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For an Australian business with no clear owner for basic cyber risk, establish that capability first—through a qualified employee or an appropriate service provider—while building AI literacy and working on defined AI use cases in parallel. That is a conditional starting point, not a universal rule: the right first specialist depends on the business’s exposure, current capability, intended AI work and the amount of ongoing work each need will create. Most businesses should define the job before hiring an “AI specialist”; applying AI to a workflow, integrating tools and developing new models require different skills.
Why the choice is not simply AI versus cybersecurity
AI capability and cybersecurity capability solve different problems, but they increasingly intersect. AI may help a business change or automate work, analyse information or build new services. Cybersecurity capability helps protect the systems, information and operations on which those activities depend. A business adopting AI therefore does not automatically need a dedicated AI researcher, nor does it become safe simply because it has hired a security specialist.
The practical question is which unmet capability presents the more immediate business need—and whether that need calls for a new hire at all. A business may be able to address a defined, limited requirement through training, an existing employee, a specialist provider or a combination of these. A persistent workload or a need for accountability and deep expertise may justify a dedicated role.
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What the Australian workforce figures do—and do not—say
The National AI Centre’s 2026 report, using hiring data through 2024, found that 1,532 organisations—3.8% of organisations hiring—sought workers with AI-related skills in 2024. In 2015, the comparable figures were 483 organisations and 2.7%. Technical AI-related skills appeared in 0.9% of job postings in 2024, up from 0.2% in 2015. These indicators show growth, but they do not mean that every employer needs an AI hire or that every posting was for a dedicated AI-development job.
Hiring was concentrated: 100 companies accounted for 58% of AI job postings, while inner Sydney, Melbourne, Brisbane and Perth accounted for 64% of position locations. Those figures describe where postings were concentrated; they are not a count of all AI roles, a measure of unmet demand, or a national estimate of a skills shortage. The report also found that postings commonly combined technical capabilities—such as machine learning, programming and mathematics—with communication, management and leadership skills.
The broader business hiring environment matters too. The Australian Bureau of Statistics reported in a 2026 release that 35% of businesses had a skills shortage in 2024–25. Among businesses reporting shortages, 57% cited specialist skills or knowledge and 48% cited wages or salary costs. These are economy-wide figures, not AI- or cyber-specific rates. They nevertheless underline why a narrowly scoped role, a realistic salary plan and careful consideration of existing staff matter.
The Australian Computer Society’s Digital Pulse executive summary reported that 77% of surveyed technology workers and 51% of surveyed workers in other roles said at least one digital skill needed for their current role was insufficient. It identified AI, data analytics and cybersecurity among common deficiencies reported by technology workers. The report edition and date were not verified in the opened extract, and the survey figures are not population-wide census findings. The same summary reported that, in its 2024 survey, only 1% of surveyed technology employers considered graduates “job ready” and 65% said they had to reskill graduates to make them effective. Those findings apply to that survey scope, not all Australian graduates or employers; they are a reminder not to treat a qualification alone as proof of job readiness.
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Decide whether cyber ownership is the first gap to close
Start by identifying who is accountable for understanding and managing the business’s cyber risks. The question is not merely whether someone has “cyber” in their title. It is whether a competent person or provider has responsibility for assessing exposure, protecting systems and information, coordinating incident response and overseeing relevant vendors.
If no one can confidently describe who performs those functions, or how the business would respond to a security incident, foundational cyber ownership is a sensible first specialist priority. That may mean appointing an internal lead, assigning clear responsibility to an appropriately capable existing employee, or engaging external expertise. It does not automatically mean hiring a full-time security engineer: the appropriate arrangement depends on the work, operating environment and available internal capability.
Cyber capability becomes especially urgent when a role or service must protect sensitive information, business-critical systems or operational technology, or when an AI use case will connect to internal data and processes. This is a practical risk-based assessment, not a claim that the sources prescribe a fixed checklist or that every AI project creates the same risk. The National AI Plan material treats cybersecurity and secure critical infrastructure as priorities alongside AI infrastructure, reinforcing that security is part of enabling AI capability rather than an alternative to it.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe APS Data, Digital and Cyber Workforce Plan 2025–30 reports that Australian Public Service agencies have difficulty attracting experienced and mid-level data, digital and cyber employees, citing low labour supply, competition and affordability. The plan repeats an AustCyber estimate of a 3,000-person Australian cyber workforce shortage by 2026, while warning that current Australian cyber labour-market statistics have limitations. That number is a cited projection—not a verified count of a present-day shortfall, nor a precise estimate for private businesses.
