This shift is happening inside today’s RFP and direct-sales process, not in some future marketplace. Buyer agents are increasingly determining which publishers are considered, how their audiences are valued, and how quickly deals move. For publishers, the implication is immediate: if your inventory cannot be understood and evaluated by these systems, it risks being overlooked entirely.

Agent readiness is the work of ensuring that doesn’t happen. It is the process of preparing publisher data, audiences, and inventory to be discoverable, interpretable, and competitive in an environment where machines are the first point of evaluation.

There are two reasons to prioritize this work now rather than waiting for the agentic marketplace to mature:

1. The return on agent-ready infrastructure shows up in the current RFP cycle, not just in some future quarter when buyer agents become the standard.
2. The standards governing agentic advertising are being written right now, and publishers who wait will inherit rules that others have already defined.

See a shift by the next RFP cycle

The work that makes a publisher’s inventory available to a buyer agent has immediate value inside the current direct-sales motion. Agent readiness depends on a handful of foundational assets:

• A unified identity graph
• Audience segments enriched with verified third-party attributes & your own structured first party data
• Inventory packaging that answers what’s available, who you reach, and at what price

Each asset strengthens your next RFP response while preparing your inventory for agentic evaluation. Buyer agents are already querying publisher inventory at machine speed, and sales agents that can directly respond to them are emerging. When that agent-to-agent communication matures, deal cycles condense from weeks of human back-and-forth into minutes of structured exchange between systems. 

Signal and audience agents are already running on the publisher side, synthesizing and packaging audience data into outputs that sales and buyer agents can act on.

The payoff will show up within the quarter, long before the agentic marketplace fully develops.

Shape the standards being written today

Agentic advertising only works at scale if buyers and sellers share a common language for agents to discover audiences, negotiate deals, exchange brand safety signals, and enforce publisher-defined rules. Built on MCP, the Ad Context Protocol (AdCP) is defining how agents transact across the advertising ecosystem, and the IAB Tech Lab is running a similar agentic track with their AAMP framework.

What matters to publishers is that open, interoperable standards prevail, and that publisher interests shape the outcome. An interoperable standard gives your premium audiences a path to buyers beyond walled gardens. It lets a buyer agent discover, evaluate, and transact on the value of your inventory directly, at prices that reflect the value of your audience, without routing that value through a closed ecosystem or requiring manual negotiation at every step.

The working groups developing these protocols are open, and they need publisher voices to weigh in. A few ways to engage:

• Join the groups shaping the standards. Organizations like the AgenticAdvertising.org and  IAB Tech Lab run open agentic tracks. Membership gets you a seat in the conversations where standards are being defined.
• Assign a data or revenue strategy operator to attend. You don’t need a senior engineer, just someone who understands your inventory well enough to read proposals and respond.
• Submit comments on the decisions that affect your business. Share your opinions on inventory representation, audience description, pricing, brand safety, and consent.

How to become agent-ready

Buyer agents are moving faster than publisher workflows can keep up with. Three operational priorities close that gap:

• Assess honestly. Run a cross-functional audit across data engineering, ad operations, revenue, and legal to map where you stand across the pillars of agent readiness: data, inventory signals, content and context signals, technical infrastructure, and organizational awareness.
• Pilot on a real workflow. Pick one property, audience, or deal type. Configure an agent against that scope, establish a control group, and run it against live direct-sales RFPs over a defined window. Document performance and evaluate how you can move forward.
• Continue shaping the standards. Keep participating in these governance efforts as you run your pilot. The decisions being made in parallel to your work will define the marketplace that emerges.

Agent readiness is the prerequisite for the future of AI-powered media selling and agentic collaboration, and investing in it now benefits your manual and programmatic workflows today.

Start the Audit, Run the Pilot, and Claim Your Sea

Agent readiness is operational work that pays off inside your current workflow and positions your organization for the emerging agentic marketplace. The publishers who start the work now will enter that marketplace with a foundation their competitors are still scrambling to build.

About the author

Bennett Crumbling is a seasoned GTM professional with over a decade of experience with data-centric SaaS, advertising and marketing technologies, and media analytics. Currently, he is the Head of Marketing at Optable, the agentic audience platform for publishers, media companies, and their advertising partners. Outside of work Bennett is a husband and a father to two young daughters. He lives in Lancaster, PA and thrives on hiking, biking, camping, and experimenting in the kitchen.

The post Get ready for AI agents now, before buyer agents scale appeared first on Digital Content Next.

The implicit deal publishers had with search – make good content, earn rankings, convert traffic – no longer holds. The publishers in the best position today recognized early that this wasn’t a temporary dip and started planning for referrals to keep declining.

Search was always a rented audience

Search was always someone else’s distribution channel. Google’s incentives lined up with publishers for a 15-year stretch, and most of the industry built acquisition strategies on that alignment. The alignment is over.

It’s a familiar pattern. Social played out the same way. Facebook referral traffic peaked around 2016 and has fallen unevenly since. Any publisher whose acquisition engine depended on organic social reach has already been through a version of what’s happening with search now.

Owned channels are what’s left. The publishers who built them early are ahead and everyone else is catching up.

From traffic intelligence to relationship intelligence

According to Parse.ly data, across the publisher network it works with (more than 400 sites with 15B+ pageviews a month) the pattern is consistent. The publishers whose audience base has held up are the ones that started investing in direct and newsletter channels years before the search decline forced the issue. The ones that didn’t are trying to build that muscle now, during the decline, which is a much harder job.

Most publisher analytics, including ours, grew up in an era when the publisher’s job was to understand what search traffic did once it arrived. Which articles held attention. Which converted. Which didn’t. That’s content intelligence, and it was the right problem to solve when traffic was abundant and external.

The new problem is different. How does a reader move from a first visit to a repeat visit to a loyal relationship? What content earns the second visit? Which acquisition sources produce readers who stay? When should the newsletter signup appear, and to whom?

That’s a different type of analysis – one that we call relationship intelligence.

Three diagnostic questions

The starting point is a traffic-mix audit. This is not to confirm assumptions, but to see where things actively stand. Most publishers are surprised by what they find. Three questions cut to the picture quickly:

1. What percentage of your traffic is direct or newsletter-driven today, compared to 12 and 24 months ago? If that figure is flat or shrinking while search declines, owned audience isn’t developing fast enough to offset the loss.
2. Which pieces of content drive newsletter signups or repeat direct visits, as opposed to the ones that get the highest raw pageviews? These are often different articles, and the conversion-first bias tends to be under-examined in editorial reviews.
3. Where did your most loyal subscribers originally come from, and what was the first piece of yours they engaged with? The acquisition path that produces a long-term subscriber is probably the most underused signal in publisher analytics.

