Some trends can be picked ahead of their time, however the explosiveness of a random tweet, call-to-action or cat video is almost impossible to pin-point.
While trends will mostly fade back and be replaced with another, the occasional and rare trend can have legitimate and measurable impacts on society. A recent example of this is the anti-plastic straw movement that took off in 2018.
It started with a terribly sad and visceral video of a straw being removed from the nose of a sea turtle – it’s likely you’ve seen it yourself. The internet is filled with images and videos relating to the impacts of pollution and climate change on the wildlife, however this video happened to stick in the social media sphere long enough to cause a stir.
In the context of environmental upset and helplessness, the plastic straw became the epitome of our harmful single-use plastic culture. In the space of a couple of months, plastic straws were disappearing from venues and public discourse stigmatised their use. Massive chain restaurants such as McDonalds and Starbucks announced plans to ban the plastic straw, as well as some cities and countries introducing bans or taxes on similar single-use products.
While this is ultimately a positive movement with good intention, rejecting the use of plastic straws is an easy and short-term relief to an overwhelming frustration with single-use consumer culture. This year we’ve been seeing similar trends emerge with the rise of keep-cup popularity and debates over plastic bags in super markets.
These trends may be tokenistic, however, they are telling of widespread sentiment and signify the public’s desire to be heard and responded too.
Loren is an experienced marketing professional who translates data and insights using Isentia solutions into trends and research, bringing clients closer to the benefits of audience intelligence. Loren thrives on introducing the groundbreaking ways in which data and insights can help a brand or organisation, enabling them to exceed their strategic objectives and goals.
An organisation’s reputation is at its core, really how people feel about them. These feelings can be based on their interaction and knowledge of the brand, or their experience with the products and services the organisation provides. This reputation is important as it can often dictate the actions or choices audiences and buyers make, impacting an organisation financially and its ability to grow. If managed and measured, the value of an organisations reputation can safeguarded and used as a source of growth by strategically influencing key consumer’s consideration over the competition and the market more broadly.
People can interact directly with an organisation more than ever before, on social media, targeted advertisements, in-store experiences, customer support to name a few.
Social media is a great place to listen to the voice of consumers and key audiences who choose to voice their experiences online. It provides insight into what your organisation has done well or needs to do better. When used in conjunction with additional data, like survey analysis it can also reveal what channels and content are contributing to this perception, and how this can be shifted. Drawing from online resources and social media, Isentia has established 3 drivers to identify and quantify an organisation’s reputation.
The first driver is about the future direction of an organisation.
Does your organisation have a strong leader? Does your organisation seek to innovate? Does it shape the way society thinks? Is your organisation authentic in its messaging? Is your organisation likely to succeed?
When an organisation shows these qualities, it raises consumer trust and confidence, but it’s important that this is authentic. An example of this is Honestbee. Honestbee’s strategy covered several of these points - they were a fast expanding and innovative Singaporean startup in the online grocery delivery business. The founders focused on being perceived as successful, with plans for rapid expansion.
However, In October 2018, Habitat, the world’s first tech-integrated multi-sensory grocery and dining destination launched. Three months after the launch of Habitat, it was discovered Honestbee was deep in financial debt. This was a shock to the industry as Honestbee had a good strategy. Their downfall had been in their inauthentic messaging which resulted in the organisation losing trust of their consumers and investors.
Culture is determined by the organisation having strong values and integrity.
Is the organisation socially responsible? Are practices fair and transparent? Do they promote a balanced workplace? Is it an environment where people aspire to work? Do they have ethical relationships with their business partners?
The growing number of organisations ‘going green’ is as good example of how the market can reflect and appeal to the values of today, in this case by demonstrating they're more environmentally conscious. In a 2019 Nielsen study, it was shown 1 in 3 consumers prefer eco- friendly products. Both Fairprice and Redmart, grocery chains in Singapore, also expressed growth in demand for their environmentally friendly products.
An organisation’s workplace culture, including ethical behaviour can also negatively impact an organisation. For example, Google was challenged for the way cases of sexual harassment were handled within the workplace. They were also challenged for questionable deals in AI technology that resulted in a protest of 20,000 employees across their offices. Google’s poor behaviour was exposed which led to criticism from Amnesty International and a backlash on social media.
Delivery is how good an organisation is at delivering on it’s day to day business.
Do people perceive the organisations products are good quality? Are the products well received? Is the organisation well known in the industry? Do customers have a good experience? Are they successful?
A good example of how delivery can be analysed is in the sphere of reputation is the case of, Razer Inc. known as an organisation passionate about gaming. With a tagline ‘For Gamers. By Gamers’, they are well known in the gaming industry for supply gaming software, hardware and accessories.
