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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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Following our webinar on 5 May, our panelists respond to the questions we didn’t get to on the day.

How comms leaders need to adapt to this new AI shift at the workplace?

AI is already shaping your organisation’s reputation — whether you’re managing it or not.

On 5 May, Isentia brought together three leading voices in communications and insights for a conversation about what’s really happening on the ground as AI reshapes the workspace. Catherine Arrow (Executive Director, PR Knowledge Hub), Russ Horell (Isentia APAC’s ex-Chief Revenue Officer) and Ngaire Crawford (Isentia and Vuelio's Executive Director for AI Strategy in PR & Comms) explored how communications leaders are navigating AI conversations with executives and boards, where pressure is increasing across risk, measurement and strategic advisory, how teams are adapting workflows and decision-making in response to AI influence, and where do communicators see the right opportunity.

The session saw many questions popping up from our audiences that we couldn't really address them all. So we went back to our panelists and asked them to respond. Below, Catherine Arrow and Ngaire Crawford share their thoughts on what attendees most wanted to know.

Catherine Arrow, Executive Director, PR Knowledge Hub

Catherine Arrow is the Executive Director of PR Knowledge Hub, a professional development and training organisation for public relations practitioners. A veteran of the communications industry with deep expertise in strategic counsel, crisis and issues management, and information disorder, Catherine is known for her clear-eyed thinking on the intersection of AI, reputation and organisational responsibility. She is a trusted voice on what AI actually means for practitioners — not in theory, but in practice.

Q1. Comms professionals often have an idea of how AI can help us, but often the C-Suite have other (less informed) ideas. Do you have examples of how you’ve tactfully pushed back or diverted focus back to where you feel it should be (outcomes focused)?

One of the main difficulties is that organisations and their leaders seldom have a clear picture of what they already have at their fingertips when it comes to AI. Many organisations, for example, use the Microsoft suite and may already have access to Copilot, but what can actually be achieved depends on the licences, payments and subscriptions in place. At the same time, leadership teams are influenced, as we all are, by the level of hype that has bubbled to the surface over the last 12 months. Too often, AI is regarded as a passive tool that lives inside a box and as practitioners we have a role to play helping leaders move beyond that limited view. We need to help them understand not only the functional use of particular tools but the bigger picture, to understand the impact AI may have on the organisation’s decision-making, relationships, reputation and licence to operate. The issue is whether the organisation understands the consequences of handing decisions, or the appearance of decisions, to AI in ways that may affect stakeholders, employees, communities of interest and others connected to the organisation’s activities.

So, when I need to tactfully push back or redirect the conversation, my starting point is usually a set of simple questions. What are you trying to achieve with this? How does it align with your organisational outcomes? Is it being applied ethically? Do you understand the consequences? What could it do to your reputation, relationships and ability to maintain your licence to operate?

That approach allows the conversation to move away from the excitement of the new shiny tools and back towards purpose, responsibility and organisational impact. From there, you can begin to workshop the options, discuss the implications, consider the real costs and identify the areas that need attention before AI of any kind is deployed.

Q2. How much is AI picking up on social media commentary as part of its description of organisations?

Yes, AI picks up social media commentary but it will only pick up what it can access. Generally, that means publicly available commentary or material available through an API connection or approved data source. So, in terms of general digital chatter, yes, AI can identify and interpret some of that activity.

The difficulty is that we have to be careful about what it is actually reading. You can already see this in some AI overviews and AI-generated summaries, where the system may refer to “chatter” or online discussion without always 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 bigger problem for communication and engagement. People are increasingly using AI to generate and optimise social media content but that is not the same as engaging with people. At the same time, many platform algorithms are designed to reward optimised content. The result is a circular loop where AI feeds AI, which feeds AI again. Human language, judgement and connection get pushed aside.

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.

So the short answer is yes, AI can monitor social media commentary. The longer answer is that it often does so in ways that require considerable caution, human judgement and a much deeper understanding of what is being surfaced, amplified and missed.

Q3. How are you maintaining credibility in a landscape flooded with AI-generated content?

Personally, I try to maintain credibility by doing my best to remain human. That is probably the best advice I would give to others as well. Use your own intelligence to understand the people and communities you want to engage with. Do not use AI as a barrier between you and them. Use it as a handy tool. Let it help you edit where necessary, test an idea or explore an angle, but do not hand over your voice, judgement or identity. The same applies to imagery. If you are creating images with AI, treat it as a collaboration rather than giving the system an idea and simply running with whatever it gives back. AI-generated imagery carries assumptions and bias, so we must question what is produced and make conscious choices about what we use.

For me, 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.

Q4. Globally, it would be interesting to learn how each country’s culture is reflected in the messaging as filtered by LLMs.

Different AI systems can reflect, distort or flatten cultural context in several ways and one of the biggest concerns is the continental drift between the major model providers. Many of the systems most widely used are strongly shaped by US language, culture, law, commercial assumptions and social norms. At the same time, Chinese models are being developed within a very different political, linguistic and cultural environment – much better at APAC languages for example. So the question is twofold: whether an AI system is “accurate” and “accurate according to whom, trained on what, governed by which assumptions and optimised for which worldview”?

Training data matters enormously. In the early days of the general release of generative AI, we saw certain words and phrases appear everywhere. “Delve” is one example, and “dive into” is another. These were signals of the linguistic patterns embedded in the data, the training process and the reinforcement layers shaping outputs. When those patterns are repeated at scale, they begin to influence the way people write, speak and frame ideas. Over time, that blunts understanding, with distinctive voices, local idioms and cultural ways of knowing pushed towards a generic machine-mediated style.