What owned relationships produce

Direct traffic converts to paid subscriptions at a higher rate than search-referred traffic. A reader typing in your URL or clicking through from your newsletter already has a relationship with your site. A search visitor often doesn’t.

Newsletters are the most concrete example. Publishers sent 28 billion emails in 2025 to over 255 million readers, with average open rates above 41%. There’s no intermediary algorithm between the publisher and the inbox, which is the whole point. The Financial Times now gets more than 70% of its subscriber traffic through its mobile app. That traffic doesn’t move if Google changes a ranking signal next quarter.

What’s missing: the audience connection

What’s missing in most publisher analytics today isn’t more pageview data – it’s relationship intelligence. The acquisition path that produces a long-term subscriber. The content that earns a second visit. The newsletter signup that started a ten-year reader relationship.

A reader who found you through a newsletter, opens your app a few times a week, and subscribed because they trust your coverage on a specific beat is not a reader Google or an AI assistant can reassign. That’s a different audience than the one search was providing for most of the last decade. It’s a much more valuable audience. And relationship intelligence is how you build it.

About the author

Bob Ralian is Head of Unified Analytics at Automattic, including Parse.ly, the content analytics platform for enterprise publishers. His team works with publishers to make sense of their audience data, what’s working, what isn’t, and what to do about it.

The post How publishers rebuild audience ties as search falls appeared first on Digital Content Next.

Ofcom’s Adults’ Media Use and Attitudes Report shows how this caution plays out. Audiences are spending more time online yet feel less positive about the experience, with only 59% saying the benefits outweigh the risks, down from 72% last year. At the same time, 89% feel confident online, suggesting they are comfortable navigating and using digital platforms. But that confidence does not always match their ability to distinguish reliable information from misleading content. These patterns point to broader shifts in how audiences engage with media, evaluate information, and build trust online. 

AI use becomes routine, but trust lags  

AI is moving into the mainstream quickly, with 54% of adults now reporting use, up from 31% last year. At the same time, 75% encounter AI-generated summaries in search. Adoption is not the issue. Trust is. 

Many users, 57%, say they trust AI-generated news less than human-written content. Widespread use does not translate into confidence. Even as AI becomes part of everyday experiences, skepticism remains high. AI may change how content gets surfaced, but it does not replace the need for visible authorship, sourcing, and editorial judgment. The gap between use and trust is not unique to AI. It reflects a broader shift in how audiences evaluate all media. 

Trust shifts while confidence holds 

Most viewers (85%) report using mainstream media, such as the BBC and The Guardian for news. But only 19% say they always trust it, while 21% say they always question it. This is not just a divide between those who trust and those who do not. It signals a deeper shift in how people evaluate information. 

Audiences now validate information socially. About 41% look at comments and reactions to judge credibility. In practice, a story’s reception can matter as much as its origin. Authority still matters, but it now competes with visible social context. Publishers no longer control how their content is interpreted once it enters digital environments. 

At the same time, confidence remains high. About 82% say they can spot scams, and 81% say they can recognize advertising. The results look different when tested, with only 52% correctly identifying paid search results. This gap highlights a difference between perceived ability and actual performance. 

Engagement is receding 

After years of expanding social media activity, behavior is starting to tighten, with posting declining from 61% to 49% this year. Only 14% of users say they explore new websites regularly. People are not leaving the internet, but they are narrowing how they use it. 

Sentiment declines alongside this shift. Only 36% say social media benefits their mental health, and 40% say their screen time feels too high most days. Less exploration and lower satisfaction point to a more cautious and selective user mindset. 

Data awareness is on the rise 

Most users understand that their data gets collected, with 89% aware of this. However, only 31% can identify how that collection happens. 

While 86% use at least one security measure, 26% still reuse passwords. People understand the risks around data privacy and security, but do not always act on them. At the same time, attitudes toward data use remain divided, with 34% comfortable and 37% uncomfortable with personalization. 

Younger does not mean more media literate 

These gaps are not evenly distributed. It is easy to assume younger audiences, particularly those aged 16–24, navigate digital environments better, but the data does not support that view. Younger users perform well in some areas, with 88% correctly identifying fake profiles. At the same time, only 52% recognize paid content in search. 

Older users, especially those aged 55 and over, often take a more cautious approach when dealing with scams or suspicious content. Media literacy depends more on behavior and experience than age, and it develops unevenly across contexts rather than following a generational pattern. 

The audience is recalibrating how it engages online. They still see value but feel less positive about the experience. This shift raises expectations. Trust is shaped by signals that show who created the content, where it comes from, and the context in which it appears. In this environment, clarity is a competitive advantage. 

The post Media audiences are engaged, but selective and skeptical  appeared first on Digital Content Next.

What seemed especially significant was not simply the scale of the change media leaders face. It was the degree to which they recognize what this moment requires. They must move from reacting to disruption to setting terms for it by defending the value of their content, deepening direct audience relationships, investing in unmistakably human differentiation, and applying AI where it creates real business advantage.

This became clear from the four big themes that stood out this year:

1. AI is redrawing the value chain around content

Unsurprisingly, AI was a nearly constant topic across sessions. One idea that cut across the event with unusual clarity, however, was that AI is not just another technology wave. It is forcing a fundamental reset in how media leaders think about the value of content, the economics of publishing, and the terms by which others get to access journalism and other professionally created content.

In his opening remarks, DCN CEO Jason Kint described AI as “the latest and most consequential event of an ongoing story” saying that it is disrupting “the value chain on the Web.”

That sense of responsibility and urgency echoed across many sessions. Nicholas Thompson, CEO of The Atlantic, talked in no uncertain terms about the fact that media leaders must work to shape the model so that publishers “will get the compensation they should,” even as he acknowledged the larger risks around scrapers, declining search, and disintermediation in an “agentic future.” Guardian CEO Anna Bateson emphasized the need to establish “the value of our IP” and protect the investment behind centuries of journalism.

Scott Havens, Chief Growth Officer and Global Head of Consumer of Dow Jones was even more direct about the value media companies bring to LLMs. In describing the way in which agentic AI is reliant on current, quality information, he said, “AI companies need our content and that’s not going to change.” The implication was unmistakable, however. If the industry does not actively assert the value of its work, someone else will define that value.

