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As generative AI reshapes how people research, learn, and make decisions, a new discipline is emerging: Generative Engine Optimization (GEO). Just as SEO helped brands get discovered on Google, GEO ensures they appear accurately and positively within AI-powered platforms like ChatGPT, Gemini, and Copilot. With millions of consumers now turning to generative engines instead of search engines, GEO is fast becoming essential for marketing and PR professionals looking to influence how their brand shows up in this new discovery layer, where conversations, not clicks, determine visibility.

What is Generative Engine Optimization (GEO)?

Why Should Marketing & Insights Teams Care About GEO?

How Does GEO Work? Key Mechanisms & Signals

What's the Difference Between GEO and SEO?

Increase Your GEO Readiness with Meltwater

Generative Engine Optimization (GEO) is the practice of ensuring that AI platforms such as ChatGPT, Google Gemini, Llama, etc., provide accurate, positive, and up-to-date information about your brand when users ask questions. These platforms are referred to as Generative Engines, Answer Engines, or Large Language Models (LLMs). They are increasingly used by consumers to research purchases and make decisions.

GEO is a way of making sure that AI platforms give people the right information about a company’s brand and products. 

For example, if a person asks ChatGPT: “What is a good family car for camping trips?” then the auto manufacturers would want ChatGPT to suggest their SUV models, and list the benefits. To make sure that happens, the auto brands need to use GEO.

GEO ensures your brand shows up correctly within these AI-generated responses.

The practice of GEO involves several steps, but it is mostly focused on creating, structuring, and publishing content in the best way to influence the responses given by AI Generative Engines. 

In this article we’ll go into the detail of how GEO works, why it’s important, and how you can use it to make sure your brand is featured positively in AI platforms.

Sometimes, generative engine optimization (GEO) is referred to as answer engine optimization (AEO). Learn more about the differences and similarities of GEO and AEO, and take a look at the best AEO tools.

For a long time marketers have been able to rely on the fact that consumers almost exclusively use Google to research buying decisions. So they have been able to use Search Engine Optimization (SEO) to ensure their brand’s website appears in the organic search results, or use paid search advertising to buy ads on relevant search results. 

This is a highly effective way of targeting the right audience at the right time. But now things are changing.

LLMs aren't just another channel. They sit on top of this ecosystem. They synthesize everything into a single conversation.

Allie Konchar, Partner & Chief Client Officer, Omniscient

Changing user behavior: AI + conversational search

Increasingly, consumers are using tools like ChatGPT to research their buying decisions. Data from Meltwater’s 2026 Global Digital Report shows that ChatGPT.com is now the fifth most visited website on the web.

While search engines remain popular, many search queries now produce an AI generated summary that often provide enough information to divert users away from clicking through to websites, because their question has already been answered. McKinsey reports that around half of all Google search queries already produce AI generated summaries, and that number is increasing all the time. 

With conventional web search, consumers are performing research manually, trying to find trustworthy sources and sifting through information to reach the answers they seek. But with Generative Engines, the AI is performing the research automatically, using its existing base knowledge alongside supplemental information pulled from more recent online sources. A search engine shows users where to find answers, while a Generative Engine just gives them the answer. 

For marketers, the key takeaway is that few top-of-funnel consumers are visiting your website, because during the awareness phase their information needs are being met by Generative Engines. This makes it more difficult to move them down into the consideration phase. 

TIP: For more data around growing AI usage and online consumer behavior, download our 2026 Global Digital Report, featuring nearly 700 pages statistics to help inform your GEO strategies next year.

You already know that everybody is talking about AI, and all of your competitors are having the same discussions as you about what it means for their marketing strategy. Those who move decisively to implement GEO, ensuring their brands are represented clearly and positively in Generative Engines, will capture the largest share of those consumers who start their buying journey with an AI query. 

Those who ignore GEO will lose ground, as growing numbers of consumers turn from search engines to Generative Engines, the size of the audiences that can be reached through conventional search marketing will decrease. 

