United States

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?

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

Today, PR teams aren’t just tracking media coverage and share of voice. They’re also measuring how their brand shows up in AI-generated answers, search summaries, and large language models (LLMs). This new LLM visibility layer is quickly becoming a critical part of PR performance.

15 Must-Have PR KPIs and How to Track Them

See how one platform can power every decision across PR, communications, and marketing.

The Meltwater Blog

Blog Topics: Generative Engine Optimization, Media Intelligence, Media Relations, Social Listening, Consumer Intelligence, Influencer Marketing

Find out how you can leverage Facebook to boost your brand's online presence and gain a competitive edge. Explore the Meltwater category page today!

Facebook Blog | Meltwater

Find out how you can leverage Instagram to boost your brand's visual appeal and gain a competitive edge. Explore our category page today!

Instagram Blog | Meltwater

Find out how you can leverage LinkedIN to grow your professional network and gain a competitive edge. Explore the Meltwater category page today!

LinkedIn Blog | Meltwater

Find out how you can leverage TikTok to elevate your brand with creative content and gain a competitive edge. Explore the Meltwater category page today!

TikTok Blog | Meltwater

Find out how you can leverage X / Twitter to engage your audience effectively and gain a competitive edge. Explore the Meltwater category page today!

X / Twitter Blog | Meltwater

Find out how you can leverage YouTube to create engaging video content and gain a competitive edge. Explore the Meltwater category page today!

YouTube Blog | Meltwater

Generative Engine Optimization (GEO) is transforming how brands show up in AI-powered search. Explore insights, strategies, and best practices to help your content rank and resonate across conversational engines like ChatGPT, Gemini, and Copilot.

Explore insights and strategies for Generative Engine Optimization (GEO) to help your brand stay visible and discoverable in AI-powered search.

Generative Engine Optimization (GEO) Blog Collection | Meltwater

Every day billions of conversations take place on social media: tweets, posts, comments, blogs, reviews, podcasts, and much more.
Learn how to use social listening to make sense of all of that data.

On our social listening category page, you find all our blogs covering topics around social listening and social media monitoring. Read more.

Social Listening Blogs | Category

Business Reputation Management: What does it take to add reputation management to your marketing strategy? More importantly, how do you keep your reputation sparkly and shiny? Here’s your all-access guide to online reputation management for the long term.

On our reputation management category page, you find all our blogs covering topics around business reputation management. Read more.

Reputation Management Blogs | Category

From your brand's logo to your website, your social media presence to your customer support—these elements combined are what constitutes a brand in the minds of customers. But, there are thousands of other businesses that have a logo, website, social media presence, and website. So, how can you position your business on the market and make it stand out from the competition?
The key to success is branding & brand management!

On our brand management category page, you find all our blogs covering topics around brand management. Build your strategy and reach your goals!

Brand Management Blogs | Category

Competitive intelligence and the act of collecting competitive data can be seen as growth drivers for a company. Our guide will help you understand the concept of competitive intelligence (CI) by answering questions such as: What is the definition of competitive intelligence? How is competitive intelligence useful to organizations? What is the goal of competitive intelligence? And more.

On our competitive intelligence category page, you find all our blogs covering topics around competitive intelligence. Learn how to stay ahead.

Competitive Intelligence Blogs | Category

Let’s dive into the customer journey framework and how to optimize the process by creating a customer journey map and using metrics that matter when analyzing the customer journey.

On our customer journey category page, you find all our blogs covering topics around mapping and understanding the customer journey.

Customer Journey Blogs | Category

What Is Data Analytics? [Beginner’s Guide 2023]
How can I grow my business? 
How do customers interact with my newly launched product?
How do I increase the productivity of my employees?
How do I know if my marketing efforts are working?
We have just two words for you...Data analytics!

On our data analytics category page, you find all our blogs covering topics around AI and data analytics. Technical and still easy to understand.

AI & Data Analytics Blogs | Category

Public relations and media relations can add impact to any marketing strategy. It’s an opportunity for brands to grow their reach beyond traditional sales messages. PR helps to shape the image you want people to have of your brand.
So, what is PR and how do you use it? This guide shows you everything you need to know to build a strong public relations foundation.

On our public relations (PR) category page, you find all our blogs covering public relations and communications topics. Step up your PR game.

Public Relations (PR) Blogs | Category

We all know exactly what the roles of sales or accounting are in a company, right? We might even have a slight idea of what the IT guys are doing.
But there is another business discipline, that plays a crucial role in organizations, even more so during the pandemic – internal communications!
But what exactly do we mean by that, what purpose does it serve and what steps can you take to ensure you and your employees will communicate effectively?

On our internal communications category page, you find all our blogs about internal comms topics. Learn about the intranet, internal newsletters, and more.

Internal Communications Blogs | Category

As the internet has taken root in more areas of our lives — everything from entertainment to shopping to paying bills and more — traditional marketing is becoming less impactful. As any marketer knows, you need to focus on where your audience is if you want to reach them. Since the average person spends about seven hours a day online, it’s no surprise that content has maintained its hold on marketers.