Perhaps the starkest articulation came from Jon Roberts, Chief Innovation Officer at People Inc. “Index for discovery is fine. Stealing our content is absolutely not.”

2. Audience strategy is replacing reach strategy

For decades, the industry’s growth logic was built around distribution at scale. Reach was the organizing principle. However, AI answers fundamentally change the math. Therefore, search strategies will no longer be reliable or sufficient to drive traffic or revenue.

Axel Springer’s Supervisory Board Chairman Jan Bayer noted that Business Insider has become “more focused on engagement and time spent now, less on reach.” That shift was emblematic of a broader theme. Again and again, leaders returned to the audience as the center of gravity: not traffic, not sheer distribution, but the depth and durability of the audience relationship.

COO Alex MacCallum described CNN as a “consumer first organization” focused on “delivering the most value to their audience.” That language resounded in other conversations about consumption habits, format flexibility, and the need to build around how audiences actually want to consume and engage with information.

At the Guardian, Bateson described a shift from being reader-funded to “audience-funded,” a semantic shift which recognizes that people are consuming journalism in many forms across video, audio, and visual formats. Award winning investigative journalist Julie K. Brown talked about the way her Substack and work for the Miami Herald reach different readers and create a bridge to new audiences. This approach is complementary, she said, rather than competitive. It allows her to use different tones, formats, and distribution models to grow the audience for her reporting.

As discovery is becoming less reliable, the business value of a direct relationship with the audience has risen sharply. This impacts product development and higher-level strategy. Media companies that know the audience, serve focused and meaningful needs, and create value across multiple formats are better positioned than those optimizing for reach. As Ankler CEO and Editor-in-chief Janice Min pointed out, the opportunity is not merely to serve narrow audiences, but to “broaden the total addressable audience” through sharper value and stronger relevance.

3. Human connection, talent, and voice may be the moat

Much of the AI conversation has been understandably dominated by automation, efficiency, and scale. But one of the most interesting through-lines of the event was the opposite idea: that the more abundant and synthetic content becomes, the more valuable human connection, recognizable talent, and editorial voice will be.

That point surfaced when COO Jen Wong described the agentic Reddit search experience as one designed not to replace conversation, but to drive people toward “human communication.” That framing stood out because it runs counter to the grain of so much AI hype. Wong says they must “never disintermediate human communication.” And, as CNN’s MacCallum pointed out: “AI can’t do the human-to-human connection,” which makes it a defensible moat.

That same logic extends to creators and talent. If human connection is becoming more valuable, then the people who embody that connection matter more, not less. In a market flooded with interchangeable output, audiences gravitate toward individuals they recognize, trust, and want to spend time with. That gives journalists, creators, and other distinctive voices a different kind of strategic value: They are not just contributors to the product. They are increasingly central to how media brands build authority, loyalty, and differentiation.

As Christine Cook, Chief Commercial Officer at Bloomberg Media put it: “Aren’t journalists the original creators?” Thus, surfacing their authority, authenticity and lived experience will build loyalty. Carlos King, Founder & CEO, Kingdom Reign Entertainment spoke about the strategic importance of “recognizable talent” and the “creator perspective” in an increasingly fragmented consumption landscape. And Min argued for “the value of voice.”

That connection between humanity and distinctiveness may be one of the most important takeaways from the event. In a noisy world with too much content, what stands out is not generic output but rather trust, perspective, and personality. As others pointed out, what matters is offering information people “can’t get anywhere else” and face-to-face experiences that create “genuine connection.” As King put it, we must harness the power of “human advantage.” Havens talked about the futility of ignoring creators and other talent ecosystems and encouraged his peers to find “ways to work with them.”

For leaders, this means the industry has to stop thinking about talent, journalists, creators, and experiences as nice-to-have complements to the brand. Particularly in an AI-dominated landscape, the people who create our content and human to human connection will define the brand.

4. Operational change is no longer optional

Many of the most grounded comments across the event were not about AI-generated content or media products. They were about AI’s impact behind the scenes to improve workflow, efficiency, product development, emphasizing the practical ways AI can help companies move faster and operate smarter.

Thompson from The Atlantic warned that too many companies are focused on AI in the “front of house” when it is “really useful in the back office.” Min drew a similar line for the deployment of AI in media companies: “On the backend: opportunity. On the front end: not so interesting.”

CNN’s MacCallum categorizes AI as something that can help create “better consumer experiences” and help media companies “be more efficient.” And Bayer from Axel Springer described the need to create experiences that are “personalized and relevant,” while still arguing that content creation itself should remain “a human area.”

These comments point to a meaningful leadership test: It is no longer whether or not to use AI, but rather where, how and to what end. Leaders who approach AI strictly as a shiny consumer-facing feature, or as a way to replace journalists risk missing the deeper opportunity to rethink internal systems, reduce friction, improve decision-making, and better align product, editorial, and business teams. For example, Havens from Dow Jones made a clear case for how AI can help business leaders accelerate the “speed to approval” to enable growth and innovation.

Guiding the future of media

Overall, the tone from the speakers and attendees at this year’s DCN Summit was one of leadership. Of stepping up and owning the challenges they face in order to shape the future.

They recognize that the old search and platform dynamics are weakening, that AI is reshaping the economics of content and that audiences want relevance, flexibility, and value on their terms. They also see that human connection, distinctive voice, and trusted talent are not being diminished by this moment. If anything, they are becoming more valuable.

Media leaders know the value of content and need to set equitable terms around access and compensation. They need to double down on business models that center on audience value rather than reach. A human-differentiated media market requires investing in journalists, creator partnerships, experiences, and products that offer unique value. But that doesn’t mean ignoring the value of AI. Rather, it requires understanding how to use AI where it strengthens the business — behind the scenes, in operations, in product intelligence, in speed and efficiency.

As ever, the future will be neither predictable nor easy. But, as the conversations at the DCN Summit made clear, it can and will be shaped by informed leadership.

The post Four forces shaping digital media and the leadership this moment demands appeared first on Digital Content Next.

But TIME is not just relying on AI companies citing their work. They have also developed and rolled out their own tool, TIMEAI. This is an interactive agent that has been trained on its 103 year archive in order to enrich the user experience on-site.

Time to train

TIMEAI, which appears on the site as a toolbar overlay, was initially tested at the end of 2024 with the annual reveal of TIME’s Person of the Year, Donald Trump. The toolbar was also put live on the three prior Person of the Year honorees; Taylor Swift, Volodymyr Zelensky and Elon Musk.