How do generative engines (LLMs) surface content?

It’s important to understand that while Generative Engines are based on similar technology, they all work a little differently. Broadly speaking they all use two different sources of knowledge to provide an answer to a query: 

<table class="table-primary alternate-color">
  <caption>How Generative Engines (LLMs) Surface Content</caption>
  <thead>
    <tr>
      <th>Generative Engine</th>
      <th>Primary Retrieval Method</th>
      <th>Sources Most Often Cited</th>
      <th>Sources Less Likely to Be Cited</th>
      <th>Notes on Behavior</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>ChatGPT</td>
      <td>Bing search + internal training data</td>
      <td>Reuters, AP News, Wikipedia, Encyclopedic sources</td>
      <td>Social media platforms (X/Twitter, Instagram)</td>
      <td>Prefers authoritative, neutral, structured sources with high trust signals.</td>
    </tr>
    <tr>
      <td>Google Gemini</td>
      <td>Google Search + YouTube + Knowledge Graph</td>
      <td>Reddit, YouTube, Google-indexed blogs</td>
      <td>X/Twitter</td>
      <td>Strongly favors user-generated content, community threads, and Google-owned properties.</td>
    </tr>
    <tr>
      <td>Perplexity</td>
      <td>Real-time web crawling + citation-first retrieval</td>
      <td>News publishers, reputable blogs, research papers</td>
      <td>Low-trust UGC</td>
      <td>Most transparent engine — always cites sources and updates in real time.</td>
    </tr>
    <tr>
      <td>Claude</td>
      <td>Internal training data + selected retrieval</td>
      <td>Academic sources, reputable news, long-form articles</td>
      <td>Short-form social media</td>
      <td>Prefers long-form text and well-structured explanatory content.</td>
    </tr>
  </tbody>
</table>

Training Data: All LLMs begin by being trained on a huge volume of existing content and data — we’re talking about dozens of terabytes (a terabyte is 1,000Gb). This training data comes from a wide variety of sources (including your website), and gives the LLM its base knowledge about the world. The LLM does not have any knowledge of anything that happened after its initial training. 

Live Web Search: To overcome the knowledge cut-off problem, the training data is supplemented with fresh information gathered from the internet. If the Generative Engine thinks that a user’s query would benefit from more up to date information than it already has, it fetches new content from trusted online sources and works that into its response. Google uses its own search engine to find fresh information, while ChatGPT uses various sources, including Microsoft Bing.

It’s also important to note that AI does not quote any of this content verbatim in its responses. Much like a human, it uses the information it has learned to inform its own answer, and that can include information from various sources, including the press, competitors, consumer reviews. So don’t expect it to always be on-message!

Another key point is that unlike a search engine, an LLM does not have an index of content which it searches to find answers. It would be impossible to search ChatGPT for all mentions of your brand — that’s simply not how it works. 

An LLM is more similar to a human brain, in that it has a lot of knowledge stored in patterns and relationships between words and concepts. It is extremely difficult, verging on impossible, to examine and understand the detail of an LLM’s learned knowledge because of the sheer scale and complexity; this is a challenge referred to as Black Box AI. 

But we do understand how Generative Engines work at a high level, and we can analyze the kind of responses they produce for relevant queries, which gives us insight into how we should optimize our content for increased visibility.

What content features improve GEO-visibility?

Bear in mind that when you’re optimizing content for Generative Engines, the goal is to write copy that is easier for AI to digest and incorporate into its responses.

Clarity: Avoid overly elaborate language. Do not use corporate-jargon. Keep it simple, professional, and to the point. Leave no room for ambiguity. 

Authority: Wherever possible back up your statements with data from reputable sources, quotes from experts or industry leaders, and reference-able proof points. 

Structure: Organize your pages well with clear titles, headings, and subheadings. Use heading tags (H1, H2, etc) correctly to specify the hierarchy of your content. 

