "reach younger audiences" into "make vertical video". Formats produce outputs, experiences produce behaviors. And repeatable behaviors connect audience value to commercial value

> In video there is a passive-active pattern. Viewers happily let the next item start, but if it shifts too sharply they have to actively decide again, and that decision point is where they leave

> So the unit of planning changes. Before launching anything ask what experience begins here, what the natural next one is, and what must stay consistent so it feels like continuation

> The Economist has launched a dedicated subscription tier for access to audio and video formats, priced lower than the core digital subscription to attract new audiences

> The "Halo Effect": From early signals, the team has observed an uptick in core Premium sign-ups. Seeing a cheaper, format-specific option next to the full product acts as an anchor, nudging undecided visitors to pay a little extra for the full suite.

> Net-New Audience: Conversions through Play have been driven almost entirely by brand-new readers rather than existing subscribers trading down.

> Play is part of The Economist's wider strategic development (alongside podcasts, short-form video and "Insider") towards building deeper, more "human" relationships with audiences

Lennart Schneider is Founder of Subscribe Now, helping decision-makers in the subscription economy attract subscribers and keep them happy.

Mark Zohar, President and CEO of Viafoura, shares why he believes that Frequency (the F in RFV) is the most important metric to be tracking in an era where content is increasingly a commodity that AI can summarize, paraphrase, and serve without ever sending a click your way.

AI broke the funnel. Community rebuilds it.

The uncomfortable truth is that the open web’s traditional acquisition funnel, SEO at the top, content in the middle, monetization at the bottom, was always borrowed infrastructure. Publishers rented audience from platforms, and now those platforms are keeping the audience for themselves.

The publishers who will win the next decade are the ones who own the relationship, not rent it. And ownership doesn’t come from content alone anymore. Content is increasingly a commodity that AI can summarize, paraphrase, and serve without ever sending a click your way.

What AI cannot replicate is community. It cannot replicate the experience of debating a local election with your neighbors in a comment thread moderated by your newsroom. It cannot replicate a live Q&A with the reporter who broke the story. It cannot replicate the feeling of being recognized, replied to, and known by a brand you trust.

This is where Frequency lives.

The Frequency Loop

When publishers invest in community-building experiences like commenting, Live Q&As, topic-based chats, polls, personalized feeds, and real-time notifications, something powerful happens. Readers stop coming back for the content alone. They start coming back for the conversation.

That shift creates what I call the Frequency Loop

A reader visits for the content. They engage with the community. They get notified when someone replies, when a new live event starts, when a topic they care about is being discussed. They come back. They engage again. They register to keep participating. They subscribe to deepen the relationship. Every loop tightens the bond, increases the visit count, and unlocks more revenue per user.

The economics of this loop are not theoretical. Viafoura‘s data shows that engaged community members visit at a rate that is 6-8 times higher than fly-by users. And the downstream impact is well documented across the industry. Piano’s Subscription Performance Benchmark has found that registered users convert to paid subscriptions at roughly 45 times the rate of anonymous visitors. Frequency is what closes the gap between the two.

More frequency means more ad impressions. More frequency means higher page RPM, because engaged sessions monetize at premium rates. More frequency means a registered-user base that’s actually worth registering. More frequency means subscription offers that land, because the reader has already demonstrated, behaviourally, that they value what you do.

What the F should you actually do?

If Frequency is the metric, here’s the honest diagnostic every publisher should run:

Do you give your audience a reason to come back tomorrow that isn’t a new article? Do you have a place on your property where readers talk to each other and to your journalists? Do you notify them when their participation matters? Do you personalize what they see based on what they engage with, not just what they read? Do you treat your registered users like a community, or like a CRM list?

If the answer to most of those is no, you don’t have a Frequency problem. You have a community infrastructure problem. And in an AI era where every other lever (search traffic, social referral, content differentiation) is getting harder to pull, community infrastructure is the lever that’s getting more valuable, not less.

The bottom line

AI is going to keep getting better at answering questions. It’s not going to get better at being a community. That asymmetry is the single largest strategic opening publishers have right now, and Frequency is the metric that tells you whether you’re walking through it.

So the next time your board asks how you’re future-proofing the business against AI disruption, don’t show them a traffic chart. Show them your Frequency curve.

And if you don’t have one, well, you know what to ask yourself.

What the F are you doing?

> Publishers have long treated one reader as two entities: the ads team chasing reach, the subs team chasing exclusivity. Ringier built a single Customer Lifetime Value combining both, and found the conflict doesn't exist

> Subscribers keep generating strong advertising value precisely because they're the most engaged. Exclusivity doesn't cost you ad revenue the way the org chart assumes

> On Blick.ch, a logged-in user is worth well over 30 times a consent-only one, and a Brand Lover over 50 times a casual reader

> But logging in isn't the magic. The value comes from what follows: more page views, and an email address that cuts churn. Ringier built "Follow My Team" so registering buys something, and doubled daily logged-in users in a year

To break this down:

• Reader Revenue CLV is calculated by multiplying the Average Revenue Per User by the subscription lifetime.
• Advertising CLV is calculated by multiplying the Average Revenue Per User by the readership lifetime.

