AI is moving fast. The harder challenge is moving the organization with it.

by Jon Holcombe, Managing Director, Australia and New Zealand 

There's no shortage of conversation about AI in marketing right now. AI search, LLM discovery, generative AI, agents, and automation are everywhere. Organizations are experimenting with what AI could mean for the way they market, sell, and engage with customers. 

But my biggest takeaway from the Martech Symposium in Hunter Valley (which was my second time in 10 years) was a different one. For all the focus on AI, the human element is becoming more important, not less. 

Technology is moving quickly. The harder question is whether our people, strategies, and organizations are moving with it. 

The marketing fundamentals haven't changed 

As more customers discover information through LLMs and AI-powered experiences, there's an understandable focus on what organizations need to do differently. But look beyond the technology, and many of the questions are still fundamentally marketing questions: 

• Who is your customer?
• What do you want them to know?
• What content will influence them?
• Where are they looking for answers? 

The channels may be changing, but the underlying challenge hasn't gone away. If anything, AI makes these questions more important. 

When a customer asks an AI system for a recommendation, the brand no longer controls the environment where that decision is made. The customer journey may begin somewhere entirely different from the brand's website. 

That creates a new challenge for marketers. They need to understand not only where customers are going, but how they consume information and what influences their decisions in those environments. 

The technology has changed, the marketing fundamentals haven't 

The same principle applies to how organizations use AI internally. There's enormous potential to use AI to improve productivity, accelerate work, and automate activity. But a strong theme at the Symposium was the importance of keeping humans in the loop. 

If everyone uses the same models and approaches problems the same way, organizations risk starting to look and sound remarkably similar. 

AI can generate, analyze, summarize, and automate. What it doesn't do is remove the need for judgment. As AI becomes more embedded in how organizations work, human judgment becomes one of the things that sets apart how effectively the technology is used. 

The opportunity isn't simply to automate as much as possible. It's to use AI to empower teams, giving people better tools and more capability while keeping the experience, creativity, context, and judgment they bring to their work. Organizations that strike this balance will be in a stronger position than those that treat AI purely as an automation exercise. 

Change enablement is the next challenge 

The second major theme I took away from the Symposium was change enablement, though I think it goes beyond the traditional definition of change management. 

The issue isn't a lack of experimentation. There's plenty of that. Teams are testing new AI tools, developing use cases, and exploring where the technology might create value. That's a positive thing, but experimentation alone doesn't necessarily translate into visible business value. 

At some point, organizations need to move from individual experimentation to organizational direction. That requires strategy, governance, and orchestration. Most importantly, it requires clarity on what the organization is actually trying to achieve. 

Without that direction, you can end up with dozens of interesting AI initiatives running independently, with no clear connection to one another or to broader business objectives. The question becomes less "What can we do with AI?" and more "What should we do with AI, and how does it move the organization forward?" 

From individual use cases to a connected strategy 

One of the risks in AI adoption is focusing too heavily on individual use cases. One team finds an interesting application, and another discovers something else. Someone builds an automation. Someone else experiments with a new model. 

Individually, these initiatives can be valuable. But organizations need to think about how they connect: 

• How do you consolidate that activity?
• How do you govern it appropriately?
• How do you give people clear direction?
• How do you turn individual use cases into something that moves the organization forward? 

That's where orchestration matters. The real opportunity isn't having more AI initiatives. It's creating coherence between them. 

AI hasn't eliminated the old martech challenges 

The martech landscape is still grappling with many of the same challenges it always has: customer understanding, content, technology, organizational silos, skills, and adoption. 

AI has enormous potential to help unblock some of these, but it also introduces new ones. Adopting technology is relatively easy compared with changing the way an organization operates around it. 

AI forces organizations to reconsider how teams work, which skills become valuable, where decisions are made, and how people interact with technology. That isn't simply a technology transformation. It's an organizational one. 

The question isn't whether AI will change work 

AI will change how teams work, and that's already happening. The more interesting question is what we want that change to look like. How can organizations create an environment where people experiment without losing sight of strategy and business outcomes? 

Technology alone can't answer these questions. They require strategic leadership, and they require organizations to be deliberate about the future they're trying to create. 

Technology moves quickly. Organizations need to move differently. 

Perhaps my biggest takeaway from Hunter Valley is that the conversation around AI needs to get broader. It's no longer just about technology. It's about people, strategy, adoption, governance, and orchestration. 

AI has enormous potential, but potential isn't the same as value. Value comes when organizations connect technology to a clear strategy, give their people the capability to use it effectively, and bring individual experiments together into something meaningful. 

New models will emerge. New capabilities will become available. New use cases will appear. We won't always be able to predict what's coming next, but we can build organizations that are capable of adapting to it. 

That's the real challenge ahead: not keeping up with AI, but creating the people, strategy, and organization capable of moving with it. And that's where the next phase of the AI conversation needs to go.