Two futures for Australian jobs in an AI era

The takeaways:

Last year's AI Jobs Barometer revealed a paradox that still holds: job numbers and wages are rising even in highly automatable roles. This year, we can explain why. What matters isn't how much AI automates, but which parts — the routine tasks or the expert ones. This distinction creates two very different futures for both global and Australian workers, and gives business leaders a clearer lens to act on.

Our analysis indicates that rather than replacing jobs, AI is reshaping them in fundamentally different ways. It is ‘Professionalising’ some roles and ‘Democratising’ others. The result is a growing divergence in job growth, skill demand, and wage outcomes. 

This year’s findings suggest two main shifts: First, Australian organisations are moving beyond AI experimentation and into execution. The rebound in AI hiring and rapid growth in demand for specialists point to rising confidence in how AI can be applied in practice. 

Second, AI’s biggest gains are likely to come not from automation alone, but from redesigning how work gets done. Globally, more AI-exposed businesses are seeing both stronger productivity growth and rising demand for specialist AI capability and human skills including judgement, creativity, and empathy. Together, these suggest the real opportunity lies in reinvention: reshaping roles, workflows, and business models to get the best from both people and technology.

AI is Professionalising work, automating away routine tasks, elevating the importance of human expertise, judgement, and creativity. It is also ‘Democratising’ work, taking on  more complex tasks, leaving people with less demanding work. This divergence is creating two tracks in the labour market with markedly different outcomes.

All jobs globally fall into one of three categories

1. Low AI exposure jobs: Jobs that include few tasks in which AI has capabilities, so AI is likely to have limited impact on the role 
   Examples: Chefs, construction workers, mechanics.  
2. Professionalised jobs: Jobs reshaped by AI to demand more expertise.
   Examples: Radiologists, employment recruiters, air traffic controllers. 
3. Democratised jobs: Jobs reshaped by AI to demand less expertise.   
   Examples: Software developers, loan officers, finance managers.

_{See Methodology appendix for more information.}

AI can increase the need for expertise in two fundamental ways. By taking on the routine tasks in a job, AI leaves the more complex and expert tasks to people. For example, AI helps lawyers with basic tasks like document summarisation, leaving people tougher challenges like building a case in court.  

On the other hand, AI can take away the expert tasks in a role, leaving the less demanding tasks for people. Consider inventory clerks. AI now performs complex tasks like managing inventory, leaving people less specialised tasks like moving stock in warehouses.  

AI’s impact on Professionalised and Democratised jobs will be widely felt. Half (50%) of advertised jobs in Australia are Democratised, while around a quarter (24%) are Professionalised and the remaining quarter (26%) have low exposure to AI.  

Professionalised jobs are pulling ahead in skill growth, pay, and volume of job postings — while Democratised jobs are falling behind 

Professionalised jobs across the world are adding new skills at twice the rate of Democratised jobs.  

The market is already reflecting this divergence. Professionalised roles are seeing 42% faster salary growth than Democratised jobs, with the gap widening since AI adoption accelerated. While both job categories are still growing, Professionalised roles are growing much faster — signalling a gradual labour market shift away from Democratised roles. 

What's driving this Professionalised premium? Trust

What sits behind the premium on Professionalised roles is not just skill — it’s trust.  

As AI takes on more of the execution, employers are placing greater value on the people who can apply judgement, navigate ambiguity, carry accountability and be trusted on what happens next. In other words, AI is not devaluing expertise; it is concentrating value around trusted expertise.

Professionalised roles are pulling ahead both in volume and wages as the premium is rising not just for what people know, but for how credibly they can apply it in the real world. AI is making human-intensive qualities more visible, not less.

Junior roles are also starting to diverge: some are shrinking, while others are being reshaped to demand capabilities traditionally associated with more senior workers. 

In many of the entry-level roles most exposed to AI, such as junior data analysts, the skill bar is rising quickly. These jobs are increasingly demanding capabilities that were once expected later in a career, including judgement, emotional intelligence, leadership, and stakeholder management. In fact, roles with the highest AI exposure are 7x more likely to require skills traditionally expected of senior workers than those with the lowest AI exposure. 

Examples of skills traditionally required in more senior roles that are now required in many AI-exposed entry level roles

• Motivational leadership 
• Team building 
• People management 
• Stakeholder management 
• Process management 
• Mentorship 
• Data-driven decision making

That rising skill bar is already showing up in job growth. Among the most AI-exposed entry-level roles, those requiring a larger number of new senior skills are continuing to grow strongly (35%), while other junior roles are declining (-10%).

The data suggests AI is not eliminating junior talent — it is reshaping and seniorising it. In some organisations graduates are already running teams of AI agents. They are setting task priorities and interrogating outputs — work that looks far closer to orchestration and judgement than traditional entry-level execution.  

