Your next AI hire probably isn\u2019t one person.<\/p>\n
There are a lot of businesses trying to write the same job description at the moment. They want someone who can set the AI strategy, fix the data, put governance in place, manage the stakeholders and still deliver something useful. Ideally, quickly.<\/p>\n
The problem is that this isn\u2019t really one role. It\u2019s several roles bundled together, usually with a title like Head of AI attached to it.<\/p>\n
From what we\u2019re seeing in the market, the organisations making the most progress are taking a different approach. They\u2019re not looking for one person to carry the whole thing. They\u2019re building teams around the work.<\/p>\n
If you asked most people who employs the most AI talent in Australia, they\u2019d probably say the banks and technology companies.<\/p>\n
They\u2019d be right. Commonwealth Bank leads the market, with Microsoft, Telstra, NAB and AWS among the other major employers of AI-skilled professionals.<\/p>\n
But that only tells us where the largest teams are today. The more useful question is: who is still growing?<\/p>\n
Some of the strongest growth is coming from the advisory firms. Two of Australia\u2019s largest are among the top employers of AI talent, and they\u2019re continuing to add capability while hiring at some of the more established players appears to be levelling out.<\/p>\n
That makes sense. Some organisations have moved from building their initial teams to putting those teams to work.<\/p>\n
What\u2019s more interesting is how the advisory firms are hiring. They\u2019re not just appointing a Head of AI or finding one person to lead a client project. They\u2019re hiring at several levels, from consultant through to associate director.<\/p>\n
In other words, they\u2019re building a team rather than hiring a figurehead.<\/p>\n
The fastest-growing roles among AI-skilled professionals in Australia aren\u2019t all highly technical.<\/p>\n
We’re seeing growth not just in my Data practice, but in other areas we recruit in too, roles including Senior Business Analyst, Principal Consultant, Delivery Manager and Operations Manager.<\/p>\n
AI Engineer isn\u2019t the only story here. This suggests AI capability is starting to spread into the roles that sit between technology and the rest of the business.<\/p>\n
That\u2019s probably overdue.<\/p>\n
Someone still needs to work out which problems are worth solving. Someone needs to understand the process being changed, manage delivery, test the outputs, document the decisions and explain what\u2019s happening to the people affected by it.<\/p>\n
An engineer may contribute to some of that work. It\u2019s unlikely they should own all of it.<\/p>\n
We wrote about this back in March after seeing businesses hire senior AI leaders into environments that weren\u2019t ready for them. The expectation was strategy and delivery.<\/p>\n
The reality was often fragile pipelines, inconsistent data and little or no governance.<\/p>\n
A Head of AI can set a direction. But if the foundations aren\u2019t there, they\u2019ll spend a significant part of their first year fixing them. That work matters. It\u2019s just probably not what either side thought they were signing up for.<\/p>\n
This is where the role starts to become difficult. The business is waiting for visible AI outcomes. The new hire is trying to make the underlying environment reliable enough to produce them.<\/p>\n
Both sides can be working hard and still feel like nothing is moving. That\u2019s usually a sequencing problem rather than a talent problem.<\/p>\n
There isn\u2019t one team structure that will suit every organisation. But there is a fairly simple question worth asking before going to market: What work actually needs to be done?<\/p>\n
If the answer includes fixing the data foundations, establishing governance, choosing use cases, delivering models and managing organisational change, you\u2019re probably describing a small team.<\/p>\n
A consultant or assistant manager might take ownership of data quality, testing, documentation or delivery coordination.<\/p>\n
Someone more senior can set the direction, make prioritisation decisions and manage executive stakeholders.<\/p>\n
The titles will vary. The important part is that the responsibilities don\u2019t all quietly land with one person.<\/p>\n
It also reduces the risk of building the entire capability around a single hire. If your one AI leader leaves, the program can stall with them. If one person leaves a properly structured team, there\u2019s a much better chance the work continues.<\/p>\n
If you have approval for one senior AI hire this year, I\u2019m not suggesting you automatically replace that role with three junior ones.<\/p>\n
But I would look closely at what you expect that person to inherit.<\/p>\n
If the answer to most of those questions is no, the job may be bigger than the title suggests.<\/p>\n
Two or three appropriately levelled hires may get further than one senior person trying to cover strategy, infrastructure, governance and delivery at the same time. The market seems to be heading in that direction.<\/p>\n
The organisations still adding AI capability are increasingly building it in layers. They\u2019re giving senior people genuinely senior problems to solve and making sure there\u2019s a team around them to handle the work underneath.<\/p>\n
Before writing the next AI job description, it\u2019s worth working out whether you\u2019re hiring for a role or trying to squeeze an entire function into one person.<\/p>\n
If you\u2019re scoping an AI or data hire and want to test whether it should be one role or a small, structured team, that\u2019s a useful conversation to have before the job ad goes live.<\/p>\n