How can Australia assess job disruption and support durable employment?
Evidence reviewed: 15 September 2026
Australia should enter the next technology era with ambition for its people. We can seek a place among the countries that develop useful systems, build capable businesses and help workers move forward.
That requires a long view of AI and employment. Technology choices made today will shape skills, careers and business capability for years.
Build the next capability. Open the next opportunity.
For a consultancy division, workforce transition belongs inside business strategy. A technology investment needs an operating plan. That plan needs people who can deliver it. Productivity and opportunity should be assessed together.
The problem and the practical response
- Problem: Workers may lose jobs, hours or earnings even while aggregate employment grows.
- Response to assess: Monitor transitions and earnings alongside the unemployment rate.
- Problem: Exposure to AI is often confused with proof that a job will disappear.
- Response to assess: Examine tasks, adoption costs and actual employer decisions before estimating displacement.
- Problem: Training can produce certificates without improving employment.
- Response to assess: Compare employer-linked programmes using sustained job and earnings outcomes.
- Problem: Displaced workers face immediate income pressure while retraining takes time.
- Response to assess: Evaluate temporary support, job matching and training as an integrated pathway.
- Problem: Technology adoption can bypass smaller firms and workers with limited access.
- Response to assess: Test practical assistance with independent evaluation of benefits and costs.
The strategic case
Job losses in Australia require careful diagnosis. A cyclical decline, local employer closure and technology-driven task change are different events. Workforce transition measures should address the cause. Judge them on sustained employment and earnings.
Measure the next job.
This report reviews published statistics and international experience. It does not estimate the national number of jobs that AGI will eliminate. The scenarios below are hypotheses for testing.
1. What the current labour data establishes
In July 2026, seasonally adjusted unemployment was 4.5%. Employment fell by approximately 15,800 in the month but remained about 191,900 above a year earlier. Trend employment increased by about 29,300 in the month. ABS: Labour Force July 2026.
Seasonally adjusted and trend series answer related but different questions. One monthly decline cannot establish economy-wide job collapse, and these aggregates do not identify AI as the cause.
Individual hardship remains important even when national totals look stable. Workers can experience lower hours, insecure work or reduced pay after displacement.
A useful monitoring system would examine unemployment duration, underemployment, vacancy patterns, regional transitions and earnings after re-employment. It should identify data revisions and uncertainty.
2. AI and employment: tasks before occupations
Jobs and Skills Australia’s 2025 generative AI study examines work, skills and the labour market. It provides a relevant Australian evidence base for studying technology adoption. JSA: generative AI capacity study.
AI and employment should be analysed at task level. A role can contain automatable documentation, interpersonal work, physical tasks and legally accountable decisions.
Technical capability does not immediately establish commercial adoption. Integration, accuracy, liability, customer acceptance and cost influence employer choices. Productivity growth could support greater output, fewer hours for the same output, changed staffing or some combination.
The outcome is an empirical question.
3. International approaches
Denmark: mobility combined with support
Denmark’s flexicurity approach combines labour-market flexibility, income protection and active employment support. OECD analysis finds that its arrangements have helped displaced workers integrate after shocks. OECD Economic Survey: Denmark 2024.
Australia cannot simply import a programme label. Transferability depends on funding, benefit rules, employer participation and administrative capacity. The useful question is which components improve transitions under Australian conditions.
Singapore: structured mid-career learning
SkillsFuture provides career-transition learning and training-allowance options. This establishes a concrete institutional model for helping people participate in learning during career change. SkillsFuture: individual initiatives.
Programme availability is not proof of its causal employment impact. An Australian assessment would need comparable outcomes, the cost of participation and information about people who did not complete or obtain relevant work.
A course is an input. Employment is an outcome.
4. Options to assess
| Option | Potential benefit | Evaluation concern |
|---|---|---|
| Early transition assistance after a closure notice | Shorter interruption and faster planning | May miss workers outside large employers |
| Employer-linked paid placements | Relevant experience and a route to hiring | Subsidising hires that would occur anyway |
| Targeted retraining | Skills for verified demand | Provider incentives focused on enrolment |
| Temporary earnings support | Capacity to undertake a transition | Cost, eligibility boundaries and incentives |
| SME technology assistance | Broader diffusion of useful tools | Paying for low-value or premature adoption |
A programme could combine assessment, basic digital skills, a verified training pathway and placement support. That remains a design option. It should not require every displaced worker to enter a technology occupation.
