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Interning at Isentia was enriching and fulfilling

I had the pleasure of interning at Isentia, and my experience was nothing short of wonderful. 

Having only just graduated from university, I could not help but feel slightly apprehensive starting my internship. However, from my very first day, I was greeted with friendly faces all around the office. Before I knew it, I was having morning coffees with my team mates, and soon my colleagues became familiar friends. I was pleasantly surprised by how inclusive and positive the culture proved to be. 

My leader and colleagues from the marketing team were patient when it came to sharing knowledge and took the time to give me tasks that enhanced my learning experience. 

During my internship I gained a deeper understanding how to execute a social media campaign. The planning that goes behind each campaign was so extensive and detailed, which I found intimidating initially, but nevertheless proved to be a great learning experience. For example, I was introduced to the concept of publishing paid advertisements, SEO and content creation. I was even given the chance to write blogs, a responsibility I took on-board with great enthusiasm. 

My experience was not limited to marketing, I was fortunate enough to get involved with the client experience team, where I learnt more about Mediaportal and the amazing insight services Isentia provides. Time flew by quickly and I am very grateful to have had the opportunity to learn and grow.

Isentia isn’t all about working hard; it provides a holistic experience with various social activities and events so everyone has a chance to get to know each other better and learn about the different roles that help to make everything happen. 

I am a strong believer in an enriching environment and Isentia has exceeded my expectations as a company, which teaches and places value in those who work there. The knowledge I have gained is invaluable, and I am thankful for the friendships I have made along the way. 

I highly recommend working at Isentia and leave the team feeling much more confident of the future ahead - a big thanks to everyone who added to my experience.

 Nicole C.
Sydney University, Marketing Graduate

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How would I sum up my experience as an intern for Isentia? Interesting, rewarding, challenging, and engaging.

Hi, my name is Allan. I am currently an undergraduate Mechanical Engineering and Business Management student studying at the University of Technology, Sydney – and have recently completed an internship with Isentia’s HR department.

First of all, you might be wondering what an Engineer is doing as a HR Intern – they don’t exactly fit together, do they? It was for this reason that I was initially hesitant in applying for the role as I didn’t know whether it would align with what I wanted out of my future, or whether I would be a good fit.

However, I soon learnt that being an intern with Isentia was a rewarding and interesting role, not to mention the fact that I was also surrounded by a group of incredibly supportive and knowledgeable people.

Having put myself forward as a mentee for the Australian Human Resources Institute’s mentoring program, I was inspired to learn more – an interest that ultimately led me to this exciting role.

Being an HR intern at Isentia wasn’t just any job – I took on this role because of the challenges it would provide to explore a different area of expertise. And yes, there were definitely new and interesting projects waiting to test my capabilities!

I do have to admit, I always seemed to find myself applying a bit of my engineering experience to the way I undertook each task, but I think this was an approach that helped bring a new and alternative perspective to the team. Who knows, maybe I taught them something new too?!

Along with the day-to-day operations of a HR department, I also gained skills across areas such as policy development, the intricacies of an intranet, and how a strategic HR function operates within a large business.

I would highly recommend Isentia for all future interns wishing to challenge themselves with something new and exciting – I certainly loved my time there and will carry that experience with me throughout my career!

Allan Soo 
Student from the University of Technology, Sydney NSW
Combined Degree in Business Management (Hons) and Mechanical Engineering (Hons)

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Optimise your customers' journey across all touchpoints to achieve a holistic and customer-centric experience

Customers now have a powerful voice in sharing their experiences and with it, comes an expectation for actions to take place as a result of their feedback. In our digital world, customer data is nearly limitless – but people are much more than data. Their lives are defined by driving wants, needs and desires with an endless amount of choice and more often than not, brands believe they are delivering a better experience to these people than they actually are.

