Yesterday we wrote about the broader consolidation in Australia’s AI consulting market. Today we’re looking at the other side of that story: the people. Because while the number of AI consulting firms has contracted, demand for experienced AI practitioners has never been higher.

The talent war in Australian AI consulting is fierce, and it’s reshaping how firms operate, where they’re based, and what they’re willing to pay.

The Supply Problem

Australia produces roughly 1,200 graduates per year with advanced degrees in machine learning, data science, or related AI disciplines, according to Department of Education data. That sounds like a reasonable number until you consider that consulting firms alone are competing for those graduates against banks, mining companies, government agencies, tech companies, and every enterprise that’s decided to build an internal AI team.

The real bottleneck isn’t entry-level talent — it’s the mid-career practitioners with three to seven years of production experience. These are the people who’ve built AI systems that actually run in production, who’ve dealt with the messy reality of real-world data, and who can translate business problems into technical solutions. There are probably fewer than 2,000 people in Australia who fit this description, and everyone wants them.

Senior AI architects — people who can design end-to-end AI platforms for enterprise clients — are even scarcer. We’re talking hundreds, not thousands. These individuals command salaries of $250K to $350K base, plus equity or bonus structures that push total compensation well above $400K at the top firms.

Who’s Hiring and How

The Big Four consulting firms — Deloitte, EY, PwC, and KPMG — are all expanding their AI practices aggressively. Deloitte’s Australian AI team has reportedly grown from around 150 to over 300 in the past eighteen months. They’re using their graduate pipeline and brand recognition to attract junior talent, but they’re struggling at the senior end where practitioners prefer smaller, more technically focused firms.

Accenture and Capgemini are taking a different approach, establishing dedicated AI delivery centres in Melbourne and Sydney. These centres operate almost like independent product teams within the consulting framework, giving technical staff the environment they want (modern tooling, interesting problems, less corporate bureaucracy) while retaining the client access that large firms provide.

Among the specialist firms, team400.ai has been steadily growing its engineering bench, focusing specifically on practitioners who can bridge the gap between AI research and production deployment. This positioning — technical depth combined with business consulting — is attractive to senior engineers who don’t want to work at a traditional consulting firm but also don’t want a pure research role.

Quantium, which straddles the line between analytics firm and AI consultancy, has been on a hiring spree as well, particularly for their work with major retail and financial services clients.

The Salary Arms Race

Compensation has escalated significantly in the past twelve months. Based on conversations with recruiters and hiring managers across the sector, here’s where things stand in early 2026.

Junior AI/ML engineers (0-2 years): $95K to $130K base. This is up roughly 15% from early 2025. The floor has risen because banks and tech companies are now matching consulting salaries at this level.

Mid-level practitioners (3-5 years): $150K to $200K base. This cohort has seen the biggest percentage increase. Good mid-level people with production experience regularly receive three or four competing offers within a week of entering the market.

Senior AI architects (7+ years): $250K to $350K base. At this level, firms are competing on total package including equity, flexible working arrangements, conference budgets, and the quality of projects on offer. Several firms have also introduced retention bonuses — essentially paying senior staff not to leave.

Principals and AI practice leads: $350K to $500K+ total compensation. These roles blur the line between technical leadership and business development, and the people who can do both effectively are extraordinarily rare.

The wage pressure is also affecting offshore strategies. Several Australian consulting firms that used to rely on offshore AI teams in India or Vietnam are finding that the quality gap for complex AI work makes local talent more cost-effective when you account for the hidden costs of coordination, rework, and client confidence.

The Retention Challenge

Hiring is only half the problem. Keeping people is arguably harder.

The average tenure for an AI practitioner at an Australian consulting firm is now around 18 months. That’s down from approximately 24 months two years ago. People are leaving for better offers, more interesting work, or to join client organisations directly. It’s not unusual for a consultant to spend six months working on an AI project for a client, build relationships and domain expertise, and then accept a direct offer from that client at a 30% pay increase.

Some firms are responding with counter-offers, but money alone isn’t solving the problem. The practitioners I’ve spoken with consistently cite three factors in their career decisions: the quality and variety of projects they work on, the technical culture of the firm (do the partners actually understand technology?), and the level of autonomy they’re given.

Firms that treat AI practitioners like generic consultants — assigning them to whatever engagement needs bodies, regardless of fit — lose people fastest. The ones that retain talent are those that give practitioners agency over their project selection and invest in genuine technical development.

The University Pipeline

Australian universities are attempting to increase AI graduate numbers, but the expansion is constrained. University of Melbourne’s Master of Applied AI and UNSW’s AI programs are both at capacity, with waiting lists for enrolment. New programs at University of Technology Sydney, Monash, and Queensland University of Technology are ramping up, but it takes three to five years for new graduates to develop the production experience that consulting firms need.

Some firms are bypassing the traditional university pipeline entirely. Bootcamp graduates with strong engineering backgrounds are being hired for junior roles. Self-taught practitioners with impressive open-source portfolios are getting interviews at firms that previously required formal qualifications. The credential bar is lowering out of necessity.

What It Means for the Sector

The talent crunch is having three observable effects on the Australian AI consulting market.

Project selectivity. Firms can’t take every engagement because they don’t have the people to staff them. This is creating a quality filter — firms are choosing projects where they can deliver well rather than chasing revenue. That’s probably a good thing for clients.

Specialisation. Generalist AI consultancies are less viable when talent is scarce. It’s easier to recruit and retain people when you can offer deep expertise in a specific domain — healthcare AI, financial services AI, industrial AI — rather than asking practitioners to be jacks of all trades.

Geographic redistribution. Remote and hybrid work has loosened the requirement to be physically in Sydney or Melbourne. Some firms are building teams in Brisbane, Adelaide, and regional centres where the cost of living is lower and competition for talent is less intense. This is a structural shift, not a temporary pandemic hangover.

The Outlook

The talent shortage in Australian AI consulting isn’t going to resolve quickly. International migration helps at the margins, but visa processing times and the global demand for AI talent mean Australia is competing with the US, UK, Canada, and Singapore for the same people.

The firms that will win the talent war are those that offer genuine technical challenges, treat their people as practitioners rather than billable resources, and maintain a culture that technical staff actually want to be part of. That sounds obvious, but it’s remarkable how few consulting firms actually deliver on it.

For the sector as a whole, the talent constraint is the binding limit on growth. The client demand is there. The technology capability is there. The people to do the work? That’s the bottleneck.