Examining the implications of automation and AI for apprenticeships, traineeships, work placement requirements, and industry engagement obligations under the Standards for RTOs 2025
The First Rung Is Cracking
Christopher Kong’s widely shared observation that artificial intelligence is eliminating entry-level positions faster than organisations can redesign their talent pipelines struck a nerve across professional networks because it named something that many in workforce development had been sensing, but few had articulated so directly. The tasks that traditionally defined junior roles, the routine administrative work, the basic report preparation, the scripted customer inquiries, the data entry and scheduling that gave new workers their first foothold in an organisation, are being absorbed by generative AI and automation systems at a pace that is outstripping the capacity of employers, educators, and policymakers to respond.
For Australia’s vocational education and training sector, this is not an abstract labour market trend. It is a structural challenge to the foundational mechanism through which VET delivers on its promise: work-based learning. Apprenticeships, traineeships, and mandatory work placements depend on the existence of entry-level roles in which learners can develop, practise, and demonstrate the competencies their qualifications require. If those roles are shrinking, transforming, or disappearing in key industries, the entire architecture of work-based learning, and the VET pathways that depend on it, must be rethought.
Drawing on more than 30 years of experience working with over 2,000 registered training organisations across Australia, this article examines the evidence on what is happening to entry-level work, the implications for apprenticeships and work-based learning models, the obligations RTOs face under the Standards for RTOs 2025, and the practical strategies the sector must adopt to ensure that VET pathways remain viable, equitable, and genuinely connected to the workplaces of 2026 and beyond.
1. What Is Happening to Entry-Level Work
1.1 The Evidence: Erosion, Not Extinction
The most accurate description of what is happening to entry-level work is erosion rather than extinction. Entry-level jobs are not vanishing overnight. They are being reshaped, with the routine, structured, and predictable components of those roles, the very tasks that made them accessible to new workers, being progressively automated while the more complex, relational, and judgment-intensive components remain or grow. The result is not that there are no junior roles but that the remaining junior roles demand a higher baseline of capability than they did five years ago, and that there are fewer of the purely routine positions that once served as the simplest entry point into employment.
Analysis of automation trends in Australia shows that the tasks most vulnerable to automation are precisely those that characterise traditional entry-level work: basic report preparation, routine customer inquiries, data entry, scheduling, and simple administrative processing. A 2025 report drawing on feedback from more than 53,000 young people across 184 countries warned that entry-level pathways are eroding fast, with frontline and junior roles particularly vulnerable and women and young workers at greatest risk of displacement.
In Australia, youth unemployment climbed back above 10 per cent in late 2025, reaching approximately 10.5 per cent, its highest level since 2021, even as overall unemployment remained relatively low. This divergence between general employment strength and youth employment weakness is a signal that the structural conditions for early-career entry are deteriorating. Jobs and Skills Australia’s generative AI capacity study reported cases in which large employers could see no logical business reason to take on junior engineers in the traditional way, because AI was already handling many of the structured, routine tasks those positions were built around. JSA warned that technology sectors may be among the first to restructure their entry-level intake entirely.
At the same time, the preparation pipeline for this new landscape is inadequate. Research from ADAPT found that only about 6 per cent of organisations mandate enterprise-wide AI training, meaning the vast majority of workers, especially those in vulnerable groups and early-career stages, are not being systematically upskilled to navigate the new task environment. Work is changing faster than the systems designed to prepare people for it.
