The Top 15 Social Media Monitoring Tools 2026

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

How can you get started with GEO? Test your GEO readiness

Which media sources to prioritize for GEO

What's the difference between GEO and SEO?

8 essentials of generative engine optimization

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, which can lead to zero-click searches, and that number keeps rising. 

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.

If you’re not optimizing for generative engines, you just might never reach your audience at all.

Kelly McKenna, Regional Director, Account Management at Meltwater

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.

Tip: Learn more about the citation layer.

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.

Reading tip: Learn how a website rebuild drove a 73% citation increase.

Test your GEO readiness: a 2-minute self-assessment.

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.

Reading tip: Your AI Visibility Score Is a Mirror, Not a Scoreboard

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 brand 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 digital 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, understand how to report on LLM visibility, and take a look at executive report for AI search.

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.  

GEO helps your brand to appear in AI platforms — but how does it work? This guide has everything you need to know to get started with GEO.

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

It’s been 15 years since we published the first of our Global Digital Reports, but data shows that digital continues to gain momentum as we head into 2027.

And the good news is that our newest report has everything you need to make sense of all the latest trends.

Your complete guide to digital in in 2027

The Digital 2027 Global Overview Report – published in partnership between We Are Social and Meltwater – delivers comprehensive insights into the adoption and use of connected tech around the world.

Weighing in at more than 900 pages, this year’s report contains detailed statistics for:

Global internet adoption, access, and usage trends

AI adoption and use, including AI use cases and LLM platform trends

The evolution of online search and its role in the purchase journey

Social media behaviours and platform preferences

Ecommerce activity, including social commerce data

Digital entertainment, covering online TV and video, digital audio, and video games

Consumption of news content via digital channels

Online marketing trends, including ad spend and engagement

Top stories in our flagship Digital 2027 report include:

Important changes in the use of search engines

And that’s not all: you’ll find a treasure trove of data, trends, and insights in the complete report, and we’ll also explore some fascinating findings in this extensive analysis article, so read on below.

Before we begin, I’d like to extend my heartfelt thanks to our wonderful research partners, who provide much of the world-leading data you’ll find in this report:

All of this analysis has been written by Simon Kemp at Manochi, without the use of generative AI [why does this matter?].

Simon has been producing the Global Digital Reports series for 15 years, so he’s intimately familiar with the global “state of digital”, and how people’s online behaviours are evolving.

If you’d like to follow Simon’s regular analyses, he’d be delighted if you connect with him on LinkedIn.

As you explore this latest report – and especially as you compare trends over time – please be aware of the following:

International sanctions mean that GWI is no longer able to field its survey in Russia. As a result, we’ve removed data for Russia from the historical GWI figures that we’ve included in this report, to deliver more representative trends over time. That means all values on the GWI timeline charts you’ll find in this particular report already reflect these changes, but please refer to individual chart footnotes to understand how changes might affect this year’s data.

Similarweb recently introduced a new methodology for its App Intelligence dataset, which has resulted in some important changes to the figures the platform reports. Fortunately, the company has been able to apply these changes to historical data too, so – once again – the timeline trends you’ll find in this latest report already reflect these changes. However, readers should avoid comparing this new App Intelligence data with figures published in previous Global Digital Reports.

The United Nations made significant changes to its approach to classifying “urbanisation” since our Digital 2026 Global Overview Report, resulting in some big changes in reported values for urbanisation and urban population.

Please also refer to our comprehensive notes on data to understand other changes to data and sources in the Global Digital Reports series.

You’ll find our complete Digital 2027 Global Overview Report in the embed below (click here if that’s not working), but read on past that to find our in-depth analysis of what this year’s numbers actually mean for you and your work.

Let’s start with a look at the latest global headlines:

The United Nations’ World Populations Prospects data shows that there are 8.32 billion people living on Earth today. That figure is 69 million higher than the total we reported this time last year, resulting in an annual growth rate of 0.8 percent. The UN’s data also illustrates humanity’s continued move towards urban societies, with 80.6 percent of the world’s population – more than 4 in 5 people – now living in towns and cities [note that the UN made fundamental changes to the way it classifies urban and rural populations in November 2025].

GSMA Intelligence’s latest research indicates that there are 5.86 billion unique mobile users around the world in October 2026, which equates to 70.4 percent of the global population. This unique subscriber figure has increased by 98 million over the past 12 months, equating to annual growth of 1.7 percent. Meanwhile, separate data from Ericsson shows that the 7.68 billion smartphones in use today account for roughly 89.6 percent of all active mobile phone handsets, and 85.8 percent of total cellular mobile connections [note that some people may use more than one smartphone].

