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Short answer: If no one is competently accountable for your business’s core cyber risks, establish that capability first—through a hire or another suitable arrangement—while building AI literacy and implementation skills around real business needs. A dedicated AI specialist is not automatically the next hire: first determine whether you need to apply existing tools, improve data and workflows, integrate systems, or develop AI models. This is a conditional decision framework, not a universal hiring order for every Australian business.
Why there is no universal hiring order
AI and cybersecurity address different but increasingly connected needs. AI capability can help an organisation apply tools, analyse information, redesign tasks and create new services. Cyber capability helps protect systems and data, assess risk, respond to incidents and oversee technology suppliers. As AI use expands, the work of integrating and governing it can touch both areas.
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That does not mean every firm needs two new specialists. The first decision is whether a critical capability is missing, not which job title is more fashionable. A business with no clear owner for security risk may have a more urgent gap than one whose staff have not yet hired an AI engineer. A firm with a well-managed security function and a specific, valuable AI project may reasonably prioritise AI implementation instead.
The available Australian evidence shows increasing AI-related hiring, difficulty attracting some experienced digital and cyber workers, and skills shortages across businesses. It does not set a mandatory sequence, establish a company-size or budget threshold for a dedicated AI hire, or prove whether a particular firm should hire, train, or outsource. Treat the order below as practical guidance based on risk, workload and current capability.
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What “AI skills” and “cybersecurity skills” mean in a hiring plan
Neither phrase describes one standard job. A useful job brief identifies the work to be done, the level of expertise required and who will be accountable for the outcome.
AI work ranges from adoption to model development
An AI need may involve choosing and applying existing tools, redesigning a workflow, preparing usable data, connecting systems, evaluating outputs, or building models and products. Those activities call for different combinations of technical and business capability. An organisation adopting AI in existing processes may need people who understand its data, users and operations before it needs someone to train a new model.
The National AI Centre’s 2026 ecosystem report describes an Australian ecosystem substantially focused on adopting and integrating AI into existing processes, alongside a growing group of companies developing proprietary tools. It identifies work clusters that include business analytics, industrial process optimisation, scientific research, medical diagnosis and training generative AI models. These examples show why “AI specialist” is too vague to use as a hiring specification; they are not a complete role taxonomy.
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- Application and workflow improvement: identifying suitable tasks, mapping a process, testing whether a tool improves it and supporting staff adoption.
- Data and analytics: preparing, interpreting and analysing information to support decisions or an AI use case.
- Integration and product delivery: connecting tools to existing systems and designing a usable service or process.
- Model development: building or adapting AI models where the intended product, data and technical workload justify that depth.
- Leadership and governance: setting priorities, coordinating delivery and ensuring that relevant risks and responsibilities are considered.
This breakdown is a way to translate business objectives into work; it is an inference from the National AI Centre’s findings, not a prescribed classification from the report.
Cyber work is about ownership as well as technical execution
Cyber capability is not limited to hiring someone who configures security tools. A business needs clear responsibility for assessing risk, protecting systems and information, handling incidents, and checking how suppliers affect its technology environment. The specialist work may be performed by employees, a service provider or a combination, but the business still needs a capable owner who can make decisions and follow up on risks.
For a smaller organisation, that distinction matters: a single full-time cyber hire may not be the only way to cover the need. Conversely, buying a service does not by itself answer who inside the business understands the risk, decides what is acceptable, and acts when the provider identifies a problem.
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What Australian hiring evidence says—and what it does not
AI-related hiring is growing, but concentrated
The National AI Centre’s 2026 report, using underlying hiring data through 2024, found that 1,532 organisations—3.8% of hiring organisations—sought workers with AI-related skills in 2024. In 2015, the corresponding 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.
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The same report found that 100 companies accounted for 58% of AI job postings, while inner Sydney, Melbourne, Brisbane and Perth accounted for 64% of position locations. These are measures of concentration, not estimates of total AI job numbers or proof of a nationwide shortage. A smaller business should not infer from prominent city hiring that it must compete for a specialist before defining the work.
AI-related postings commonly included machine learning, programming and mathematics as well as communication, management and leadership. That mix reinforces a practical point: technical depth matters when the work requires it, but the ability to implement a useful change across a business may call for different skills.