Care, construction, maintenance and other fields may require very different capabilities. Demand should be checked locally.
5. Financial and outcome evaluation
Assume a hypothetical programme spends A$5 million on 500 participants. Its gross cost is A$10,000 per participant.
If 300 obtain sustained employment, dividing cost by those outcomes gives about A$16,667 per observed placement. But that is not the cost per additional job. If a credible comparison indicates 250 would have obtained sustained work anyway, only 50 outcomes are additional, implying A$100,000 per additional outcome.
This simplified example illustrates why a counterfactual matters. It is not a cost estimate for an existing programme.
Assessment should also include earnings, hours, job quality and persistence. A higher initial programme cost may be justified by stronger long-term outcomes, but this needs evidence.
6. Proposed pilot structure
- Diagnosis: select a demonstrable transition problem, establish baseline outcomes and consult workers and employers.
- Design: set eligibility, verify training relevance, identify likely hiring demand and define independent evaluation.
- Delivery: combine appropriate support with practical training and placement; retain accessible alternatives for people facing digital barriers.
- Follow-up: track outcomes at six, twelve and twenty-four months where possible.
- Review: compare benefits with costs and discontinue elements that do not improve outcomes.
Payment solely for placements could encourage providers to select easier cases. Evaluation should therefore adjust for participant circumstances and examine people who were refused, withdrew or remained unemployed.
Employers, training organisations and public employment bodies would have different roles. Employment and privacy obligations would require detailed review before implementation.
7. Scenarios for a changing economy
Gradual adoption: firms redesign some tasks and workers have time to adapt. Practical training and incremental job redesign may be useful.
Rapid displacement: some tasks become commercially replaceable quickly. Income support and transition capacity may face pressure before new work expands.
Disappointing technology gains: expensive tools produce limited benefits. Programmes narrowly tied to fashionable products may underperform.
These AGI economic scenarios do not establish an arrival date. The analysis should test options across them.
A sovereign investment fund, if available, would be a financing source rather than proof that a training programme works. Assistance should not depend on optimistic investment returns arriving when workers need support.
Adoption is a business decision. Transition is a human reality.
Direct answers
Is AI already responsible for all recent job losses? The reviewed aggregate evidence does not establish that claim. Attribution requires more detailed employer and worker evidence.
Should everyone learn coding? No single course suits all transitions. Training should match capabilities, accessible opportunities and verified demand.
How should success be measured? By additional sustained employment, earnings and job quality after accounting for programme costs and what would otherwise have happened.
The DivineLab Worx approach: turn setbacks into evidence
Dainu Devis founded DivineLab Worx to bring business strategy, technology and infrastructure into one practical discipline. His founding belief is simple: failures should become evidence for a better system.
The team’s guiding principle is to treat technical failures as hard engineering data. In business and public-service delivery, that also means examining costs, outcomes and people’s experience. A mistake becomes useful only when it is investigated and the lesson changes what happens next.
Record the failure. Find the cause. Test the improvement.
In workforce transition, a programme that produces certificates without sustained employment needs investigation. Was the training relevant? Did employers have vacancies? Could participants access the opportunities?
The programme is what needs testing. A worker’s setback is not proof of personal failure. Use outcomes and participant feedback to improve the support system.
Learn from the outcome. Improve the pathway.
This is the discipline DivineLab Worx aims to bring to Australia’s economic transformation. Tighter feedback. Clear accountability. More resilient systems. Our ambition is an Australia whose ability to learn and deliver earns trust around the world.
The long view
Australia’s position in the next economy will depend partly on how well people can move into useful, rewarding work. Economic resilience requires broader access to opportunity as technology changes.
DivineLab Worx’s perspective connects business demand, practical skills and technology adoption. Identify the work. Design the transition. Support the person. Measure the outcome over time.
Australia should aspire to contribute knowledge and useful technology to the world. Building that capacity begins with the people who will create, operate and improve it.
The future needs capable people. Invest accordingly.