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.

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.

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. 

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Discovery and recommendations drive excitement 

Survey results show that audience interest in AI centers around a small number of practical use cases tied directly to discovery and viewing quality. Better recommendations emerge as the leading area of excitement, cited by more than one third of respondents. Improvements in production quality and tools that help viewers decide what to watch next follow closely, each attracting interest from roughly 30% of consumers. Personalization also ranks among the top areas of interest.  

By contrast, more experimental applications, such as appearing in content or fully interactive experiences, draw meaningfully less enthusiasm. The data shows that audiences respond most strongly to AI when it supports relevance, quality, and ease of navigation within the viewing experience.  

Industry investment follows applied use cases 

Industry activity increasingly aligns with these audience priorities. The research documents growing experimentation with AI in production workflows such as editing and visual effects, areas where efficiency gains remain largely invisible to viewers.  

Interest in applied AI also appears in rising participation at industry AI conferences, which more than doubled in attendance over a two-year period. At the same time, the study emphasizes that these investments only succeed if audiences value the resulting experiences, reinforcing the importance of consumer acceptance.  

AI adoption grows alongside consumer expectations 

Consumer adoption of AI continues to accelerate. Nearly 73% of respondents say they have used generative AI tools, up from 57% the year before. Familiarity also increases, with about 75% reporting that they understand AI and how it works.  

Audience expectations have risen alongside usage. Almost 90% believe AI will have a big impact on everyday life, and about one third expect it to change daily life for everyone. These findings suggest that AI already feels present and substantial to many consumers, even as opinions about its long-term effects continue to form.  

Audience concerns center on authenticity and trust 

Despite growing familiarity with AI among the average consumer, concerns remain pronounced. The most common worry involves losing the ability to distinguish what is real. More than 60% cite unauthorized use of personal likeness as a concern, while nearly as many worry about not knowing whether content is authentic.  

Job loss also ranks high among concerns, cited by more than half of respondents, followed closely by concerns about copyright infringement. These issues persist even among consumers who report high levels of comfort with AI. 

Comfort varies by who uses AI and for what

Acceptance also depends heavily on who uses AI and for what purpose. About 40% of respondents say they feel completely comfortable with regular people using AI for personal tasks. That figure drops to roughly 20% when influencers or companies use AI to build audiences or generate revenue. The data underscores that audiences apply stricter standards to commercial uses and expect greater responsibility and oversight from organizations.  

Knowledge continues to shape acceptance. Among respondents who describe themselves as most familiar with AI, about two-thirds express interest in generating content using well known entertainment IP. More than half of this group also considers that an ethical use of AI. As understanding increases, openness expands, particularly when audiences perceive clear boundaries and responsible application.  

Transparency around AI remains a baseline audience expectation 

Across all segments, transparency in the use of AI stands out as a requirement among audiences. Nearly 90% of respondents believe companies should disclose when AI plays a role in creating content. Disclosure does not register as a differentiator but as a baseline expectation. Audiences want clarity around when AI contributes and how it fits alongside human decision-making.  

Audiences show the strongest interest in AI that improves recommendations, enhances production quality, and simplifies discovery. They express greater caution around uses that affect authenticity, identity, or trust. For media companies, success depends less on how advanced these applications become and more on how closely the use of AI aligns with audience priorities. 

As AI becomes more embedded in media workflows, the opportunity lies in focus and execution. Viewers already signal what they value most: better ways to find content and better experiences once they start watching. The challenge now centers on delivering AI-based benefits clearly, responsibly, and in ways audiences recognize and trust. 

The post Audiences have clear expectations when it comes to AI appeared first on Digital Content Next.

AI moves into the center of the tech and media stack 

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

Search integrates AI summaries and changes discovery 

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

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

Computing demand rises and infrastructure strains 

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

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

Content formats shift toward shorter and more visual experiences 

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

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

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

Navigating hidden tradeoffs in a complex environment 

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

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

The post The trends rewiring tech, media and discovery   appeared first on Digital Content Next.

According to Gracenote’s 2025 State of Play report, audiences still love streaming, but the thrill of seemingly infinite choices has become an endless maze. The challenge isn’t that viewers don’t want to watch; it’s that they can’t easily find what they want. Streaming is maturing, and the next phase of growth depends on improving how people discover and engage with content. 

