10 minNovember 8, 2026
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Try It FREEAI-powered advertising is expanding across conversational search, social feeds, and retail platforms. But while advertisers are exploring new ways to reach customers, the competitive intelligence tools used to understand the market have not evolved at the same pace.
The traditional advertising research process was built around relatively visible environments: search engine results, public ad libraries, display placements, and landing pages. Marketers could examine competitor creatives, monitor keywords, and use visible campaign activity to develop a picture of the competitive landscape.
That picture is becoming incomplete.
Consumers now discover products through AI assistants, conversational search, short-form video, and retail recommendation experiences. These environments can influence which brands enter a customer's consideration set before a traditional search or advertising interaction takes place.
Research from AdExchanger discusses the emergence of AI platforms as advertising environments, while Search Engine Journal examines how AI-assisted search and agentic commerce may change the visibility and economics of traditional search advertising.
For performance marketers, this creates a new challenge: understanding where competitors appear, how they communicate their value, and how customers encounter their offers across increasingly fragmented environments.
ChatGPT, Gemini, and TikTok represent different parts of this shift. Each introduces new discovery behaviors, advertising opportunities, and visibility challenges.
This article explores why traditional competitive intelligence is no longer sufficient, what marketers should understand about these emerging platforms, and how to build a cross-channel spy stack that connects advertising observations into a more useful research process.
AI-powered advertising is moving beyond experimentation. More platforms are introducing conversational discovery, automated recommendations, and advertising opportunities that place brands closer to customer decisions.
A review of Q2 2026 from AdExchanger discusses the growth of AI media and the development of advertising infrastructure around conversational experiences.
Retailers are also bringing AI into shopping. Amazon and Walmart are developing conversational shopping experiences that allow customers to describe what they need rather than relying entirely on traditional product searches.
These developments create new places for brands to reach potential customers. But they also make it harder to understand the full competitive landscape.
For years, search advertising offered marketers relatively familiar visibility indicators:
These metrics were never a complete picture of customer behavior, but they provided a useful starting point for understanding how brands competed for search visibility.
AI-generated answers are changing that environment.
Google's AI Overviews and AI Mode can occupy prominent areas of the search experience, changing how users interact with traditional results. Search Engine Journal discusses advertiser observations about declining traditional search impressions and the growing role of AI-assisted experiences.
Google is also exploring advertising formats designed for these environments, including placements and offers that fit into more conversational shopping experiences.
The competitive challenge is no longer limited to securing a position among a list of search results. Brands increasingly need to understand whether they appear within the shorter set of options presented by AI-assisted discovery.
Microsoft's framing of the web's evolution describes three broad user expectations:
The third stage introduces a different kind of discovery experience. Instead of browsing numerous pages and comparing every available option, users may increasingly rely on assistants to evaluate alternatives and present a smaller set of recommendations.
This creates what some industry observers call the shortlist economy.
When an assistant presents only a handful of product or service options, inclusion can become an important competitive advantage.
A Search Engine Journal analysis also discusses a Semrush survey in which U.S. shoppers reported discovering brands through AI experiences.
These findings suggest that AI-assisted discovery deserves attention as part of a broader brand visibility strategy. However, a brand mention or recommendation should not automatically be interpreted as a purchase or a measurable advertising conversion.
The growth of AI advertising does not automatically mean marketers have the same visibility they enjoy in more established channels.
New advertising products may offer familiar campaign controls, reporting, and conversion measurement features. But understanding the complete influence of a conversational interaction is more complicated.
MarTech examines the challenges around advertising revenue, measurement, and proving incremental value in emerging AI advertising environments.
An interview recap from AdExchanger also discusses the measurement and transparency questions advertisers face as ChatGPT advertising develops.
Google's AI performance insights in Merchant Center represent another approach to understanding visibility on AI-powered surfaces. Search Engine Journal discusses these reporting developments.
The broader issue is that advertisers are beginning to spend money in environments where traditional competitive benchmarks may be unavailable or incomplete.
A marketer may know how many sponsored impressions or clicks a campaign generated without knowing how often the brand was considered, how frequently competitors appeared in similar conversations, or how an AI-assisted interaction influenced a later purchase.
The central challenge is visibility.