According to their annual report, their revenue last year, hit an all-time high of 712 billion US dollars. While online reviews of their mostly praise the high quality of Razer products, a common complaint on sites such as trustpilot.com, Reddit and Forum Hardwarezone are about slow or unhelpful customer support. Some customers even expressed that due to the poor customer support for products, they were even considering switching brands. This signals an opportunity. While Razer Inc has performed well financially and seemingly has a message that appeals to their key consumer, by improving their touchpoint experience and capacity to deliver they could potentially eclipse the competition and swing those who were apathetic towards other brands.
This is just a small glimpse of how your organisation’s reputation can be analysed and measured by a combination of social media data and more traditional market research techniques. Executing a broad analysis of your organisation based on the 3 drivers of Strategy, Culture and Delivery, we can assist in gauging your organisation’s reputation and how it fares against competitors. With a clear metric for overall reputation and a breakdown of performance by driver, Isentia's Reputation Analysis helps your organisation identify areas for improvement and where there are opportunities to strengthen PR, marketing and engagement strategies.
Request a sample of Isentia's Reputation Analysis here.
An organisation’s reputation is at its core, really how people feel about them. These feelings can be based on their interaction and knowledge of the brand, or their experience with the products and services the organisation provides. This reputation is important as it can often dictate the actions or choices audiences and buyers make, impacting […]
A customer evaluating a brand, a journalist researching a CEO, and a policymaker looking up a public agency are all doing the same thing — discovering organisations through AI. The difference is that they may each receive a different description, supporting evidence, and overall impression.
This shift represents a major change in modern communications.
Large language models (LLMs) now interpret information for audiences, rather than just helping them find it. Instead of listing search results, they retrieve content, select authoritative sources, and generate a single synthesised answer that many users accept without reviewing the original articles.
For PR, communications, and marketing professionals, this presents a new reputation challenge. Your organisation now has multiple AI-generated reputations, each shaped by the model your stakeholders use. Understanding these differences is becoming as important as understanding media coverage – in fact, the two are often closely linked.
A common misconception is that ChatGPT, Gemini, Claude, and Perplexity all access the same information. In reality, they don’t.
At a high level, all leading LLMs are built on similar foundations. They're trained on vast collections of books, websites, news articles, public documents and licensed datasets that help them understand language and generate human-like responses. Increasingly, they're also capable of retrieving live web information, allowing answers to incorporate recent events rather than relying solely on historical training data.
However, their similarities end there.
Each LLM uses a unique combination of training data, retrieval architecture, and ranking logic. As a result, each model answers three key questions differently before generating a response:
These decisions fundamentally shape how organisations are represented. To explain this better, we summarise how different AI models consult different publications and finally cite them in AI answers:
LLMs are not impartial, but come with their own weights and balances. In a sense, it reflects dynamics we already see at play across PR & Comms.
If four experienced journalists had to write a profile of the same CEO after attending the same press conference and have access to the same reports, one may write about leadership, another financial performance, another governance and another might frame the story around innovation. None are necessarily wrong—they're simply viewing the event through a lens of individual expertise, and therefore, making different editorial decisions.
LLMs behave in similar ways. As a result, organisations are now represented by multiple AI-generated narratives, rather than a single authoritative digital narrative.
These differences are most apparent when users ask AI questions that require judgment rather than simple factual recall.
Consider a prompt like: "Which are the leading banks in Southeast Asia?" Across ChatGPT, Gemini, Claude, and Perplexity, you will likely see many of the same names — DBS, UOB, OCBC, and Maybank. This overlap occurs because all four models recognise these institutions as major regional banks.
However, the models differ in their explanations of why these banks are considered leaders.
ChatGPT may highlight DBS's digital banking leadership and customer experience. Claude is potentially more likely to discuss governance, regional strategy, and long-term institutional strength. Gemini may emphasise recent awards and publicly available web information, while Perplexity often presents answers as research comparisons supported by multiple citations. These changes are not necessarily so big as to be immediately noticeable, but over time the results accumulate.
An organisation may not meaningfully change between two users asking two different LLMs , but the reputation narrative shifts accordingly. This distinction matters because stakeholders rarely ask AI for isolated facts. They ask questions like:
AI responds by interpreting credibility, authority, and context, rather than simply retrieving documents.
For communications teams, this means your organisation is increasingly evaluated through comparative prompts, where competitors, industry peers, and institutional benchmarks appear alongside your organisation by default.
