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Over the past several years, AI use in advertising has steadily increased. It’s now delivering measurable gains in speed and output, empowering teams to plan smarter, buy faster, sharpen targeting, and personalize creative at scale.
At the same time, the technology is transforming consumer behavior. On the other side of the (not so proverbial) screen, people are spending real, measurable, and fast-growing time inside AI environments—so much so that AI is starting to be talked about and measured as its own digital media channel.
Time spent with AI is still modest compared to other channels. US adults will spend an average of 16 minutes per day with generative AI platforms in 2026, representing just a sliver of the nearly 13.5 hours people spend with all media. But that modest share is climbing quickly. Generative AI is the fastest-growing channel, with daily time up 62.8% this year after more than tripling the year before. Among active users, the average already reaches 32 minutes per day and is projected to climb to 45 minutes by 2028.
For advertisers, that growing time spent in AI environments makes understanding AI as a channel all the more important. Consumers are asking questions, comparing options, and forming preferences within these AI spaces. To shape how their brands or clients are represented within them as they mature, teams can focus on organic visibility, track how paid placements evolve, and adapt their measurement approaches for zero-click environments.
Key Takeaways:
Generative AI represents only about 2.9% of the time US adults spend with digital media, and it currently ranks last among key digital activities. In other words, most people aren’t trading their TikTok scrolling or their streaming hours for a chatbot…yet.
How fast that use is growing, however, is what should put it on advertisers’ radar. Time spent tripled last year and is forecast to grow another 62.8% this year, a sign of a habit taking hold. CTV, which now commands three hours of daily viewing, followed a similar trajectory, starting as a smaller share of time spent before spiking and growing into a critical channel for advertisers.
Treating AI as a channel now, while the numbers are still small, gives advertisers room to learn the environment before competition for attention intensifies. Teams who wait for the time-spent figures to grow risk arriving after audience behavior has already settled.
Time spent with gen AI tools is distributed unevenly. Though ChatGPT has commanded substantial market share since its release just a few years ago, it recently slipped below 50% as other players like Anthropic’s Claude, Google’s Gemini, and Microsoft’s Copilot, among others, gained ground. That said, ChatGPT still accounts for over 1 billion monthly users worldwide, far outpacing any single competitor.
Where people use these tools tells an interesting story as well. After more than a decade of decline, desktop and laptop usage is rebounding, with some attributing the reversal largely to gen AI, which is more computer-centric than mobile-first. In fact, about 63% of gen AI time now happens on desktops and laptops. As advertisers consider how to connect with users in these spaces, defaulting to mobile-first assumptions likely will not fit how people are actually engaging.
For advertisers, these patterns help guide where effort should go first. Attention still concentrates on just a few platforms, so teams can concentrate their generative engine optimization (GEO) and early paid testing (where available) on the platforms where audiences already are—ChatGPT given its scale, then Claude, Gemini, and Copilot as usage spreads—rather than trying to cover everything at once. And because so much of that engagement happens on desktop, teams building creative and optimizing for organic reach should be thinking around longer, more considered sessions rather than the mobile-first, scrolling-centric behavior that defines so many other digital channels.
Knowing where audiences spend their AI time begs the question of what they’re doing with that time. And increasingly, shopping is a big part of that answer. Time spent in AI environments is progressively shaping real purchase decisions rather than staying purely informational.
Among shoppers who use AI, it now ranks as the second most influential source in the customer journey, trailing only search engines and outranking retailer sites and recommendations from friends and family. And research on the 2026 holiday shopping season finds that 40% of consumers are open to using the technology for shopping, with Gen Z leading adoption. Primary use cases include using it to find deals, generate ideas, and compare products.
However, there's a meaningful limit to how much shoppers are willing to use AI (at least for now). Consumers want AI to help them make shopping decisions, but they don't want AI tools to make those decisions for them. For advertisers, that clarifies where to focus. Because AI use clusters around discovery and evaluation, the highest-leverage work is making sure a brand is surfaced and accurately represented when those comparisons happen. To do so, teams need to prioritize the structured, factual content AI systems pull from (i.e., product pages, specifications, reviews, and comparison-friendly details) through GEO, rather than conversion-focused tactics that fit later, decision-stage moments.
Presence in AI environments comes with a caveat that other digital channels don't carry to the same degree: Consumers remain wary of the technology itself. Half of US adults say the growing use of AI in daily life makes them more concerned than excited, and only 10% say the opposite.
In particular, consumers are concerned about the accuracy of AI answers. Among the small share of adults who get news from AI chatbots, about half say they at least sometimes encounter information they believe is inaccurate. Those inaccuracies can negatively impact brands who appear in the same environments: A brand surfaced by an AI tool the user half-trusts inherits some of that doubt, which can damage the brand’s credibility.
That challenge is compounded by how little control brands have over how they appear in AI environments. In AI answers, an algorithm decides which sources to surface, synthesize, and recommend—so a brand can be summarized, compared to competitors, or left out entirely, all without any input from the brand itself.
The strategy that gives brands the most influence here, at least at present, is GEO. GEO involves shaping content so AI systems can find, understand, and accurately represent it. Because AI systems reward structured and factual material, clear product pages, specifications, and FAQs are both what AI systems surface most reliably and what holds up when a cautious user scrutinizes the result. Though GEO won’t give brands full control over how they’re portrayed in answer engines, it’s the best way they can shape what shows up for users.
Paid access to advertising in AI environments is still nascent. Google, Microsoft, and OpenAI have all begun offering or testing ads within their AI search and chat products, but availability is limited as is research on consumer sentiments towards ads in these spaces. Tracking how these placements work and where they’re headed is worthwhile now, as is early experimentation, even if buying in at scale isn’t yet realistic for most teams.
As such, the most immediate work in terms of building readiness is organic. Large language models decide which brands to cite and recommend, and optimizing for that inclusion through GEO is available to any team today. If brands fail to make such information easily available to LLMs, they likely won’t be included.
Measurement needs to evolve as well, which calls for a shift in what counts as success. Impressions alongside AI-generated summaries or within AI chatbots influence purchase decisions even when no one clicks, so click-through rate alone understates the value of showing up. Statistical modeling and brand lift studies help capture influence that last-click reporting misses.
Finally, the channels teams already control carry more weight while influence over AI environments is limited. Video, CTV, social, and display remain the places where advertisers fully control the message, anchoring brand presence as AI environments mature. Coordinating all of these channels gets harder as platforms multiply, each with its own formats and reporting. Managing that channel spread from a single, unified platform—where planning, performance, and optimization live together—keeps teams nimble as AI media environments mature and the rules change.
The habits forming inside AI tools right now will likely shape how audiences discover and choose brands for years. And forward-thinking advertisers are already building their understanding of it as a true media channel. People are spending fast-growing time in AI environments, and that time is starting to shape what they discover, consider, and buy.
As audiences continue to spend time, form preferences, and make real decisions with these tools, advertisers who work to influence how they show up in AI environments today will be better positioned than those still waiting for the ad inventory to mature or time spent to look more impressive on paper. Teams can focus on keeping track of available advertising offerings, earning organic visibility, adapting measurement to capture influence that clicks miss, and holding steady presence across the channels where they have more control.
Consumer habits inside these tools haven't hardened yet, so the work advertisers do now—learning how their brands appear in these environments and shaping that appearance where they can—is what will set them apart once they become more standard.
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Looking for more insights specifically on advertising in tools like ChatGPT, Copilot, and AI Overviews? Check out What Do Marketers Need to Know About Advertising in AI Environments?