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Indicator |
Evidence |
Implication for VET |
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Youth unemployment rising |
Youth unemployment climbed above 10% (approximately 10.5%) in late 2025, its highest level since 2021, even as overall unemployment remained low |
The first rung of the career ladder is weakening for young people, the core demographic for apprenticeships and traineeships |
|
Entry-level tasks are being automated |
Basic report preparation, routine customer inquiries, data entry, and structured administrative tasks are now replicated by generative AI and automation systems |
The tasks that traditionally defined junior roles, and that gave apprentices and trainees their first workplace learning experiences, are shrinking |
|
Employers restructuring junior intake |
Large employers report seeing no logical business reason to take on junior engineers in the traditional way; tech may be among the first sectors to restructure entry-level hiring |
The assumption that employers will always need entry-level workers in the same numbers and configurations is no longer safe |
|
Apprenticeship numbers declining |
Active apprenticeship contracts declined by approximately 7.8% in 2024 after government wage subsidies ended mid-year, reversing pandemic-era growth |
VET’s primary mechanism for work-based learning is cooling, and technology alone will not compensate for reduced employer demand |
|
AI training not reaching workers |
Only about 6% of organisations mandate enterprise-wide AI training; most workers, especially those in vulnerable groups, are not being systematically upskilled |
Workers entering automated workplaces are not being prepared for the new task landscape, creating a readiness gap that VET must help close |
|
Global pathway erosion |
A 2025 report drawing on feedback from over 53,000 young people in 184 countries warns that entry-level pathways are eroding fast, with frontline and junior roles particularly vulnerable |
This is not an Australian anomaly but a global structural shift that will reshape VET pathways across all industries and occupational levels |
1.2 The Task Shift: From Routine to Judgement
Jobs and Skills Australia’s generative AI analysis stresses that entry-level jobs may transform rather than diminish, with the human component of junior roles shifting toward monitoring, exception handling, human interaction, and judgment tasks. This is a critical insight for VET. It means that the work-based learning experiences RTOs design must themselves shift: away from training learners on the routine tasks that AI already does, and toward the higher-order human capabilities that are growing in importance even at the entry level.
The following table illustrates this task shift and its implications for what work-based learning should prioritise.
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Tasks Declining (Increasingly Automated) |
Tasks Growing (Uniquely Human) |
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Basic report preparation and document formatting |
Troubleshooting automated systems and handling exceptions |
|
Routine data entry and records management |
Managing complex client relationships and escalations |
|
Standard customer inquiry responses (scripted) |
Exercising ethical judgement in ambiguous situations |
|
Manual scheduling and calendar coordination |
Cross-functional collaboration and stakeholder coordination |
|
Simple bookkeeping and invoice processing |
Interpreting AI outputs and verifying accuracy |
|
Repetitive quality inspection (visual/pattern-based) |
Training, mentoring, and supporting team members through change |
For RTOs, the practical implication is direct. Workplace logbooks, placement assessment tasks, and apprenticeship training plans must be redesigned around the tasks in the right-hand column, not the left. A trainee whose logbook is filled with entries about data entry, document formatting, and routine scheduling is being trained for a workplace that is rapidly ceasing to exist. A trainee whose logbook documents troubleshooting of automated systems, management of complex client interactions, and collaborative problem solving in cross-functional teams is being prepared for the workplace that is emerging.
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The Core Dilemma Work is changing faster than early-career pathways. The routine tasks that once defined entry-level roles, and that gave apprentices and trainees their first workplace learning experiences, are being automated. VET’s work-based learning architecture was built on the assumption that these roles would always exist in sufficient numbers and with sufficient substance to support genuine competency development. That assumption is no longer safe. |
2. Apprenticeships and Traineeships: The Cooling Market
After the pandemic-era boom in apprenticeship numbers, driven in large part by generous government wage subsidies, Australia’s apprenticeship market cooled significantly in 2024. When the Australian Apprenticeships Incentive System’s major employer subsidies ended in mid-2024, active apprenticeship contracts declined by approximately 7.8 per cent. By March 2025, there were still 41,710 more apprentices in training than there had been in 2019, and trade completions were at their highest level in a decade. But the trend had clearly turned downward once the financial incentives were removed, confirming what many in the sector had long suspected: a significant portion of employer demand for apprentices during the pandemic period was subsidy-driven rather than organically sustained.
The Strategic Review of the Australian Apprenticeships Incentive System, with its final report released in January 2025, emphasised the need to increase completions, improve support for women and other under-represented cohorts, and better align incentives with national priority occupations. The review signalled that the apprenticeship system is being redesigned for more targeted, higher-value pathways rather than the broad-based volume approach of the pandemic era. This is a significant policy shift. Incentives and subsidies are likely to be more tightly focused on occupations where skills shortages are acute and where work-based learning delivers genuine, measurable outcomes, increasing scrutiny on whether apprenticeships and traineeships produce real learning rather than cheap labour.
For RTOs, this creates a convergence of pressures. Automation is reducing the task content of many entry-level roles. Employer demand for apprentices and trainees is softening as subsidies are withdrawn. Policy is shifting toward quality over quantity. And the Standards for RTOs 2025 maintain strong expectations that training is industry-relevant, that industry engagement is ongoing, and that outcomes for learners and employers are demonstrably positive. The assumption that an RTO can always find a host employer willing to take on an entry-level worker and provide meaningful work-based learning is no longer safe. The sector needs new models.