Cyber and digital recruitment has constraints, but shortage figures need care
The Australian Public Service Data, Digital and Cyber Workforce Plan 2025–30 reports difficulty attracting experienced and mid-level data, digital and cyber employees. It cites low labour supply, competition and affordability as factors. The plan also warns that current Australian cyber labour-market statistics have limitations. It repeats an AustCyber estimate of a 3,000-person Australian cyber workforce shortage by 2026; that is a cited projection, not a verified count of a current shortfall or a precise measure of private-sector vacancies.
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 figures are economy-wide, not specific to AI or cyber roles, but they help explain why a narrow, expensive job specification can be difficult to fill.
Survey evidence points to skills gaps, not a national census of competence
The Australian Computer Society’s Digital Pulse executive summary reports 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. The report identifies AI, data analytics and cybersecurity among commonly reported deficiencies for technology workers. These are survey results, not population-wide census findings; the edition and date were not verified in the opened extract.
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The same summary says that in 2024 only 1% of surveyed technology employers reported graduates were “job ready,” while 65% said they had to reskill graduates to make them effective. Those figures describe the surveyed employers, not all Australian employers or graduates. They are a reason to assess practical capability and role fit rather than treating a qualification as a guarantee of readiness.
AI adoption can change jobs as well as create specialist work
Jobs and Skills Australia’s Generative AI Capacity Study says: “The Study finds that Gen AI is more likely to augment human work than replace it.” The overarching report is dated 14 August 2025, and the final release was 30 September 2025. The study also considers automation of routine tasks and changes to work. For employers, that supports considering job redesign and training alongside external recruitment; it does not mean that every role will be augmented in the same way or that specialist hires are never needed.
The National AI Plan material frames workforce capability as necessary to build infrastructure, develop AI solutions and apply them effectively. It also includes cybersecurity and secure critical infrastructure among AI infrastructure priorities. That policy framing treats AI and security as complementary needs, but does not direct private businesses to hire one job title first.
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1. Find the unowned cyber risk
Start by asking whether a competent person or provider has clear responsibility for the basics. Identify who assesses risk, protects business systems and data, coordinates incident response, and oversees relevant suppliers. Check whether that person can explain the current arrangements and who makes decisions when a problem is found.
If no capable owner exists, establish that accountability before adding AI deployment that could increase the number of systems, data flows or suppliers to oversee. Depending on the business and its current team, the answer might be a dedicated employee, a qualified external provider with an internal counterpart, or training and additional responsibility for an existing role. The reviewed evidence does not prescribe a particular staffing model.
2. Write down the AI outcome before opening a requisition
Describe the business problem, the people affected and the change you want to make. Then list the work required: applying an existing tool, redesigning a process, preparing data, integrating systems, building a model, or coordinating oversight. Name the skills already present and the work that cannot be done reliably with current capacity.
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This step prevents two common mismatches: hiring a model developer when the real need is process and data work, or expecting a generalist to build a technical product without the required expertise. The National AI Centre’s findings on adoption, integration and diverse work clusters support scoping a role around the task rather than assuming one standard AI job.
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Some needs are broad enough to address through staff development. Employees using AI tools can benefit from basic understanding of what the tools do and how their work may change. People responsible for a particular implementation may need deeper skills in data, systems or delivery. Cyber awareness should likewise reach the people whose choices affect business risk, while specialist security work needs suitably capable ownership.
The APS workforce plan encourages agencies to provide AI fundamentals training for all employees and additional role-specific training when required. That is guidance for public service agencies, not a requirement for private businesses, but it illustrates a layered approach: broad literacy plus targeted learning for jobs that need it. Jobs and Skills Australia’s augmentation finding also supports considering whether current roles can adapt rather than assuming every new use case demands a new position.
4. Recruit for persistent gaps, not a fashionable title
Consider a dedicated specialist when the work is recurring, consequential and too technically demanding or time-consuming to cover responsibly with existing staff or providers. A sustained model-development programme, a major integration pipeline or complex security responsibilities may call for specialist capacity. A limited experiment or occasional need may not justify a permanent full-time role.
Before hiring, compare the same factors for each gap:
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- Urgency: Is there an immediate operational need, or can the work be planned?
- Persistence: Is the workload ongoing, or a bounded project?
- Access and impact: Does the role touch sensitive information, core systems or important business operations?
- Required depth: Does the work call for specialist engineering or security experience, or can a capable generalist deliver it?
- Available capacity: Can existing staff be trained, or can an external provider cover the need with adequate internal oversight?
- Practical hiring constraints: Can the business recruit and retain the needed capability at a sustainable cost and in its location?