The paradox of streaming abundance  

The data shows a streaming market still expanding. The number of free ad-supported streaming (FAST) channels continues to climb, and global streaming services keep expanding their catalogs. As the supply of entertainment keeps rising, viewers bounce among apps and subscriptions in search of something to watch. 

Nearly half of all streaming viewers say it’s getting harder to find what they want. They spend an average of 14 minutes searching before pressing play, with younger audiences spending even more time. Nearly 50% state they would consider canceling a service because they can’t find something to watch. 

The challenge is especially evident in live sports. To watch every NFL game, fans need access to several different services. That complexity turns loyal fans into frustrated detectives. Streaming freed audiences from linear schedules, but freedom without guidance risks undermining engagement.  

Audiences aren’t turning away from streaming; they are asking for better experiences. Many viewers want a service that tells them where to find a specific program, even if it’s on another platform. Others want recommendations shaped by their own preferences such as release year, mood, or country of origin. And 84% say layout, images, and program descriptions define the value of a service 

Streaming is no longer just about access to endless content. It’s about how people feel when they engage with a service. The viewer experience has become the product, and personalization now sits at the center of every strategy. 

AI and the future of streaming discovery

Gracenote’s report identifies a powerful accelerant. Generative AI and large language models (LLMs) can transform how audiences search, browse, and decide what to watch.  

Traditional search depends on keywords. Type “Seattle TV shows” and you get a static list. LLM-driven discovery understands nuance: “What’s a good comfort show set in Seattle?” or “Where can I watch the Dodgers game tonight?” 

AI models trained on harmonized entertainment data can connect viewers with accurate, real-time results. They can unify metadata across multiple catalogs and rank results by popularity, critical acclaim, or mood. 

For media content companies, these capabilities mean stronger engagement. Better discovery leads to less searching, fewer abandoned sessions, and lower churn. AI-enhanced interfaces can reintroduce a sense of curation, the element many viewers miss from traditional TV, while still offering the flexibility of streaming. 

Common standards for streaming navigation

The real opportunity isn’t to compete for every minute of attention, but to help audiences navigate abundance. Unified discovery doesn’t require every service to merge libraries; it requires smarter metadata, richer taxonomies, and collaboration on common standards. 

Companies that take this approach can turn fragmentation into differentiation. They can become a trusted guide, not just another destination. By understanding how mood, time of day, or current events influence viewing decisions, they can deliver more relevant recommendations and seamless journeys. Currently, when looking for something to watch, only 28% of streaming viewers report choosing content based on a service recommendation (30% in the U.S.). 

Viewers don’t resent moving between services; they resent confusion. Helping them find something to watch, even if it’s hosted elsewhere, builds loyalty through transparency. This is about expanding the value exchange between viewers and brands. Companies that empower discovery, even beyond their own platforms, strengthen trust and remind audiences that the success of streaming depends on serving people first. 

Search for streaming success  

For media executives, Gracenote’s data affirms what many already sense. Engagement isn’t just about how much people watch; it’s about how confidently they navigate the streaming environment. When viewers spend 14 minutes searching, that’s 14 minutes of potential disengagement. When they give up entirely, that’s a lost connection and possibly a lost subscriber. 

Fragmentation won’t reverse itself. If anything, it will deepen as new services, FAST channels, and specialized platforms emerge. The solution isn’t to rebuild the old cable bundle. It’s to create bridges of intelligent, data-driven, audience-centered pathways that make the ecosystem easier to explore. AI can help.  

Success comes down to intention: seeing curation not as nostalgia, but as streaming’s natural next chapter. Engagement thrives when innovation is paired with clarity and when abundance feels accessible rather than overwhelming. Elevating content discovery will define the future, not by expanding catalogs, but by guiding viewers through them. This is a moment to transform data into discovery, and discovery into delight. 

Opening image source: Netflix TechBlog 

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Disney’s ESPN set out to solve the sports discovery problem with its new “Where to Watch” feature. Offered on its main app and website, the feature helps viewers instantly locate any sports event appearing on ESPN platforms and elsewhere, including cable and broadcast networks or streaming services. ESPN is aiming for this feature be comprehensive across the market, not just for ESPN and ABC properties, because the goal is to solve fan fragmentation and frustration.