3. Work-Based Learning Under the Standards for RTOs 2025: Obligations Do Not Disappear
The Standards for RTOs 2025, which commenced during 2025, give providers more flexibility in how they meet quality expectations but maintain strong requirements around industry relevance, learner outcomes, and authentic training and assessment. Outcome Standard 1.1 emphasises that training must align with training package requirements and reflect current industry practice. Outcome Standard 1.2 requires effective engagement with industry, employers, and community representatives to inform training design, delivery, and assessment. ASQA’s FAQs on the 2025 Standards clarify that industry engagement should be ongoing to ensure training remains relevant, fit for purpose, and reflective of current practice, while deliberately not prescribing a minimum number of engagements and instead placing responsibility on providers to choose appropriate strategies.
For apprenticeships, traineeships, and placement-heavy qualifications, these requirements mean that RTOs must still secure, supervise, and document authentic work-based learning even if entry-level roles are thinner, more automated, or harder to source. Providers cannot simply drop work placement requirements without renegotiating the training product, and they cannot claim compliance with industry engagement obligations if their engagement does not grapple with the reality of how automation and AI are changing the tasks and capabilities that define the occupations their qualifications serve.
This last point deserves emphasis. Industry engagement under the 2025 Standards is increasingly a substantive, not a procedural, obligation. An RTO that engages with employers solely to confirm that their training package units still look familiar is not meeting the spirit of the Standards. The engagement must address what is actually happening in workplaces: which tasks are being automated, which human capabilities are growing in importance, what the real experience of junior workers looks like in an AI-augmented environment, and how training and assessment should adapt accordingly. If an employer reports that their entry-level roles now involve monitoring automated systems and handling exceptions rather than performing routine administrative tasks, the RTO must reflect that in its training and assessment strategy, its assessment tools, and its work-based learning design.
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Standards for RTOs 2025: Key Obligations for Work-Based Learning Outcome Standard 1.1: Training must align with the training package requirements and reflect current industry practice, including how automation is changing job tasks at the entry level. Outcome Standard 1.2: Industry engagement must be ongoing and must address how AI and automation are reshaping the occupations and tasks that qualifications prepare learners for. Self-Assurance: RTOs must demonstrate, through evidence, that their work-based learning arrangements produce quality outcomes for learners and employers, including in sectors where entry-level roles are being restructured. |
4. Rethinking Work-Based Learning: Five Models for an Automated Landscape
The response to shrinking entry-level roles is not to abandon work-based learning but to diversify how and where it happens. Work-based learning remains the most powerful mechanism for developing the competencies that VET qualifications require, precisely because it places learners in the conditions where they must apply their skills in real or realistic contexts, under the pressures and expectations of actual workplace performance. What must change is the assumption that work-based learning can only occur in traditional, long-duration, single-employer placements in roles that may no longer exist in their previous form.
The following table presents five models for work-based learning that RTOs can adopt, adapt, and combine to maintain authentic workplace learning in an environment where traditional entry-level placements are becoming harder to source or less substantial in their learning content.
|
Model |
Description |
Strengths |
Limitations |
|
High-Fidelity Simulation |
VR/AR scenarios, digital twins, and simulated workplaces replicating automated environments |
Scalable, repeatable, safe for practising high-risk tasks; accessible when real placements are scarce |
Cannot fully replicate workplace culture, interpersonal dynamics, or unpredictable real-world conditions; must be supplemented with real exposure |
|
Shared and Pooled Placements |
Multiple employers share early-career workers through GTOs, industry clusters, or coordinated RTO-employer arrangements |
Distributes the hosting burden; exposes learners to diverse workplace contexts; viable when individual employers cannot sustain a full placement |
Requires significant coordination; quality and consistency of learning experience varies across employers; supervision can be fragmented |
|
Micro-Internships and Short WBL Bursts |
Shorter, more frequent work experiences aligned to specific skill sets rather than long traditional placements |
Flexible and adaptive; can target specific AI-augmented tasks; easier for employers to offer in tight labour markets |
May lack the depth and continuity of longer placements; requires careful design to ensure sufficient evidence of competence across contexts |
|
Hybrid Virtual-Physical Models |
Theory and some practical components delivered online with interactive modules; essential hands-on skills developed in workshops and on-site |
Widens access, reduces geographic barriers, lowers cost, and maintains quality through targeted physical components |
Requires strong digital infrastructure and learner support; risk of over-reliance on virtual components that do not build embodied skills |
|
Project-Based Employer Partnerships |
Learners work on defined employer projects, individually or in teams, that address real business problems involving AI-augmented workflows |
Authentic, outcome-focused, directly valuable to employers; develops problem-solving and collaboration alongside technical skills |
Requires employers willing to invest time in briefing and mentoring; project outcomes may not cover all unit requirements |
No single model will be sufficient for all qualifications or all industries. The most resilient approach is a blended one: combining elements of simulation, micro-internships, employer project partnerships, and traditional placement into a work-based learning strategy that is tailored to the specific occupation, the local labour market conditions, and the learner cohort. The Standards for RTOs 2025 provide the flexibility for this kind of innovation. The question is whether RTOs have the strategic capacity, the employer relationships, and the assessment design capability to take advantage of it.