Recency: AI platforms look for more recent information from trusted sources to supplement their base knowledge, so in both owned and earned media, aim to publish fresh content recently.

In many respects GEO shares principles with good SEO practice. If you write consistently high quality content which addresses the needs of your target audience, and structure it correctly on well designed, standards compliant web pages, you will succeed.

First you need to understand your starting point. Meltwater’s GenAI Lens is a tool that gives brands insight into how they are discussed by all major Generative Engines, including ChatGPT, Claude, Gemini, Perplexity, Grok, and Deepseek. 

With GenAI Lens you’ll be able to understand how your brand is already featured in LLMs, what source websites are used in those responses, and whether outdated or inaccurate information is being used. 

We know that LLMs pull content from the web, which they use to inform their responses. So whatever is said about your brand online will significantly affect how Generative Engines talk about your company. 

Start with your own website, this is the first place LLMs will look for information about your organization. But also focus on trusted, high authority third party sites like the mainstream news and specialist media within your industry, because these are the kinds of sources that LLMs will use. GenAI Lens can show you which websites the leading LLMS use to source information about your brand. 

Once you have a comprehensive audit of how your brand is currently portrayed online, you can perform a gap analysis against your desired messaging, and build a plan for how to reach that objective.

Tip: AI brand monitoring, AI media monitoring, and LLM monitoring will help you understand your online footprint. Learn how to track your LLM visibility and how to track LLM brand mentions.

An important difference between GEO and SEO is understanding how people search differently when using Generative Engines. Users interact conversationally with AI, and are likely to ask more complex questions in natural language, compared to simple keywords or short queries in a search engine. So it’s useful to get an idea of what kind of questions people will ask about your brand or industry, to help plan your GEO content.

Tip: Learn more about AI visibility for retail brands.

Unlike search engines, at present the leading Generative Engines do not share data on popular user queries, so it’s not easy to find out exactly what people are asking about your business. But you can look at popular keyword searches associated with your brand, and brainstorm the most likely questions people will ask Generative Engines. 

You can also use a social listening platform, like Meltwater Explore+ to analyze social media conversations around your brand and industry, to gain insight into common questions. Once you have mapped out all of the most likely questions people might ask, you can start creating content that answers those questions.

Tip: Learn how to connect social listening with generative engine optimization. 

Conventional SEO was built around search queries but GEO requires thinking in concepts, context, and authority — you need to focus on the intent of the query, rather than simply trying to match keywords. 

LLMs prioritize sources that provide comprehensive, well-structured, and contextually rich content. Focus your content strategy on:

Educational depth: Long-form resources that answer questions end-to-end. Publish explainers, FAQs, and data-backed insights that answer “how” and “why.”

Credibility signals: Citations, quotes, and data from reputable sources (analyst studies, thought leadership, or trusted media).

Rich media: Generative Engines pull information from videos and images as well as text content, so make use of varied content formats. 

Consistency across channels: LLMs learn from media, social, and owned content together; ensure your narratives are aligned across them.

The key thing is to ensure that your content is genuinely helpful in answering those questions you mapped out. If your content is too focused on sales messages, it’s less likely to be used by the LLMs.

TIP: Discover innovative AI-generated content examples to enhance your digital strategy.

You can optimize the content published on your website and owned channels, but LLMs also source information from trusted third party websites. That means PR has a key role to play in ensuring your brand is featured in high authority earned media, including news features, expert comment from your spokespeople, product reviews, and any other kind of brand story traditionally generated through a media relations campaign.

Tip: Learn more about the importance of earned media for GEO and about PR Pitch GEO optimization.

The tricky part is that unlike advertising, which can tell exciting and attention grabbing stories, PR professionals are limited to working only with the facts. But while you cannot always control the outcome of media outreach activity, ensuring that PR and GEO are aligned will help increase the volume of third party sources that feature the right messaging.