Marketers are testing new AI media opportunities, but many are still relying on fragmented platform dashboards and traditional reporting systems to understand a customer journey that no longer happens in one place.
ChatGPT advertising has attracted attention because it introduces sponsored placements into an environment where users are already asking questions, exploring options, and looking for recommendations.
For advertisers, that creates an interesting opportunity: reaching people in the middle of an active conversation.
But access to an advertising placement does not necessarily mean access to the information needed to understand the entire competitive environment.
Coverage from Dash Two discusses the development of ChatGPT advertising, including its evolving buying options and commercial model.
The appeal is easy to understand. A conversational environment can provide context about what someone is trying to accomplish.
For example, someone troubleshooting a leaking faucet may also be interested in plumbing supplies, repair services, or replacement parts.
That creates an opportunity for relevant advertising to appear alongside a conversation about a specific problem.
However, the economics of an emerging advertising product should not be treated as permanently established. Pricing, minimum spend requirements, targeting options, and availability can change as a platform develops.
Traditional search advertising often provides a clear relationship between a keyword and an advertisement.
Conversational advertising can be more context-dependent.
An advertiser may provide contextual signals or campaign guidance, while the platform determines when an advertisement is relevant to a conversation. Dash Two's breakdown discusses this approach to ChatGPT advertising.
This changes how marketers should think about competitive research.
Instead of focusing only on individual keywords, they need to understand the situations and questions that could create an opportunity for a sponsored message.
A customer might ask:
These questions reveal context, needs, and commercial considerations that may not be represented by a single keyword.
1. Share of voice is difficult to benchmark
In traditional search, advertisers can use impression share and related metrics to understand their visibility relative to competitors.
Conversational advertising introduces a different challenge.
Advertisers may see impressions and clicks for their own sponsored placements, but that does not necessarily reveal how often their brand appears in organic AI recommendations, how often competitors appear in similar conversations, or how frequently a brand is eligible for a placement but does not receive one.
Google's AI performance reporting is an example of the kind of visibility measurement advertisers would like to see across other AI environments. Search Engine Journal discusses Google's reporting approach.
2. Attribution remains fragmented
A conversational interaction may influence a customer before they visit a website, search for a brand, or make a purchase through another channel.
A platform's own reporting can provide useful information about its advertising activity, but it may not fully explain how that exposure contributes to downstream behavior.
AdExchanger discusses the measurement and transparency challenges surrounding ChatGPT advertising.
This is why advertisers are interested in cross-channel measurement approaches that connect platform interactions with other customer activity.
3. Independent measurement is still developing
Partnerships involving advertising platforms and data infrastructure providers are an important part of this transition.
For example, Marketing Dive reports on LiveRamp's partnership involving ChatGPT conversion measurement infrastructure.
These developments may help advertisers connect more signals across environments, but they do not eliminate every attribution limitation or guarantee that a particular conversation caused a conversion.
MarTech discusses the argument that ChatGPT advertising should currently be approached as an intent-driven opportunity rather than an automatic replacement for established media channels.
That is a useful planning perspective.
Rather than shifting a large portion of a mature campaign budget into an emerging environment without sufficient evidence, marketers can:
The opportunity is real, but the research process needs to account for what advertisers can and cannot observe.
Cheap clicks are not enough. Marketers need a better understanding of where their advertisements appear, which competitors are present, and whether the exposure contributes to meaningful outcomes.
Gemini's competitive position is about more than the quality of its conversational experience. It also benefits from its relationship with Google's wider ecosystem.
That distribution creates opportunities for AI-assisted discovery to appear across multiple environments where people already search, browse, shop, and communicate.
For advertisers, the implication is important: AI visibility cannot be understood by looking at one standalone assistant alone.
The competition between AI assistants is often discussed in terms of product capabilities, user experience, and adoption.
But distribution is another important factor.
Google already operates a broad collection of consumer products and advertising environments. AI experiences can be integrated into search, mobile devices, browsers, shopping, maps, video, and other parts of that ecosystem.
A MarTech analysis discusses the contrast between ChatGPT's position as an AI product pioneer and Gemini's connection to Google's established distribution network.
This does not mean Gemini automatically wins every interaction or that every Google surface delivers the same advertising experience. It does mean that marketers should consider the wider ecosystem when evaluating how AI is changing discovery.
