HOT OFFERINGSThe next ad format isn’t a banner, a carousel, or a pre-roll. It’s the conversation itself.
In our previous deep dive, “Disrupting the MarTech stack: How generative AI is reshaping traditional marketing,” we explored the breakdown of once-reliable marketing channels. SEO, social campaigns, email blasts, paid media, and influencer outreach are all buckling under the combined pressure of saturation, rising costs, and platform hostility. As 2025 unfolds, it’s clear: the playbooks that once delivered reach have turned into background noise. Users, overwhelmed by irrelevant messages, scroll past without pause.
Take SEO — once a cornerstone, now a rigged game. Brands spend arduous months optimizing content, only to find themselves outranked by informal Reddit threads or summarily penalized by Google’s relentless algorithm tweaks.
For instance, News Corp’s flagship UK brand, The Sun, witnessed a devastating 50% loss of organic traffic in late 2024, a direct consequence of Google’s algorithm changes that explicitly punished broad, listicle-style publishing in favor of community discussions.
The New York Post experienced a 27% traffic decline during the same period, signaling not isolated blips but a structural reinterpretation of authority by Google, shifting away from high-volume generalist publishers.
Even HubSpot, once the quintessential blueprint for B2B content marketing, suffered a dramatic loss in visibility across thousands of high-intent queries, with its vast archive of SEO-first content, often thin on topical authority, deprioritized by the Helpful Content Update. Hubspot’s internal metrics suggest up to a 40% decline in organic leads from Google since December 2024.
Independent publishers have suffered even more. DMARGE, an Australian lifestyle site, spent $200,000 to regain lost traffic, only to see monthly visits crash from 8 million to 300,000. Programmatic revenue shrank to 3–5% of previous levels. With editorial cuts and falling ROI, the team shut down its site and shifted to Instagram and newsletters. Google no longer rewards independent content; it buries it.
And it’s not just SEO. Most scalable channels are in decline:
- Influencer marketing burns budgets for minimal return.
- Email campaigns struggle with low deliverability and engagement.
- Paid media faces rising CPMs and rampant click fraud.
- Affiliate and referral programs are bogged down by fraud and admin overhead.
- PR might build awareness, but rarely converts.
The aggregated effect is a landscape where everything that once worked has been diluted into mere noise, leaving users disengaged and exasperated.
AI discovery: Why conversations are replacing search
As traditional marketing channels deteriorate, a new discovery layer is rapidly taking shape: generative AI. Tools like ChatGPT, Perplexity, and Microsoft Copilot are transforming the way users interact with information. Discovery no longer begins with fragmented keywords in a search bar. It begins with context-aware, interactive conversations. A query like “best credit cards for travel” becomes an ongoing dialogue shaped by prior inputs and refined recommendations.
This shift is producing measurable changes in user behavior. According to Adobe Analytics, traffic from AI referrals consistently outperforms traditional sources across key engagement metrics. Users spend 8% more time on site, view 12% more pages, and have a 23% lower bounce rate. These figures point to higher-quality, more engaged sessions.
Engagement growth is matched by traffic acceleration. During the 2024 holiday season, Adobe recorded a 1,300% year-over-year increase in AI-driven retail visits, with Cyber Monday traffic rising by 1,950%. By February 2025, AI referral traffic was up 1,200% compared to just seven months earlier. Similar trends are playing out across other sectors, with banking and travel seeing 1,200% and 1,700% increases, respectively. While AI referrals still represent a smaller share of total traffic than paid search or email, their rapid expansion signals a major realignment in digital discovery.
The driver behind this trend is intent. Semrush’s comparison of ChatGPT and Google reveals a distinct behavioral difference. Over 52% of ChatGPT queries are informational, compared to 36.4% on Google. Navigational queries dominate Google at nearly 50%, but drop to just 34% on ChatGPT. Users interacting with AI systems are seeking answers, evaluating options, and engaging in multi-step problem solving. Generative AI is not simply modifying how people search. It is redefining the entire process of exploration and decision-making online.
AI-native ads: How monetization works inside conversational interfaces
As this shift accelerates, platforms adapt to a new user engagement model. ChatGPT now routes traffic through homepage-weighted links, while Microsoft Copilot creates personalized session flows. Analytics providers like Similarweb are building the infrastructure to track and interpret AI-originated traffic. They signal a fundamental reordering of how users discover, evaluate, and act online.
Discovery no longer flows through SEO, email, or influencer traffic as it once did. Generative AI introduces a new interface with its own logic and mechanics. Brands that understand how these systems interpret, rank, and surface information will gain a lasting competitive edge.