“There were several hundred articles about all four of them that we trained [the AI] on,” explained Mark Howard, Chief Operating Officer at TIME. “Person of the Year is our single biggest editorial event of the year. So it’s not like we put it on some back catalogue just to see if we could get some basic data. We put it on our most prominent release.”

The tech used was a small language model rather than a large one (LLM), so it was more straightforward to deploy. But it still needed training on TIME’s editorial styles, and guardrails added. TIME’s Editor in Chief Sam Jacobs and some of the newsroom team were brought in to help train TIMEAI in terms of style and tone.

“We had to, given the personalities involved, make some decisions about how it was going to handle questions that had nothing to do with anything we had written about,” Howard said. Even though there is a lot of publicly available information on these public figures, they decided they didn’t want the AI to use content outside of TIME. “It’s not a complete picture when somebody wants to converse, but we made a lot of those trade-offs in a very contained way,” he added.

The version deployed in December 2024 enabled users to translate the article, summarize it in various lengths, chat with it, and even speak to it using voice and audio in 13 different languages. Rather than just having one type of function, Howard said that being able to mix and match requests – for example, to ask for an audio summary then go into detail via text – sets this AI tool apart from many others on the market.

A wider rollout of TIME AI agent

The initial launch was a success. Howard said that the original toolbar was kept live while the team worked on bringing more TIME content into the AI. This wasn’t a straightforward task.

“Because of the way media companies operated over the decades, we had [content] in five different databases going back to the 1920’s, some of them were just PDFs of magazines that needed indexing and digitizing,” he explained. “Now it’s trained on well over three-quarters of a million pieces of content.”

Since November 2025, the TIMEAI agent has been visible on almost every piece of TIME content. Newly published pieces are also indexed in near real-time, ensuring that the answers given are up-to-date.

The team have also been experimenting with ways to get users to interact. The agent has a number of preset prompts to show how sophisticated queries can be. These can range from, ‘Try reading this article out loud’ to ‘Debate AI’s impact on humanity’ using TIME’s reporting and opinion archives.

The results so far have been encouraging. Users who engage with TIMEAI are 139% more likely to return to TIME when compared to those who don’t. Those using the AI agent also double their time spent on the TIME website.

Now, the focus is on encouraging those who may not be as familiar with AI tools to engage. Howard said that this may include putting more instructional overlays in, or feeding more preset prompts into the toolbar. But they are cautious about the extent to which people are routed into the full AI experience, rather than the standard web page. 

For now, Howard sees TIMEAI as an opportunity to increase engagement, rather than a revenue generator. “If we can get people to engage, every surface represents a monetization opportunity,” he added. “But the focus has been on the product itself in the beginning.”

A trusted agentic AI source

Given TIME’s licensing deals with other AI engines, there is a question about why users would come to TIME, rather than ask an AI which pulls in a wider range of sources, including TIME itself.

Howard was emphatic that trust is something the other AI tools don’t yet have, but TIME does. “A lot of people grew up with TIME, knowing and trusting that the red border stood for something, and there was a level of quality that is behind it,” he explained. “We want to carry that forward.”

ChatGPT and other answer engines do cite TIME content, but alongside a wide range of other sources, including sites like Reddit. These answers aren’t always trustworthy, and Howard said that there is a high burden on the consumer to deconstruct what is accurate and what isn’t.

“We think [TIMEAI] can be a trusted source of information to try and help explain the world in a way that TIME can, because of this vast archive,” he said, pointing out that users will only encounter TIMEAI if they’re already on the TIME site. This already signals a level of intent and interest in TIME’s reporting.

“It’s a way for us to pull our archives, our history, our heritage and our brand equity forward in a way that we can present more through an AI-based interface and experience.”

Howard is also clear on the role AI has to play in TIME journalism going forward. “Our journalists get information that only humans could by speaking to their sources and doing the work,” he emphasised. “AI can never do anything like that. It’s not intended to do anything like that. 

“Its purpose is to take the great journalism and then be able to make it accessible to those who want audio briefings, roundups and other formats to be able to better understand everything that we’re publishing.”

Walking the tightrope between using AI tools to help reporting, and letting them take over is something a number of publishers have fallen down on in the past months, with potential long-term implications for audience trust. 

Interactive agents present an opportunity for quality publishers to surface decades of reporting to add context and enrich the user experience. TIME’s approach is cautious, and they are wary of detracting from a carefully-designed web experience. But as similar tools roll out rapidly, readers will become more familiar with what they can do and how to interact with them. TIMEAI’s multi-feature functionality will set it apart as a leader.

The post Inside TIME’s rollout of its TIMEAI interactive agent appeared first on Digital Content Next.

Publishers understand what they’re up against. They’ve done the math. They know they need to engage audiences across social video, YouTube, audio platforms, and emerging AI interfaces, environments where discovery is driven by algorithms, not direct visits. Every day they work to balance maximizing short-term revenue while maintaining the user experience that builds and keeps an audience over time.

But execution is hard. And it’s getting harder.

To operate across more platforms and environments requires people and know-how that most publishers no longer have. Short-staffed teams can’t juggle dozens of disconnected tech vendors. Data doesn’t flow where it needs to flow. And the operational debt from years of patching together point solutions is making it harder to move fast.

Create. Transform. Distribute. Engage. Monetize.

To compete in a market now dominated by platforms, creators, and AI-driven discovery, publishers need to reorganize their operations around a clear set of functions: creating content, transforming it for different environments, distributing it effectively, driving engagement, and monetizing it across channels.

Create

Every successful content creator–from influencers to the best known media brands– has their secret sauce; their unique style or point of view. Many are rightly concerned that unconstrained use of AI will commoditize quality content, or that a torrent of AI slop will drown out the good stuff. This is why publishers have to be maniacal about quality and authenticity to create real consumer engagement. 

Transform

But how do you scale that quality content across today’s fragmented consumer landscape?  Here is where AI finds purpose. It turns out that AI is really good at taking content and adapting it to different environments and formats. With some expertise and guidance, it can maintain brand standards of quality, trust, and authenticity across many surfaces. 

Today, the words “publisher” and “website” cannot be synonymous. Content has to be created to meet the consumer where he/she lives. That includes the social platforms where the goal to drive traffic back to the publisher’s website is in opposition to the platforms’ imperative to keep the audience within their walled garden. The content then has to do double duty; yes drive traffic, but also maximize monetization programs that encourage customer engagement within even if the audience is experiencing your content outside of your site or app. 