Traditional search gave users a list of results to explore.
AI Overviews and AI Mode can provide synthesized answers and recommendations within the search experience itself.
This may reduce the number of traditional results a user needs to visit before making a decision.
Search Engine Journal examines the impact of AI-powered search experiences on traditional advertising visibility.
For advertisers, this creates a different competitive question:
Are customers encountering our brand when AI-assisted search helps them compare options?
That question is broader than asking whether a particular keyword has a strong average position.
Consider someone asking an AI assistant:
What are the best running shoes for wide feet?
The assistant may provide a concise set of recommendations rather than requiring the shopper to browse many separate product pages.
The brands included in that answer may receive more consideration than brands that are absent.
This is the shortlist economy discussed by industry observers and covered by Search Engine Journal.
A Semrush survey cited in the same coverage also suggests that consumers are discovering brands through AI-assisted experiences.
These trends make AI visibility an increasingly relevant research topic.
However, marketers should distinguish between appearing in an AI-generated answer, appearing in a paid placement, and generating a measurable conversion. They are related but different outcomes.
Google's AI performance insights in Merchant Center are an example of how platform reporting may evolve.
According to Search Engine Journal, these insights can help merchants understand their visibility on AI surfaces relative to similar brands.
This kind of information could help advertisers identify changes in their competitive position.
It also creates a useful model for the broader industry: marketers need ways to compare brand presence across AI-generated recommendations, not just traditional search rankings.
Gemini and Google's AI experiences should not automatically be treated as separate from the wider search and shopping strategy.
Instead, marketers should examine how AI-assisted discovery affects:
Google's ecosystem creates a distribution advantage, but it does not remove the need for competitive research.
Marketers still need to understand which brands appear, what messages they communicate, and which product or content signals may be associated with visibility.
Search is no longer limited to a traditional search engine.
Consumers increasingly discover products through social video, retail platforms, conversational assistants, and recommendation feeds.
These experiences may not look like traditional search, but they can serve similar purposes: helping people identify products, compare options, and decide what to buy.
TikTok is one of the clearest examples of this change.
TikTok combines entertainment, discovery, product research, and shopping within a feed-based environment.
A user might discover a product through a video, look through comments, watch another creator review it, and then visit a retailer or brand website.
The customer journey can move from discovery to validation without a conventional search query.
For competitive intelligence, this creates a challenge.
A marketer studying only search keywords may miss the creative concepts, creator partnerships, and product messages influencing customer decisions on TikTok.
Amazon and Walmart are also developing conversational shopping experiences.
As discussed in AdExchanger's review of AI media in Q2 2026, retailers are bringing AI into shopping environments, making it easier for customers to describe their needs in natural language.
Consider a shopper looking for:
The best non-comedogenic sunscreen under $30.
That request could be made through a search engine, a retailer's AI assistant, a smart speaker, or a general-purpose AI assistant.
The customer intent remains commercial even when the discovery environment changes.
This means marketers need to look beyond the traditional search box when researching where buying decisions begin.
1. Social discovery as a form of search
TikTok, Instagram Reels, and YouTube Shorts can help consumers discover products and learn about different solutions.
A person searching TikTok for styling ideas or product recommendations is using the platform to answer a question, even if the interaction is categorized as social activity in a marketing report.
For marketers, useful observations include:
2. Retail media as a shopping-intent environment
Retail platforms have access to shopping-related activity and product information within their own ecosystems.
Conversational interfaces can make product discovery more natural while creating new opportunities for retail advertising.
However, much of the data and recommendation logic remains inside each platform.
Advertisers need to distinguish what they can observe publicly from information that is available only through a platform's own reporting or commercial relationships.
3. General-purpose AI as a discovery layer
ChatGPT, Gemini, and Copilot can help users compare products, explore services, and research possible solutions.
These assistants may influence which brands a person considers before visiting a marketplace or search engine.
But there is not yet one universal, reliable competitive report that shows how every brand performs across all AI assistants and commercial prompts.
The traditional research process often focused on:
Those sources still matter. But they do not fully represent how customers discover and evaluate products across AI and social environments.
Marketers now need to understand three overlapping layers.
