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We spoke at Marketing Interactive's PR Asia 2025 recently in Singapore around authenticity, trust and how these are at a strain, specifically in this new AI-powered world. We were amongst top leaders in the PR industry who touched upon how crisis and reputational threats need to be dealt with authentically. Most importantly, companies must be ready for any kind of crisis communications to be activated with statements from senior leadership, without a sense of "doing everything reactively", i.e., the logistics need to be in place so that teams have enough time to be responsive rather that reactive.

Audience perceptions of AI: do we know what's real?

Russ Horell, Chief Revenue Officer, APAC touched upon a few cases that set the tone around how audiences have not been able to clearly identify which online content is real and have ridden the wave until someone figures it out. The two main examples that were touched upon were around how Mia Zelu, a virtual influencer on Instagram became the face of Wimbledon this year, until everyone realised she's not real.

The other case was around former Astronomer CEO Andy Byron's fake statement that was circulated - although not AI, it gives us an insight into how trust in CEOs is at an all time low, with this incident taking it further underground. In this world of fakes, audiences have given up on trying to decide what's real. This needs to be urgently addressed by PR leaders when it comes to brand communications, especially during a crisis.

Our CEO for Pulsar Group, Joanna Arnold was in attendance of the speaking session and at our booth to support and motivate as always. This gave us an extra level of confidence to interact with the visitors at the booth and to speak with them about who we are, what we do and more insight into our content.

Assigning cues to audience reactions

With all this in mind, we wanted to understand how leaders, specifically PR leaders can own their content strategy and decision making when it comes to responding effectively.We analysed posts by top executives and c-suite leaders on LinkedIn and audience behaviour to those posts. We then assigned cues - cues that identify which post is the most authentic in terms of cultural relevance, identity, tone & style, trust, information accuracy etc. Prashant Saxena, Vice President, Revenue & Insights, SEA expanded upon how these cues can be utilised to increase engagement 3-fold. This transforms authenticity from subjective performance into an executable framework that any leader can deploy. The pattern is clear, and posts with multiple authenticity cues consistently outperform those relying on tone alone.

Booth interactions

Jenna Wang, Business Development Director and Christian Chan, Business Development Manager for Isentia, Singapore were having engaging and insightful discussions with attendees, considering the topic at hand is an important one with an almost "what to do" playbook that leaders can use effectively in their communications. We knew many would be keen on understanding and wanting to know more as a follow up to the speaking session. Nikita Gundala, SEA Marketing Lead, managed the content and the logistics around the booth display along with timely updates on our social media.

We had a wonderful experience at PR Asia this year and we look forward to being a part of (and hosting) more such events where we can bring together industry leaders to understand how they navigate new challenges and what can be done about them.

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Interested in learning more? Email us at info@isentia.com

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A World Of Information Without Noise 

Big data is more than just a buzzword. It’s one of the biggest challenges and opportunities facing almost every industry, business and brand today. With the potential value that it holds, investment in big data, machine learning and AI will be crucial for any business that wants to remain relevant through the ages.

Big Data

noun : extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.

Each day 2.5 quintillion bytes of data is generated – a number that continues to grow exponentially. While we have seen improvements in the collection of data over recent years, the ability to synthesize meaning from this data is demanding more from engineers and their technology than ever before.

The problem that we face is sorting through these huge chunks of data to separate the noise from what is important to individuals and their organisation. While automation has offered speed, simplicity and efficiency, the ‘why’ is where the untapped value and excitement lies.

“Contextualisation is key. It's not about just collecting data, it’s about how that data can provide clear information that enables and inspires action”

Richard Spencer, Chief Marketing Officer at Isentia.

Rather than reflecting on past performance, answering the ‘why’ has the potential to lead action that focuses on influencing the tomorrow.
Beyond big data, the 'why' behind AI and machine learning may raise new questions. For instance the wider interplay behind machine learnings ability to  translate to a language without any knowledge or assumptions about that language.

