AI Success Starts With Data Not With Tools

Reflections on ANZ Marketing Academics Conference and Martech for 2026

I spent Wednesday at the ANZMAC Future of Work panel, taking part in a fascinating discussion with academics and industry leaders on how to prepare the next generation of marketers.

The questions were smart, the intentions earnest, and the debate spirited. But after digging into the Martech for 2026 report from Scott Brinker and Frans Riemersma (Dec 2025), it’s clear that the most persistent question in the room — “How do we teach students the tools?” — is already outdated.

Both the discussion and the report point to the same truth:
Marketing’s biggest AI challenges have nothing to do with tools.
They have everything to do with the invisible infrastructure underneath them.

1. The Real AI Gap Isn’t Tools — It’s Data, Readiness and Context Engineering

The Martech for 2026 report is unequivocal: the top blockers to AI success are foundational, not technical. According to the AI & Data section of the report, the three biggest challenges teams face are:

• Poor data quality: 56.3%
• Org/process readiness: 52.4%
• Integration friction: 50.5%

None of these are “tool proficiency” problems.

So what do we actually mean by “AI success”?

Not cool demos.
Not faster content generation.
Not saving 10 minutes writing an email.

AI success is AI that is:

• Contextually intelligent — operating with connected, accurate, enriched data
• Operationally embedded — integrated into real workflows and governance
• Reliable and auditable — producing outcomes the business can trust
• Aligned with the business — accelerating strategy, not generating noise
• Agentic — capable of taking meaningful action, not just generating text

This aligns with one of the report’s most important ideas:context engineering — the integration of internal and external data sources that gives AI the context it needs to behave intelligently, not blindly.

This is where the gap really is. And it’s exactly what the ANZMAC conversation kept skirting around.

There was a genuine desire to “teach students the tools,” but AI doesn’t become valuable because someone knows where the buttons are. AI becomes valuable when the data is clean, the processes are clear, the systems are integrated, and the organisation is ready.

• Tools don’t fix weak foundations.
• AI doesn’t fix weak foundations.
• AI exposes weak foundations.

2. AI Doesn’t Patch Foundation Problems — It Exposes Them

One of the strongest lines in the Martech for 2026 report is:

“You don’t have an AI strategy if you don’t have a data strategy.”

At the ANZMAC session, several panelists (myself included) spoke about the importance of data fluency, the digital layer, and understanding how systems fit together. But the academic focus kept drifting back to:“Which tools should we teach?”

The answer is that with AI now embedded inside almost every martech platform, the skill gap has shifted upstream. AI pushes pressure onto:

• data quality
• metadata
• taxonomy
• workflow design
• governance
• cross-functional alignment
• compliance
• integration patterns
• measurement systems

These are the real bottlenecks. And they’re the exact capabilities traditional “tool training” fails to build.

3. Execution Is Becoming Automated — But Thinking Isn’t

The Martech for 2026 report shows that 62% of teams are already using AI agents embedded directly into their martech tools. Execution is becoming software-driven. The UI is becoming simpler. The cognitive load of “operating” the tool is dropping fast. But the cognitive load of using the tools effectively has never been higher.

The marketer of 2026 doesn’t need to click faster, they need to think better. They need to:

• interpret outputs
• diagnose AI failure modes
• connect systems
• design processes
• manage data
• set guardrails
• ask higher-quality questions
• integrate AI into messy workflows
• ensure ethical and compliant operation

These are not tool skills. They are capability skills. And they’re exactly what can’t be automated.

4. The Future of Marketing Ops Has Already Evolved

One of the strongest models in the Martech for 2026 report is the evolution of MarketingOps:

• Ops 1.0: Tool Admins
• Ops 2.0: Use Case Onboarders
• Ops 3.0: Business Value Engineers

Academia is still training people for Ops 1.0, while industry urgently needs Ops 3.0 — professionals who can orchestrate across people, data, process, and platforms to deliver measurable business value.

Not operators. Not button-pushers. Engineers of value.

This shift requires an entirely different skill set, one universities and many organisations still aren’t preparing people for.

5. Why This Matters Now

The report warns that organisations who invest deeply in AI will leap ahead of competitors. Those who stay in pilot mode will fall behind, permanently.
The difference won’t come from:

• who can write the best prompt
• who knows the most tools
• who has the biggest martech stack

It will come from:

• who has reliable data
• who has strong processes
• who has clear governance
• who can orchestrate complexity
• who invests in context engineering
• who understands the digital layer

Those foundations drive performance. Everything else is noise.

Final Thought: Teach the Layer Beneath the Tools

The ANZMAC discussion was an encouraging sign — academia wants to get this right. But the Martech for 2026 report makes something crystal clear: The interface isn’t where the future is. The infrastructure is.

🔺 Clean data.
🔺 Clear processes.
🔺 Integrated systems.
🔺 Strong governance.
🔺 Cross-functional coordination.
🔺 Digital fluency.
🔺 Context engineering.

These are the foundations of AI success — and they cannot be taught by “tool training.”  If we want marketers who can thrive in the agentic era, we need to stop teaching the tools first and start teaching the layer beneath the tools. Because AI doesn’t make weak foundations stronger. It makes weak foundations visible.

If your organisation wants to build the foundations for AI success — data clarity, process design, governance, and capability — I run workshops and advisory sessions that help teams make immediate progress.

If you want AI to amplify strength, not chaos, let’s start with your foundations.