This makes junior talent more valuable, more quickly. It also changes the employee value proposition: if graduates are contributing at a higher level earlier, they can no longer be treated as easily replaceable, or as part of a workforce model that relies on attrition. 

Organisations will need to recruit more intentionally, invest earlier in capability, and rethink the traditional workforce pyramid model. 

If AI is used for growth and reinvention — not just efficiency — some organisations may end up hiring more graduates, not fewer, because junior talent becomes a source of future advantage, rather than a cost line to manage. 

That potential is not automatic; it requires organisations to invest in capability before the gap widens, and to do so early. For example, from 2027, all graduates joining PwC Australia's Early Careers Program will take part in a three-day immersive AI experience — combining hands-on work with real client scenarios, responsible AI practice, and the human skills that define professional readiness in an AI-enabled environment. The goal is to build trusted habits early, which will compound across their career. 

Organisations that redesign their graduate models, rebuild their onboarding around senior capability development, and treat early-career investment as a strategic priority will build a talent pipeline that compounds in value over time. Those that default to the old pyramid — high volume at the bottom, attrition as the management tool — will find themselves short of the judgement, creativity and leadership they need precisely when competition for those capabilities is most intense. Getting this right is not a workforce planning question; it's a competitive one. 

The equity challenge in an AI labour market 

If AI is raising the bar for junior roles, the next question is: who gets the chance to clear it? This is where AI starts to become a social equity issue as well as a workforce one.

The workers most exposed to AI disruption are not evenly distributed across the labour market, and the risks are likely to fall hardest on those who already face barriers to opportunity. Women, for example, are disproportionately represented in administrative and clerical roles¹ — jobs more exposed to democratisation, where AI can take on more of the expert tasks and leave workers with less specialised work. 

The risk compounds when access to AI training, career visibility and new pathways into AI-enabled work is uneven. Countries such as Singapore² and Malaysia³ are moving deliberately to address this, with free or subsidised national AI training programs. Australia will need to think just as seriously about how to make those opportunities visible and accessible to a broader group of workers. 

Across the board, for both Democratised and Professionalised jobs, the ones most exposed to AI are changing far faster than those least exposed. 

Globally, the skills required in the most AI-exposed roles are now changing at more than twice the rate of the least exposed roles, and that gap has widened sharply (up 75%) over the past year.  

The same pattern is visible in Australia. Occupations with the highest AI exposure have added an average of 187 new skills per role, compared with 93 for the least exposed occupations over the past six years. 

Along with the shift in skills, new tasks are also being added to AI-exposed roles globally, and they are 2.5x more likely to require human-intensive capabilities.  

What kinds of capabilities are rising in value? Drawing on MIT's EPOCH framework⁴ — which maps the human abilities most resistant to AI automation — five categories are pulling ahead: 

• Empathy & Emotional Intelligence: Compassion, understanding, and building meaningful connections. 
• Presence, Networking & Connectedness: Physical presence, networking, and navigating social contexts, crucial for building trust and collaborating. 
• Opinion, Judgment & Ethics: Navigating open-ended, ambiguous, or ethical scenarios. 
• Creativity & Imagination: Original thinking, improvisation, and generating novel ideas. 
• Hope, Vision & Leadership: Grit, perseverance, initiative, and setting strategic direction.

Our data shows that AI is increasing the value of human skills, not reducing it. As more work is done in partnership with AI, the skills that matter most are increasingly the ones machines cannot easily replicate, such as empathy and judgement.

This creates a pressing challenge for organisations. Today, only 56% of workers say they are gaining skills that help their careers, while those who are supported to upskill are 73% more motivated than those who aren’t.

Employers need to do more than deploy new tools — they need to build AI fluency and confidence across the workforce so people can use them regularly, safely, and well. That means making AI tools accessible and relevant in day-to-day work, offering practical training in their safe application, and creating the cultural conditions that make it safe to experiment, fail, and learn. A practical first step is to assess current capability across the workforce, including baseline AI skills, readiness, and responsible use. 

Many of the skills demanded in an AI economy, including judgement, adaptability and communication, take time to develop. That means the response cannot sit with employers alone. AI is not simply reshaping work inside organisations; it is driving a broader economic and social transition. How Australia builds trust, skills and inclusion will shape whether the benefits are widely shared. That will require strong collaboration between business, higher education, government, industry, and unions — and a serious national conversation about how the gains from AI are shared across the workforce and the community.  

The most AI-exposed companies globally consistently achieve stronger productivity growth (measured as revenue per employee) than their least AI-exposed peers. The top 20% achieve growth that is around 5x higher than the average for AI-exposed companies overall.  