As teams start to ask these questions, the data starts to be reimagined. The perception of a data point transforms into breadcrumbs of a narrative that can tell a bigger story, and ultimately influence our thinking.

The question is, when big data becomes manageable and meaningful – how fast will it move into being predictive? And even beyond this, be able to simulate what is ‘likely’ to happen.

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It’s official: artificial intelligence has arrived. But how will this disruptive technology transform businesses in the near future?

After more than a few false starts, artificial intelligence (AI) is finally here, and it’s powerfully disrupting the way business is done. We don’t need to ask if or when businesses will adopt AI – the question is where and how widely it will be employed.

AI is already a big player in the technology industry. In particular, there is a growing use of AI in IT’s backroom functions like cybersecurity and tech support. A Tata Consultancy Services (TCS) survey of 835 company executives found that nearly half of respondents were using AI to detect and fend off intrusions – the most frequent use of the technology. But a number of other industries are also opting for AI.

Early adopters

In entertainment, companies like Netflix and Amazon are using machine learning to help their movie recommendation engines. Health care has seen myriad applications, including virtual assistants for doctors, apps that can interpret test results and even AI-based spine surgery technology. In the financial sector, AI has been put to work in regulatory compliance and fraud prevention – PayPal uses a combination of its own AI program and human analysts to combat fraud, for example, and HSBC has teamed up with Silicon Valley startup Ayasdi to automate anti-money-laundering investigations.

Worldwide spending on cognitive and AI systems is expected to reach $12.5 billion this year, according to IDC, a whopping increase of 59.3 percent over 2016. Much of this growth is powered by use cases like the examples above. But there’s another area where AI is rapidly being adopted: automated customer service agents, or chatbots as they’re more commonly known.

Customers now expect AI to be used by companies and they are comfortable interacting with the technology (up to a point). Research from HubSpot found that nearly half of people are happy with the idea of buying products from a chatbot. Perhaps more importantly, 40 percent of respondents said they were indifferent about receiving customer support from either a chatbot or human – provided they got the help they needed fast and easily.

Dealing with data

Whether patrolling a computer network for intrusions or trawling through financials for signs of fraud, AI is most often employed to intelligently handle vast amounts of data quickly. “AI is best deployed in companies with significant amounts of data and robust data systems,” says Andrea Walsh, Isentia’s CIO.

Gartner predicts that, in 2018, half a billion users will save two hours a day as a result of AI-powered tools. Every time a business gains efficiencies, it saves money – and that is AI’s chief benefit.

AI’s smarter processing power is also helping companies generate more quality leads on new customers, using IBM’s Watson AI, for example. Finding, contacting and closing new sales is a time and resource-heavy activity. But AI-based sales assistants can tirelessly work on reaching out to people, while intelligently analyzing data on leads. This can then be effectively communicated with point-of-sale staff.

When employees hear the word “efficiency,” they often assume it will lead to lay-offs. While there is no question that some jobs will be replaced by AI programs, the naysayers are largely exaggerating their mass-redundancy predictions.

AI is a data-cruncher, and it is often employed to take care of something that didn’t even exist 30 years ago: big data. When it accomplishes its analysis, a human is still needed to interpret the results, such as in cybersecurity and anti-fraud scenarios. Even in the case of customer service chatbots, these will mostly be applied to routine queries and simple support functions, augmented by human representatives for complex problems. “AI should not stand alone as a technology,” say Walsh.

Enhancing existing infrastructure

As with all industrial revolutions, AI will create jobs even as it replaces them. There are already glaring shortfalls in STEM-trained employees across the world, and that’s likely to continue as the rapid pace of technological transformation outruns educational reforms. But eventually, new generations will be trained and educated to do jobs created by innovative technologies like AI.