Manochi’s analysis shows that the total number of people using the internet had reached 6.15 billion at the start of October 2026, which puts global internet penetration at 73.9 percent. Reported internet user figures have increased by 54 million (+0.9 percent) over the past year, but our analysis indicates that delays in reporting mean that the actual growth figure over the past 12 months is probably considerably higher than these numbers suggest. Despite continued improvements in internet access, however, close to 2.17 billion people around the world remained offline at the time of report production.

Manochi’s research also reveals that global social media user identities have now reached 5.81 billion, with that figure equivalent to 69.9 percent of the global population. This global user identities figure increased by 2.7 percent in the 12 months to October 2026, thanks to the addition of 151 million new user identities [note that “user identities” may not represent unique human individuals.]

According to Manochi’s latest analysis, there are now 3.24 billion active users of generative AI platforms. This figure equates to 38.9 percent of the total population, but – like our social media user identities figure – it’s important to stress that this GenAI user figure may not represent unique human individuals. Caveats aside though, active GenAI users increased by more than 1.2 billion between October 2025 and October 2026, resulting in an impressive annual growth rate of 57.2 percent.

But beyond overall adoption trends, how have people’s online behaviours and preferences been evolving?

Manochi’s latest analysis reveals that more than 1 in 3 people around the world now uses generative AI (GenAI) each month.

Our research indicates that 3.24 billion users will access at least one of the top LLM platforms in October 2026, equating to a worldwide adoption rate of 38.9 percent [learn how we calculate these numbers in this article].

It’s important to stress that – while we strive to deduplicate figures across different platforms – the base user figures reported by the platforms themselves may include some degree of duplication, because the same individual may use multiple accounts on the same platform (e.g. one for personal use, and one for work).

As a result, we recommend treating this GenAI user figure in much the same way as our social media “user identities” figure.

But having said that, research conducted by GWI indicates that ChatGPT already had more than 1.1 billion monthly active users amongst adult internet users in Q2 2026, and once we factor age and geography, this finding correlates closely with the ChatGPT figures that we use in our calculations.

Manochi’s analysis also indicates that monthly active GenAI users have increased by more than 57 percent over the past 12 months, thanks to the addition of 1.2 billion new users.

For perspective, figures reported by OpenAI indicate that ChatGPT alone has added roughly half a billion weekly active users over the past year, which our analysis suggests translates to an increase of roughly 700 million monthly active users.

And for added perspective, Similarweb’s App Intelligence data shows that active users of ChatGPT’s mobile apps have more than tripled over the past 2 years.

Meanwhile, CNNIC’s research shows that GenAI users in China have increased by at least 150 million over the past 12 months.

But perhaps the most interesting trend is the growth in “non-ChatGPT” platforms, especially Google’s Gemini and Anthropic’s Claude.

For example, Similarweb’s data shows that active users of Gemini’s mobile apps have increased more than fourfold over the past year, while Gemini’s app MAU figure for August 2026 is more than 23 times higher than the equivalent figure for August 2024.

Moreover, that figure only includes use of Gemini’s standalone mobile apps; if we were able to factor OS integrations too, that figure could easily be even higher.

Claude has delivered this year’s standout GenAI growth though, with Similarweb’s data indicating that the platform’s active app user figure for August 2026 was a whopping 17 times higher than the equivalent figure for August 2025.

And in an even more impressive achievement, Similarweb’s data suggests that monthly active users of Anthropic’s flagship app in August 2026 were almost 80 times higher than the equivalent user figure for August 2024.

So how does monthly use of these platforms compare?

Well, Similarweb’s latest data reveals that ChatGPT is still the clear GenAI leader as we head into 2027, with twice as many monthly active users as any other platform.

However, as we’ve just seen, both Gemini and Claude have made significant gains over the past few months.

Microsoft’s Copilot app also seemed to be gaining momentum, but the company’s recent announcement that it will merge its Copilot and Copilot 365 platforms suggest that Copilot’s future will focus more on enhancing Microsoft’s other products like Office, rather than acting as a standalone chatbot.

There’s no easy way to deduplicate platforms’ user bases across their web interfaces and native mobile apps, so we report these datasets separately, especially because each one tells a different story.

Similarweb data indicates that mobile apps claim the lion’s share of GenAI activity today, which demonstrates just how quickly these platforms have become a fixture on our smartphones and in our daily lives.

ChatGPT tops the latest rankings, with the platform attracting more than 3 times as many monthly active users in August 2026 as its next nearest rival, Gemini [but note that figures for Gemini use do not include Android OS integrations].

Copilot ranked third, with an indexed MAU value of 16.66, indicating that the platform’s mobile apps had six times fewer MAUs than ChatGPT did in August.

Claude ranked fourth in August 2026 with an index value of 14.79, while Deepseek rounded out the top 5, with an index of just over 11.

Note that these figures do not include data for China.

Meanwhile, figures for web use tell a slightly different story.