Tip: Learn how GEO is reshaping PR strategies.

Meltwater’s GenAI Lens gives you the ability to track your GEO performance over time, so you can understand whether your strategy is working well and spot areas where you need to improve. The tool provides visualizations that show brand sentiment, emotion, key phrases, people, products and things mentioned, alongside citations.

Armed with this information you can identify gaps in your GEO strategy where you might need to create more or better content. You can also discover which third party sources have the greatest impact on your GEO performance, and use that information to optimize PR outreach to focus on the most important media. 

Like SEO, this is a process of constant iteration and improvement. GEO should not be seen as a one-off project that will generate positive results in perpetuity, but rather an ongoing activity that needs to be integrated with the broader marketing mix.

Tip: Learn How to Measure PR Performance Across LLMs and Generative Search, and understand how to report on LLM visibility.

Which Media Sources to Prioritize for GEO

When an LLM processes a user query, it can often detect that the response might require more recent information than it has access to in its original training data. In that situation it will look online for more up to date content that will help it to form a more accurate answer, but where exactly do Generative Engines look for this information?

At Meltwater we used GenAI Lens to research which content sources the leading AI platforms were most likely to reference in their responses, and there were distinct differences between them all:

Open AI ChatGPT: Heavily favors text-based news and newswires (Reuters, AP, PR Newswire) and reference sites (Wikipedia). Much less likely to cite social media and UGC. 

Google Gemini: Leans towards high quality UGC from sites like Reddit and YouTube, as well as other online sources within the Google ecosystem. Does not index X/Twitter content. 

Google Search AI Overviews: Highly focused on UGC from Reddit and YouTube, as well as Facebook, Yahoo and LinkedIn. 

xAI Grok: Strongly skewed towards conversational UGC from X and Reddit. Less likely to cite traditional reference sources like Wikipedia, YouTube or academic sites. 

Unlike SEO, where most practitioners were happy to optimize only for Google, in GEO we have to consider the nuances of several different platforms; at the very least ChatGPT and Google AI Overviews. In short, it’s good practice to ensure you have up to date content propagated across a wide range of platforms where LLMs might look for supplemental information.  

In terms of which online sources are most likely to be referenced overall, we looked at several categories and charted the most frequently cited websites:

<ul class="medium">
	<li><b>Social Media & UGC</b></li>
    <ul>
		<li>reddit.com: 43.1%</li>
		<li>youtube.com: 17.9%</li>
        <li>facebook.com: 11.1%</li>
		<li>linkedin.com: 9.2%</li>
        <li>quora.com: 5.6%</li>
	</ul>
	<li><b>Consumer Reviews</b></li>
	<ul>
		<li>glassdoor.com: 21.6%</li>
		<li>indeed.com: 20.6%</li>
        <li>trustpilot.com: 15.7%</li>
		<li>bbb.org: 9.9%</li>
        <li>yelp.com: 8.0%</li>
	</ul>
	<li><b>Travel</b></li>
    <ul>
		<li>tripadvisor.com: 83.2%</li>
		<li>expedia.com: 10.0%</li>
        <li>booking.com: 6.8%</li>
	</ul>
	<li><b>News & Media</b></li>	
    <ul>
		<li>yahoo.com: 26.5%</li>
		<li>prnewswire.com: 10.8%</li>
        <li>reuters.com: 9.0%</li>
		<li>forbes.com: 6.7%</li>
        <li>usnews.com: 6.7%</li>
	</ul>
    <li><b>Academic & Science</b></li>	
    <ul>
		<li>nih.gov: 53.6%</li>
		<li>researchgate.net: 14.5%</li>
        <li>sciencedirect.com: 10.0%</li>
		<li>jhu.edu: 7.5%</li>
        <li>cdc.gov: 5.8%</li>
	</ul>
    <li><b>Market Research & Consulting</b></li>	
    <ul>
		<li>statista.com: 18.8%</li>
		<li>mordorintelligence.com: 16.3%</li>
        <li>gartner.com: 14.9%</li>
		<li>mckinsey.com: 13.5%</li>
        <li>cbinsights.com: 13.0%</li>
	</ul>
    <li><b>Business & Financial</b></li>	
    <ul>
		<li>investing.com: 14.1%</li>
		<li>morningstar.com: 13.2%</li>
        <li>nasdaq.com: 11.4%</li>
		<li>nerdwallet.com: 10.4%</li>
        <li>seekingalpha.com: 8.8%</li>
	</ul>
    <li><b>Government & Public Sector</b></li>	
    <ul>
		<li>weforum.org: 20.6%</li>
		<li>europa.eu: 17.8%</li>
        <li>canada.ca: 12.7%</li>
		<li>imf.org: 11.5%</li>
        <li>sec.gov: 10.1%</li>
	</ul>
</ul>