Where to Watch, which debuted in August, showcases tens of thousands of events across dozens of leagues. Included are events from the NFL, NCAA football, NCAA men’s and women’s basketball, MLB, NHL, NBA, WNBA, NASCAR, UFC, F1, PGA Tour, MLS, tennis majors, Premier League, Champions League, and other live sports events that air on Disney’s ESPN platforms—with plans to grow.

We recently spoke with Casey Grabbe, senior director of ESPN Strategy, and Chris Jason, executive director of ESPN product management, on the development and objectives of this ambitious feature.

The feature aims to solve fragmentation

“Where to Watch is an easy-to-use guide for sports fans to locate any sports event on ESPN platforms and beyond. That includes broadcast, cable and regional sports networks and streaming services,” Jason explained. “From Where to Watch, fans can view all the sports events for an entire day, along with the network or service on which to find them, with quick one-click access to ESPN network streams for pay TV authenticated users and ESPN+ subscribers.”

Beyond just ESPN, fans are also linked directly to select partner networks, which currently include regional sports networks such as NESN and Monumental Sports, Jason said. Fans can search for events, filter, and customize the guide to prioritize their favorite teams and leagues.

“This makes for a fast and easy to discover what they care about most, all tied to their ESPN profile and personalization preferences,” Jason explained.

The motivation behind the Where to Watch feature was simple: reduce complexity.

Disney’s internal research found that sports fans are confused about where to find games, according to Grabbe. As sports viewing has become fragmented across many TV networks and streaming platforms, it has also become difficult and confusing for people to know where they can watch their favorite teams, players, and sports.

“We are hoping to solve that consumer pain point by creating a centralized home for sports viewing information with an intuitive interface that is easily accessible from within their daily routine of visiting ESPN.com and the ESPN App,” Grabbe explained.

How Where to Watch works

Where to Watch is designed to be a simple, scrollable, time-based guide of sports events, Jason said. It is powered by a proprietary event database, managed by the ESPN Stats and Analysis team.

The event database aggregates ESPN and partner data feeds along with originally sourced information and programming details from more than 250 media sources, including television networks and streaming platforms, Jason explained. 

“We currently support coverage of tens of thousands of events across dozens of sports and leagues, and other live sporting events airing on ESPN platforms,” Jason said.

In order to watch an event, fans need only press boldly colored “watch” buttons on live game selections, which takes the viewer directly to the broadcast. That is, provided that they are a subscriber to ESPN+ or a pay-tv service. Fans can also customize the feature to highlight a specific sport or league.

Event-driven database drives discovery

Where to Watch is currently available for free to all ESPN App and ESPN.com users, which do not require a paid subscription. The feature employs an event database that was created by and is managed by the ESPN Stats and Information Group. The Stats group aggregates and analyzes data from ESPN and partner feeds. It combines that data with that of more than 250 other media sources. This includes television networks and streaming services.  ESPN has a partnership arrangement in which it links users on the ESPN App directly to partner feeds to view content, in an effort to cut down on the friction of finding and assessing sports content.

Sports fans using the Where to Watch service see two primary features: A Favorites element and the Guide. If the fan has a favorite team, sport or league they wish to watch, they can set that information into the feature and it will display upcoming games or events at the top of their screen. The viewer need only click on the event they want to be directed to. The viewer can personalize or change favorite settings at any time. Otherwise, the Guide feature will display all of the options available to watch at a given time on a given day.

Early feedback says Where to Watch is a winner

Jason notes that the Where to Watch feature was designed with the sports fan desires in mind, and that seems to have paid off so far.

“Fan feedback has been overwhelmingly positive, primarily in that this is focused on solving a real pain point for sports fans,” Grabbe said. “We see this sentiment reflected on social media, through various media outlets following launch, and ongoing interactions with sports fans. Several million fans have already used the feature, which is a really promising sign that this can become an indispensable utility going forward.”

Initial partnerships have been formed with only a few regional sports networks – NESN and Monumental Sports – to link fans directly with their programming, with plans to increase the number of these partnerships.

