LinkedIn’s 2026 Jobs on the Rise report puts AI Engineer at the top of the country’s fastest-growing roles, with average salaries reaching $190,000 and hundreds of live postings at any given moment.
Hiring AI engineers accelerates what gets built. It says nothing about whether teams are asking the right questions before the build starts. And the gap between those two things, between execution speed and decision quality, is where most AI investment quietly dissolves.
Benjamin Humphrey, CEO and co-founder of Dovetail Software, spent years at Atlassian before starting Dovetail in Sydney. He describes a dynamic that remains familiar in organisations of every size: decisions shaped less by evidence than by whoever holds the room. He calls it “vibes-based” decision-making, and it has a more formal cousin: the HiPPO, the highest-paid person’s opinion.
The HiPPO problem is not a personality flaw. It is a structural outcome. When customer data is fragmented across sales tools, support queues, and customer success platforms, each locked behind per-seat licensing that most of the company does not have, the people closest to customers cannot easily share what they know. A sales leader summarises call themes in a slide deck. A CX analyst selects which support trends to escalate. By the time that information reaches a planning meeting, it has passed through at least one layer of editorial judgment that was never requested.
“It’s not really like you’re coming up with ideas out of nowhere,” Humphrey has said. “Everything has some sort of connection back to some kind of evidence.”
The organisations where that is true have solved an access problem, not a talent one.
More AI engineers means faster iteration. That is genuinely valuable. Faster iteration on the wrong thing is still the wrong thing.
PwC’s 2026 AI Performance Study found that nearly three-quarters of AI’s economic gains are captured by 20% of organisations, and the separating factor is not headcount or tooling. The leading companies are using AI to pursue new revenue opportunities and reimagine workflows. The majority are applying it to processes that already exist.
The Deloitte 2026 State of AI in the Enterprise report found a similar split: two-thirds of organisations report productivity gains from AI adoption, but only a third are genuinely redesigning their workflows rather than layering AI onto existing ones.
Hiring the engineer is the easy part. Knowing what to ask them to build requires something the hiring market is not currently measuring.
Australia’s AI hiring surge is a real and meaningful shift. The roles being created are not decorative. They will build and ship things faster than the generation of engineers before them.
The question is what they are pointing at. An AI engineer working from a well-constructed brief, grounded in structured customer intelligence, will build something different from one working from an executive’s instinct about what the market wants. The brief is the constraint. And the brief comes from wherever the organisation stores its understanding of customers.
In most organisations, that understanding is fragmented, curated, and controlled by whoever holds the relevant license.
The LinkedIn data captures a genuine market signal: Australia wants more people who can build with AI. Dovetail Software’s argument is that the return on that investment depends on what those engineers are given to work with. Faster building without better knowing is not a capability upgrade. It is a more expensive version of the same problem.
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