ChatGPT still claims top spot outside of China, but its lead over second-placed Gemini is considerably less than the lead we saw in the mobile app ranking.

Indeed, Gemini’s web user base is now over 60 percent as large as ChatGPT’s, and Similarweb’s data shows that Google has made significant strides in closing that gap over the past year.

Claude ranked third in Similarweb’s ranking of web-based GenAI platforms in August 2026, with its browser-based offering attracting close to a quarter the number of unique users as ChatGPT.com.

Deepseek claimed fourth spot, while X’s Grok platform closed out the top five.

However, Similarweb’s data indicates that both Deepseek and Grok had fewer than 10 percent of the monthly active web users that ChatGPT did in August 2026.

And it’s also interesting to note that Copilot falls further down this web ranking, which may help to explain why Microsoft has made such sweeping changes to its Copilot strategy.

More than 1 in 3 people using GenAI as adoption surges

Digital 2027 Global Overview Report

Getting a convincing answer out of AI can take seconds, but deciding whether to build a campaign, change a message or advise your CEO on the strength of it takes more judgment. 

At Meltwater Summit on Tour London on October 1, 2026, speakers explored how PR and marketing teams can make AI useful in practice, from improving access to insights to understanding its growing role in brand discovery. Along the way, Professor Hannah Fry introduced some surprisingly talented pigeons. Here are six lessons from the day, and how to apply them.

Keep human judgment at the center of AI use

Make AI visibility part of reputation management

Create content for people and AI discovery

Professor Hannah Fry’s keynote gave the audience an unusual way to think about AI: pigeons trained to identify cancer in medical images. Their ability to recognize patterns was impressive, and it also illustrated how performing a specific task well differs from understanding the wider responsibilities of a profession.

She explored the problems that arise when machines interpret our instructions differently from how we intended them, and when people put too much faith in a machine’s output. For PR and marketing professionals, those problems can show up in a research summary, a campaign recommendation or a piece of copy that sounds plausible until someone checks it.

The live Data Malarkey podcast brought that lesson into the workplace, with host Sam Knowles, Canon EMEA’s Gareth Crew and Golin Ketchum’s John Brasington discussing the risk of handing over tasks without staying mentally involved in the work. Brasington described finding AI particularly useful in areas where he already had the expertise to evaluate its answers.

That distinction helps teams decide how to use AI responsibly, because  a quick read for typos won’t reveal a flawed methodology or a recommendation built on the wrong assumptions. The person reviewing an AI output needs enough expertise to challenge it.

Build review into the workflow so people can check the evidence, assess the context and challenge the recommendation.

Heineken’s Narek Garit offered a practical starting point for AI adoption: work backward from what a stakeholder needs to achieve.

It’s easy to start generating reports once you have data, dashboards and an AI assistant, but an impressive report can still spend its life unopened.

Garit recommended defining the use case first, then selecting the data, analysis and delivery method that serve it. He also stressed connecting social intelligence with relevant media, research and internal information, so teams can interpret what they find in context.

For a communications team, the question might concern whether an emerging narrative requires a response, while a marketing team might need to understand which audience concerns a campaign should address. Those are different decisions, even when they draw on some of the same conversations.

Define the decision before building the workflow. Agree on who needs the answer, what they will do with it and how quickly they need it, then determine where AI can help with gathering evidence, analyzing information or presenting the findings.

Garit’s advice was to start small and keep future scale in mind - one useful application gives you something concrete to improve and a reason for colleagues to support the next one.

Meltwater’s Antony Cousins described a problem he called “shadow insights”: business users asking general AI assistants for answers without the data or methodological checks those questions require.

The convenience is understandable, because an assistant responds immediately, whereas an insights specialist might first ask which market, which audience and which time period the question concerns. Those clarifications improve the research, but a colleague facing a deadline may see them as another hurdle.

Cousins shared an example involving a question about women’s attitudes toward electric vehicles. The apparently useful response relied on a single, outdated blog post, but the answer looked much more comprehensive than the evidence behind it.

His session explored how Meltwater’s MCP connector can bring relevant data and existing searches into AI workflows. He also discussed capturing expert methods in reusable skills, so colleagues can draw on a consistent approach to recurring questions.

Make reliable research easier to access by agreeing on sources and methods for common requests, and include the limits of the evidence in the answer itself.

That last step is particularly useful, because social conversation can reveal themes worth investigating, while a representative survey answers a different kind of question. 

Gener8 founder Sam Jones showed why AI deserves attention across the customer journey, and described using it to plan a family holiday and select a stroller, then buying the first stroller it recommended.

His presentation drew on an analysis of more than 100 million AI conversations from over 200,000 people across the US and UK. In the situations Gener8 compared, people were 3.6 times as likely to visit a brand’s website when it appeared in a commercially relevant AI response, compared with an answer that featured a competitor and omitted the brand.