Any business can benefit from AI programs, but when it comes to how broadly they adopt AI, companies need to look at how the technology can augment their existing capabilities. Instead of replacing staff, current AI should be used to support them and put their invaluable human minds to the best use, saving tedious, data-crunching work for the machines. For customers, AI needs to be a helpful, timesaving addition to their experience, and companies should never try to create the false impression that a human is doing the work. People are ready for AI; companies need to be too.

Andrea Walsh, Isentia's Chief Information Officer

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Why PR and comms teams need to take LLM visibility seriously — and what to do about it

The next time a journalist, investor or potential customer wants to know about your organisation, it’s now increasingly likely they won’t Google you. They'll ask an AI.

They'll type a question into ChatGPT, Claude or Gemini, something like "Who are the leading renewable energy companies in Australia?" or "What's the best PR agency for healthcare in Singapore?" and the AI will give them an answer. The question is whether your own organisation shows up in that answer.

The implications are significant for communications professionals, whether they’re in the agency-side working with clients or in-house managing a brand. The rules of reputation and discovery are being rewritten, and there’s a new kind of playbook that we all need to adapt to. That’s what’s going to take us forward.

The shift no one saw coming, but perhaps should have

For decades, earned media has been the backbone of credibility. A strong piece in a respected outlet signalled trust, authority and relevance. This hasn't particularly changed, but the way that coverage gets used has.

Large language models (LLMs) are trained on vast amounts of publicly available content - news articles, company websites, industry reports, social media, expert commentary. When someone asks an AI a question, it synthesises all of that material into a single answer. If an organisation has a strong, consistent, well-sourced presence across those channels, it is more likely to show up. If it doesn't, it becomes invisible and is absent from the conversation entirely.

Gartner's latest predictions for Chief Communications Officers underline how serious this shift is. They forecast that as LLMs increasingly replace traditional search, PR and earned media budgets will double by 2027. What they say is that this is a communications challenge, one that requires PR expertise to build trust, secure quality coverage, and maintain consistent messaging across stakeholders.

Their research also predicts that by 2029, 45% of CCOs will be using narrative intelligence technologies to monitor reputation amid rising disinformation, a recognition that the old keyword-based approach to media monitoring simply can't keep up with the way stories now form, spread and multiply. 

The AI-generated content loop and why it matters

One of the less obvious risks in this new landscape is what happens when AI starts feeding on itself.

Catherine Arrow, Executive Director of the PR Knowledge Hub, raised this point during Isentia's recent Inside the AI Shift webinar. As she explained, "AI can identify and interpret some publicly available commentary. The difficulty is that we have to be careful about what it is actually reading. You can already see this in AI overviews where the system may refer to online discussion without digging deeply enough into whether the original sources are genuine, reliable or themselves AI-generated. So we end up with AI nested inside AI, nested inside AI."

That creates a real problem for anyone in communications. If the content landscape is increasingly populated by AI-generated material which is optimised to be found by algorithms rather than to inform real people, then the signals that LLMs rely on to build their answers become less trustworthy. Human judgement, original thinking and genuine expertise become harder for these systems to find, precisely because they're being drowned out by content that was designed to game them.

Catherine puts it simply, "People can become immune to this kind of content because it does not sound like the way we speak to each other, nor does it reflect the way genuine relationships are built. Then, when conflict or outrage is layered on top, the environment becomes even harder to interpret."

For PR and comms teams, it's not enough to produce more content. The right content needs to be produced, one that is original, expert-led, and well-placed in the channels and formats that LLMs are most likely to surface.

What this means in practice

So what does it actually look like to build LLM visibility into your communications strategy? It starts with the fundamentals, but applied with new intent:

• Expert commentary placed in credible publications. 
• Thought leadership that's genuinely distinctive, not a rehash of what everyone else is saying. 
• Consistent messaging across channels. 
• Media coverage that's authoritative enough for an AI system to treat it as a reliable source.

This is where the gap between media monitoring and media intelligence becomes critical. Monitoring tells you what's been said. Intelligence tells you how stories are forming, which perspectives are shaping them, and where your organisation sits within those narratives — including how AI systems are representing you.