Here's a closer look at what content marketing is and why it matters more than ever in our digital-first economy.

On our content marketing category page, you find blogs about content marketing topics. Learn about strategies, KPIs, tips, and more. Make your content king!

Content Marketing Blogs | Category

The rise of the influencer marketing space – today a $10 billion industry, growing 50% per year – is one of the clearest examples of how social media is transforming traditional marketing. These days having a business website, presence on social, PR coverage, and your own content isn’t enough to compete.
We'll dive into the intricacies of influencer marketing to help ensure that you're equipped with the insights needed to be successful in this space.

On our influencer marketing category page, you find blogs about influencer marketing topics. Build your strategy, find influencers, and measure results.

Influencer Marketing Blogs | Category

Have you just launched a new product or service that you want to get the word out about? Maybe you want to share with consumers why it is awesome and how it is so much better than the one your competitors offer.

Well, then, there is one thing you can't do without...Marketing!

On our marketing category page, you find blogs about marketing topics. Learn about all the different angles and types of marketing and improve your results.

Marketing Blogs | Category

In this guide, we want to give you a peek into the definition of social media management, the daily schedule of a social media manager, social media platforms to promote your business, and the do’s & don’ts of social media management.
Are you ready to begin the journey of becoming a social media superstar?

On our social media management category page, you find blogs about social media management topics. Build and manage your online community.

Social Media Management Blogs | Category

Building a following and using social media platforms to reach a wider audience requires a well-planned social media marketing strategy. In this guide, we’ll be looking at why social media marketing is so essential and how you can make it work for your business.

On our social media marketing category page, you find blogs about social media marketing topics. Learn how to get the highest paid social ROI!

Social Media Marketing Blogs | Category

In the fast-evolving realm of digital marketing and public relations, utilizing the right tools is crucial for success. Our interdisciplinary category showcases all the pertinent tools for digital marketing & PR. These tools help in enhancing your online presence and effectively reaching target groups, whether through reputation management, influencer marketing, or social media management.

On our digital marketing and PR tools category page, you find all our blogs covering topics around mapping and understanding the customer journey.

Digital Marketing & PR Tools Blogs | Category

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.

Many believe they can identify AI content, but most worry that others cannot, contributing to broader uncertainty and distrust.

How important is AI transparency for brands?

Transparency is essential. Most consumers expect brands to clearly disclose when AI is used to create content.

Should brands use AI in content creation?

Yes, but carefully. Brands should use AI where it adds value, maintain human oversight, and align usage with audience expectations.

How can brands build trust when using AI?

To build trust, brands should disclose AI use, avoid misleading content, apply AI in appropriate contexts, and prioritise human input.

Influencer marketing is evolving, and so is the way brands measure success. That’s why Meltwater is proud to announce a new partnership with YouTube, integrating YouTube’s Creator Partnerships API directly into our Influencer Marketing platform.

As one of a select group of partners chosen by YouTube, this collaboration brings verified, first-party data into every stage of the influencer marketing workflow, marking a significant step forward for how brands approach YouTube as a marketing channel.

The Challenge: Growing Investment, Limited Visibility

A Strategic Shift for Influencer Marketing

More Than Discovery: End-to-End Campaign Management

Why this Partnership Matters for Influencer Marketing

FAQ: Meltwater Influencer Marketing & YouTube’s Creator Partnerships API

YouTube has become one of the most powerful platforms for creator-led marketing. With long-form content, deeper storytelling, and highly engaged audiences, it plays an increasingly central role in brand strategy.

But as investment in YouTube grows, so do expectations. Marketers are facing a persistent challenge: public metrics only tell part of the story, making it difficult to fully understand true performance. At the same time, audience fit is hard to validate before campaigns even begin, and campaign impact is often fragmented or unclear once they are underway. 

As a result, brands are investing more in YouTube, but without full confidence in performance or ROI.

The Solution: Verified Data, Built Into the Workflow

Through this new partnership, Meltwater Influencer Marketing integrates verified, non-public YouTube data directly from creators in the YouTube Partner Program into influencer workflows, unlocking a new level of transparency and control.

But there’s more to this story than simply providing additional data for marketers - it’s about transforming how YouTube campaigns are planned, executed, and measured.

The Meltwater Blog

AI-Generated Misinformation: Detect Harmful Narratives in LLMs

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

Introducing Verified YouTube Data in Meltwater Influencer Marketing

PR Crisis Management: 10 Steps to Protect Your Brand in 2026

The Top Branded Influencer Posts of Coachella 2026

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

The 13 Best Market Research Tools in 2026

Top 8 Customer Intelligence Platforms & Software 2026

Platform Deep Dive: 7 Trends Defining Beauty Reddit Right Now

Brand Risk Management: Strategies to Protect Brand Value

How to Measure Share of Voice to Improve Brand Visibility

How to Gather Market Intelligence: 5 Steps To Prove ROI