Measuring users on two dimensions

Testing this new metric out, the team started with Ringier’s digital-only, German-speaking reach brand in Switzerland, Blick.ch.

While the project started as a complex manual spreadsheet exercise, the team is currently working on automating the process directly within their data lake. Patrick emphasized that these preliminary results are meant to show “t-shirt sizes” (relative proportions) rather than precise, absolute financial figures, the main goal being to prove that the ads vs subs conflict doesn’t exist.

The Blick user base was analysed across 2 distinct dimensions: funnel depth and loyalty.

Dimension 1: Funnel Depth

Dimension 2: Loyalty

What the data revealed

1. Funnel depth unlocks LTV

The data clearly proved that moving a user deeper into the funnel unlocks exponential lifetime value. Preliminary results suggest:

• Giving consent increases the value compared to “No Consent”.
• A logged-in user delivers a significantly higher CLV, well over 30 times that of a “Consent Only” user
• Moving from a login to a monthly subscription doubles the value, and a yearly subscription multiplies that even further

2. Subscribed users still drive ad value

The results exploded the myth of the ads vs subs conflict: subscribed users continue to generate strong advertising value. When looking at monthly and yearly subscribers, their advertising value remains high because they are highly engaged with the platform. 

This was also something shared by Bonnier Media at WAN-IFRA’s World Congress in Marseille: subscribers not only have a subscription ARPU of €300, but an advertising ARPU of €75.

3. The more loyal, the higher the LTV

When analysing the loyalty dimension, the lifetime value growth was even more staggering. A Brand Lover has a total CLV well over 50 times higher than that of a casual or one-time reader.

The final monetisation matrix

When combining these dimensions into a heat map, the value disparity becomes clear. In short, a high CLV directly correlates with high loyalty and a deep funnel stage.

While the high-value buckets are incredibly lucrative, the core issue for Blick.ch, and most reach publishers, is that the vast majority of the audience currently sits in the “Consent Only” bucket, which yields relatively low monetisation per user.

Speaking on a panel about registration as a solution to this challenge, Patrick highlighted how this data provides a clear financial incentive to log audiences, guiding users deeper into the funnel. 

However, Patrick clarified that registration isn’t a magic wand – the value does not come from the act of logging in itself. Rather, it comes from two secondary effects:

1. Engagement: Logged-in users consume more page views, creating more ad impressions and higher ad revenue.
2. Lifetime: Having a user’s email address establishes a direct relationship, reducing churn and making it far easier to eventually convert them into monthly or yearly subscribers.

Putting CLV into practice

To put these CLV insights into practice, Ringier Media Switzerland has shifted from just gating content to building features designed to deepen user relationships.

Because Blick.ch is well-known for its sports coverage, they launched a feature called “Follow My Team.” Sports fans can select and follow their favorite local clubs, national teams, or specific sports, creating a highly personalized feed and specialized team detail views. Of course though, if a user wants to save these preferences and access this tailored experience, they must register and log in.

By building a product that naturally incentivises identity over anonymity, they are successfully guiding casual users into the logged-in ecosystem.

Patrick highlights that understanding user value is becoming yet more critical with the rise of AI search and AI browsing modes. In an AI-driven ecosystem, publishers are most likely to lose the traffic of users with low loyalty who visit anonymously without a login or subscription. Building a walled garden of logged-in, loyal users is no longer just a revenue optimisation strategy, but a survival mechanism.

By aligning their teams and product strategy with a holistic Customer Lifetime Value, Ringier Media Switzerland doubled their number of daily logged-in users over the last year.

For the first time, the publisher has the hard data to prove that investing heavily in deep, direct user relationships is entirely worth the effort.

1. The strategy gap: moving engagement to the heart of the business

Newsrooms are recognizing that deep audience engagement is the primary lever to unlock long-term financial sustainability. In fact, audience engagement was the most frequently selected top-three goal for 2026.

However, a significant execution gap remains: 25% of newsrooms still make daily editorial decisions purely on instinct or reactivity, and 42% operate with only loose structural alignment.

Furthermore, the data proves that editorial growth stems directly from operational discipline. Newsrooms that systematically review and discontinue low-impact initiatives are nearly twice as likely to experience budget growth compared to those that manage portfolios on an ad hoc basis (50% vs. 28%).

