As AI becomes more and more embedded in workflows, it’s important not to neglect the human element, says MBM’s chief digital officer Emily Isle.
Your brain matters. I have a feeling someone needs to tell you that.
You are brilliant, creative and uniquely human. Do not let this be challenged by feelings of inadequacy in the face of AI.
AI is a people story, not just a tech story. Adoption succeeds or fails based on the capability, receptiveness and experimentation mindset of people in an organisation, not on tools alone. As AI touches more of our modern knowledge work, the risks become clearer when the human element is neglected: motivation drops, anxiety about replacement creeps in, and inboxes fill with AI‑generated work that passes problems on instead of solving them. This isn’t an AI problem. It’s a leadership problem. And it quietly erodes efficiency, performance, trust and morale.
So how do we empower our people toward better AI outcomes?
Building the right foundations and fostering a culture of learning and engagement around AI is a new leadership muscle we’re all developing. Here are a few of my observations.
Progress over perfection
AI rarely gets it right the first time – and neither will you. That’s ok.
Organisations can miss the opportunity to build confidence, capability and trust where it matters most: with their people. Progress comes from experimentation, and from creating the conditions where learning feels safe rather than intimidating.
With this in mind, we started with small, low risk, high value use cases that could make daily work easier and enhance learning development and skills for our people. Think: AI-assisted meeting notes, training Copilot to learn a writing style, or simple ways to prioritise inbox overload. Many of the most useful applications began as personal tools – but over time, the best of these were shared, refined, and adopted within teams.
We paired this with hands-on training, access to a secure sandbox environment thanks to Publicis global tools, and a small ‘flash team’ of people keen to lean in and prioritise use cases for a test-and-learn roadmap.
It’s not fancy and honestly, that’s the point. AI theatre is distracting. Real productivity gains compound quietly over time. Democratising AI, rather than centralising or mystifying it, makes it feel accessible and empowering.
From individual wins to shared collective advantage
Once a learning culture starts to hum, individual gains show up at the workflow level in how teams share knowledge, make decisions, and reduce friction across everyday processes. This is where AI moves from being a personal efficiency tool to a source of collective advantage.
In practice, that often means applying AI to the ‘behind-the-scenes’ parts of work. At MBM, that has included introducing smart search and data solutions for our clients, as well as new tools that automate pacing, reporting and analysis. Other applications have focused on advanced audience targeting, creating conversational analytics, and advancing our capabilities around social listening and emerging search behaviours like AEO and GEO – not as standalone innovations, but as extensions of how people think and work.
Individuals have gone from thinking they don’t have the knowledge or skills to deliver on an idea, to being able to transform that idea into reality with the help of AI. This encourages more ideas. Clients benefit from these ideas, along with clearer insight, better decision making, and future-fit capability. But the critical point is this; those compounding benefits are powered by people, not replaced by machines.
Building the future of work
As AI becomes more embedded in how work gets done, the real question isn’t what the technology can do, but how deliberately we choose to lead, learn, and equip our people to work alongside it.
AI at MBM is about augmenting human intelligence, not automating creativity away. The human brain – with its ability to apply context, exercise judgment and imagine new possibilities – remains the differentiator behind great work. It’s what creates lasting advantage, and it’s what gives people pride and purpose in what they do.
As AI capabilities evolve, we have a responsibility to localise how they’re understood and applied. There’s a long‑term opportunity in investing through the early, often uncomfortable stages of technological change; building capability deliberately while nurturing a sustainable pipeline of New Zealand home‑grown talent. If our market adopts quickly, we hold a feedback loop advantage across both data and productivity.
The future is also focused on talent change management. Roles are shifting, and this change is necessary to balance tool development with the training and talent orchestration needed for its use. Innovation is a process, not a product.
That’s how we secure the future of work with AI.
I wouldn’t call myself an AI expert. But I am embracing it and constantly absorbing from the hands-on AI experts on my team.