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Define the AI work before writing a job description
“AI specialist” can mean several substantially different jobs. The National AI Centre describes an Australian ecosystem that is focused substantially on adopting and integrating AI into existing processes, alongside a growing group of companies developing proprietary tools. Its eight work clusters include business analytics, industrial process optimisation, scientific research, medical diagnosis and training generative AI models. Those examples span routine business implementation and highly specialised development; they should not be collapsed into one generic vacancy.
Before deciding to recruit, write down the intended use cases and identify the main work they require:
- Applying existing tools: selecting and using AI-enabled products within an existing workflow. This may call for product knowledge, sound judgement and role-specific AI literacy rather than model-building expertise.
- Redesigning a process: working out which tasks should change, how people will use the output and where human review is needed. Process knowledge, communication and change leadership may matter as much as technical skills.
- Preparing and analysing data: checking that relevant data can be accessed and used appropriately, then turning it into useful analysis. The required depth could range from business analytics to more technical data work.
- Integrating systems: connecting AI capabilities to existing tools, data and workflows. This calls for implementation and systems skills; it is not necessarily the same as developing a model.
- Developing or adapting models: building proprietary AI capabilities or doing more specialised model work. This can require substantial technical expertise, such as machine learning, programming and mathematics.
- Providing oversight: setting expectations for use, decision-making and accountability as applications change. The needed mix will depend on the application and the organisation’s existing governance and cyber capability.
This breakdown is a hiring-design inference from the work described by the National AI Centre, not a prescribed occupational classification. It gives an employer a more useful starting point than assuming that every AI goal requires a machine-learning engineer. The National AI Plan material also describes a need for workers who can build infrastructure, develop solutions and apply them effectively, which points to a range of capabilities rather than one universal role.
Compare the capability you need, not just the job titles
Use the following distinctions to decide what each proposed hire or development path must accomplish. A person may cover more than one area in a small organisation, but a broad title should not obscure missing responsibilities or expertise.
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| Capability | Typical business need | When to prioritise it | What it does not automatically provide |
|---|---|---|---|
| Cybersecurity ownership | Accountability for assessing cyber risk, protecting systems and data, coordinating incident response and overseeing relevant vendors. | When these responsibilities have no competent owner, or when exposure and operational importance make the gap pressing. | It does not by itself select AI use cases, redesign workflows or deliver AI model development. |
| AI application and workflow capability | Finding suitable ways to apply existing AI tools and changing work so the application serves a defined business need. | When use cases are clear enough to test and current staff need help applying tools effectively. | It does not necessarily cover systems integration, specialist data work or development of proprietary models. |
| Data, analytics or integration capability | Preparing and analysing data or connecting an AI-enabled capability to existing systems and processes. | When a viable use case depends on ongoing technical work that current staff cannot cover. | It is not a substitute for cyber risk ownership or, automatically, for advanced AI research expertise. |
| AI model-development expertise | Building or adapting models where that technical work is central to the business objective. | When the business has a sustained, specific development workload and a credible case for doing it in-house. | It does not by itself ensure a model is integrated into operations, adopted by staff or governed appropriately. |
| Broad staff literacy and role-specific training | Helping employees understand and use relevant AI capabilities, alongside security awareness appropriate to their work. | When many roles need foundational knowledge but only some require specialist depth. | It does not replace expert accountability or deliver specialist work that employees are not equipped to perform. |
Use a hiring sequence that reflects risk and workload
There is no evidence-based rule that every Australian business must recruit a cyber specialist before any AI role, or the reverse. The following sequence helps make the choice explicit while leaving room for businesses whose immediate needs differ.
- Map ownership and exposure. Identify who is responsible for cyber risk, systems and data protection, incident coordination and vendor oversight. Note critical systems, sensitive information and any AI use cases that depend on them. Where competent ownership is absent, address that gap before expanding into specialist AI development.
- Write down the business outcome for each AI use case. Name the process or service, what improvement is sought and what work must happen to test it. Separate adoption of existing tools from workflow change, analytics, integration and model development.
- Assess the capability required to deliver and maintain the work. Be specific about technical depth, access to data or operational systems, management responsibility and the expected persistence of the workload. If cyber and AI work overlap, record the security responsibility separately instead of assuming the AI role covers it.
- Compare hiring with training and external support. Check whether current staff have transferable skills, whether role-specific learning can close the gap, and whether a provider can cover a bounded requirement. Consider a dedicated hire where work is persistent or accountability and expertise cannot reasonably be supplied through the alternatives.
- Reassess when adoption changes. As a use case moves from a small trial into routine operations—or new use cases are added—review whether the workload, system connections, data exposure and oversight requirements have changed enough to warrant additional internal capability.
