State of AI development in Australia 2026

Executive summary

Seven findings the published evidence supports.

1. Adoption has jumped but is still a minority position. 12% of Australian businesses used AI in 2024–25, up from 1% in 2021–22 [1]: around 35% of large, 22% of medium and around 11% of small and micro businesses [2].
2. Most adoption is shallow. Two-thirds of the firms the RBA surveyed had adopted AI in some form, but nearly 40 per cent reported minimal use [3]. Only 7% of SMEs make broad use of AI [4], and 5% of SMBs using it are fully enabled to realise the benefits [5].
3. Pilots stall before production. Only 28% of Australian respondents to Deloitte's 2026 enterprise survey have moved 40% or more of their AI pilots into production, against 25% globally [6]. Gartner found at least 50% of generative AI projects abandoned after proof of concept [7].
4. Trust and skills are the binding constraints. Around 65% of non-adopting SMEs cite distrust in AI decision-making or a preference to keep human control [8]. Only 28% of Australian CEOs say they can attract high-quality AI talent, against 42% globally [9].
5. Capital has moved to AI. 61% of Australian startup capital raised in 2025 went to companies with an AI offering [10], AI was the top-funded sector at $1.0B [10], and in Q2 2026 AI-first, AI-enabled and AI-infrastructure companies took roughly three-quarters of capital [11].
6. Infrastructure is being built at scale. Amazon plans AU$20 billion of Australian data-centre investment from 2025 to 2029 [12] and Microsoft A$25 billion by the end of 2029 [13]. The ABS attributed a 6.5% rise in private capex in the March quarter 2026 to data-centre equipment [14].
7. The regulatory settings changed direction. Mandatory guardrails will not proceed at this time [15], the Voluntary AI Safety Standard was superseded by six essential practices on 21 October 2025 [16], and a Privacy Act transparency obligation for automated decision-making commences on 10 December 2026 [17].

Methodology

This report is desk research on sources published between 2024 and September 2026. The adoption figures come from the ABS Business Characteristics Survey, a random sample of approximately 7,000 businesses with an 87.9% response rate [1]; the National AI Centre's SME AI Pulse, monthly waves of at least 400 SME decision-makers [8]; an RBA liaison survey of 105 medium and large firms [3]; and a Deloitte Access Economics survey of more than 1,000 SMBs [5]. The rest draws on Australian cuts of global surveys, government workforce and policy publications, industry salary guides, venture funding data and vendor pricing and availability documentation. Every source was accessed on 16 September 2026 and is listed under References.

Four limits apply.

• Definitions differ. "Using AI" covers everything from staff with a chat assistant to a system a process depends on. Surveys report different rates because they ask different populations different questions. We report each figure with its sample and do not average them.
• Australian cuts of global surveys are small. Where a source states the Australian sample size we repeat it; otherwise treat the figure as indicative.
• Some things are not publicly measured. No Australian source publishes a breakdown of deployed AI systems by architecture, model provider, hosting region or build-versus-buy route, and none publishes inference spend. Where that is the case this report says so.
• Prices are a snapshot. Model and cloud list prices are as published on 16 September 2026 and change often.

Adoption and maturity

The ABS is the most reliable measure of breadth. In 2024–25, 12% of Australian businesses used AI, up from 1% in 2021–22 [1]. Innovation-active businesses adopted at more than three times the rate of the rest, 20% against 6% [1]. Size and industry explain most of the spread.

SME panels report far higher rates because they count any level of use. The National AI Centre's tracker put SME adoption at 43% for December 2025 to February 2026, down marginally from 45% [8]. The Productivity Commission, citing the same tracker, reported 82% of firms with 200 to 500 employees had adopted AI in the first quarter of 2025 against 33% of firms with 0 to 4 employees [18].

Depth is the better question, and every source that asks it gets the same answer. The RBA found two-thirds of surveyed firms had adopted AI in some form, with nearly 40 per cent reporting minimal use [3]. Assistant Minister Andrew Leigh put broad use among SMEs at 7% [4]. Deloitte Access Economics found two-thirds of SMBs using AI but 5% of those users fully enabled [5]. On the supply side, the National AI Centre and CSIRO identified 1,533 AI companies in Australia, 1,121 private and 412 public [19], and CSIRO found AI-adopting firms posted 36% more non-AI job ads over time than non-adopters [20].

What Australian businesses are building

Among SMEs using or planning to use AI, the most common applications are content generation and data analytics at 54% each, then cybersecurity and threat detection at 48% [8]. Hiring follows the same pattern: PwC recorded a rise of 19,300 in postings for AI user roles against 1,300 for AI developer roles [21]. Most of what Australian businesses call AI use is people using AI tools, not systems built around models.

The next layer is forming. SEEK reports demand for agentic AI skills up 174.4% year on year [22], and just over half of Australian companies name talent and skills gaps as a significant barrier to agentic AI [6]. Investors have moved past the label: Cut Through Venture notes they weight workflow integration, differentiation and defensibility over standalone AI branding [10].

ComplxAI's view: four recurring shapes

No public source classifies Australian production systems by type, so this is observation, not measurement.