Many teams focus on keywords, backlinks, and meta tags, but LLMs prioritize context, authority, and relevance. GEO requires content that answers full questions with clear, verifiable information, not keyword stuffing or link chasing.

2. Ignoring source credibility and transparency

AI models reward trustworthy, well-cited content. Publishing unverified claims, outdated statistics, or missing citations can lower visibility or lead to inaccurate AI summaries of your brand. Always include reputable data sources, authorship, and dates.

3. Failing to monitor how your brand appears in LLMs

Without ongoing media monitoring, brands miss when GenAI models misrepresent their products or replicate misinformation. LLM tracking tools like Meltwater’s GenAI Lens can surface these blind spots and help correct or update narratives quickly.

What is Generative Engine Optimization (GEO)? & How to Do It

Ask an AI tool what your brand is known for, and the answer may look credible, while still containing distortions.

AI systems could position a discontinued product as current. They might frame a minor customer complaint as a defining weakness or name a competitor in connection with your business for no legitimate reason.

None of that needs to go viral to affect perception.

That’s what makes AI-generated misinformation different from older reputation threats. It enters through synthesized answers people use to compare vendors or make buying decisions. It’s bad enough when a model gets a fact wrong. But repeating weak or inaccurate signals to the point it becomes the narrative carries a heavy, costly weight. 

It’s a reputation issue that communications teams can’t treat as incidental.

Why Misinformation Is Harder to Spot in AI Answers

The Types of Harmful Narratives Brands Should Watch For

A Framework to Detect Harmful Narratives in LLMs

How to Respond to AI-Driven Misinformation

The Role of UGC in Correcting Distorted Narratives

How Meltwater Helps Detect Narrative Risk

The Future of Reputation Management in AI

AI-generated misinformation includes misleading, outdated, or distorted information produced or reinforced by AI systems.

Sometimes it looks obvious, like fabricated statistics without clear sources or invented partnerships. AI might also hallucinate product claims that don’t exist.

AI systems might say that a software company is “frequently criticized for poor onboarding” based on a handful of forum complaints from years ago, even when current customer sentiment says otherwise. The output contains fragments of truth, but the synthesis changes the meaning.

Most misinformation generated through LLMs is not intentional deception. Instead, it comes from reconstructing uneven inputs, like old media coverage, edge cases, singular reviews that don’t account for the big picture, or third-party summaries. 

When AI picks up on the wrong patterns and returns them as authoritative answers, it can distort how a market understands a brand.

A weak signal repeated often enough can begin behaving like a consensus.

Consider a product launch that triggers complaints about onboarding friction. The issue gets resolved quickly. But dozens of posts remain indexed and cited.

Months later, AI summaries start citing “difficult onboarding” as a common drawback.

Inputs may include user forums, reviews, syndicated media coverage, comparison sites, and aggregated content. They may not be decisive by themselves alone. But together, they can harden into something larger.

Why Misinformation is Harder To Spot in AI Answers

Traditional misinformation looks like a false claim you can identify and challenge. AI-generated misinformation is harder to detect because it may appear inside polished, balanced answers that seem credible upon first read. 