Dr Nici Sweaney, Founder and Director of AI Her Way, made this distinction sharply during Isentia's AI as a New Stakeholder webinar. "What will set people apart, and what AI cannot replicate is the human lens. The judgment, the relationships, the institutional knowledge, the strategic read of a room. The organisations that lean into supporting their people to harness these tools, rather than just deploying the tools, will be the ones best placed.”

That's an important framing. The answer to AI disruption is to get clear on what only humans can do and then make sure the tools we’re using actually support that.

Staying credible when the noise is deafening

There's a temptation, when faced with a challenge like this, to throw more content at the problem – more posts, more articles, more releases. But Catherine Arrow points out the risks of that approach.

"Maintaining credibility and authenticity means being yourself and not allowing AI to suffocate your identity. That will become harder to do as digital twins, synthetic voices and other tools make it easier for organisations to use it as a mask. The real challenge is not so much maintaining credibility. It is about maintaining humanity, empathy, kindness and a genuine wish to connect with others beyond the AI-intermediated space.”

That advice matters just as much for organisations as it does for individuals. Brands that let AI do their thinking, generating bland, interchangeable content at scale, will find themselves blending into the noise rather than cutting through it. The brands that show up in LLM answers will be the ones with a clear, consistent, well-evidenced point of view.

Dr Nici Sweaney reinforced this from the operational side. "Ethical use is not about not using AI. It’s about using it with intention, honesty, and a clear sense of what good looks like on the other side.”
She was also direct about the risks of rushing in, "Don’t add new shiny AI projects on top of already overloaded teams. That creates resentment, not buy-in. Start by solving the problems people already have."

The cultural dimension

There's another layer to this that often gets overlooked and that’s the cultural one.

Catherine Arrow raised important concerns about how different AI systems can distort or flatten cultural context. Many of the most widely used models are shaped by US language, commercial assumptions and social norms. Chinese models operate within a different political and cultural framework. For organisations working across the Asia-Pacific region, it directly affects how the brand, messaging and the market are understood and represented by AI.

"Different AI systems may distort cultural context by privileging dominant languages, simplifying complex meanings, mistranslating concepts, omitting local histories or reproducing the worldview of their developers and training environments. They may flatten culture by making everything sound the same.”

For communicators operating across diverse markets, this means paying close attention to where content sits, who produced it, and whether the AI systems the audiences are using can actually interpret it with the nuance it deserves.

Where Isentia's platform fits with its new toolkit for AI visibility

This is precisely the challenge that Isentia's Lumina suite was built to address. Lumina is an intelligent suite of AI tools trained on the language, workflows and realities of modern public relations and communications, designed to empower, not replace, the human element of communications strategy.

Isentia's Lumina AI View feature will allow organisations to track how their brand, competitors and key topics are described by leading LLMs, with auditable claims, citations and transparency with regards to the sources. It's the difference between wondering whether AI is getting your story right and actually being able to see for yourself. These aren't generic AI features bolted onto a monitoring tool. They're intelligence systems built for the way communicators actually work.

The bottom line

The communications landscape has shifted. AI isn't just a tool the team might use, it's a stakeholder in its own right, actively shaping how an organisation is discovered, understood and evaluated.

For PR and comms professionals, the priorities are to ensure experts, commentary and evidence are placed widely enough for LLMs to find them and include them in their answers. Intelligence is imperative and required to how narratives are forming across both traditional media and AI platforms. All of this needs to be done without losing the human credibility that makes communications worth paying attention to in the first place.

As Dr Nici Sweaney put it, "The people who get the most from AI aren’t the ones who use the most tools, they’re the ones who understand their work deeply enough to know exactly where AI can add the most leverage."

That's the opportunity. The question is whether we’re set up to take it.

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To explore how Isentia's Lumina suite can help your team navigate AI visibility, get in touch or discover Lumina.

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