We found the same pattern in our 2026 AI Performance study— the biggest rewards are accruing to a relatively small group of companies. It also revealed that those companies have moved beyond productivity gains and are using AI to reinvent their business model and grow into new sectors.   

The story doesn't end with productivity. The most AI-exposed companies are also hiring and paying more. Since 2022 when AI use soared, their headcount growth has been roughly double that of the least AI-exposed companies. Among the top 20% by productivity growth, wages have increased by an average of 68%. 

From AI activity to business value  

Task-level AI is not a productivity story. For many organisations, it has been an expensive lesson.  

While it may be tempting to apply AI to individual tasks or plug capacity gaps, the gains are modest — once the costs of tools, governance, and implementation are factored in. 

In our conversations with business leaders, those companies pulling ahead are focused on connecting AI activity — pilots, tools, and use cases — to value, and the outcomes that matter most: growth, revenue, cost and risk. AI is being used to transform entire functions across areas such as finance, marketing, and operations.  

Those achieving the greatest gains are thinking bigger still. Rather than asking, "How can we do what we do more efficiently?", they are asking, "How can we do things differently?" They are using AI to reimagine their business and operating models more broadly, with a focus on value creation and associated growth through new products and services, personalised customer experiences, and entering new markets. 

This shift from efficiency to reinvention may help explain why a small group of AI companies are pulling away from the pack.

Earlier, we saw that the most AI-exposed companies are paying higher wages overall. This data reveals another dimension of that story: the market is placing vastly different values on AI skills depending on the industry. 

Australia's AI wage premiums follow a U-shaped pattern. At one end are industries such as Manufacturing, where the wage premium for AI skills is 57% higher than for similar workers without AI skills, and Consumer Markets (42%). This likely reflects the scarcity of AI-capable talent in sectors that are earlier in their AI adoption journey. At the other end are sectors such as Technology, Media and Telecommunications, and Financial Services, where AI is already deeply embedded, and competition for skilled talent is intense (59% and 43% premiums, respectively).

A talent migration risk for Australia? 

This creates a potential challenge for sectors where AI premiums remain relatively low. Workers can see where their skills are most highly valued, and over time this may encourage talent to migrate towards sectors offering stronger rewards for AI capability. 

This matters because some of Australia's largest employers sit among the lower-premium sectors. Government and Public Sector, and Health, account for a significant share of national labour demand (almost 23% and 17% of total job postings, respectively) yet offer some of the weakest AI wage premiums. As competition for AI talent intensifies, attracting and retaining these skills may become increasingly difficult. 

These wage premiums provide one of the clearest market signals of where organisations believe AI is creating the greatest value — and where competition for AI talent is likely to be most intense.   

Companies are accelerating their investment in AI systems and the people who build, run, and optimise them. In Australia, hiring of AI specialists (workers with advanced AI skills like machine learning) grew by over 80% while user roles more than doubled over 2025.   

Australia's surge in AI specialist hiring reflects something more fundamental than growing enthusiasm for the technology. Organisations are moving from experimentation to execution — now enabled by the necessary infrastructure.. 

For years, serious AI deployment at scale was constrained by missing infrastructure: hyperscale data centres, computing capacity, and reliable electricity. In the September 2025 quarter alone, asset classes associated with data centres accounted for 75% of the increase in private investment growth. 

The companies creating the most value from AI are doing more than hiring technical specialists. They are uniting builders (those who develop and deploy AI systems) and users (those who apply AI tools across business functions) into cross-functional teams where business strategy, not the technology itself, drives the AI agenda. 

Organisations that bring these capabilities together are better positioned to capture the gains from AI — but only if the strategy driving them is clear. 

This year's AI Jobs Barometer makes one thing clear: AI is not a distant disruption. It is already reshaping jobs, skills, and wages — and in Australia, it’s accelerating. AI hiring doubled between 2024 and 2025. The most AI-exposed companies are growing headcount at roughly double the rate of their least-exposed peers. For business leaders, this isn’t a warning; it's a signal. 

But the gains are not automatic, and they are not evenly distributed. Our analysis points to a widening divide in how AI affects roles. Both tracks are already visible in job growth, wages, and skill demand. 

The organisations pulling ahead aren't simply automating more. They are reinventing smarter — embedding AI into the core of how work gets done, using it to pursue growth, not just cut costs. That also means investing in people before the gap widens. The fasting growing roles reward judgement, creativity, and and the ability to apply them in ways that build trust  — capabilities that take time to develop. Organisations that equip their people now with the skills and AI fluency to operate in either track create the conditions for lasting advantage. 

Australian organisations that move now, with intention not just activity, will be best placed to shape what comes next, for their people and their business.