“We want ESPN to be a part of every sports fan’s daily routine,” Grabbe stressed. “Providing fans with this added functionality is helping to further strengthen ESPN’s position as the preeminent digital sports platform. We are always thinking about how we can put the sports fan’s needs first.” ESPN also plans to launch a new stand-alone direct-to-consumer product in 2025, and hopes to include its Where to Watch feature.

“Our near-term focus is to expand coverage across more sports events and leagues,” Jason said. “We are also working on adding additional utility within the experience, for example giving fans the ability to set reminder alerts for games they are interested in. In parallel we continue to monitor fan feedback to evaluate additional ways to improve the experience.”

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From traditional TV to streaming

As cord-cutting and “cord-never” populations continue to grow, the number of viewers who rely solely on traditional pay-TV services is dwindling. According to Hub’s findings, more than twice as many viewers use both traditional pay-TV and streaming platforms rather than just one type. Audiences find that streaming platforms offer more options and flexibility than traditional TV. Deloitte’s Digital Media Trends report echoes this, noting that many consumers find streaming services more aligned with their viewing needs. They prioritize content that matches their schedules rather than set broadcast times.

Viewers’ SVOD stack

Viewers’ video-on-demand (SVOD) “stacks” are getting larger, with many people subscribing to at least three different services. The report shows that the percentage of consumers using three or more SVODs more than doubled since 2020, illustrating the growth of multi-platform use. This expansion is partially due to the massive libraries each SVOD offers; for instance, Netflix, Hulu, and Disney+ have extensive catalogs covering different genres and audience segments.

Yet, while viewers may stack multiple services, only a few platforms become their “default.” Netflix leads this default category, with 26% of respondents choosing it first. This trend toward Netflix as the initial go-to aligns with its status as a pioneering platform with an established reputation for both quantity and quality of content. Hulu and Amazon Prime Video follow while Disney+ and Max (formerly HBO Max) fall slightly behind.

Online streaming is the new “home base”

Hub’s findings underscore that, for most viewers, the default experience of “turning on the TV” starts with online streaming. About one-third of viewers say they now go directly to a built-in smart TV app, showing a 50% increase in usage since 2021.

The appeal of smart TV apps lies in their convenience. They provide immediate access to various streaming platforms without additional hardware. The transition to smart TV apps represents a natural evolution of how viewers experience TV.

Research from the Leichtman Research aligns with this trend, finding that 87% of U.S. households own a device connecting their TV to the internet, from smart TVs to streaming media players. This widespread connectivity facilitates using apps like Netflix, Hulu, and Prime Video, solidifying them as the primary sources of TV content.

SVOD loyalty driven by “favorite shows”

Hub’s report highlights a crucial driver of platform loyalty—exclusive content. When viewers have a specific favorite show exclusive to a particular SVOD, they’re more likely to remain loyal to that platform. This “stickiness” effect is essential in a crowded market where content variety can make or break viewer retention.

On the other hand, traditional MVPDs still hold an edge on live TV, particularly for sports and news. These content categories remain strongholds for pay-TV providers, appealing to a demographic that values real-time events. However, this loyalty is eroding. The report notes that nearly a quarter of MVPD users would consider canceling their service if forced to choose between platforms.

The growth of FAST

FAST services are also becoming a mainstay for many viewers, especially those who prioritize content variety over exclusivity. FAST platforms appeal to cost-conscious consumers who prefer a broad selection of programming without additional monthly costs. Their rise complements subscription streaming by offering a fallback for when paid services are unavailable or too costly.

FAST providers such as Pluto TV and Tubi are gaining traction as they offer a unique blend of on-demand and live content with a more traditional TV-like feel. According to a survey from eMarketer, over half of U.S. adults now use FAST services. For these viewers, the trade-off of ads in exchange for free content is more appealing than paying for an additional SVOD, further underscoring the complexity of the modern TV ecosystem.

Will MVPDs adapt?

The rapid decline of MVPD set-top boxes poses an existential challenge to pay-TV providers, who now face pressure to innovate or risk further market loss. Some MVPDs are pivoting to streaming bundles or hybrid solutions to capture traditional and digital audiences. However, Hub’s report suggests these changes may be too late. As smart TV apps and streaming services become the “default” choice for viewing, MVPDs could be relegated to a niche role unless they compete with streaming platforms on convenience, affordability, and exclusive content.