Distribute

Once you’ve got content that’s tailored and transformed, the next problem is getting it everywhere it needs to be really fast. You cannot brute-force this. There aren’t enough hours in the day and there aren’t enough people on your team.

The market for consumer attention shifts constantly. The lifespan of a piece of content is finite: hours or days for news and longer for evergreen, explanatory, or enthusiast content. You need a real-time feedback loop telling you what’s still relevant, what’s gaining traction, and what’s already dead. Without that, you’re flying blind. And a piece of content that could have driven real revenue at hour two is worthless by hour six.

Speed isn’t a nice-to-have. It’s the whole game.

Engage

The goal of distribution is to drive engagement, because engagement drives revenue. The challenge is that the best format to engage with you may not be the same as what’s needed to engage with me. Some in your audience will prefer long-form video, some will prefer audio, some still prefer reading, and others will opt for short-form video. Other consumers will respond to more interactive experiences, like community boards, polls, quizzes, and games. Getting that right, at scale, for each individual is the engagement opportunity. And the publishers who solve it  are rewarded with more content consumed, more time spent, and more frequent repeat visits. 

Monetize

Let’s be honest about something. The platforms were not designed to make publishers rich. They were designed to keep audiences inside their walls, and they’re very good at it. For years, the monetization math outside your owned-and-operated properties was ugly, and most publishers knew it.

But something has shifted. Not because the platforms suddenly became generous. But because the pressure on them to attract and retain quality professional content has forced them to open doors they used to keep firmly shut.

YouTube now offers monetization models that generate real revenue for creators who treat their channel like a full-scale media business, not an afterthought. Its dynamic ad insertion tools give serious content owners the ability to operate more like TV networks, swapping sponsored segments in and out, extending the lifespan of sponsorships, and unlocking new monetization opportunities within existing content. Last year, Facebook made meaningful changes to its creator program, and publishers who wrote it off are quietly revisiting that math.

None of this is a windfall. The platforms will always take their cut. But “the platforms take a cut” and “there’s real revenue to be captured” are not mutually exclusive statements. The publishers extracting value from these channels aren’t doing it because the platforms are benevolent. They’re doing it because they’ve built the operational infrastructure to move fast, transform content for each environment, and actually work the monetization programs available to them.

That’s the opportunity. 

Shifting Thinking

While most publishers know they need expertise to help them extract value from their content wherever it is experienced, most are looking at the current moment with a clear-eyed view to extract as much value as they can. They are adapting to the current circumstances and are seeking out new partners who help them succeed. The partners who can consolidate data flows, simplify workflows and harness AI to automate processes will be their best friends. 

The post The new publisher challenge appeared first on Digital Content Next.

As a result, notes AdExchanger editor Anthony Vargas, “Publishers, typically a tight-lipped crowd, have been surprisingly candid about losing 20%, 30% and in some cases even as much as 90% of their traffic and revenue over the past year.”

A survey of media leaders featured in the Reuters Institute’s latest annual predictions report revealed that publishers anticipate a further decline in traffic from search engines of more than 40% over the next three years. “Not quite ‘Google Zero’, contends author Nic Newman, “but a substantial impact none the less.”

In response, companies need to focus on how to address this challenge. And how to do so quickly, as traditional sources of referral traffic continue to hemorrhage. 

Five core factors to address Google Zero

Here are the five core factors that publishers should incorporate into their strategy and workflows.

1. Grasp the size of the problem

The term Google Zero stems from a question posed by The Verge’s Editor-in-Chief Nilay Patel who asked what would happen to businesses if their Google traffic were to go to zero?

Changes to social media and search algorithms have reduced referral traffic for some publishers. In the AI era, these dynamics are becoming even more pronounced. The impact of AI-snippets at the top of Google search results means that when users ask a question, they find answers at the top of the page and many don’t click through for more detail or scroll down for more options. 

Data from the Pew Research demonstrates the impact of this: when an AI summary appears, users click traditional search results only 8% of the time, compared to 15% without one. 

Meanwhile, for publishers pinning their hopes on Google Discover, it’s worth remembering that most of the growth in this space comes from breaking news, content which is often excluded from Google AI summaries. 

“Google Discover traffic is mostly a mirage,” contends the media analyst Simon Owens, recommending that media companies “avoid optimizing their content operations around it.” “Publishers never owned those audiences and therefore should never have counted on them,” he added.

Lastly, the situation is further exacerbated by content in replacing carefully crafted headlines from publishers with those generated by AI. Initially confined to content in Discover, this is now happening in Search too. As Sean Hollister, senior editor at The Verge, put it, “This is like a bookstore ripping the covers off the books it puts on display and changing their titles.” 

All of this is to say that if you’re still heavily reliant on Google as an engine for traffic, it’s time to think again. 

2. Understand that this is part of a wider shift in user behavior

Much of the coverage to date has focused on the supply side, centering on publishers and platform dynamics. Far less attention has been paid to the demand side and evolving user needs.

Traditional search feels increasingly outdated. In its place, users are turning not just to AI-generated summaries within Google, but to AI tools like Claude, ChatGPT, and Perplexity as they begin their information journey.

A recent study from Eight Oh Two Marketing, surveying 500 active AI users, found that 37% of those sampled now begin their search with these types of AI tools rather than traditional search engines. “Consumers are not choosing AI because it is trendy. They are choosing it [AI] because search has become too noisy, too effortful, and too slow.” 

At the same time, 85% of respondents said they still double-check AI-generated answers using traditional search, using these platforms for verification and deeper exploration.

Publishers need to recognize the implications of these behaviors. Those absent from the first phase, AI-driven discovery, may never be found in the second. Equally, those only visible in the verification phase are absent from the critical entry point of this new information funnel. If they’re not present in both of these environments, then they risk being overlooked and left behind. 

3. Recognize that visibility is increasingly binary

AI environments are far more winner-takes-most than traditional search. Previously, a publisher that ranked fifth on a search query would still earn traffic, as might those even lower. AI-mediated discovery cites a couple sources and if you’re not on the shortlist, you’re unlikely to be discovered.

For media companies, understanding how AI-generated answers are created is critical for ensuring that your content is featured in the places and spaces that LLM’s crawl. The foundations of good SEO, such as authority, clarity, credibility, still matter. Build on your SEO foundation; you don’t need to fully reinvent the wheel. 

