Martech advisor · Educator · Speaker
Jodie Dunkley
Putting the pieces together across marketing technology, AI and evolving operating models.
I am a marketer at heart, and improving customer experience is the ultimate goal. How we do that gets interesting when you mix martech, AI, strategy, operating models, legacy technology and organisational politics. This is where I thrive, helping clients turn complexity into results.
I teach martech to postgraduate students at Deakin University and speak about what modern marketing looks like in practice.
Martech Advisory is where I bring together my work and musings
across martech leadership, education, research and speaking.
Martech Advisory
Thought Leadership through Experience
My work spans marketing, technology, customer experience, data, strategy and delivery. I spend a lot of time translating and negotiating between big ideas and what it takes to make them happen, including the work, business risks and architectural implications.
I was once the reverse mentor on martech to NABs CMO and have had the opportunity to speak at a variety of vendor and girls-in-tech conferences, from debating the pros and cons of AI to war stories of implementation.
“Thank you for your endless enthusiasm, support and guidance as we’ve navigated this journey together.”
Delivery Lead, Top Australian Brand
“Whenever I have a question about martech, I always want to know Jodie’s opinion.”
Martech Manager, Australian University
Speaking · Teaching
Making martech and AI more accessible
At Deakin University, I show students how organisations use marketing technology beyond the theory. I use current examples, honest stories and practical demonstrations to connect strategy, data, platforms, campaigns and measurement.
I give guest lectures and speak at conferences, on panels and in industry discussions. My goal is to make martech more accessible to students and marketing professionals.
“Jodie’s knowledge of the marketing technology discipline is outstanding and is a huge asset to the Department of Marketing.”
Associate Head of School (Teaching)
Department of Marketing, Deakin University
“Jodie was an outstanding teacher who made Fundamentals of MarTech genuinely engaging and enjoyable. She explained complex MarTech concepts in a clear and accessible way.”
Fundamentals of MarTech student feedback
Deakin University, Trimester 1, 2026
Looking for some free advice?
Questions leaders should be asking about martech
Should I be worried about AI discoverability?
Yes, but Google is still a huge player in this market. Just ask your AI where it got its info from…probably a Google search. I’m researching how recommendations are made in this space and the results are surprising. Now that the reddit funnel has been switched off, AI needs new cues to determine the best in each category. My suggested first step in this space – start monitoring so you have a benchmark and can track the results of changes you are making.
Where should I start if I want to sweat my martech stack?
I think people are bored of being told to check your fundamentals, but auditing your data, your integrations and your measurement are great places to start to find the gaps. Use your AI tool suite to crunch the numbers and look for gaps, overlaps and duplicates.
What does the future look like for marketing graduates?
Marketing graduates need to be fluent in marketing, data and now AI to be successful. Most likely you’ll be a “marketer-in-a-loop” and need to understand how the AI loop works so that you can optimise outcomes. Stay focused on customer experience – even if the customer employs agents to do its buying, there is a human with emotions and cognitive dissonance buried in there somewhere!
How do I make experimentation part of my team’s culture?
Great question, especially when experimentation and optimisation could potentially be delegated to AI tools. Without the culture to support experimental thinking, the AI could get up to all kinds of mischief. Experiments should be outcome-focused. Everyone in the team should understand what a hypothesis is and how to measure it. Share the results and interesting findings to encourage the team to come on the journey. And allow opportunities to fail fast.
Where should I start with agentic AI in the content supply chain?
This use case is prime for orienting your agents around OUTCOMES not ROLES. I foresee a short term future where we create so many agents doing small pieces of the work they become hard to orchestrate. Combine activities where it makes sense to do so and have an agent thats responsible for the outcome (e.g. production of media) and a separate one (e.g. challenger agent) that pushes each agent to work harder. Avoid automating a sh*t workflow with expensive agents. It’s still a sh*t workflow.