Feed-driven discovery
Which competitors are consistently appearing in relevant TikTok and short-form video experiences?
Retail AI recommendations
Which brands appear when customers ask shopping assistants for generic product recommendations?
AI-generated shortlists
Which companies appear when people ask ChatGPT, Gemini, or Copilot to compare commercial options?
These questions require different research methods. Some information can be gathered through public observation and structured testing; other information requires access to platform reporting.
The irony is that advertising platforms are developing more advanced internal measurement systems while external competitive visibility remains fragmented.
For example, Marketing Dive discusses LiveRamp's work involving ChatGPT conversion measurement, while Search Engine Journal covers Google's AI-related reporting developments.
These systems may improve advertisers' understanding of their own campaign activity. They do not automatically provide a complete view of competitor performance across platforms.
The problem is not that customer intent has disappeared. It is that the signals are spread across more environments, each with its own visibility limitations.
Every time a new advertising format appears, marketers face the temptation to adopt it immediately.
But adopting a new format does not automatically create a competitive advantage.
When customer discovery is spreading across ChatGPT, Gemini, TikTok, and retail AI, the more valuable opportunity is to build a research system that helps marketers understand how these environments connect.
That system can be thought of as a cross-channel spy stack.
Instead of treating every platform as a separate research project, a spy stack organizes the available intelligence into a consistent process.
A review from AdExchanger discusses the development of AI advertising infrastructure and the growing ecosystem around new AI media environments.
At the same time, data infrastructure providers are working to connect advertising activity with conversion measurement. Marketing Dive discusses LiveRamp's partnership involving ChatGPT conversion measurement.
These developments highlight a larger opportunity: marketers need a consistent way to interpret advertising observations across multiple channels.
A practical spy stack can be organized into four layers.
The first layer is understanding the questions and situations that create commercial intent.
In traditional search, marketers often begin with keywords. In conversational environments, the starting point may be a natural-language question.
For example:
Dash Two's discussion of ChatGPT advertising explains the role of contextual signals in the platform's advertising approach.
Marketers can use their own available campaign data, customer questions, and structured research to identify relevant conversational themes.
Those themes can then inform:
The objective is to understand the customer's problem across channels rather than simply reproducing the same advertisement everywhere.
The second layer is understanding which brands appear in AI-assisted recommendations.
Google's AI performance insights in Merchant Center are an example of reporting designed to help merchants understand visibility on AI surfaces. Search Engine Journal discusses this development.
Where reliable reporting exists, marketers should monitor:
Where platform reporting is unavailable, controlled manual testing can provide directional observations.
However, prompt-based testing is not equivalent to a complete share-of-voice report. Results may vary based on location, account context, timing, personalization, and the wording of the prompt.
The third layer focuses on creative patterns.
A brand may use different formats across platforms while communicating a similar customer benefit.
For example:
Rather than evaluating each asset in isolation, organize them into creative concept families.
Useful categories include:
This allows marketers to compare how similar concepts are adapted across different advertising environments.
It also makes creative testing more systematic. Instead of assuming that one platform's winning format will work everywhere, teams can test whether the underlying message transfers between channels.
The final layer is connecting research observations to actual business outcomes.
An advertising interaction may contribute to a later branded search, a website visit, a social interaction, or a retail purchase.
That does not mean every later conversion was caused by the initial exposure.
A stronger measurement process should combine:
Microsoft's framing of the web's evolution—from helping users find information, to choose, to act—provides useful context for the growing role of automated systems. Search Engine Journal discusses this transition.
For marketers, the objective is not to assume that AI agents have replaced human buyers. It is to understand how both human and automated interactions may contribute to a purchase journey.
The advantage of a cross-channel spy stack is consistency. It gives teams a repeatable way to organize what they observe, compare competing messages, identify research gaps, and connect hypotheses to measurable campaign results.
A modern competitive intelligence system needs to do more than collect advertisements from familiar platforms.
It needs a repeatable way to investigate what competitors are doing in environments where placements, recommendations, and customer journeys can change from one interaction to another.
This is particularly challenging across ChatGPT, Gemini, and TikTok.
Each environment has different discovery mechanics, different visibility limitations, and different ways of presenting commercial information.
The research process must adapt accordingly.