Research on AI citation patterns shows that AI systems – like search engines before them – tend to favor sources with strong off-page authority. So, media companies will want to ensure that their content demonstrates the E-E-A-T (Experience, Expertise, Authoritativeness, and Trust) principles. 

That means having a presence on high-authority external platforms, citations in credible databases, earned media in leading publications, as well as content across the information ecosystem. This includes your own website, to thought leadership demonstrated by execs on LinkedIn and in publications like Forbes, through to engagement on Reddit forums, and material produced for other platforms such as company podcasts, YouTube videos, and so on.

A recent SEMrush analysis of 325,000 prompts across AI platforms found LinkedIn ranking second only to Reddit as a source for AI chatbot responses, particularly for professional queries. 

4. Build content that AI can’t replicate

Not all content is equally vulnerable to AI summarization. Sites that prioritize original storytelling, exclusive imagery, and strong visuals, can still thrive. 

According to The Digital Bloom, People.com saw a 27.52% year-over-year traffic increase through September 2025 by adopting this approach. Similarly, Substack witnessed a 40% year-over-year growth in July 2025. “This growth reflects users seeking authentic voices and first-hand perspectives,” they wrote. These are “content types that Google’s Liz Reid [VP, Search] specifically identified as gaining traffic in the AI era,” they added, referencing a discussion on wider online behavioral shifts that she had with The Wall Street Journal last year.

Conversely, if your content can be summarized in three bullet points by an AI, it is a commodity. That’s one of the reasons why a lot of evergreen content has been cannibalized and its traffic for many publishers has tanked. 

In response, many media companies need to simply produce less content and ensure that what they are doing is better. Content needs to be more meaningful. More impactful. More distinctive. Can it help to drive a conversion – in the form of a registration, a newsletter signup, or a subscription – so that audiences come to you first, and not their AI platform of choice? (And if not, should you be doing it at all?)

Similarly, media companies need to continue to invest in products that reduce dependency on external discovery channels. The New York Times is perhaps the best known proponents of this. Nearly half of Times digital subscribers now pay for more than one Times product, attracted by a mix of games, cooking, audio, news, opinion, product reviews and sport. Each of these are elements that can help to create habits and drive engagement that is independent of search.

5. Rethink what success looks like

The narrative is not so much that “AI is killing search,” but that AI should force us to rethink what search looks like. Search is no longer just a driver of traffic, it’s a multi-faceted arena covering discovery (AI chat), synthesis (AI chat and snippets), and verification (actually going to your content to dig deeper). 

As a result, publishers have to look more broadly at metrics they measure and value. In the AI-era, software company ClickRank, for example, points to areas such as citation frequency, brand mention rate, share of voice within AI answers, AI-driven referral traffic and sentiment of brand references.

And all of this sits alongside core metrics such as subscriber lifetime value, churn rates, and time on site. Chartbeat’s data shows us that “your most valuable traffic source might already be on your site,” reminding us that while addressing shift in search matters, engagement with existing visitors remains a key area of focus. 

As part of this conversation, at an industry level, we also need to move beyond discussing a reduction in referrals to better understand its impact on revenue. We know that clicks are down, but we know much less about what that means for subscriptions and ad yields. Hopefully publishers are already joining up these dots internally, but a wider industry conversation about this would also be beneficial. 

The bottom line

Worries about Google Zero are well founded, although its impact is uneven. Publishers that are most exposed to this shift are typically those with the heaviest dependence on evergreen, easily summarized content and platform-dependent traffic. Meanwhile, those that are best positioned to navigate these changes, are those who produce content that needs to be seen onsite or in-app, and who already have strong direct and habitual relationships with audiences. Off-platform discovery is part of their playbook, but they are not reliant on it.

There’s no point asking whether AI-powered search will disrupt the traditional referral model. We know the answer. Subsequently, Google Zero encourages us to think about distribution and engagement strategies at a time when search traffic is less predictable and the economics are increasingly hard to quantify.

As Press Gazette describes it, search is not dead, it is fragmenting. What the most successful publishers understand is that Google Zero doesn’t require a single response. It requires a range of them. That includes potential partnerships with tech companies and AI providers, optimizing distinctive content for generative engine optimization (GEO), as well as doubling down on user experience within your properties.  

These media companies recognize that we have moved beyond clicks to a more fragmented and distributed media ecosystem, one where value is defined not just by traffic, but by presence, influence, and direct relationships with audiences.

The post The publisher’s playbook for the Google Zero era appeared first on Digital Content Next.

Most publishers now accept that AI licensing will become part of future business models. The challenge is operational. Converting content usage into revenue requires infrastructure that connects traffic signals, pricing frameworks, and payment workflows into a cohesive system.

AI licensing is moving from policy to execution

Early conversations about AI and publishing focused on access rights, attribution, and platform accountability. Those debates still matter. But publishers are now entering a more practical phase of the market centered on execution.

This shift requires answering several basic questions:

• Who is using the content?
• What are they allowed to do with it?
• What is that usage worth?
• How does the publisher get paid?

Today, these answers are often scattered across tools and teams. Analytics platforms may identify bot activity. Legal teams negotiate licensing terms. Commercial teams structure agreements. Finance teams handle billing and reporting. Without integrated workflows, AI monetization strategies remain fragmented and difficult to scale.

The need for usage-based AI monetization infrastructure

For AI licensing to become a durable revenue stream, publishers will need systems built around usage-based economics. In practice, this means enabling workflows that can:

Identify and classify AI traffic.

Publishers need visibility into how AI systems interact with content, including frequency of access, depth of engagement, and types of material consumed.

Apply flexible licensing models.

AI agreements are unlikely to follow a single template. Some will involve flat-fee partnerships, while others will rely on usage-based pricing or dataset licensing. Infrastructure must support experimentation without requiring new operational processes for every deal.

Convert usage signals into billable events.

Operationalizing AI monetization requires translating content access into economic transactions. This includes assigning rate cards, tracking consumption, and generating revenue statements that support negotiation, compliance, and financial reporting.

Settle payments and route revenue.

Once pricing is applied, publishers need systems that can manage invoicing, revenue allocation, and partner payouts across multiple licensing structures.

Emerging solutions are beginning to address parts of this workflow by bringing usage measurement, pricing logic, and settlement processes into a unified environment. The goal is not to replace existing systems, but to create an operational layer that allows publishers to run AI licensing as an ongoing business function rather than a series of bespoke agreements.