4.1 The Non-Negotiable: Real Workplace Exposure Must Remain
While diversified models are necessary, it is important to be clear about what cannot be replaced entirely. For many qualifications, particularly in trades, healthcare, aged care, community services, and construction, real workplace exposure remains non-negotiable. You cannot learn to weld, to administer medication, to manage a classroom of children, or to operate heavy machinery in a virtual environment alone. High-fidelity simulation can prepare learners for these experiences and reduce the time required in real workplaces, but it cannot substitute for them entirely. The Standards for RTOs 2025, training package requirements, and the Rules of Evidence all require that assessment evidence be authentic and sufficient, and in many occupations, authenticity can only be established through performance in a real or closely supervised workplace environment.
The distinction RTOs must draw is between qualifications where the entire work-based learning component can be redesigned around alternative models, which may be possible in some administrative, business, and technology qualifications, and qualifications where a core of real workplace exposure must be preserved, and the alternative models serve to supplement, extend, and enrich that core. Making this distinction explicitly, and documenting the rationale in the training and assessment strategy, is itself a form of the self-assurance that the 2025 Standards expect.
5. Three Imperatives for RTOs: Tasks, Equity, and Evidence
5.1 Re-Specify Workplace Tasks Around What Humans Still Do Best
Industry engagement under the 2025 Standards must now ask a question that was not necessary five years ago: which tasks in this qualification’s target job roles are being automated, and which human tasks are growing in importance? The answers should directly reshape workplace logbooks, placement projects, and assessment tasks. If employers report that their entry-level workers now spend more time troubleshooting automated systems than performing routine data entry, then the RTO’s work-based learning design must reflect that reality.
This has implications for training plan design in apprenticeships and traineeships. Rather than structuring workplace learning around a progression through routine tasks of increasing complexity, the traditional model, training plans should be structured around the human capabilities that remain essential: complex problem solving, client relationship management, ethical judgement in ambiguous situations, cross-functional collaboration, and the ability to interpret, verify, and act on AI-generated outputs. These are the tasks that define the new entry level, and they are the tasks that work-based learning must prepare learners to perform.
5.2 Protect the Integrity and Equity of Work-Based Learning
Even if entry-level positions contract, ASQA’s focus remains on the quality of outcomes and the integrity of training. Work-based learning must still give learners fair opportunities to demonstrate all aspects of competence, not just shadow an AI system or perform the diminished remnants of a once-substantial role. An apprentice who spends their placement watching an automated system process invoices while occasionally clicking an approval button is not receiving work-based learning of the quality or substance that the Standards require.
The equity dimension is equally critical. Youth unemployment and the erosion of junior tasks disproportionately affect disadvantaged learners: young people from lower socioeconomic backgrounds, First Nations learners, learners from culturally and linguistically diverse backgrounds, and learners in regional and remote areas where employer options are already limited. VET pathways that rely on “find your own placement” models will worsen these inequities if RTOs do not actively broker opportunities and support learners through the work-based learning process. The Standards for RTOs 2025, with their emphasis on learner support and equitable access, reinforce the expectation that RTOs will take greater responsibility for ensuring that all learners, not just those with pre-existing employer connections, can access meaningful work-based learning.