As more people rely on streaming services and smart TVs, the influence of traditional MVPDs is waning. Netflix’s leading SVOD “default” position reflects its early mover advantage and vast content library. Meanwhile, traditional TV’s staples—live news and sports—feel less essential as viewers increasingly favor on-demand content.

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Deloitte’s “2024 Digital Media Trends” study found that 60% of Generation Z video consumers are more likely to watch user-generated content because they “don’t have time to spend searching for what they want to watch.” Half of all respondents say they “abandon an entertainment experience because they can’t find what they’re looking for.”

The findings are problematic for entertainment giants, which are committing large budgets toward the production of original content that aren’t attracting significant audiences. Deloitte says the top six subscription streaming platforms are likely to spend $100 billion on original content production and marketing in 2024 alone. Jeff Loucks, the Executive Director of the Deloitte Center for Technology, Media, and Telecommunications who co-authored the Digital Media Trends report, said that investment is likely to be wasted effort if streamers can’t easily connect to those shows and movies.

The discoverability dilemma

According to Loucks, one big reason why shows and movies aren’t being watched is because streaming platforms make it difficult for that content to be readily discovered, likening the experience of sifting through shows and movies on a streaming service to “the old days of Blockbuster.”

“You’re searching through a bunch of titles, and you can’t agree on what to watch. It’s going to take an hour and a half of your time,” Loucks said. “The content discovery has got to get better.”

Industry experts who spoke with Digital Content Next said they were largely unsurprised by Deloitte’s findings that streamers — particularly younger audiences — were increasingly turning to user-generated content platforms like TikTok and YouTube for their entertainment needs. One often overlooked reason is that platforms like YouTube and TikTok have made heavy investments in their search and discovery algorithms that identify what a person is watching on the regular, and then serve up more content that caters to their interests.

“YouTube and TikTok are scarily accurate and predictive and elemental, whether the content is large-scale or bite-sized,” said Tim Hanlon, the founder and CEO of the media consultancy firm Vertere Group. “Those are all independently describable and ascribable elements, data-rich elements that can be mixed and matched together.”

Loucks agrees that younger viewers are increasingly attracted to short-form content, which can be easily skipped for something new if it isn’t appealing. “Sometimes, people are telling us that they’ve got a lower attention span, and smaller, snackable content is something they’re willing to watch — it’s easier to consume,” Loucks said.

Addressing fragmentation

Embracing short form and the push-based UI of user-generated content platforms won’t serve as a silver bullet that will solve the complex challenges of streaming search and discoverability, however. There are still plenty of consumers who prefer to be entertained by watching feature-length films and episodic TV series — and they’re having the same challenges finding interesting things to watch across apps and platforms, too.

According to a report from Accenture, about 36% of people say they’re exhausted from having to constantly look across platforms and services to find something they want to watch. And 60% of consumers say they’ve churned out of a service because a movie or TV series was dropped, or they thought they’d watched everything there was to watch.

Fragmentation is accelerating these trends, because content that is relatable to a person is spread across different services. “The net impact of fragmentation is the fact that consumers can’t simply find content in consistent places where they want to spend their time,” pointed out Dallas Lawrence, a former communications executive for Roku’s platform.

Lawrence spent a lot of time thinking about this problem at Roku. He noticed that companies spend a lot of time and marketing money drawing customers into their streaming services only to “fail at the five-yard line.”

“They’ve failed to actually consumers with a piece of content they want to watch, and that’s probably one of the biggest challenges today, both from a streamer perspective — to keep people from cycling out — but also from a consumer perspective.”

Lawrence is now the chief strategy and communications officer for Telly, a startup that grabbed headlines last year after promising to offer a free, dual-screen smart TV. Telly packs a lot of features that are meant to entice consumer interest — from a premium screen to an integrated high-fidelity sound bar. They also promise to play nice with any streaming platform that a customer wants to use.

Telly is rooted in the idea that the TV will pay for itself over time through advertisements shown on a secondary screen that sits beneath the main display. Lawrence said Telly is uniquely positioned to help ease the challenges of streaming discovery for consumers and services alike because the device is able to evaluate what someone is watching across any service.