Flexibility will define the next phase of AI monetization

The AI market is evolving quickly, and publishers will need optionality. Direct licensing agreements, collective negotiations, and marketplace models may all coexist. Some organizations will prioritize strategic partnerships with major AI platforms. Others will focus on monetizing specialized datasets, archives, or real-time information.

Infrastructure that supports experimentation will be essential. Publishers must be able to test pricing models, analyze usage patterns, and refine commercial strategies without rebuilding workflows each time the market shifts. This mirrors earlier transitions in digital publishing, where scalable advertising and subscription technology enabled new revenue streams to grow.

AI content monetization will only become meaningful if publishers move from fragmented signals to repeatable revenue systems. Visibility into AI usage is the starting point. The real opportunity lies in building the infrastructure that makes licensing measurable, manageable, and financially actionable, turning content consumption into a predictable commercial engine.

The post Turning AI content usage into revenue appeared first on Digital Content Next.

But, across conversations with media leaders at Axios, Hearst Newspapers, Consumer Reports and Forbes, the prevailing approach is measured rather than panicked. What’s happening is less about SEO replacement than an evolution of existing best practices. SEO still delivers traffic, but with an added GEO layer.

While this last year has introduced a new set of acronyms like AEO, GEO, and AIO (see glossary), search teams are pushing past the alphabet soup and finding that the fundamentals of SEO haven’t changed as much as the hype may suggest. AI answer engines still rely on traditional search databases, making authority and clarity advantages for publishers.

“SEO equals GEO. GEO transcends SEO,” says Gideon Grudo, executive managing editor at Consumer Reports, who oversees the SEO and GEO/AIO team. “All the work that we’ve been doing, that we will be doing for search, we will continue to do, because it will benefit us in the answer engines. Study after study, data point after data point continues to prove that if you’re ranking well in search, you are ranking well in the answer engines.”

“There’s news every day about what matters, and what doesn’t matter, and what might be important, and what’s important in this year, and in the next five years,” says Grudo. “And, there are mainstay SEO search foundational realities that have not changed an inch.”

Re-structuring content for machine-readability

Andy Crestodina, co-founder of Orbit Media Studios, frames it in structural terms. In the traditional search model, a user query goes to a search engine, which returns results. Now, he says user prompts go to AI, which queries search, summarizes results, and surfaces an answer. The abstraction layer, which sits on top of the visual internet and allows us to talk to it, is new. The underlying mechanics are not.

“Traditional SEO still matters,” he says. “If you have a page that’s not discoverable in search, it’s usually for one of three reasons: a technical problem, the page isn’t relevant, or you don’t have any links and you’re not a credible website.”

For publishers, the practical implication is that content needs to be more rigorously structured. Bridget Williams, chief product and strategy officer at Hearst Newspapers, is tracking which content is being scraped, what is most frequently retrieved by AI systems using RAG, and how that retrieval correlates with actual clicks.

“These topics, these URLs are scraped the most by OpenAI. These topics are getting surfaced the most. And then these topics are getting the most people clicking through. What does that all mean?” she asks. “It’s very nascent for us.”

Binti Pawa, VP of audience growth and development at Forbes, says AI optimization means making sure that both search engines and AI engines can retrieve Forbes’ content. She notes that the best practices for traditional SEO and for answer engine optimization overlap. “Your content should be well-organized and clear. Content should be for people and not AI agents or bots. That’s not going to change with any of the strategies.”

Pawa believes that SEO and answer engine optimization are complementary, not competing, and that ignoring either one means leaving visibility on the table. “It’s becoming more important to think about your brand and your brand mentions and the citations, which will return a link, which will return a referral, and track it back to your site,” she says.  

Original reporting is a competitive advantage

Structural SEO work only goes so far without editorial to back it up. As information becomes increasingly commoditized by AI, the most successful publishers are doubling down on original reporting and subject-matter expertise, content that is harder for AI to replicate.

Ben Berkowitz, head of news at Axios, says the Axios strategy is to double down on exclusive reporting and analysis from subject-matter experts. “AI prizes original information, so the depth and quality of reporting really is the optimization.”

One of the strategies that can be effective in the AI era is to publish original content and research, Crestodina says. He has noticed that in the traffic from AI sources to websites, the URLs that get the most traffic are the ones that have data points, statistics, or research studies, he explains.

“Most people don’t fully trust AI, for good reasons. And when they look for something backed by data, they’re likely to want to click through and see it,” he says. “They might want to cite the original source. So, when a prompt indicates the visitor wants hard numbers, and the AI response summarizes those numbers, the reader will click through. Because they want to see: is this legit?”

Expanding beyond clicks to visibility and reputation

Publishers say that standard SEO metrics are still tracked, but they are no longer telling the whole story. For media executives, the focus has shifted to holistic KPIs like brand visibility, citation tracking and conversion metrics.

Where traditional SEO focused on what lived on a publisher’s own site, GEO and AIO requires thinking about the brand’s presence across the entire web: third-party mentions, Reddit comments, social signals, and anywhere else a large language model might go looking for evidence of credibility.

“It’s not just what’s on your site, it’s what exists on all these other platforms. So, we’re starting to think about all these other platforms as well,” Grudo says. “That means I want to think about how we exist outside of our site, and what that means.”

Grudo says his team is also looking at where traffic originates. While bot traffic might be minimal right now, as it continues to grow, will it become important, he wonders. “What is intention like from traffic from bots? What’s crawl like from bots? Is that important? How do we keep an eye on that to determine optimization for it?”

SEO teams are now working more closely with social and audience teams. AI models draw on signals from across the web, including platforms like Reddit and YouTube, to assess a brand’s authority and determine how to describe it to users.

Williams notes that Hearst Newspapers subscription base gives it an advantage, with those direct relationships providing a stable foundation. “We are focusing more and more on direct relationships,” she says. “We have this amazing subscription base of people that really care about local news and we are very differentiated in the market.”

According to Pawa, standard SEO KPIs continue to be important to Forbes. But, Forbes has learned it ranks among the top cited sources within certain AI search environments, intelligence that is now feeding into how the team thinks about audience engagement. “Our KPIs are evolving with that,” she says, adding, “Our strategy is shifting to quality and how do we engage loyal users? Do we know enough about our users? How do we engage better with them to actually move them down the funnel?”

