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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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If your New Year’s resolution is to get ahead of the tech curve, you’re in luck.

As artificial intelligence (AI) transitions from novel to normalised in 2018, there are many ways you can integrate advanced technology into your day-to-day life, making you more productive at work and at home. Andrea Walsh, one of Australia’s most successful CIO’s, shares tips on how everyone can use machine learning to squeeze more out of the day.

Decision making

You may not trust a computer to make important decisions for you, but it can help guide your choices. Committed to read more in 2018? Amazon will analyse your previous purchasing behaviour to recommend books you might like. If you’d simply like to reconnect with old friends or spend more time with new ones, Facebook will flag friend suggestions for you. If you’re in the market for a new job this year, let LinkedIn’s algorithms suggest jobs you may be interested in or people you should be networking with. Embrace these tools to help cut through the noise and then use your own insight to make decisions on a narrowed, personalised field.

Be more punctual

If you are perennially late and have vowed to be more punctual in 2018, Google Maps is your new best friend, helping you avoid time-sucking activities like getting lost in parking lots or being caught in heavy traffic. Using data from your smartphone, Google is able to provide you with directions to where you parked your car. On the road, Google will analyse your position together with anonymised data from other smartphones to suggest the fastest route to your destination. If driving full-stop is your peeve, then you will be pleased to hear that California authorities will allow self-driving cars to be tested alongside cars driven by humans on roads this year. Experts predict this could result in a 90% reduction in accidents (which will arouse all sorts of ethical debates as to whether humans will still be able to drive cars), 75% less cars on the road and reduce the work commute by almost half.

Boost creativity

With the rise of machine learning comes the fear of job losses. “The development of full artificial intelligence could spell the end of the human race,” Stephen Hawking told the BBC.

An Oxford University survey suggested that 47 per cent of the world’s jobs could be replaced within decades. Autonomous cars present one example of how jobs in transport and logistics may be replaced by robots. With this uncertainty comes the understanding that routine work is far more likely to be automated than jobs requiring skills like creativity or emotional intelligence. Machines may be adept at processing large volumes of data, but they can’t make insightful or creative decisions. The good news is that as machines become smarter, humans are freed from mundane tasks and can become more creative. If you’re in a small business, using accounting products like Xero to manage your financial reporting. This allows you to turn your attention to business boosters like problem solving, improving customer service or creating new products. If you’re in big business, tools like Amazon Transcribe or Amazon Translate can perform laborious tasks like producing and translating documents with lightning speed and accuracy, allowing you to focus on big picture thinking like strategy and profitability.

Stay on top of current affairs

In my work at Isentia, we use machine learning to process seven million news items each day. Not long ago this was a task relegated to humans with the mind-numbing task of flipping through newspapers in search of stories that might relate to a client. Machines trawl video, audio and digital content across more than 5,500 new sites at a rate of 234 stories per second and present meaningful summaries to clients in real-time. Whether a story breaks on Twitter and then spills across news platforms and onto television and radio, machine learning can track and analyse how a story evolves with 99% accuracy. Use these tools to stay on top of the issues or people relevant to your industry – in real time.

Make your mark in 2018

The robots aren’t ‘coming’, they are well and truly here. Without realising, we interact with ‘smart’ technology at almost every touch point of our daily lives. As a technologist, I am excited by machine learning not only because I see its profit boosting value, but also for how much it can improve our working lives each and every day.

If you learn one thing this year, take the time to discover how AI can help you be a more creative and productive version of you in 2018.

Headquartered in Sydney, Australia, Isentia is a media intelligence company operating since 1982. The company is backed by over 1,200 employees with 18 offices across Australia, New Zealand, Asia, Europe and the US. Isentia provides more than 5,000 clients, including many of the world’s leading brands, companies and governments, with media intelligence software and services that help drive more informed and timely business and communication decisions.

Originally featured on Women Love Tech.

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Ngaire Crawford, Director of Insights, ANZ

In leadership meetings across the industry, a single question has become unavoidable: "What is our AI strategy?" Behind this question is often the unspoken hope for an "AI Easy Button": a mythical, one-click solution to our most complex measurement challenges. As someone who spends a large portion of my time designing these new frameworks, I'm infinitely more excited about the blueprints and the foundations than what colour the house is painted.

For the first time in my career, we have the tools to stop using proxies and start building what we've always wanted: true, at-scale, sophisticated measurement. The real opportunity isn't in automation, which lets the AI decide;  it's in the architecture and design of systems for the AI to follow. For decades, I’ve been frustrated by proxies. I’ve watched organisations use metrics like Impressions and Share of Voice as proxies for impact and influence. Too many people have been measuring the loudness of their voice, not whether anyone was actually listening.

Much of the history of communications measurement has been a story of 'good enough' data. And in some cases, data that wasn't even good at all (*cough* AVEs). 

Why a blueprint still needs an architect

But before we can harness the potential of AI, we have to be honest about the technology and tools we're working with. As anyone who's ever used a "smart" tool knows, they can be... well, confidently wrong.

