By Amrita Sidhu, Managing Director, Medianet

For those of us at the C-suite level, the conversation around artificial intelligence (AI) has often been framed as an either-or: human versus machine, creativity versus code. But that framing misses the point. The real question isn’t whether AI will impact our industry, but how we choose to lead through that disruption.

In media and communications, much of the anxiety I’ve witnessed comes from teams concerned about losing the human creativity that underpins trusted storytelling. That fear isn’t unfounded. According to the Medianet 2025 Australian Media Landscape Report, 88% of media professionals expressed concern about generative AI and large language models’ impact on journalistic integrity.

Yet there’s growing recognition of AI’s potential. Edelman’s 2024 report on generative AI in PR and marketing notes that AI is helping democratise access to insights, enabling non-technical users to work with complex data via natural language inputs.

AI is a process, not a product

So, how do we lead through this duality?

First, we can’t treat AI as a “set and forget” solution, especially in media, where one misstep in tone or context can trigger reputational damage. 

When we explored AI with Medianet’s Insights team, it wasn’t smooth sailing. The reaction wasn’t enthusiasm but resistance. People were sceptical. Some were anxious. And as the leader, I had to sit with that discomfort and guide the team through long discussions, shifting workflows, and moments of doubt.

Still, my conviction was clear: foraying into AI wouldn’t diminish our human-centric work; it would elevate it. Not from blind optimism, but because responsible transformation is necessary.

Our goal wasn’t to hand over control to AI, but to elevate what makes our people invaluable: interpreting nuance, identifying trends, and challenging assumptions. AI now handles the repetitive heavy lifting—entity recognition, author signals, baseline measurement, and reporting—while our analysts interrogate, contextualise, and shape the story. They also train the models, creating a feedback loop where machines learn from humans, and humans sharpen their insights and measurement with machine support.

Efficiency is just the start 

The impact? Tangible. AI-assisted workflows cut manual processing time by over 75% and improved efficiency by 2.5x, giving analysts more capacity to add value where it matters most. But more importantly, this shift didn’t happen because we brought in new tools. It happened because we led it.

We invested time, built trust, and made space for scepticism. That was a leadership decision; staying the course when the team wasn’t sure, and setting success metrics that go beyond speed and cost to include depth of insight, client impact, and yes, P&L outcomes.

As executive leaders, isn’t that how we’re ultimately measured? The 6-figure positive impact on our P&L is how I was measured. 

Until AI demonstrably enhances performance against strategic objectives and the bottom line, what’s being achieved?

This, I believe, is what’s missing from most AI conversations. Augmentation isn’t just a technical choice; it’s a “leadership” model. It takes vision and confidence to say, “We’re not automating this away; we’re elevating it.”

At Medianet, our AI stack is built on a simple principle: automate where it makes sense, augment where it adds value, and keep humans in the loop where it matters most. From sentiment analysis to trend detection and tagging, we balance technology with context and ethics, ensuring robust, human-informed media measurement drives every insight.

The allure of full automation, promising efficiency and cost savings, is powerful across industries. Yet, in creative fields like media, public relations and advertising, where nuance and accountability are paramount, full automation comes at too steep a cost. One misread headline or an oversimplified insight can shatter reputations – a risk we cannot afford.

Better questions, better outcomes

Whether you’re leading a global organisation or a five-person team, the principles remain the same. Lean teams can lead confidently in AI by focusing on strategic outcomes over shiny tools, while larger enterprises must balance innovation with governance, risk management and cross-functional alignment.

If you are a leader in the media space grappling with the AI question, my advice is this: stop asking how to “implement AI” and start asking what outcomes you want to improve. For us, it was about amplifying the value and scalability of our insights and measurement practices without compromising the craft. Your answer may differ, but the approach is similar: set goals, prioritise human input, and lead with conviction.

We’re still refining, still learning, still healthily sceptical, but more confident than ever in the results. Because here’s the truth I’ve learned firsthand: AI can accelerate your operations, but only intentional leadership can deepen the impact and deliver sustainable value.

And that’s the real differentiator. When AI is the great equaliser, creativity may set you apart—but it’s leadership that carries you forward.

Ends………….

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