But visibility is only step one. The larger opportunity is monetization, embedded directly inside the interface. AI-native ad formats are beginning to surface within conversations, APIs, and assistant workflows. The interface itself becomes the inventory.
In this model, attention isn’t measured by impressions. It’s measured by engagement: follow-up questions, real-time filters, co-piloted decision flows, and conversational prompts. Display banners are being replaced by dialogue.
Here’s how this new advertising layer is taking shape in 2025.
Microsoft’s Copilot: Ad voice, compare & decide ads, and showroom experiences
Microsoft is moving fast to integrate advertising into Copilot’s conversational environment. Its “Ad Voice” feature is designed to embed sponsored content naturally into the flow of conversation, guiding users from query to recommendation without breaking context. The ads are labeled and disclosed, but presented as a native part of the user’s interaction.
Another emerging format is the “Showroom ad,” which transforms the assistant into a virtual product advisor. A question like “What’s a good running shoe for flat feet?” triggers a dynamic filtering experience, surfacing tailored options based on user-specified criteria such as “eco-friendly,” “under $100,” or “waterproof.”
Another emerging format is the “Showroom ad,” which transforms the assistant into a virtual product advisor. A question like “What’s a good running shoe for flat feet?” triggers a dynamic filtering experience, surfacing tailored options based on user-specified criteria such as “eco-friendly,” “under $100,” or “waterproof.”
Early results are strong. Copilot search ads outperform traditional search placements, with a 25% improvement in ad relevance metrics and a 76% higher conversion rate. These formats are already live within Performance Max (PMax) campaigns, meaning advertisers using PMax are automatically reaching Copilot users. Adoption is particularly relevant for Gen Z, who now represent over 30% of the Copilot mobile user base, and are reshaping digital commerce behaviors in real time.
Amazon’s Rufus: Contextual product recommendations
As of January 2025, Amazon has begun embedding Sponsored Ads directly into Rufus, its generative AI shopping assistant. Designed to deliver highly personalized product recommendations, Rufus analyzes product metadata, customer Q&As, reviews, and images to understand intent and serve contextually relevant results.
Rufus now appears above the fold on desktop and ranks higher on product pages, increasing the impact of AI-aware listing strategies. The assistant functions like a product analyst, adapting to user behavior such as recent searches, click-throughs, purchase history, and demographic trends.
For sellers, success requires listings that are structured for AI consumption. That includes using diverse keyword types: intent-driven, solution-specific, quality-descriptive, and comparison-based. Relevance is determined through context signals, not just keyword density.
Snapchat’s MyAI: Personalized engagement and adaptive ad formats
Public details on Snapchat’s MyAI ad placements remain limited, but the assistant is a key part of the company’s broader “AI-powered advertiser solutions,” introduced in May 2025. The focus is on improving content ranking and personalization using advanced AI and machine learning models.
Recent additions include “Smart Campaign Solutions,” which feature Smart Bidding and Smart Budget tools to optimize campaign performance. Snapchat has also rolled out new ad formats such as “First Snap,” a single-day takeover that appears in Chat feeds and opens as a full-screen video, and “Web and App Auction Ads,” which expand inventory across platforms.
MyAI’s personalization capabilities suggest a future role in mid-dialogue product suggestions and integrated sponsored content. As Snapchat invests in longer-form content and creator monetization, the opportunity grows for brands to embed messaging directly into user-generated interactions without disrupting the experience.
Perplexity AI: Comet browser and behavioral data targeting
Perplexity AI’s monetization strategy has become a focal point for privacy concerns in 2025. The company plans to collect extensive behavioral data through its upcoming Comet browser to build highly personalized advertising profiles. While this model supports more precise ad targeting, it has drawn scrutiny for its scope and potential privacy trade-offs.
CEO Aravind Srinivas has confirmed that Perplexity aims to capture data beyond direct AI queries. The objective is to construct rich user profiles based on browsing patterns, content interactions, and inferred intent. This data will likely fuel ad placements in Perplexity’s “Discover feed.”
The assistant’s conversational flow also plays a role. Follow-up prompts within chats allow the system to map intent progression across queries, creating additional context signals for ad delivery. This behavioral layer gives Perplexity an edge in targeting, but also intensifies the debate around surveillance-level data collection.
Perplexity’s Comet browser, set to launch in 2025, is designed to collect user data far beyond chat-based interactions. CEO Aravind Srinivas has stated that the goal is to capture comprehensive behavioral patterns, including purchase history, browsing habits, and location-specific preferences, to build richer advertising profiles.