RTOs should also be embedding AI literacy, digital capability, and adaptability skills into workplace preparation programs so that learners arrive at their placements equipped to add value in augmented workplaces. A learner who can work alongside AI tools, who understands how automated systems operate, and who can contribute to exception handling and quality assurance processes is a more attractive proposition for employers who are restructuring their junior roles around higher-value tasks. Preparing learners for the new entry level, rather than the old one, is both a quality imperative and an equity strategy.
5.3 Evidence Outcomes with Better Data
Jobs and Skills Australia’s VET National Data Asset is designed to track short-term and long-term outcomes after training, including employment, income, and further study, allowing more nuanced analysis of which pathways and which providers actually deliver sustainable careers. Under an outcomes-based regulatory approach, RTOs that can demonstrate strong apprenticeship completion, employment outcomes, and wage progression, even in disrupted sectors, will be better positioned with both ASQA and government funders.
This means that RTOs must invest in systematic tracking of work-based learning outcomes. Not just whether placements were completed, but what happened afterwards: did the learner receive a job offer, did they progress into higher-skill roles, did employers report satisfaction with the learner’s capabilities, did the learner report that the placement prepared them for the realities of their occupation? These data points should feed directly into the RTO’s self-assurance processes and continuous improvement activities. In a regulatory environment that increasingly measures providers by the quality of their outcomes rather than the completeness of their paperwork, the RTOs that can show evidence of genuine, sustained employment outcomes from their work-based learning programs will have a significant competitive and compliance advantage.
6. Conclusion: Innovate, Do Not Retreat
The question Christopher Kong raised, what happens when entry-level jobs disappear, is not a hypothetical exercise for VET. It is an operational reality that is already affecting apprenticeship numbers, placement availability, employer engagement, and the substance of work-based learning experiences across multiple industries. The evidence is clear: youth unemployment is rising, entry-level tasks are being automated, apprenticeship contracts are declining as subsidies end, employers in key sectors are restructuring their junior intake, and the majority of organisations are not yet preparing their existing workers, let alone their new hires, for an AI-augmented task environment.
The response from VET must be to innovate, not retreat. Work-based learning remains the most effective mechanism for developing genuine workplace competence, and the Standards for RTOs 2025 provide the flexibility for RTOs to design new models of work-based learning that are resilient to the disruptions in the entry-level labour market. High-fidelity simulation, shared and pooled placements, micro-internships, hybrid virtual-physical models, and project-based employer partnerships are all viable strategies that can complement and extend traditional placement arrangements.
But innovation must be accompanied by discipline. The new models must still produce assessment evidence that is valid, sufficient, authentic, and current. Industry engagement must genuinely grapple with how automation is changing the occupations that qualifications serve. Work-based learning must be redesigned around the tasks, capabilities, and relationships that automation cannot replace: complex problem solving, ethical judgement, human interaction, and the capacity to work alongside AI systems as a competent, critical, and capable human professional. The first rung of the career ladder is cracking. VET’s job is not to pretend it is still solid. It is to build the next one.
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Summary: What RTOs Should Do Now
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References and Further Reading
ABC News (2025). Workers Face Career Change from AI Technology. https://www.abc.net.au
ADAPT (2025). AI Is Displacing Australia’s Entry-Level Jobs, Leaving Women and Young Workers Most Exposed. https://adapt.com.au
ASQA (2025). 2025 Standards for RTOs Commence. https://www.asqa.gov.au/news-events/news/2025-standards-rtos-commence
ASQA (2025). 2025 Standards FAQs – Version 3. https://www.asqa.gov.au
DayJob (2025). Virtual Apprenticeships: Future of Skilled Trades. https://www.dayjob.com.au
Department of Employment and Workplace Relations (2025). Skills for Tomorrow: Shaping the Future of Australian Apprenticeships. https://www.dewr.gov.au
Information Age / ACS (2025). We Hire Fewer Entry-Level Engineers: Aussie Firms on AI Impact. https://ia.acs.org.au
Jobs and Skills Australia (2025). Generative AI: Augment and Advance the Way We Work in Australia. https://www.jobsandskills.gov.au
Jobs and Skills Australia (2024). Technical Paper: Strong and Responsive VET Pathways. https://www.jobsandskills.gov.au
StoneX (2025). AI Takes Toll on Entry-Level Jobs in Australia. https://www.stonex.com
WA Government (2025). Fact Sheet: Training Within the 2025 Standards. https://www.wa.gov.au