The new challenge isn't just "Garbage In, Garbage Out." The new challenge is that the AI has become a high-speed, frighteningly convincing echo chamber. When a machine delivers a flawed insight, it does so with the resolute certainty of a supercomputer, laundering that flaw into a "fact."As architects, our job is to audit the blueprints and stress-test the materials before we build the house. When my team and I test these models, we're not just looking for what they do right. We're methodically hunting for where they go wrong.

Where we continue to see a critical need for human intervention and expertise:

• Context Blindness: AI is a brilliant pattern-matcher, but it has limited real-world context and struggles to identify the intent of what’s being analysed. It can miss the nuance of language, the authority of a source, or whether something is fact or speculation.
• Language Bias: This is my personal favourite and takes a few forms. AI is trained on text, but it isn't (yet) trained on human subtext. This can look like missed nuance for slang used by younger audiences or emerging shifts in the meaning of language. Models are ultimately impacted and biased by their training data, so this can also mean larger systemic biases are amplified and not appropriately interrogated.
• Viewpoint Collapse: While AI can sometimes get locked into a perspective based on its training, it can also collapse multiple, distinct viewpoints (like a speaker's sarcastic intent vs. the literal text) into a single, flat monolith. This drastically changes the outcomes of your analysis and ultimately the understanding of your audience.

This is the methodical, behind-the-scenes work that often goes unseen, and it is the crucial due diligence needed. It’s not as flashy as writing a press release faster, but it’s the only way to build a tool you can actually trust to make a strategic decision.

New tools, same bedrock principles

This testing isn't just about finding technical bugs or funny hallucinations. We’re testing these new AI models against the foundational, hard-won principles of communications measurement that our industry has spent years formalising.

AI is an incredibly powerful new tool, but it doesn't get a free pass. It still has to follow the rules of good measurement.

• Measure outcomes, not just outputs: This has always been our goal. An AI-driven approach that only counts outputs (like mentions or sentiment) 1,000 times faster is still just a faster measure of noise. It doesn't tell you if a single mind was changed or a single action was taken.
• Demand transparency: A metric is useless if you can't explain how it's calculated. This is my biggest critique of the current "plug-and-play" approach to AI. If a vendor provides a proprietary 'Reputation Score' of 7.2, and they can't (or won't) tell you the formula, it's not a metric. It's marketing.
• Link activity to business objectives: This is the most important rule of all. The only reason to measure is to inform a strategic decision that ladders up to a business goal. A tool that just produces data, but no clear insight linked to your specific objectives, has failed.

When we stop seeing AI as a magic box and start seeing it as a powerful, scalable engine, one that we must build and steer based on these principles, then it becomes truly transformative.

The payoff: the tools are finally catching up to our ambition

A new frontier of opportunity is here. Such as the capability to move from being reactive to being predictive, and it takes careful design to get this right. Our traditional analysis has been brilliant at explaining what has just happened. Now, as architects of these new systems, we are building and testing AI models that can scan the horizon for the faint signals that precede a major narrative shift.

We can empower movement from broadcasting and the old spray and pray approach; to precision, deliberate engagement of stakeholders and audiences. This is another area where the craft of measurement design is essential. AI gives us the power to see the micro-communities and specific, high-authority voices that actually shape opinion. The work is in designing the models that can identify them accurately.

Finally, we can (at last!) move from quantifying to qualifying at scale. For me, this is the most exciting and complex challenge. For 20 years, I’ve had to choose: a large-scale quantitative study (which missed nuance) or a small-scale qualitative review (which couldn't be scaled). As architects, we can now design frameworks that don't just give a "positive" score but confirm that a specific strategic message landed, with the right audiences, and in the intended context.

That is the opportunity. It's not magic. It's the methodical, patient engineering we've been waiting for. It’s the difference between a "plug-and-play" gimmick and a truly strategic asset. The real payoff isn't just faster reporting, it’s about fundamentally upgrading behaviours and expectations of measurement. This isn't an overnight shift. As any research leader will tell you, a new methodology takes time, testing and refinement to get right.

The future we've been waiting for

For my entire career, we’ve been strategic thinkers working with tools that could only show us the past. We were forced to be historians, meticulously analysing what had already happened to predict future behaviour. The key to using this new, complex technology effectively is; strong communication, articulation and critical human thinking. The power of any AI is unlocked by the quality of the question you ask it. It's a system that rewards clear, precise, and strategic language.

This is a massive homefield advantage for communicators, who have spent their entire careers honing the exact skills required to be the architects of this new era. The AI we are using today is the worst it will ever be. It will only get better, faster, and more capable from here. This is what's so thrilling, and it's just the beginning. This new generation of AI driven approaches doesn't replace our intuition, it amplifies it. As communicators (and researchers!) this is the moment to level up. We get to be the explorers and the strategists who connect communications directly to business, policy and societal outcomes. 

We're not just building better measurement and deeper insights; we're leading a more intelligent, more responsive and more impactful profession. What an incredibly exciting time to be in this industry.

Ready to be the architect of your own measurement strategy?

To learn how to build the right KPIs and tell a compelling story with your data, register for our live webinar:

• Topic: Making Communications Count: Build your KPI confidence and storytelling"
• Date & time: 12 November, 11am AEDT/ 2pm NZT
• Hosted by: Ngaire Crawford, Director of Insights for ANZ, Isentia.

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