Trusted AI Adoption: Building the Skills for an AI-Enabled Workforce
AI adoption is accelerating, but access to technology alone does not determine which organisations realise value from it. The capability of the people using, overseeing and making decisions about AI matters just as much.
AI is already changing how work gets done. In software development, AI can accelerate coding and support tasks such as testing and documentation. In healthcare, AI can be used to retrieve lost revenue.
These examples point to a broader workforce challenge. As AI takes on more routine tasks, people need the skills to direct it, assess what it produces, understand its limitations and recognise when human judgement is required.
In Episode 27 of KJR’s Trusted AI: From Hype to Impact, KJR founder Kelvin Ross speaks with ACS National President Beau Tydd about the skills Australia will need as AI becomes embedded across workplaces, education and everyday life. The discussion raises an important question for Australian organisations: are we developing AI capability at the same pace that we are adopting AI?
AI is changing the skills behind the work
AI can create capacity by reducing time spent on routine and administrative tasks, but realising that value depends on having the skills to use and oversee it effectively.
The opportunity is already visible in practice. A study conducted by Children’s Health Queensland found that an AI medical scribe reduced the median time to finalise outpatient correspondence from 7.9 days to just 14 minutes. In software development, AI can similarly accelerate coding, testing and documentation, shifting greater emphasis towards human review, judgement and verification.
As AI becomes embedded in everyday work, employees need to be able to direct these systems, critically assess their outputs and recognise when human intervention is required. The value lies in combining AI-enabled efficiency with the human judgement needed to maintain quality, safety and accountability. According to the 2025 ACS Australia’s Digital Pulse, 77% of technology workers surveyed reported insufficient capability in at least one digital skill required for their role, compared with 51% of workers in other industries.
For organisations, the implication is clear: AI investment and workforce capability need to develop together. Without the skills to apply and evaluate AI effectively, access to more capable technology will not necessarily translate into better business outcomes.
AI literacy is becoming a workforce capability
The next generation entering the workforce will have a very different relationship with AI. Growing up as “AI natives” may give younger workers greater familiarity with these tools, but familiarity alone does not guarantee the critical judgement required to use them effectively. Workforce readiness will increasingly depend on the ability to assess AI-generated information, question its reliability and understand the implications of how data is used.
Education has an important role in building this capability. As AI becomes part of professional practice, approaches to teaching and assessment need to reflect the skills graduates will require in the workplace. Developing AI literacy means equipping people to apply AI appropriately, evaluate its outputs and understand where human judgement remains essential.
As Beau explains:
Don’t try and block it, recognise that it’s been used, and use your assessment to assess how you use an AI
The value of this approach extends beyond education. If AI use is becoming a workplace capability, learning environments can help develop the judgement required to use these tools responsibly before graduates enter professional roles.
This need continues throughout a person’s career. AI can support continuous professional development by helping people explore unfamiliar technologies, build on existing expertise and test their understanding of new concepts. As roles evolve, the ability to learn and adapt alongside emerging technology will become an increasingly valuable workforce capability.
For employers, this makes continuous learning a strategic consideration. Building AI capability cannot be treated as a one-off response to adoption; organisations need pathways for employees to continually develop their skills as the technology, its applications and their responsibilities evolve.
Critical thinking remains central to trusted AI
AI literacy is as much about judgement as it is about using the technology. Employees need to recognise when an output requires scrutiny, understand the implications of sharing business or personal information, and consider the consequences of relying on an incorrect result.
As Kelvin explains:
Our users of AI need to really start thinking about risk management
This capability becomes particularly important as employees adopt AI tools independently. “Shadow AI” can reduce visibility over how organisational information is being used, while restricting access alone may not prevent employees from seeking the efficiencies these tools offer. Clear guidance and practical AI skills can help organisations manage this risk while enabling employees to use AI more confidently and appropriately.
For trusted AI adoption, critical thinking becomes an organisational safeguard. Equipping employees to question outputs, assess data risks and recognise when human oversight is required helps organisations capture the benefits of AI without losing sight of quality, accountability and trust.
AI capability cannot stop with technical teams
As AI becomes more closely tied to business performance and risk, leadership capability will increasingly influence the quality of AI investment and governance decisions. Executives and directors need sufficient understanding to evaluate where AI can create value, scrutinise associated risks and establish appropriate oversight.
Strong AI literacy also helps leadership teams move beyond broad expectations of what the technology might deliver. By clearly defining the operational problem, expected outcome and measures of success, leaders can make more informed investment decisions and assess whether an AI initiative is delivering meaningful business value.
This capability is also important for accountability. As with cybersecurity, AI-related risk cannot sit solely with technical teams. Leaders who can ask informed questions about data, governance, risk and outcomes are better positioned to provide effective oversight as adoption expands across the organisation.
KJR is supporting this capability through its executive-focused AI strategy workshops, helping leadership teams assess AI opportunities, establish practical roadmaps and strengthen decision-making around investment, risk and governance.
Reskilling as a Strategic Enabler of AI Adoption
Reskilling gives organisations an opportunity to combine existing industry expertise with new AI capabilities. Rather than allowing valuable knowledge of customers, operations and business context to become disconnected from emerging ways of working, organisations can equip experienced employees to apply that knowledge more effectively as their roles evolve.
A workforce that can use AI, evaluate its outputs and recognise its limitations is also better positioned to identify where the technology can deliver genuine value. This can help organisations move from isolated experimentation towards more purposeful adoption tied to the work and outcomes that matter.
Treating AI skills as an ongoing organisational capability strengthens the value of existing expertise while preparing teams to adapt as responsibilities and technology continue to change.
Building Australia’s AI Capability
The organisations best positioned to navigate AI will need more than access to the latest tools. They need people who understand how those tools fit into their work, how to evaluate their outputs and when human judgement is required.
That capability needs to extend from employees using AI in everyday tasks through to executives making decisions about investment, risk and governance. It also needs to evolve as the technology changes.
For Australian organisations, reskilling is therefore becoming part of the AI adoption journey itself. Building capability deliberately, through practical experience, clear expectations and continuous learning, can help organisations realise the benefits of AI while maintaining the judgement and oversight required for trusted adoption.
AI capability cannot stop with technical teams
As AI becomes more closely tied to business performance and risk, leadership capability will increasingly influence the quality of AI investment and governance decisions. Executives and directors need sufficient understanding to evaluate where AI can create value, scrutinise associated risks and establish appropriate oversight.
Strong AI literacy also helps leadership teams move beyond broad expectations of what the technology might deliver. By clearly defining the operational problem, expected outcome and measures of success, leaders can make more informed investment decisions and assess whether an AI initiative is delivering meaningful business value.
This capability is also important for accountability. As with cybersecurity, AI-related risk cannot sit solely with technical teams. Leaders who can ask informed questions about data, governance, risk and outcomes are better positioned to provide effective oversight as adoption expands across the organisation.
KJR is supporting this capability through its executive-focused AI strategy workshops, helping leadership teams assess AI opportunities, establish practical roadmaps and strengthen decision-making around investment, risk and governance.
Build practical AI capability within your organisation
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