When ChatGPT, Gemini, Claude, or Perplexity answer a question, they first retrieve documents from distinct information ecosystems. A recent study analysed 17.2 million AI citations across ChatGPT, Gemini, Claude and Perplexity and found that each model retrieves from substantially different source ecosystems rather than a shared pool of webpages. Two LLMs can answer the same question accurately while relying on entirely different publications.
Instead of viewing these as technical differences, it is more useful to consider them as citation behaviours. Each model consistently references different types of publications when explaining organisations.
ChatGPT functions more like an executive briefing writer than a traditional search engine.
Rather than listing multiple links, ChatGPT typically combines information from several credible publications into a coherent narrative. If mainstream media, company information, and industry commentary consistently describe an organisation as a market leader, ChatGPT is likely to reinforce that positioning, regardless of the original source.
For a prompt like "Tell me about Singapore Airlines”, a typical ChatGPT response integrates its history, customer experience, awards it has won over time, its financial performance, etc., into a cohesive description. Individual articles become almost invisible, as the model prioritises a coherent narrative in its answer over transparently showcasing all citations.
For brands, this means consistency across publications is very valuable. ChatGPT values credible signals that repeat across multiple publications over one off news moments.
Gemini approaches organisations differently, as its retrieval is closely linked to Google's broader information ecosystem.
When we ask "What has Enterprise Singapore done to support AI businesses?", Gemini is more likely to include recent programme announcements, official government webpages, and newer online reporting. Its responses often feel more current because they draw from a web ecosystem designed to reflect continuously updated information.
For government agencies, this has practical implications. Official announcements and well-structured public information become machine-readable assets that help AI explain policy more accurately. These become important information touchpoints into how audiences understand government through AI.
Claude's defining feature is its emphasis on context.
Other models often prioritise concise answers but Claude frequently elaborates on why an organisation is respected. Questions about leadership, governance, ethics, and institutional reputation tend to produce more detailed explanations rather than brief summaries.
For a prompt like "Why is DBS considered one of Asia's leading banks?", Claude is more likely to discuss how the bank has regionally expanded, its digital transformation, what’s unique about its leadership etc. Its responses resemble analyst reports more than search summaries, as it favours high-authority editorial and institutional content.
For executive communications, this makes Claude particularly influential. Leadership narratives and corporate values often receive more contextual treatment than with other models.
Perplexity changes the experience by treating citations as a central feature rather than a supporting detail.
A question comparing two sustainability leaders, for example, typically returns numerous linked sources from business media and research publications. Users can immediately review the origin of each claim.
For PR teams, this creates greater transparency. Publication quality becomes visible within the user experience and is not hidden behind an AI summary. This means media strategy influences the evidence presented alongside the perception of the organisation.
Although LLMs behave similarly across sectors, the questions users ask differ fundamentally.
Consumers rarely ask AI what a brand does. They definitely do ask which brand is better.
Questions like:
1. Which airline has the best customer experience?
2. Is Salesforce better than HubSpot?
3. Which bank is most innovative?
4. Who are the leaders in cloud computing?
These questions are inherently comparative.
As a result, your organisation is often introduced alongside competitors before stakeholders visit your website.
During a recent Isentia webinar on Measuring your brand’s visibility in AI answers, we analysed airlines across multiple LLMs with Lumina AI View. Using identical prompts, different models highlighted different airlines and emphasised varying strengths, such as premium service, operational performance, innovation, and customer experience. Competitive positioning shifted depending on the model, even though the underlying organisations remained unchanged.
For commercial communicators, competitor association is becoming as important as share of voice. AI evaluates brands in context, not in isolation.
Public sector organisations encounter a different reputational challenge. Audiences increasingly ask questions that begin with how, why and can I trust:
1. What support does this agency provide?
2. Has this ministry achieved its policy goals?
3. What AI initiatives has this department introduced?
4. Has this programme faced criticism?
LLMs then synthesise official publications and institutional information into a single accessible explanation. This changes the role of public communications. The objective is not to ensure AI models have sufficient credible, authoritative context to explain complex policy accurately.
As AI becomes the primary interpreter of government information, communications teams are responsible for managing both visibility and understanding.
Reputation has been shaped across three familiar environments: media, search and social. AI introduces a fourth environment.
LLMs condense dozens of publications into a single response, reducing the traditional discovery process between a question and an opinion.
During the recent webinar, Prashant Saxena, VP of Revenue and Insights says, “We used to Google it. Now we're ChatGPT-ing it.”
This behavioural shift is significant. Reuters Institute research found that only a small proportion of AI chatbot users regularly click through to original articles.
Practically, every piece of earned coverage now has two audiences:
Both shape reputation.
This is the challenge Lumina AI View was designed to solve.