• Knowledge assistants. Retrieval over internal documents and records, with permissions enforced at retrieval time. The work is in ingestion, retrieval and evaluation, not the chat interface.
• Document processing. Extraction, classification and reconciliation of invoices, forms and correspondence, feeding an existing system of record. The DisabilityAssessments engagement on our case studies page is this shape: automated matching, alerting, invoice processing and reconciliations.
• Agentic workflows. Multi-step operations where a system acts in other software and a person approves the exceptions. The engineering is in tool design, state, retries and the approval step.
• AI features inside products. Capabilities added to an existing platform, where the constraints are cost per user at scale and behaviour on the long tail of inputs.

What separates the ones that reach production from the ones that stay demos is set out under generative AI consulting and AI engineering.

Build vs buy

The published evidence here is thin and mostly global. McKinsey's 2026 survey found 32% of respondents had decided against buying one or more software products or features because they could be built in-house with agentic coding tools [23]. Gartner expects AI coding costs to overtake the average developer's salary by 2028 [24]. The Deloitte Access Economics finding that 5% of SMB users are fully enabled [5] is the other side of the coin: buying a tool is not the same as being able to use it.

No Australian source records which route organisations take. In our own work the decision is made per use case, and "defer" is recorded as a decision in its own right. AI strategy consulting exists to make those build, buy and defer calls in writing before anything is funded.

Models and platforms

Australian teams choose between the same providers as everyone else: OpenAI, Anthropic and Google through their own APIs, a wider catalogue through AWS Bedrock, and open-weight models. List prices sit in clear tiers.

The tiers matter more than any single price. A production system routes each step to the cheapest model that passes its evaluation, and that routing is the main lever on running cost. Prices also move on a schedule: Google's Flash pricing doubles from 1 January 2027 [27], and OpenAI's gpt-5.6-sol is on promotional pricing through at least 21 November 2026 [25].

Running models from Sydney

AWS's regional-availability documentation lists no current Anthropic Claude model with native in-region support in ap-southeast-2 (Sydney). Claude Opus 5, Sonnet 4.6, Sonnet 4.5 and Haiku 4.5, among others, are available through Geo (Australia geography) and Global cross-region inference; Claude Sonnet 5 and Fable 5.1 are Global-only. Models with native in-region Sydney support include Amazon Nova, Titan Text Embeddings V2, DeepSeek V3.1 and V3.2, Gemma 3, Mistral Large 3, gpt-oss, Qwen3 and GLM 5 [29]. Geo and in-region inference carries a 10% uplift over Global cross-region rates in Sydney: Claude Opus 5 at $5.50 input and $27.50 output rather than $5.00 and $25.00 [30].

The practical position for an Australian build: the system is deployed in the region you need, and for Australian data residency that is the AWS Sydney region. Data can stay in Sydney for the models with native in-region support, and for the frontier Claude models the question is whether Geo (Australia) inference meets your obligations, which is a contractual and legal question as much as a technical one. The OAIC's guidance states that privacy obligations apply to personal information entered into an AI system and to outputs containing it, and recommends not entering personal information into publicly available generative AI tools [31]. Community expectations run ahead of the law: 71% of Australians are uncomfortable with service data being used to train AI, and 25% find automated eligibility or risk-based decisions acceptable [32]. The architecture side is covered under AI engineering and AWS consulting.

Skills and teams

The technology workforce contracted for the first time on record. ACS puts technology employment at around 967,000 in 2025, down 0.3 per cent, while estimating Australia needs an additional 259,000 technology workers over the next decade [33]. DISR counted 949,172 technology-related jobs in May 2025, a 3.7% decline while the broader labour market grew 2%, and rated its 1.2 million-jobs-by-2030 measure "Not met" [34]; the Tech Council's method gives approximately 977,000 [35].

Software engineering is the exception. Software and Applications Programmers number approximately 203,200, growing 7% year on year in each year from 2022 [36]. Yet advertised roles fell from approximately 8,000 a month in 2021 to approximately 4,000 in 2025 [36], the fill rate rose from 63% in 2023 to 85% in 2025, 98% of employers mandate prior experience and the average experience required climbed from 3.8 years to 4.6 years [36]. Software engineers are no longer in shortage in any state or territory [37].

Demand has shifted to AI skills within those roles. Postings seeking AI skills rose from 20,000 in 2024 to 41,000 in 2025, and AI-skilled workers command an average wage premium of 62 per cent [21]. In June 2026, 12.9% of ICT job ads mentioned AI skills, and demand for AI-related skills across all job ads was up 64.1% year on year [22]. Training has not kept pace: 60% of employees use AI regularly at work but 22% have had training [38], and 24% of Australians have undertaken AI-related training against 39% globally [39]. RBA respondents reported difficulty finding data engineers and scientists [3].

ComplxAI's view. The systems in this report are built by senior software engineers who can work with language models, with data engineering, product and design, and the domain experts who write the evaluation sets. The scarce skill is not prompt writing; it is evaluation, integration and operating a probabilistic system after launch. Whether the team that builds a system is the team that runs it is the question to ask of any delivery plan, and the reason documentation and handover are part of how we work.