While some AI use cases remain task-focused, Srinivas emphasized that deeper personalization comes from everyday behavioral signals. Comet’s infrastructure is explicitly intended to gather this data across contexts, reinforcing the company’s strategy to power hyper-targeted advertising through full-spectrum user profiling.
The broader insight: Advertising is native, contextual, and deeply personal
The overarching insight that advertisements are moving intrinsically within the product experience is affirmed and rapidly accelerating. This profound evolution is driven by AI’s unparalleled ability to comprehend deep context and intent, facilitating highly personalized and often subtly integrated ad placements.
Hyper-personalization and conversational commerce
AI enables dynamic segmentation based on real-time engagement patterns, creating truly personalized experiences across every touchpoint. This capability transcends static demographics, delving into subtle behavioral insights. Chatbots are explicitly transforming into tools for “conversational advertising,” adeptly capturing detailed user queries to yield insights into consumer intent that far surpass traditional search algorithms.
Seamless integration and native formats
The prevailing focus is on embedding ads directly into content environments. Emerging formats like in-scene media and virtual product placement enable brands to integrate seamlessly into videos, shows, and interactive experiences. For example, Warner Bros. Discovery’s “Shop with Max” technology allows streamers to purchase products marketed on select programming in real time through a curated second-screen experience.
This uses QR codes, audio, and visual cues to identify relevant themes and on-screen elements, allowing marketers to reach audiences engaged with specific content topics. The ultimate objective is for advertisements to feel like an organic part of the viewing experience, rather than an intrusive disruption.
Ethical and privacy considerations: The dawn of surveillance 2.0
The rise of generative AI has amplified long-standing concerns around data privacy. General-purpose AI models carry inherent risks, including memorization of training data and limited user control over personal information. Challenges persist in fully deleting user data, even under mandates like GDPR and CCPA. The opacity of model behavior remains a critical issue, often referred to as the “black box” problem. A growing concern is the repurposing of personal data, using it in ways beyond the original scope, which raises questions about consent, legality, and user trust.
As AI assistants take on more contextual and persistent roles, the depth of data they collect surpasses anything enabled by cookies. Emotional tone, behavioral signals, and even implied intent are captured through ongoing interactions. This creates a powerful targeting framework and a potential surveillance infrastructure. The industry is entering what many now call “Surveillance 2.0,” with increasing pressure to implement privacy-by-design principles and offer users more visibility and control.
Technological safeguards are advancing, including PII redaction, synthetic data, differential privacy, confidential computing, and cryptographic protections. However, most involve a trade-off between privacy and utility. Reduced access to granular data can impact model accuracy, particularly in general-purpose systems. Striking a balance between personalization and privacy will remain a central challenge in AI deployment and regulation moving forward.
AI agents with memory and emotional intelligence
Modern AI assistants are now designed to retain context across sessions, enabling follow-ups, reminders, and long-term personalization. Many are also being trained to detect tone, mood, and even stress levels, allowing for more empathetic and adaptive interactions. This level of contextual awareness supports the delivery of highly relevant mid-conversation ads that align with user intent without breaking flow.
In 2025, AI-driven embedded advertising has entered a phase of rapid maturity. Conversation itself has become the interface and the ad unit. This creates powerful new opportunities for brands to engage users through personalized, context-aware experiences. At the same time, it raises critical questions about privacy, data ownership, and responsible system design. The tension between innovation and regulation is now a defining factor in the future of AI-powered commerce.
Navigating the new advertising architecture
Generative AI transforms advertising from an external layer into a core system function. Interfaces have become intelligent environments where discovery, evaluation, and monetization occur simultaneously, often without users realizing they’ve left the search phase. This new architecture is driven by systems that analyze context in real time and act on behavioral signals with increasing precision.
As conversational interfaces become the primary gateway to information, the economics of attention are shifting. Visibility now depends on alignment with AI reasoning: how systems interpret relevance, confidence, and commercial intent. Brands and platforms must learn to operate within these logic models or risk losing their position in the discovery flow.
The regulatory landscape will lag behind technical capability, but pressure is mounting. Consent frameworks, auditability, and data minimization will become critical components of any scalable strategy. The companies that treat trust, explainability, and user control as product features will be best positioned to lead.
What’s emerging is not just a new channel, but a new foundation for how influence is built and value is exchanged. Advertising is no longer a message delivered within content. It is a responsive, context-aware layer inside the interaction itself, and its rules are still being written.