Fact-checking alone is no longer enough; brands must now also detect distortion hiding inside partial truths in synthesized answers.

For example, an AI answer comparing vendors may accurately summarize your pricing model and product strengths, then insert an outdated claim about implementation risk drawn from old complaints.

Because the answer looks balanced, the error hides inside otherwise credible info.

Citation transparency makes this harder. In many AI systems, users don’t see which sources shaped the synthesis. Even when links appear, the attribution may be incomplete or mismatched. That makes distortion harder to isolate before it spreads.

Every distorted narrative carries a different risk. These are the ones brands should watch for first:

Legacy narratives outlive strategy changes.

A company that moved to the enterprise years ago may still appear in AI summaries as a niche or low-cost provider. Such a claim affects category perception and could dissuade your target audience.

Brands can become linked with the wrong competitors, use cases, sectors, or controversies.

For example, we have seen fintech firms become associated with crypto volatility, simply because source content overlapped. Association drift can alter buyer assumptions before a sales conversation starts.

Small issues can become defining attributes: a limited recall, a temporary outage, or a support complaint trend that lasted for weeks.

Repeated enough, those can show up as persistent brand weaknesses, even when the initial issues resolve.

Partial truths can cause more damage than fabricated ones.

An AI answer may mention litigation without noting dismissal. It may reference layoffs without talking about an acquisition. This context loss changes meaning.

Hallucinations still matter, and they’re still common despite improvements in AI models. 

False executive names, nonexistent certifications, product capabilities that don’t exist, or company awards or achievements become AI’s truth if repeated often enough. These tend to demand a faster response because the error is clear and material.

Narrative risk is difficult to manage without a repeatable detection process. These steps help separate isolated anomalies from patterns worth responding to. 

Step 2: Identify recurring narrative patterns

AI output reviews should function like ongoing monitoring. Instead of catching isolated errors, your goal is to understand whether distortions are repeating often enough to form a narrative.

That starts with running consistent prompts across multiple systems: 

How does [brand] compare with competitors?

Then run those same prompts again over time. The value is in repetition. You are looking for language that persists or criticisms that begin appearing as standard descriptors rather than singular observations. Some misinformation detection solutions offer automated monitoring that can save you a lot of time.

Tip: Learn more about LLM monitoring, LLM sentiment analysis, and brand KPIs for LLM monitoring

One odd answer may not mean much; it’s the repeated phrasing that deserves attention.

If multiple models begin citing the same criticism or framing, even with different wording, there may be a narrative taking shape.

When this happens, your monitoring shifts into risk detection.

Once a pattern appears, the next goal is to find what’s reinforcing it.

Sometimes the source is easy to spot, such as a spike in negative mentions after a product launch. Other times, it emerges through changes in media tone or recurring complaints in reviews and support communities.

A distortion driven by earned media requires a different response than one fueled by customer conversation. 

Meltwater’s media intelligence and social listening help teams map where those signals originate, which makes response decisions much more precise.

Not every distortion needs an active response, which is why risk assessment matters.

Focus on whether the misinformation is likely to affect perception or decision-making. An outdated product description may call for monitoring, while a false security concern in AI-generated comparisons could require immediate correction.

Evaluate impact and spread separately. A narrative can be highly damaging without broad reach yet, just as a widely repeated distortion may have limited consequence. Those are different response problems.

After assessing risk, the response should match the nature of the narrative.

When a harmful narrative begins influencing coverage, customer perception, or trust, the response may need coordination across PR, content, support, and sometimes product teams.

You cannot directly edit an LLM’s outputs, but you can influence the signals feeding them.

Start with improving source material. Specific, updated content tends to travel farther into synthesized outputs than vague messaging.

Corrective UGC also matters. Reviews and community responses often move faster than owned channels.