For a consistent decision, rate each proposed capability against the same questions: what is the business risk if it is missing; how soon is the work needed; how persistent is the workload; what sensitive data or operational systems will it touch; how much technical depth is required; what is the local talent availability; what is the total employment cost; and could training or a managed provider cover the need? This is a practical decision aid, not a published scoring model. It prevents a fashionable title or a single urgent project from being mistaken for a lasting full-time job.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Train existing staff where the work supports it
Training can be a practical part of the answer, particularly when a business needs broad awareness or wants to test a limited use case. Jobs and Skills Australia’s Generative AI Capacity Study finds that generative AI is more likely to augment human work than replace it, while recognising that routine tasks may be automated and work reshaped. That framing supports considering training and job redesign alongside recruitment rather than assuming every adoption effort needs a new specialist position.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThe APS Data, Digital and Cyber Workforce Plan 2025–30 encourages agencies to provide AI fundamentals training to all employees and additional role-specific learning where needed. That is guidance for government agencies, not a private-sector requirement, but the layered approach is relevant: give people a foundation, then deepen training for roles with more direct responsibility. The National AI Centre’s finding that AI postings combine technical and organisational skills also supports paying attention to communication, management and leadership—not only coding.
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Training is a poor substitute when the business needs accountable specialist judgement, a sustained technical workload or a capability existing employees cannot reasonably develop alongside their other responsibilities. Conversely, hiring for a short, clearly bounded task may not make sense if appropriate existing staff or external support can handle it. Define what a trained employee is authorised and expected to do, and what must be escalated to a specialist or provider.
Plan for recruitment and capability constraints
Recruitment may be difficult even when the job is well defined. The APS plan’s account of challenges attracting experienced and mid-level digital and cyber employees is specific to agencies, so it should not be presented as a measured private-sector vacancy rate. It does indicate that experienced talent is contested. The National AI Centre’s evidence of concentrated AI hiring also cautions against assuming that AI expertise is evenly distributed across Australian locations.
For a business with a limited candidate pool or budget, sharpen the role before broadening the search. Specify the work outcomes, the capabilities that are genuinely essential on day one, and the skills that can be learned after appointment. Distinguish a need to apply a tool from a need to build a model; distinguish a security responsibility from general technology support. Consider transferable experience and demonstrated ability alongside formal credentials.
The ACS executive summary describes employers drawing on degrees while also showing interest in vocational education and training (VET) and industry certification. It does not establish one qualification pathway as a definitive measure of capability. Use interviews, work-relevant scenarios and evidence of prior responsibility to assess fit for the actual role; do not treat a credential or a survey statistic about graduate readiness as a substitute for evaluating an individual candidate.
Match the first move to the business situation
- No clear cyber owner; AI plans are early: establish competent cyber responsibility first, while giving staff foundational AI literacy and scoping a limited, useful AI application.
- Cyber ownership is already credible; a defined AI use case has a persistent workload: recruit for the missing AI capability—whether application, analytics, integration or model development—rather than automatically adding a generic “AI specialist.”
- AI use is spreading across teams, but specialist work is intermittent: build broad staff capability, nominate clear internal coordination and obtain specialist help for work that exceeds the team’s expertise.
- An AI project touches sensitive information or critical operations: make sure security responsibility is explicit before expanding access or relying on the application in routine work; AI implementation and cyber oversight may need to progress together.
- Both cyber and AI capability are missing: prioritise accountable cyber ownership where risk management is unowned, define an achievable AI use case and develop basic literacy in parallel. Decide whether additional specialist hires are justified by the workload and risk, not by a presumed universal sequence.
How to know when a dedicated AI hire is warranted
A dedicated AI role is easier to justify when the work is not simply an aspiration or a short-lived experiment. Look for a continuing stream of defined work, a clear business outcome, and a capability gap that cannot reasonably be filled by current staff, training or external support. Identify whether that stream consists of implementation, data and analytics, integration, model development or a mix; the role should reflect the actual work.
There is no company-size or budget threshold in the cited evidence that determines when to make this hire. A small firm can have a sustained specialist need, while a larger firm may have only a limited requirement. The decision should rest on the firm’s use cases, exposure, available capacity, recruitment constraints and the cost and continuity of each staffing option.
AI adoption also changes the capability mix over time. A trial may begin with workflow design and staff learning, then require more integration or oversight as it becomes part of routine operations. The National AI Plan’s paired attention to AI capability and cybersecurity supports revisiting both needs as deployment expands; it does not specify which job title a private employer should recruit next.
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