In a recent Digiday article, Karl Wells, The Washington Post’s chief revenue officer, said that users coming from AI platforms show 4-5 times higher subscription conversion rates than those from traditional search and tend to spend more time on site.

Wells’ insight may reflect something broader about how AI is changing discovery. Preliminary research from Stanford and Cornell researchers tentatively suggests that LLM adoption may complement rather than replace traditional search, as using AIs lead to a sustained increase in the number of unique websites people visit. The study found users increasingly integrate LLMs and search engines into multi-step, exploratory workflows for tasks, who visit significantly more distinct domains than those using search alone. While preliminary and awaiting peer review, the study hints LLMs disperse attention across a wider, more diverse range of websites.

Same SEO work, new AI search vocabulary

Publishers navigating this transition most effectively aren’t the ones who pivoted quickest to GEO frameworks or overhauled their content operations quickest. They are the ones treating the new environment as an extension of existing discipline: maintaining a strong SEO foundation, structuring content more rigorously, building original reporting that AI cannot easily replicate, and paying closer attention to how their brands are understood and cited across the web.

Berkowitz says, “It’s funny how many times the industry has pivoted content formats to chase whatever algorithm was most rewarding that year. And always, every time, it comes back to ‘just do good original reporting and write it well.’ The publishers that are doing the best these days are the ones embracing Journalism-with-a-capital-J. Nothing’s going to eat your lunch if you have the information and insight no one else has!”

Grudo believes there’s an overemphasis on GEO as a replacement for SEO. “The data keeps showing that that’s a great mistake,” he says. “We keep seeing the LLM companies themselves say that they are mining search databases for responding to queries. They don’t have their own, so they’re relying on what Google and Bing and Yahoo have.”

For other publishers, Pawa cautions that there is a lot of AI advice out there with little to no proof. She suggests they rely on their own data and let best practices guide their strategies.

Practical priorities for AI-driven discovery

Across discussions with executives actively shaping SEO and audience strategy, several consistent priorities are coming into focus:

• Structure content for machine readability
• Clear organization and strong SEO fundamentals still determine whether content is retrieved and surfaced by AI systems.
• Track retrieval and citations, not just clicks
• Teams are beginning to measure what content is scraped, retrieved, and cited by AI, and how that connects to traffic and conversion.
• Treat SEO and AI optimization as complementary
• Answer engines still rely on traditional search infrastructure, so strong search performance carries over into AI visibility.
• Invest in original reporting and data
• Exclusive content, research, and proprietary insights are more likely to be surfaced and drive engagement.
• Build authority beyond your own site
• Brand signals across platforms like social, forums, and third-party mentions influence how AI systems assess and describe credibility.

The post How media leaders are rethinking SEO in the age of AI appeared first on Digital Content Next.

A new form of search visibility is emerging, and it isn’t measured in rankings. 

As generative AI assistants increasingly answer questions directly, the brands that appear inside those responses are shaping consumer decisions before a click ever happens. Instead of navigating lists of links, users increasingly receive synthesized answers that combine comparison, explanation, and recommendation in a single response.  

In this environment, visibility no longer depends on ranking position alone. Inclusion within the AI-generated answer determines which brands consumers encounter. New research from Similarweb’s 2026 AI Brand Visibility Index shows how this shift is already reshaping competition across six industries: beauty, consumer electronics, fashion, finance, travel — and news. 

Across industries, three patterns consistently shape which brands appear inside AI-generated answers: 

1. Visibility concentrates among a small group of brands. 
   A limited number of companies dominate AI mentions and frequently become default reference points within their category. 

2. Momentum varies across brands. 
   Some brands rapidly increase their presence in AI responses, while others plateau or decline despite strong consumer recognition. 

3. Authority often outweighs demand. 
   Specialist and education-led brands frequently achieve higher AI visibility than their branded search demand suggests. 

AI reshapes early discovery in the purchase journey 

The research also highlights a shift in how consumers move through the purchase journey. AI increasingly dominates the upper stage of discovery, when consumers seek inspiration and explore options. As purchase intent strengthens, many users return to traditional search engines to navigate to specific sites and complete transactions. 

This behavior increases the importance of early visibility. Brands that do not appear in the initial AI conversation risk exclusion from later stages of the purchase journey. 

Visits to AI platforms continue to grow, yet referrals from these platforms show a disconnect. AI assistants evolve into all-in-one environments that keep users inside the platform. In this minimal-click environment, AI visibility becomes a critical metric for brands and publishers. 

News visibility reflects authority and partnerships 

For publishers, the news category reveals two forces shaping visibility inside AI responses: topical authority and platform access. 

Specialist and reference-driven publishers often achieve strong AI visibility even when overall brand demand remains lower. Publications such as ScienceDirect, PC Gamer, and Taste of Home rank highly because their content answers specific, structured questions across scientific, technical, and lifestyle topics. 

Commercial partnerships also appear among many highly visible news brands. The top ten news sites in the index include Reuters, The Guardian, AP News, CBS News, The Washington Post, Fox News, The Wall Street Journal, The New York Times, Variety, and The New York Post. Many of these publishers maintain commercial relationships with AI platforms, while CBS News and Variety do not. 

Together, these signals suggest that both topical authority and platform access influence which publishers appear inside AI-generated answers. 

From search optimization to answer optimization 

The shift toward AI-driven discovery introduces a new focus on optimization for AI responses. Core principles from traditional search optimization remain relevant. Brands benefit from strong onsite content, trusted external references, and sound technical infrastructure. 

AI systems identify signals of authority across multiple sources. Visibility increases when brands appear across trusted sources that answer specific user questions. 

This dynamic reinforces the authority signal identified earlier in the research. Brands with strong category expertise and durable digital presence often achieve higher AI visibility than search demand alone would predict. The research also identifies overachieving brands that outperform expectations relative to branded search demand, demonstrating how specialist expertise and structured informational content can compete with scale. 

AI visibility becomes a critical marketing metric 

AI visibility now plays a growing role in digital discovery. As AI assistants deliver answers directly within their interfaces, inclusion inside those responses increasingly determines which brands consumers encounter. This shift increases the importance of tracking AI visibility alongside traditional search metrics. Competitive benchmarking, authority signals, and structured informational content now play a larger role in determining digital presence. 

As generative AI continues to reshape discovery, inclusion within AI-generated answers will increasingly signal digital influence. Thus, the brands and publishers that appear in those answers will shape the choices consumers make. 

The post Inclusion in AI answers is becoming a discovery advantage appeared first on Digital Content Next.