And sometimes, the right response is addressing the underlying issue. If implementation complaints keep appearing because the experience is broken, the problem is not the narrative alone. The model may be exposing something real.

The Role of UGC In Correcting Distorted Narratives

User-generated content can fuel harmful narratives, but it can also help correct them.

A burst of complaints during a service disruption may distort perception well beyond the incident itself. But sustained corrective feedback after resolving the issue can begin changing the pattern those narratives draw from.

What changes narratives over time is rarely isolated positive commentary. It is repeated, credible signals moving in a different direction. That’s one reason UGC monitoring belongs inside reputation strategy, not solely within social programs.

Narrative risk is difficult to manage when teams only see fragments; Meltwater helps connect those fragments.

Media monitoring can show whether the tone around a brand is changing across outlets. Social listening can reveal whether complaints consolidate around a specific issue. Consumer intelligence can help teams distinguish isolated noise from an emerging pattern. 

Track your presence in LLMs with GenAI Lens

AI-Generated Misinformation: Detect Harmful Narratives in LLMs

Generative AI is everywhere now. You see it in content, campaigns, customer experiences, and even internal workflows. 

It promises speed, scale, and efficiency, but there’s a harder question sitting underneath all that progress; Do your audiences actually trust it?

That’s what we set out to understand in our latest report with YouGov, based on responses from nearly 10,000 people across seven global markets. The results point to something many brands are starting to feel in practice - AI is a double-edged sword, offering cost savings and productivity gains, but potentially damaging customer trust.

Consumers Aren’t as Excited as You Might Think

The Real Concern Isn’t AI, It’s What AI Enables

Context Shapes Acceptance More than Capability

AI Can Cost You Trust if You’re Not Careful

Consumers Aren’t as Excited by Gen AI as You Might Think

There’s a gap between how the industry talks about AI and how people actually feel about it.

You might assume that excitement is high because, after all, AI dominates headlines and product roadmaps for seemingly most companies. But when you look at consumer sentiment, the picture is more mixed. In fact, more people (51%) feel uncertain or sceptical than excited (39%) about a future shaped by generative AI.

That hesitation means your audience isn’t automatically on board with AI-driven content, no matter how advanced or efficient it is. By introducing a new tool into your workflow, you’re introducing a new layer of doubt into how your content is perceived.

Younger audiences are more open, with 48% of those aged 25-34 reporting that they are excited by AI, compared to only 31% of those aged over 55. 

Some markets are more optimistic than others too; Germany (56%) and Singapore (55%) show the highest levels of excitement towards AI, while the UK (23%) and the U.S. (25%) are the least excited. But overall, enthusiasm is far from universal. You can’t assume broad AI acceptance, you have to earn it.

The Real Concern isn’t AI, it’s What AI Enables

When people talk about AI risk, they’re rarely talking about the technology itself, they’re talking about outcomes.

Consumers are deeply concerned about how AI-generated content could be used. Fake news, scams, misleading information, and the erosion of authenticity all rank high on their list of worries. These aren’t fringe concerns; they’re held by a large majority of respondents across every market we studied.

That tells you something important: your audience is not evaluating AI in isolation, but judging it based on the potential for misuse.

So when your brand uses AI, people don’t always see the efficiency and cost savings that drove you to adopt the technology; they see risk, and unless you address that risk head-on, it can quietly undermine your credibility.

Many consumers believe they can spot AI-generated content, but at the same time, an overwhelming majority worry that people in general won’t be able to tell what’s real and what isn’t.

That contradiction creates a strange dynamic in which, on a personal level, people feel confident, but on a societal level they feel uneasy.

For brands, that means trust is fragile. Even if your content is accurate and well-intentioned, it exists in an environment where people expect confusion, misinformation, and blurred lines between human and machine.

For brands, the battle is more than fighting for consumer attention now, they’re competing to earn trust. 

Not all AI use is judged equally; the context of how and where it’s used matters when audiences form their opinions. 