Instead of measuring whether an organisation appears in ChatGPT, AI View measures how different LLMs represent the organisation. Using consistent question sets across ChatGPT, Gemini, Claude, Perplexity, Grok, and Google AI Overviews, it compares narratives, citations, and competitive positioning through a unified reputation methodology.
The framework is built around four familiar dimensions: Direction, Performance, Integrity, and Innovation. Rather than tracking keywords alone, AI View identifies which reputation pillars dominate AI responses, which publications influence those narratives, and how representation differs across models. It also gives the organisation an AI score based on all the above factors.
The tool also shows organisations which publications are covered by different AI models. If the organisation’s AI score on the tool is high for ChatGPT, but struggles with or is a lower score on Gemini or Perplexity, the tool allows you to see where you’re underperforming. If there are publications that are covering the wrong information, or the important ones just don’t show up, then that’s an indication to why the AI score is low on that platform.
The communications profession has always adapted to new discovery channels, from newspapers to search engines, and from social media to digital news.
AI is different because it does not simply help audiences find information, but it increasingly determines how organisations are introduced.
For communications leaders, this changes media strategy in three key ways.
Organisations that succeed in this new environment are those that have leadership, performance, integrity, and innovation represented consistently across every major AI model, regardless of where audiences and stakeholders begin their search.
Want to see which sources are shaping how AI describes your organisation? Get in touch about Lumina AI View.
" ["post_title"]=> string(50) "How do different LLMs represent your organisation?" ["post_excerpt"]=> string(136) "Uncover how different LLMs treat your brand, explore key model capabilities, and learn how PR teams can measure and shape AI visibility." ["post_status"]=> string(7) "publish" ["comment_status"]=> string(4) "open" ["ping_status"]=> string(4) "open" ["post_password"]=> string(0) "" ["post_name"]=> string(49) "how-do-different-llms-represent-your-organisation" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2026-09-21 02:42:00" ["post_modified_gmt"]=> string(19) "2026-09-21 02:42:00" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(32) "https://www.isentia.com/?p=50056" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" }Uncover how different LLMs treat your brand, explore key model capabilities, and learn how PR teams can measure and shape AI visibility.
Audiences are no longer finding information through traditional search engines that favour established news outlets. AI models now highlight highly relevant and contextual information to audiences to often include niche and regional publications alongside major news media. This change challenges the old media hierarchy around tiered publications and pushes organisations to reconsider how and where they need to show up to stay visible in an AI-first world.
Yes, organisations must focus on optimising their own content for LLMs, but will that always drastically increase the chances of AI models picking up your page? Probably not always. Smart strategy means targeting the specific publications your actual target audience reads — because those are the sources AI models retrieve when answering niche questions.
Generative Engine Optimization (GEO) is changing how brands approach online visibility. For years, traditional SEO meant focusing on your own site—optimising keywords, building backlinks, and improving on-page content. But AI models work differently. Instead of just using your website, these AI engines rely on trusted third-party sources to answer questions. This shift is taking place gradually, of course. LLMs increasingly source from earned media (where it is accessible) and even offsite links from trusted sites. Owned media is still where the organisation has maximum control of how it’s own content travels, but a pivotal strategy shift is needed to match what AI models are picking up and citing.
To succeed with AI search, comms professionals need to think more like a digital PR strategist than a SEO expert. The best way to stand out is by earning mentions, quotes, and citations in the external publications your audience—and the AI systems they use—trust most. This does not make a distinction between Tier 1 or Tier 2 media. If AI models are crawling sites that mention an organisation, but the organisation does not acknowledge or even know those sites are being prioritised by LLMs, they risk falling behind in being the right kind of visible.
To make this strategy work, looking beyond common metrics like traffic to the site or domain authority is not enough. Even a respected industry site will probably not influence AI answers as much if its content is behind a paywall or blocked from search engines. For AI visibility, accessibility to the site or page, structured data that can be crawled, and strong audience alignment are important. Since AI systems use both slow training cycles and fast real-time web searches (RAG), being featured on accessible, relevant niche sites helps an organisation show up accurately when models learn and when they search the web in real time.
According to Isentia's report How AI is destabilising trust and reputation amongst audiences, LLMs cite industry and trade publications about twice as often as traditional news sources. Company content and industry press make up over 60% of the share of voice LLMs use, while traditional news is twice as likely to generate negative sentiment. Thus, tier 1 outlets no longer automatically dominate AI-generated responses and may sometimes have the opposite effect.