These are decision axes, not a published scoring model. Use them to document why a hire, training plan or provider is the better fit; do not turn them into a numerical score without evidence to support one.
5. Revisit the mix as AI use changes
Review responsibilities when a pilot becomes an operational tool, when more staff or data become involved, or when a system is connected to other business processes. AI deployment can create further integration, data, oversight and security work. A capability mix that was adequate for a limited trial may not cover a wider rollout.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Hire, train or use a provider?
These approaches are alternatives for covering work, not mutually exclusive strategies. A business can train current staff in baseline skills, recruit for a role that needs sustained specialist depth, and use an external provider for defined work. The right combination depends on the stakes, frequency of the work, the competence already available and whether the business can retain enough knowledge to oversee the result.
| Approach | Most useful when | What to plan for |
|---|---|---|
| Hire a specialist | The work is persistent, consequential and requires depth that the current team cannot reasonably develop or supply. | Define the deliverables and decision rights; assess practical ability against the work, not just a title or qualification. |
| Train existing staff | The need is baseline fluency, adjacent to current responsibilities, or a defined skill gap that can be developed in time. | Allow for practice and role-specific learning; do not treat an introductory course as a substitute for specialist competence. |
| Use an external provider | The requirement is bounded, intermittent or needs expertise that is difficult to recruit permanently. | Keep an internal owner who can set expectations, understand advice and follow up on the work; outsourcing delivery does not remove business accountability. |
The ABS figures on specialist skills and salary costs make it sensible to define a position carefully and consider transferable skills, training and external capacity. They do not establish that outsourcing or training is cheaper or more effective for any particular employer. Compare the actual scope, quality and ongoing workload before choosing.
Role design: what the first hire might actually do
Job titles vary across employers, so specify responsibilities and outcomes in the position description. The examples below are role-design options, not a recommended staffing chart or a claim that every business needs each role.
| If the main gap is… | Describe the work as… | Do not assume… |
|---|---|---|
| Unclear security accountability | Ownership of risk assessment, protection priorities, incident coordination and supplier oversight, with authority to escalate issues. | Buying security tools alone creates accountable capability. |
| Applying existing AI tools to business work | Use-case selection, workflow mapping, practical evaluation and implementation support with relevant business teams. | The job necessarily requires a machine-learning engineer. |
| AI work blocked by data or system constraints | Data preparation, analysis or systems integration, matched to the actual technical environment. | Hiring an AI researcher will resolve an operational data problem. |
| Developing a proprietary AI product or model | Technical model or product development with the engineering and mathematical depth the project requires. | A general AI-literacy course is sufficient for specialist development work. |
| Broad staff capability gaps | Baseline learning for affected teams, followed by role-specific development where the work calls for it. | One training session makes every worker an AI or cyber specialist. |
These distinctions align with the National AI Centre’s observation that postings combine technical capabilities, including machine learning, programming and mathematics, with communication, management and leadership. The correct mix depends on the work, not on whether a candidate can claim a broad label such as “AI skilled.”
A simple decision rule for Australian businesses
- No capable cyber owner: establish cyber accountability first, while developing AI literacy and defining use cases in parallel.
- Cyber ownership exists, but no concrete AI use case: do not hire a dedicated AI specialist merely because demand is rising; build understanding, identify a valuable task and scope the work.
- Cyber ownership exists and a valuable AI workload is persistent: recruit or develop the specific AI, data, integration or product capability the workload requires.
- Both gaps are material: prioritise the more urgent uncovered risk, use external or internal capacity where appropriate, and set a plan for the second gap rather than treating the choice as permanent.
Australian hiring data show that AI-related demand is growing, but the report’s concentration figures and the reported cyber labour-market limitations caution against reading national trends as a prescription for one employer. The strongest first move is to make the missing responsibility visible, then match capacity to the actual work.
How to tell whether the plan is working
Review whether the person or arrangement you chose is covering the responsibility that justified it. For cyber, verify that risk ownership, response coordination and supplier oversight are clear and active. For AI, assess whether the defined business work is being delivered and whether the chosen skills match the work that emerged. If the workload has shifted from a trial to ongoing deployment, revisit whether the original training, provider or hire still fits.
This is especially important because the evidence describes a changing labour market rather than a settled staffing formula. AI postings have increased, and both the APS plan and ACS survey results point to challenges in attracting or developing digital capability. Keep role definitions focused on outcomes and reassess them as business needs evolve.
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