Consumers are more comfortable with AI in areas like entertainment and advertising, where 53% and 47% respectively said they found it acceptable, perhaps because these are spaces where creativity, experimentation, and even a bit of artificiality are expected, so the stakes feel lower.

But shift that same AI into news (21% acceptable), politics (18% acceptable), or other high-trust environments, and attitudes change quickly. Acceptance drops and resistance rises.

This is one of the clearest signals from the data; People don’t reject AI outright, instead they evaluate it based on context, intent, and perceived impact.

So the question for your brand isn’t just “Should we use AI?” It’s “Where does AI make sense, and where does it damage trust?”

One of the strongest findings in the report is the impact of AI on brand trust. A significant portion (32%) of consumers say they would trust a brand less if they knew its content was generated using AI, whereas only a small minority (15%) say it would increase their trust.

That imbalance should get your attention, since it means AI doesn’t come with a built-in trust advantage; rather, in many cases, it starts at a disadvantage.

It gets worse when certain lines are crossed, for example, if AI-generated content feels misleading, if it’s used without disclosure, or if it replaces human creativity entirely, trust drops sharply.

So if you’re using AI, you need to think beyond efficiency and consider the impact on audience perception. 

If there’s one takeaway from the Trust in the Age of Generative AI report that you act on, make it this; Consumers expect you to be transparent about your use of AI, as a basic requirement.

An overwhelming majority say it’s important that brands clearly disclose when content has been created using generative AI, and that expectation holds across every market in the study.

This is an interesting shift in how trust works, because in the past, brands could focus on outcomes, but now the process matters too. People want to know how content is made, not just what it says, and if they feel you’re hiding something, even unintentionally, it can erode trust quickly.

Transparency has become a signal of credibility.

AI is not going away. You’re going to use it, your competitors are going to use it, and your audience knows that. The difference will come down to how you use it.

Brands that win in this next phase won’t be the ones that adopt AI the fastest, but the ones that use it thoughtfully, communicate it clearly, and keep human judgment at the centre of what they do.

That means being deliberate about where AI adds value, setting clear guidelines for disclosure, and monitoring how your audience responds, not just assuming they’ll accept it.

Most of all, it means recognising that trust is now part of your AI strategy.

This is just a snapshot of what we found. The full report breaks down how attitudes differ by country, age group, and content type. It also explores where AI is gaining acceptance, where it’s facing resistance, and what that means for your communications strategy.

If you’re thinking about how to use AI without damaging trust, it’s worth a closer look.

Download the full report to see the complete data and insights.

Most consumers are sceptical about AI-generated content. Over half feel uncertain, and many say it can reduce trust in brands using it.

Trust issues stem from concerns about misinformation, fake news, scams, and lack of authenticity enabled by AI-generated content.

Does AI-generated content hurt brand trust?

Yes, it can. Around one-third of consumers say they trust brands less when they learn content is AI-generated, especially without disclosure.

Acceptance depends on context. Consumers are more comfortable with AI in marketing and entertainment, but less so in news, politics, or high-trust industries.

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Generative Engine Optimization (GEO)

AI-Generated Misinformation: Detect Harmful Narratives in LLMs

Trust in the Age of AI: What Consumers Really Think About AI Generated Content

How LLMs Recommend Sunscreen Brands: An Analysis of AI Search Visibility in SPF

Generative AI in Market Research: Enhancing Insights and Decision-Making

From Coverage to Insight: How AI Assistants Turn Media Mentions into Strategic Recommendations

How to Track Brand Mentions in Claude AI

Gemini AI Brand Mention Tracking: Strategies & Tools

AI Visibility for Retail Brands: What It Is & How to Win

How Social Media Impacts AI Search Visibility

Perplexity Brand Monitoring: How to Monitor Your Brand Mentions on Perplexity AI

Narrative control in 2026: PR strategies for AI & GEO

Tracking Google AI Overview Performance: Brand Monitoring Guide

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