There is important work being done by Māori researchers and others on the cultural impact of AI, particularly in relation to language and data sovereignty, indigenous knowledge and the right of communities to determine how their knowledge is represented, protected and used. The research is still developing but the concern is real. AI systems can absorb, repackage and reproduce cultural knowledge without context, consent or accountability. They can also misread or flatten concepts that do not translate neatly into dominant languages or Western knowledge structures.

That is why the homogenisation of culture and language is something we need to understand and contest. In many ways, AI becomes a form of digital colonisation. Knowledge is scraped, curated, classified and reproduced by systems that may have no meaningful relationship with the people, histories or communities from which that knowledge came. In some instances, it risks rewriting history, or at least a narrowing of it, where contested, local or marginalised perspectives are buried beneath the most available, most optimised or most dominant version of events.

So, 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. And that presents a real danger, not only for communication professionals but for society more broadly, because shared understanding, cultural memory and social cohesion all depend on our ability to recognise difference, preserve nuance and respect the knowledge that communities hold for themselves.

Q5. Where can we find Catherine’s upcoming sessions on misinformation and AI?

The Managing Information Disorder session will stream live on 2nd July. Please register here.

In case you can't make it, you can always signup and access the live recording. As part of the session, you will also receive the Information Disorder Framework and the practical tools that accompany it, designed to help you recognise and respond to misinformation, disinformation, mal-information, narrative attacks, deepfakes and other risks in the current information environment.

If you would like to know more about AI, the AI in Public Relations – What’s New, What’s Next and What Now? session is also available. It is designed to help you get up to speed with the latest developments, understand what they mean for public relations practice and identify what you need to do next.

You can also access some of the resources Catherine mentioned during the webinar, including the Chaos Compendium, which is freely available. It exists to help you think through what is happening now, prepare your organisation for the months ahead and take practical steps to manage the risks, issues and pressures already coming into view.

Ngaire Crawford, Executive Director, AI Strategy

Ngaire Crawford is Executive Director for AI Strategy, with a mandate spanning both Isentia and Vuelio to ensure the Group’s AI strategy is coherent, credible and commercially effective. A driving force behind Isentia’s insights and measurement capability for a number of years, Ngaire is a well-respected voice across the communications measurement industry — with customers, at industry events, and in the broader conversation about the future of PR and communications. Her curious, thoughtful approach, deep expertise in measurement, and early adopter mindset with AI have helped shape much of what Isentia is building.

Q1. What are some of the top errors or mistakes you see communications leaders make in regards to AI?

If we assume people are already off the first rung and past treating AI as a workflow assistant for drafting and summarising, the more interesting mistakes tend to start after that.

The one I’d put first is assuming this is a more neutral information environment than it actually is. It’s a tempting thing to believe after years of algorithmic outrage, the idea that AI hands everyone a calmer, more balanced version of events is genuinely appealing. But I don’t think the echo chamber disappears with LLMs; it just gets dressed differently. Social platforms built echo chambers by amplifying whatever made you angry. LLMs have a gentler version of the same habit, they’re built to be helpful and agreeable, so if you ask a leading question you’ll often get an answer that politely validates your framing. And the more personalised they get, the more pronounced that becomes. So when you’re thinking about how your audiences are forming views through these tools, what matters isn’t just what the system “says” — it’s who’s doing the asking, how they’re asking, and what the system has already learned about them.

And then a more practical one: getting the order of operations wrong when you build out intelligence capability. The instinct to bring more of this in-house is understandable, but it often gets handed straight to a data or tech team, and however good the pipeline they build, you can end up with something impressive that produces information nobody quite knows how to act on. What’s signal versus noise for this organisation, what’s actually useful to a comms leader — are communications questions, not engineering ones. Sort those out first and the technology tends to slot in behind them; do it the other way round and you usually get the impressive-but-unusable version.

Q2. Would it be accurate to say content with an overt evidence base will “perform” better in an AI information environment?

The thing is, “perform” is doing two jobs. There’s visibility (does evidence-rich content get cited more?) and there’s reputation (when you do get cited, is the picture the system paints one you’d actually recognise?) They’re not the same question, and an evidence base does fairly different things for each.

On visibility, it’s, broadly yes. Well-sourced, clearly structured, quotable content does tend to get picked up more, there’s research pointing that way, though honestly it’s mostly from controlled studies and it moves around a lot depending on the topic and the platform. But what’s getting rewarded there is just clarity, good sourcing, consistency, authority. Which is less a shiny new lever and more the basics of communications.

Reputation is where “perform better” can start to lead you astray. Getting cited isn’t the same as being represented well. You can have a flawless evidence base, get pulled into an answer, and still find that answer describes you in a way you’d never have approved because the model’s also leaning on everything everyone else has said about you. You can definitely nudge your visibility, but how you’re represented is downstream of your whole information environment, and that’s a slower, longer term shift.

So yes, a real evidence base matters, but not because it’s a button you press to perform better. It matters because being genuinely worth referencing is what trusted sources cite, and it’s those sources, built up over time, that shape how these systems talk about you. What I’d be wary of is treating an “overt evidence base” as something you manufacture to game your way in.

The conversation continues

What comes through clearly in both Catherine’s and Ngaire’s responses is that AI is a shifting set of conditions that communications professionals need to understand, question and actively work within, not just hand over.

The organisations that will navigate this well are not necessarily those with the most sophisticated AI tools. They are the ones asking better questions earlier, about purpose, about accountability, about what it means to remain genuinely credible and human in an environment where both are increasingly easy to fake.

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If you missed the webinar or want to revisit it, access the recording here. Watch this space — there’s more to come.

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