ChatGPT advertising introduces a different research question.
Instead of focusing only on which creative appears for a particular keyword, marketers need to understand the kinds of customer situations and questions associated with relevant advertising opportunities.
Dash Two discusses contextual targeting and the use of context hints in ChatGPT advertising.
A structured research process can include:
1. Create a standardized prompt library
Develop a consistent set of prompts representing the customer journeys relevant to your category.
For example, an online store platform might investigate prompts such as:
Use these prompts consistently to compare observations over time.
2. Record visible sponsored placements and recommendations
When advertising or sponsored content is actually visible, document:
Keep paid placements separate from organic recommendations. They represent different types of visibility.
3. Compare observations over time
Repeat the same research under consistent conditions.
Record whether the brands, messages, and visible placements change.
Because conversational outputs can vary, a single test should not be treated as definitive evidence of a competitor's overall campaign strategy.
Gemini research requires a slightly different approach because of its relationship with Google's broader ecosystem.
As MarTech discusses, Google's existing distribution network is an important part of the competitive landscape.
The objective is to understand which brands appear in relevant discovery and comparison experiences.
A practical process should answer three questions.
1. Are we appearing in relevant recommendations?
Use available reporting tools and consistent test prompts to understand whether your brand appears for relevant product or service needs.
Where available, examine Google's AI performance insights in Merchant Center. Search Engine Journal discusses this reporting approach.
2. Which competitors appear alongside us?
Run consistent comparison prompts using the same location and other relevant conditions.
Record recurring brands, product recommendations, organic citations, and any clearly identified sponsored placements.
3. What product and content signals might be relevant?
Compare visible information such as:
These factors can help identify areas worth investigating. They should not be treated as proof of the exact ranking logic used by an AI assistant.
TikTok research is less like traditional rank tracking and more like observing how creative content enters relevant feeds.
The platform's recommendation environment is influenced by user activity and other signals that are not fully visible to outside researchers.
A responsible research process can include:
Where controlled test accounts are used, they should be handled in accordance with platform rules and applicable privacy requirements. Do not use artificial engagement or misleading account behavior to manipulate recommendations.
The goal is to understand visible creative patterns, not to claim access to TikTok's internal recommendation system.
The final step is connecting competitive observations with your own campaign and business data.
Marketing Dive discusses LiveRamp's work involving ChatGPT conversion measurement and the wider challenge of connecting advertising signals beyond browser-based tracking.
A useful intelligence record should answer:
This transforms competitive research from a collection of screenshots into a structured record that can support better decisions.
There may not be one universal dashboard that reveals every competitor's advertising activity across ChatGPT, Gemini, TikTok, and retail AI.
Some information is publicly observable. Other information is available only through platform reporting, approved research access, or a brand's own measurement systems.
That is why the research process matters as much as the tools.
A well-designed spy stack uses standardized tests, clear documentation, repeatable observations, and measurable hypotheses to understand emerging advertising environments.
For Anstrex users, the broader lesson is to keep competitive intelligence connected to the advertising landscape as it changes. Traditional ad research remains valuable, but marketers should increasingly consider how creative messages, offers, and landing pages work across the wider discovery journey.
The teams that build this process early will be better prepared to evaluate new advertising formats without chasing every platform announcement or assuming every new channel is automatically profitable.
AI advertising is expanding the places where brands can reach customers. ChatGPT is introducing advertising into conversational experiences, Gemini benefits from Google's broad distribution network, and TikTok continues to blur the line between content discovery and product research. Retail platforms are also developing more conversational ways to help shoppers find products.
But more advertising environments do not automatically mean better competitive visibility.
The challenge for marketers is understanding what happens across these environments without relying on incomplete data or assuming that every visible pattern proves a competitor's strategy is working.
A modern competitive intelligence process should connect four things:
Anstrex and other competitive intelligence tools can contribute to this process by helping marketers study visible advertising activity, creative approaches, and landing-page strategies.
The next step is to connect those observations with structured AI discovery research, platform reporting, and first-party performance data.
The goal is not to predict every platform's future or chase every new advertising format.
It is to build a research process that makes new environments easier to understand, compare, and test.
The future of competitive intelligence will belong to marketers who can connect the signals—not just collect more of them.
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