“If I’m watching Bridgerton on the top screen of my Telly, the device is recognizing that, and we’re going to say, hey, maybe you’d like to watch Gilded Age on Max as well,” Lawrence said. “We can throw that ad on the second screen, and when you’re done watching Bridgerton, you just pick up your remote and click into Gilded Age. The ability for us to recognize what someone is watching now, and then pull them into new content with a single click before they’ve turned off the TV or cycled out, that will have huge benefits.”

Streaming discoverability beyond the EPG

For now, Telly is the only dual-screen device on the market that can seamlessly pull off this experience.  However, the idea of using viewing habits to deliver personalized results and improve streaming discoverability is not unique and can be franchised by other services.

Instead, streaming services seem to be defaulting to antiquated ways of browsing across content, complains former CBS executive Adam Wiener, who now operates his own media consulting firm Continuous Media.

Wiener says that subscription-based platforms have adopted the “endless scroll,” which allows consumers to quickly flip through movies and TV shows. Often, however, this approach fails offer personalized content recommendations the way user-generated platforms like TikTok and YouTube do. Free, ad-supported platforms and some premium pay TV services that deliver linear content are even worse, Wiener notes. That’s because they’ve embraced the grid-style electronic program guide (EPG) that was used by cable and satellite platforms for decades, but hasn’t kept up with the times.

“The problem with the EPG is that it’s the clunky thing of yesteryear, and it also doesn’t include all the things that you may be interested in,” Wiener opined. “An EPG should know that I never, ever watch reality shows, and it should never show those things to me.”

Endless scrolling and EPGs also reveal another problem: There is a lot of stuff to watch. According to Nielsen’s State of Play report, there are now more than 2.7 million unique titles across hundreds of streaming services, and the sheer volume of content libraries can leave consumers feeling extremely overwhelmed and make finding something to watch seem impossible.

“It’s the paradox of choice,” Wiener says. “The age-old discussion that it becomes tiring to scroll through a screen, the thought that maybe if I continue scrolling right, I might find something more interesting…and then it feels like you can’t choose, like you have to settle for something, and you just hope that it’s good.”

Hanlon agrees: “When everything is a choice, there’s a paralysis that occurs when you either revert to something you know from the past, or you’re looking for signals to grasp,” Hanlon said. “What services wind up having to do is dumb things down and simplify it to the point where it becomes pages and pages of tiles or, worse, a search box.”

AI leads the way for streaming discoverability

Wiener and Hanlon both point to generative artificial intelligence (AI) as a solution that can help ease a lot of the pain points associated with streaming search and discovery. At least one company is already embracing the idea: Earlier this month, Cineverse said it was working on a new AI-powered content recommendation engine called cineSearch.

Using metadata provided by Nielsen’s Gracenote and an AI platform powered by Google’s Gemini language model, cineSearch will power a forthcoming consumer chatbot called Ava that aims to offer personalized TV shows and movies across apps and services — even if the content isn’t offered by Cineverse itself.

“Our partnership with Gracenote increases the number of films and TV shows that are discoverable by users and allows us to offer cineSearch users the highest-quality title information with intensity rankings – when paired with a user’s viewing history, streaming service filters and content preferences – will help solve a major consumer issue and the leading cause of viewer frustration,” Tony Huidor, the Chief Technical Officer at Cineverse, said in a statement.

Deloitte’s Media Trends report suggests companies like Cineverse are on the right track. According to the report “streaming services should look to the engagement models of social media services to improve their own content delivery strategies, making a more concerted effort to leverage user data and AI technologies to target content toward individual viewers.” Loucks affirms that video platforms should want to embrace generative AI solutions to improve their content recommendation engines.

“Each service is going to have to work on having a user interface that is good and that works better, and I think generative AI is going to be a big part of that,” Loucks said.

Discoverability delivers streaming audiences

Audiences expect personalized experiences. According to a November 2023 report by Google Cloud, 81% of streaming video viewers “expect streaming services to provide highly personalized experiences,” and 31% will switch out of a service if they can’t find something they want to watch. Worse, nearly half of respondents say they’ve canceled a service in the past “if they couldn’t find something to watch,” Google Cloud revealed. Clearly improving streaming content discoverability is critical for success.

The experts and the data reveal common themes: Streamers don’t want to spend a lot of time trying to find something to watch. And if a video platform makes an investment in personalized search and discovery, that is where audiences — especially younger viewers — will spend their time.

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