AI use is rising among Australian businesses, and skills are a recognised constraint—but national data do not yet show whether adoption is outpacing capability inside the same organisations. The latest ABS business survey measures whether firms use listed information and communication technologies, not how deeply they deploy AI or whether their staff are trained to use it. Separate evidence of skills shortages and training is useful context, not a direct measure of AI readiness.
What the latest figures say about business AI use
In the Australian Bureau of Statistics’ Characteristics of Australian Business, 2024–25 financial year, released on 25 June 2026, 12% of businesses reported using AI, compared with 1% in 2021–22. The ABS says the question records whether businesses used the listed technologies; it does not measure the intensity or extent of use. A business using one AI tool for a limited task and one that has integrated AI across multiple operations can both count as users.
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The survey was redeveloped to combine previously alternating innovation and digital-activity modules in a biennial framework. Treat its latest figures as a current snapshot, rather than assuming every earlier comparison came from an identical questionnaire.
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ABS results show that business AI use varies considerably across these groups in the 2024–25 reference period:
| Business group | Innovation-active | Non-innovation-active |
|---|---|---|
| Large businesses | 37% | 29% |
| Small businesses | 19% | 4% |
Share reporting AI use, Australian Bureau of Statistics, 2026; reference period 2024–25.
Across businesses overall, the corresponding figures were 20% for innovation-active businesses and 6% for non-innovation-active businesses. “Innovation-active” is an ABS business category; it should not be read as a measure of AI maturity. The figures indicate that the national average conceals very different adoption conditions, particularly for small businesses.
What the skills evidence does—and does not—show
The same ABS release provides evidence of broader workforce constraints, but does not identify them as AI-specific. In 2024–25, 35% of businesses reported some skill shortage. Among businesses with shortages, 57% cited specialist skills or knowledge and 48% cited wage or salary costs as reasons. These responses describe skills shortages generally, not a count of firms unable to use AI.
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Among businesses reporting shortages, 38% increased on-the-job or internal training, 35% increased wages, salaries or conditions, and 26% invested in employee upskilling or reskilling. These are responses to skill shortages overall; the ABS figures do not establish that the training covered AI.
Separately, 16% of businesses said insufficient staff skills and capabilities limited their ICT use in 2024–25, while 13% cited uncertainty about ICT costs and benefits. This signals that workforce capability can constrain technology use, but the measure applies to ICT broadly rather than AI alone.
The ABS reported that work-related training participation among people aged 15–74 was 19% in 2024–25, down from 23% in 2020–21. Among people who faced barriers to training, 44% cited too much work or not enough time. These are general work-related training figures, not AI-training rates among employees of AI-using businesses; the 2024–25 release is also described by the ABS as its final four-yearly release.
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AI skills demand is growing, but job ads are not a readiness score
The National AI Centre’s 2026 analysis of 2024 labour-market data found that 1,532 organisations—3.8% of hiring organisations—sought workers with AI-related skills, compared with 483 organisations, or 2.7%, in 2015. Technical AI-related skills appeared in 0.9% of job postings in 2024, up from 0.2% in 2015. These figures measure advertised demand, not the number of workers who need basic AI literacy or the capability of businesses already using AI.
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A National AI Centre SME AI Pulse summary covering December 2025 to February 2026 found that 54% of non-adopting businesses considered AI irrelevant to their business, while 19% of SMEs did not know how to use AI in their business. The Pulse is a monthly weighted survey with at least 400 Australian small- and medium-business owners and decision-makers per wave. These results are specific to that survey period, not permanent national rates.
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Use an adoption-to-maturity lens, not a yes-or-no count
Jobs and Skills Australia’s Our Gen AI Transition – Adoption describes adoption as a progression from initial use towards integration and maturity, with leadership, data, skills and governance as relevant enablers. That distinction matters: starting to use a tool is not the same as having the people, processes and oversight to use it reliably across an organisation.
Formal deployment is not the whole picture either. JSA notes that “Shadow use (workers adopting Gen AI without formal approval) signals early adoption and bottom-up innovation.” Informal experimentation may reveal demand or useful ideas, but an organisation should not count it as governed, organisation-wide capability without understanding what tools staff use and how work information is handled.
JSA’s broader labour-market study says “Gen AI is more likely to augment jobs than replace them.” The Department of Employment and Workplace Relations’ 8 July 2026 monitoring summary found no evidence to date of broad labour-market upheaval, while noting suggestive but non-definitive evidence of slower employment growth in some highly exposed occupations. That is early monitoring, not a forecast of future employment outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How an organisation can test whether capability is keeping pace
Because there is no national, like-for-like measure of AI use and AI-specific workforce capability across the same businesses, leaders need to assess the relationship within their own organisation. A practical review can distinguish experimentation from operational readiness:
- Map actual use. Identify approved AI tools, business processes that rely on them, and informal or “shadow” use. Record whether each use is a trial, a team workflow or an integrated service.
- Define the capability each use requires. For each workflow, specify the human skills needed to select tools, assess outputs, protect information, check accuracy and make decisions. Include role-specific expertise as well as general AI literacy.
- Check the enablers together. Assess whether accountable leaders, usable and appropriately managed data, staff skills and governance are in place. A training course alone cannot compensate for unclear ownership, poor data or absent safeguards.
- Track evidence of capability. Measure who has received relevant training, whether they can demonstrate the required tasks, what work is reviewed by a person, and how errors or incidents are handled. Compare these measures with the maturity of the use cases, not simply the number of AI tools purchased.
- Prioritise the gaps. Start with uses that have meaningful business value and manageable risk. Set a named owner, provide time for learning, and review whether the workflow and staff capability improve together.
JSA’s consultation asks both how generative AI is being adopted and what barriers shape its pace and depth, and how industry can work with education and training to develop capabilities for a Gen AI-enabled economy. That is the right organisational question too: not merely whether AI has arrived, but whether people and systems can use it effectively and responsibly.
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