Two main factors are driving this shift in which media is picked up by LLMs:
Which type of media gets cited relies upon how AI models scan different pages. If these models are citing much more niche media outlets, we can assume that a lot of these pages that are consulted could be a part of very relevant Tier 2 media that ends up actually getting cited, and that we’re seeing more and more examples of in AI answers. At the IABC APAC and Isentia webinar on measuring brand visibility in AI answers, Prashant Saxena, Isentia's VP of Revenue and Insights for SEA, stated that in the search era "we would get sources on our page one, page two, mostly page one", and people would click through to form their own opinions. The combined click-through rate in that era was 35 to 40 per cent. Nowadays, he says, "it's just four to five per cent" — since LLMs provide a smooth, ready-made answer and "most of us aren't really checking the citations".
Communications teams now face a new consideration: the distinction between sources that are consulted and those that are cited. At the IABC APAC and Isentia webinar, Takeo Apitzsch, Hoffman Agency’s Chief Digital and AI Officer, explained that AI models scan hundreds of pages to generate an answer but cite only a select few to users. This means that the audience sees only a small, curated portion of the sources that actually influenced the AI's response and a lot of what actually shapes the AI answer doesn’t get visible credit. Therefore, organisations need to make sure they reach out to those publications that AI models can actually crawl and audiences trust the most.
We are seeing four practical shifts:
Structurally, as Ashley Knapp, Head of Brand and Corporate Affairs, East Asia at Schneider Electric noted during the webinar, these efforts can no longer remain siloed. Owned, earned, shared, and paid media have traditionally been managed by separate teams. Now, because of AI visibility, this required a unified approach, as models do not distinguish between departments but are first to detect inconsistencies.
This also means reconsidering the PESO (paid, earned, shared and owned) strategy deployed by organisations since the way that LLMs access and prioritise them has changed. They prioritise brevity in content due to the high costs of GPUs and data centres. As a result, the shortest, clearest, and most trusted answers are favoured which benefits brands with strong reputations. Earned media remains important, but its influence now depends more on the credibility of the analyst than the platform. Shared content amplifies messages more than ever but is also where misinformation spreads fastest. Paid media is becoming more prominent in some models, though brands are still learning how this impacts visibility.
This shift requires media monitoring companies to evolve. Tracking mentions and sentiment across media channels has been central to media intelligence, but AI visibility has added a new dimension to this. This means monitoring not only what is said about an organisation, but also which sources AI models use when answering questions about that organisation, and assessing how current, authoritative, and consistent those sources are. This gives media monitoring organisations an opportunity to own what they’ve developed and also be thought leaders in this space. Stakeholders value the “so what” advice much more than just knowing “this is what is being said about you in the media”.
Lumina AI View addresses this by tracking which sources ChatGPT, Gemini, Claude, and other models cite when representing an organisation, benchmarks citations against competitors, identifies narrative shifts before they reach stakeholders, and regularly scores AI visibility against four reputation pillars: Direction, Performance, Integrity, and Innovation, These pillars have always supported reputation management, now applied to a largely unseen audience.
If you're weighing up where a tool like this sits alongside the rest of your stack, our own comparison, Best AI Tools for PR & Comms Teams (2026), breaks down how AI-assisted coverage, measurement, crisis response and reporting tools stack up, Lumina included.
Because that’s really the mindset shift comms teams, and the firms advising them both need to make. As Takeo put it on the IABC APAC and Isentia webinar: “I fear that this is the mindset shift communications teams and their advisors must adopt. I fear that AIs will be your secondary, and if not, at least equal… audience in the future.” Beyond human visibility, reputation is about being accurately represented by the systems that mediate access to your audience, which is an additional layer that cannot be trivialised anymore.
Want to see which sources are shaping how AI describes your organisation? Get in touch about Lumina AI View.
" ["post_title"]=> string(97) "How relevant is Tier 1 and Tier 2 media hierarchy in impacting how organisations show up in LLMs?" ["post_excerpt"]=> string(243) "The hierarchy that exists between Tier 1 & 2 publications today is being challenged. AI models are the new way audiences discover information requiring organisations to rethink how they show up to remain visible in an AI-mediated environment. " ["post_status"]=> string(7) "publish" ["comment_status"]=> string(4) "open" ["ping_status"]=> string(4) "open" ["post_password"]=> string(0) "" ["post_name"]=> string(96) "how-relevant-is-tier-1-and-tier-2-media-hierarchy-in-impacting-how-organisations-show-up-in-llms" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2026-09-09 02:24:17" ["post_modified_gmt"]=> string(19) "2026-09-09 02:24:17" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(32) "https://www.isentia.com/?p=49871" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" }The hierarchy that exists between Tier 1 & 2 publications today is being challenged. AI models are the new way audiences discover information requiring organisations to rethink how they show up to remain visible in an AI-mediated environment.
Get in touch or request a demo.