What your team can do:

• Diversify the strategy table: Break down the church-and-state silos. Include audience engagement and platform leaders directly in your long-term strategy and investment conversations. Structurally aligned newsrooms over-index significantly on bringing these non-traditional roles to the table.
• Implement “portfolio discipline”: Set up a formal quarterly review to audit your newsletters, verticals, and content formats. Be ruthless about killing initiatives that fail to build a loyal public so you can redirect scarce editorial resources toward high-impact journalism.
• Shift from destination-first to audience-led commissioning: Currently, 64% of newsrooms still design stories for a single primary legacy channel (like print or a homepage template) and adapt them later. Instead, adopt commissioning frameworks—potentially assisted by internal AI tools—that force editors to identify a defined user need or specific audience group before a story is assigned.

2. The audience trust gap: rethinking participation and storytelling

Trust has evolved from rigid, institutional authority into relatable, relational signals. While newsroom leaders heavily prioritize audience relationship building, their workflows tell a different story: reporters spend a staggering 38% of their week on technical production drag, but a meager 11% on post-publication work like community building and responding to reader feedback.

When it comes to distinctiveness in an abundant content ecosystem, the ‘evergreen explainer’ of the past is being disrupted. Publishing teams are pivoting toward deep background reporting (+6pp in structurally aligned newsrooms) while actively steering away from low-margin daily breaking news (-10pp).

What your team can do:

• Bring community in-house: Stop relying on volatile, algorithmic social media comment sections to house your audience network. Take inspiration from pioneers like Newpress (building algorithm-free spaces for co-creation) or the Financial Times (leveraging interactive digital forums like “Ask an Expert”) to turn passive readers into highly engaged, recurring subscribers.

• Lean into service-oriented co-creation: Consider launching a reader-initiated reporting vertical. New Zealand’s Stuff Digital launched “Solving Stuff,” an interactive project prompting readers to submit local problems for journalists to actively investigate. This approach generated massive reader investment, with average time-on-page and completion rates soaring well above site averages.
• Be candid and transparent: Build ‘Behind the Story’ formats into your editorial mix to pull back the curtain on how your journalism is sourced and verified. Relational trust relies on showing your audience your journalistic motivations and methods.

3. The capability gap: shifting AI from efficiency to strategic maturity

Publishers are widely trapped in a Level 1 AI maturity mindset: 42% use time savings as their primary KPI for AI success, focusing strictly on automating existing administrative tasks like transcription or copyediting.

However, the biggest barriers holding back AI integration are people-based rather than technical, namely skills gaps (61%) and cultural skepticism (52%). Crucially, the study found that 57% of organizations do not have explicit AI representation in the newsroom, which directly correlates to the lowest literacy and adoption rates.

What your team can do:

• Embed AI roles directly in editorial: Do not leave technology procurement solely to isolated corporate IT or CTO functions. Move your tech budget closer to production by placing editor-coders, hybrid technologists, or dedicated newsroom engineers directly within your reporting desks. High AI usage and literacy jump dramatically (to 46%) when technical leads are embedded in the newsroom.
• Modernize backend metadata: Your AI tools are only as powerful as your archive. Publishing teams that systematically structure and tag their content assets with robust metadata report exponentially higher levels of successful AI integration and content retrievability.
• Reframe the AI conversation: Move beyond generic, mandatory tool-click training. Adopt a strategy like Bonnier News, which ran voluntary, playful workshops focusing on demystifying what AI fundamentally is and cannot do. This organically transforms open-minded early adopters into internal ambassadors who win over newsroom skeptics by proving how technology frees up capacity to do more investigative work.

4. The skills gap: transitioning from generalists to sharper specialists

Confidence in the future readiness of editorial talent drops sharply when media leaders look three years ahead. To thrive in a visually and technically demanding landscape, the industry is transitioning from broad generalism to a tightly bundled set of hybrid skills.

The primary skills priorities identified for future readiness include tech-enabled journalism (e.g., open-source intelligence, AI prompting), audience data fluency, and production expertise (short-form video editing, visual design). Yet, 61% of newsrooms still provide no formal training for these vital new competencies.

What your team can do:

• Formally define hybrid reporting profiles: Modern reporting roles require a multi-disciplinary approach. Audit your hiring pipeline to look for cross-functional profiles, such as Open Source Intelligence (OSINT) reporters who combine coding with investigative craft, or “Podcast Correspondents” capable of owning a niche category across written text, audio recording, and visual presentation end-to-end.
• Invest heavily in creator-like on-camera training: 41% of newsrooms want to upskill existing staff into multi-platform visual creators, yet 68% of them offer no formal video or presentation coaching. If your strategy relies on launching visual content on TikTok, YouTube, or Instagram, you must systematically allocate budget to presentation, vocal coaching, and platform algorithm fluency.
• Evolve compensation models to retain niche talent: Because audience affinity is increasingly shifting from legacy institutional corporate brands to recognizable individual personalities, media companies must rethink retention. A dominant 78% of media executives agree that alternative payment structures, such as bespoke revenue shares, hybrid contracts, or performance-linked bonuses, are becoming essential to protect and incentivize your most prominent, community-driving journalists.

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Download the full Future Newsrooms Study 2026 report from FT Strategies here.