For marketers, that makes AI recommendations a meaningful area to investigate. For PR professionals, the information inside those recommendations raises an equally important question: is the brand being represented accurately?

In the panel hosted by Meltwater’s Jenny Force, Snap Inc.’s Alicia Edmonds-Smith emphasized a strong, consistent narrative and factual accuracy, while Unilever’s Laura Thomas discussed the product attributes a brand needs to be known for, including the complications of managing different names and associations across markets.

The Data Malarkey conversation extended AI visibility to other stakeholders, explaining that employees, investors and others may also use AI to research an organization, so its answers can shape perceptions well beyond a product shortlist.

Start by reviewing questions relevant to your audiences, then examine where your brand appears, how it is described and which sources the answers cite. Include the markets and languages that are relevant to your business.

Accuracy deserves a place alongside visibility in your reporting. An increase in mentions is hard to celebrate if those mentions misstate your product, repeat an outdated claim or misrepresent your organization.

AI discovery gives content teams another reason to answer customer questions clearly.

Jenny Force shared Meltwater’s experience of rewriting key website pages in plain language around questions customers were already asking, and reported a 73% increase in AI citations across the pages measured within six weeks of the website launch.

Chloe Singleton of eight&four described work for Sandals Resorts that began with detailed customer questions and community discussions. The team then used GenAI Lens to benchmark AI responses and identify content gaps.

Both examples point toward a useful approach: learn what people need to know, publish clear answers and investigate whether AI reflects that information accurately.

Deloitte Digital’s Nick Purdon added a second dimension, discussing the rational information that helps people and machines assess a product, alongside the emotional connections that help brands participate in culture. Specifications, ingredients and evidence serve one purpose. Humor, taste and lived experience serve another.

His discussion of creators emphasized cultural relevance and lasting behavior change. Creators understand the communities they serve, and that knowledge can help a brand contribute something people value.

Use AI to assist with research, organization and analysis while giving people room to develop the idea and judge how it will land. A creator brief can set out the product facts and the desired outcome, while leaving space for the creator’s understanding of their audience.

Clear information helps discovery, while distinctive, relevant storytelling gives people a reason to care when they find you.

Meltwater CEO John Box and Chief Product Officer Chris Hackney opened Summit with a vision of connected intelligence across monitoring, discovery and action.

The demonstrations showed how AI can help teams investigate brand visibility, explore emerging conversations and work with Meltwater data through an AI assistant. In the keynote, an example from Havas Red described using custom Mira prompts to accelerate recurring reporting and monitoring. That is a valuable opportunity for busy PR and marketing teams, because gathering coverage, preparing briefings and organizing evidence can take up time that people need for planning, interpretation and relationships.

The Data Malarkey discussion raised a related concern: when people hand over too much of the thinking, they may lose the opportunity to develop their own understanding, which makes it important to decide which activities AI should handle and which experiences people still need.

Automate work where AI performs well and where you can assess the result, then decide how to use the capacity it creates. You might spend more time interviewing customers, testing a campaign idea, strengthening journalist relationships or helping leadership understand an emerging risk.

Purdon highlighted humor, taste and judgment as sources of competitive advantage - saving time on production creates room to develop those qualities.

In her closing remarks, Meltwater Chief Strategy Officer Alexandra Bjertnæs emphasized the value of curious, adaptable PR and marketing professionals. She encouraged the audience to keep asking why, going beyond the first answer to understand what sits underneath it.

That’s a useful habit for working with AI. Question the brief, examine the evidence and stay interested in the people behind the data.

Choose one application your team can test. Agree on the business question, establish a baseline and decide who will evaluate the output. Track whether the work becomes faster, whether the findings improve and whether people can make better decisions as a result.

An AI summary can be produced in seconds. Giving your team more confidence to act is the outcome worth working toward.

How can PR and marketing teams use AI effectively?

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PR & Communications

Digital 2027 Global Overview Report

Lessons on AI in PR and marketing from Meltwater Summit on Tour London

Intelligence is always in style: Kim Cattrall stars in Meltwater’s new campaign

Meltwater Summit on Tour: London - Key Takeaways

PR Crisis Management: 10 Steps to Protect Your Brand

Meltwater vs Muck Rack: Media Database & PR Platform

The Basics of Public Relations (PR): Meaning & Examples

Media 3.0: The Rise of Independent Social Journalists

Best PR Measurement Tools and Analytics Software (2026 Comparison Guide)

Small Team, Big Impact: PR Automation for Lean Comms Teams

Conversations with Comms Leaders #9/9: André Pechmann (Microsoft)

Conversations with Comms Leaders #8/9: Tom Roberts (Barratt Redrow)

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