Budgets and cost expectations

Australian leaders rate AI highly and fund it cautiously. 70% of Australian CEOs rated AI a top investment priority, yet 29% were committing less than 10% of their investment budget to it, against 17% globally [44]. 65% of Australian respondents to Deloitte's enterprise survey intend to raise AI investment next year, against 84% globally [45]. Globally, 28% of organisations spend more than 10% of their ICT budget on AI, and about 20% say operating costs, including token costs, constrained their use [23]. The backdrop is Australian IT spending forecast at A$172.3 billion in 2026 [46] and worldwide AI spending of US$2.59 trillion [47]. For product companies, funding rounds set the ceiling: median Australian deal sizes in 2025 were A$1.0M at angel and pre-seed, A$2.5M at seed, A$11.0M at Series A and A$30.0M at Series B and beyond [10].

No Australian source publishes what individual AI systems cost to build. The ranges below are ComplxAI indicative ranges: the modelling assumptions behind our AI project cost calculator. They are not published data, not ComplxAI rates and not quotes. Treat them as starting points for a scoping conversation.

The drivers that move a project within those ranges, and the inference and infrastructure costs that continue after launch, are the subject of the Australian AI development cost report.

Barriers

Two things stand out. The barriers are organisational before they are technical: trust, know-how, policy and alignment dominate, with technology and data availability behind skills and cost. And the pilot-to-production gap is global: Gartner's abandoned projects cite poor data quality, inadequate risk controls, escalating costs and unclear business value [7], and McKinsey found 44% of organisations scaling AI across the enterprise, up from 38% [23]. Microsoft adds the cultural detail: 51% of Australian workers say it feels safer to focus on current goals than to rethink work with AI [50].

ComplxAI's view. The barrier an organisation names in conversation and the one that stalls a project are rarely the same. The named barrier is trust or skills; the one that stops a project is access to the data, an integration nobody owned, or the absence of an evaluation set that would let anyone say the system was good enough. A scored use-case map from AI consulting surfaces the second kind before money is committed.

Regulation and governance

Australia has moved from a proposed mandatory regime to voluntary guidance plus targeted law.

The National AI Centre, established in 2021 and part of DISR, is the lead body for industry adoption [60]. The July 2026 safety priorities also flagged a Digital Duty of Care, a second tranche of privacy reform and an automated decision-making framework for federal agencies [56].

For a business building or buying an AI system in 2026 the binding obligations are the Privacy Act and the OAIC's guidance, the ADM transparency requirement from 10 December 2026 [17], and whatever the exposure draft becomes. The six essential practices are voluntary, but they are what a procurement questionnaire or an insurer will ask about. The governance we recommend designing in from the start covers ownership of each system, an audit trail of model calls and actions, an approval step wherever a system acts on someone's behalf, and a documented evaluation set. Those are engineering deliverables, cheaper to build than to retrofit.

Infrastructure investment

Venture funding is smaller but points the same way. Australian startups raised $5.4B across 390 announced deals in 2025, up 31% [10], with 61% of capital going to companies with an AI offering [10]. Q1 2026 brought $1.8 billion, the strongest first quarter since the 2022 peak [67]; Q2 added $1.7 billion, taking the first half to roughly $3.5 billion, though sub-$5M rounds fell to 31 against a 2025 quarterly average of 56 [11]. AI featured in 81% of seed deals in Q2 [11].

For a business that only needs to run models, the relevance is capacity and region: more in-region compute in Sydney and more models served from Australia over time, which matters where Australian data residency is a requirement. It does not change the engineering between a model endpoint and a dependable system.

Outlook for 2027

This section is ComplxAI's view. It contains no figures and records the expectations of one engineering team, to be checked against the evidence when this report is repeated.

• The depth gap narrows for a minority. Organisations that treat AI as an engineering discipline, with evaluation sets, integration and ownership, will put later systems into production more cheaply than the first. The rest will keep piloting.
• Agentic workflows with approval steps become the default shape in operations and finance. The unit of work shifts from "answer a question" to "complete a task and route the exceptions to a person". Fully autonomous systems will stay rare where a human approval step is appropriate, and that is the right outcome.
• Procurement asks better questions. Residency, automated decision-making disclosure, an audit trail and an evaluation set will appear in tender questionnaires alongside security. Systems built without them will be retrofitted or replaced.
• Model prices keep moving, in both directions. Scheduled increases and promotional pricing both appear in current price lists. Architectures that route between models and providers will absorb that; architectures wired to one model will not.
• The team shape settles. Demand for people who use AI tools keeps growing faster than demand for builders, and the builders hired will be senior software engineers who can work with models rather than a separate AI discipline.
• More models served from Australia. The infrastructure commitments above should widen what can be run in-region, simplifying the residency conversation without removing the need to design for it.

If any of these is wrong, the next edition will say so.

References

All sources accessed 16 September 2026. Figures are quoted as published by the source named; where a source is a media release or news article reporting another organisation's data, the publisher listed is the one we consulted.

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