For those brands or organisations that choose to close the experience gap and embrace maximising the customer experience (CX), are finding themselves in a race to the top. By understanding what drives your customers’ decisions and the other influences that are out there, you can improve overall business growth and success over your competitors by making decisions based on customer intelligence.

Optimising the customer experience

A customer’s feedback has the power to transform your organisation through innovation and by improving their overall experience it can reduce customer churn. No matter where your organisation is in terms of CX maturity or customer feedback management, it is important to have access to customer insights in order to implement strategies to retain them. 

Here are 3 steps to maximising the customer experience:

1.       Illustrate the customer journey

The customer experience is made up of many customer journeys – the path customers take to solve a problem or need. The better experience your customers have with your brand or service, the more engaged they become, and the more opportunities become available. Having a great customer experience can also promote customer loyalty and as long you continuously optimise every element along their journey you will have satisfied customers.

Understanding the steps of your customers journey through various touchpoints, engagements and interactions with your brand will help to properly target your customers and understand their requirements and their pain points. Divide the customer journey into phases and pay close attention to each component by measuring the outcomes, collecting feedback and applying this feedback where possible. This will maximise customer success.

2.       Drive value from experience data

Looking at both quantitative and qualitative approaches across various facets of your business must be considered to give a complete picture of your customer data. Looking at one source will only give an incomplete representation.

Customer experience is more than sending surveys and collecting feedback – having this information is important but it’s also about enriching and humanising the experience and using these unique experiences to create a positive customer centric culture. Sharing insights and developing processes to improve the customer experience and create business value allows the best experience possible. It also generates the maximum return on your efforts. Obtaining this information can be done through swapping knowledge between cross functional groups by identifying where there are gaps as well as what's working well. A team dashboard can also be created that specifically looks at different touchpoints and their success. Whatever data you do gather, turn it into actionable insights that directly improve your customers 'experience.

3.       Learn from churn when it happens

Reducing customer churn is always sought after, however is quite difficult to achieve. Churn happens from poor experiences (both operational and strategic) and can have a drastic effect on your bottom line but it can also be helpful and insightful for your brand to learn and improve. For the customers you’re not able to prevent from churning, be sure to find out why they decided to move on. Conduct a short exit interview with the customer to understand their experiences and their pain points and take this knowledge to make improvements.

Fundamentally, it’s important to ensure a positive customer experience to encourage your customers to build brand loyalty. Customers hold the power in today’s business landscape which is why seeking feedback on their experiences is valuable to your brand or organisations' performance and reputation.

Happy customer, happy life.

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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.

There isn't one AI version of your organisation

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:

• What information should I retrieve?
• Which sources should I trust most?
• What deserves emphasis in the final answer?

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.

The same prompt can produce four different narratives

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:

• Should I work with this company?
• Which university is most innovative?
• Has this government agency delivered on its commitments?
• Who are the market leaders in this sector?

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.

Every LLM has its own citation fingerprint

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 – Building consensus from multiple sources

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 – Reading the living web

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 — explains the ‘why’, in addition to the ‘what’

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 — makes your media strategy visible

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. 

How commercial brands and government agencies are represented differently

Although LLMs behave similarly across sectors, the questions users ask differ fundamentally.

• For brands, AI compares before customers get the chance 

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.

• AI becomes the interpreter of policy for government agencies

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.

Measuring AI reputation with Lumina AI View

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:

• The people reading it.
• The AI models learning from it.

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. 

What this means for PR and communications professionals

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. 

• First, publication quality becomes more important than quantity, as different LLMs repeatedly reference authoritative sources when constructing narratives around your organisation.
• Second, visibility for the organisation’s spokespeople now extends beyond interviews and articles. The expertise attributed to leaders increasingly shapes how AI explains an organisation's direction and credibility. 
• Finally, competitor positioning is now continuous rather than campaign-based, as AI naturally compares organisations whenever users ask for recommendations or leadership insights.

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.

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Want to see which sources are shaping how AI describes your organisation? Get in touch about Lumina AI View.

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