The CHRO Guide to Reskilling in the Age of AI

As we transition from a focus on AI adoption to AI impact, there is a sense of urgency around making sure Australians have the capabilities they need to add value to their organisation and future-proof their careers.

Despite this, recent Workday research reveals a significant gap: in 89% of organisations, fewer than half of all roles have been updated to reflect AI capabilities. So, how can we make sure that we're not forcing 2026 tools into 2016 job descriptions? Where do we start?

I believe reskilling in the age of AI requires CHROs to lean into their expertise as organisational architects – understanding what skills already exist, where the gaps are, and how AI can reduce friction across their workforce.

Only then can we identify the training and support our people need to become more productive, innovative and prepared for the future of work. Here are five ways to improve reskilling for the AI age.

What are the Key Steps to Reskilling in the Age of AI?

1. Create a Skills Taxonomy
2. Identify the Human Skills AI Can’t Replace
3. Build Universal AI Fluency
4. Drive Adoption With Reverse Mentoring 
5. Use Skills-Based Hiring

Step 1: Create a Skills Taxonomy 

What often holds organisations back from taking this approach is that they lack a standard skills taxonomy.

In my recent conversation with Dr Barney Glover on the InnovationAus podcast, he explained how Jobs and Skills Australia is creating a national skills taxonomy that will provide a common language for describing the skills Australians gain, develop, and use across education and employment.

This is exactly what CHROs need in their own organisation – not a granular list of tasks, or a list of every skill across the organisation. And forget job titles. That's a straight line towards re-creating the past.

Instead, HR leaders need to think about critical job families – customer service, sales, operations – in terms of how they map to business strategy. Anchor your taxonomy in what the business is trying to deliver over the next 12-36 months.

It’s vital to make skill descriptions highly practical – and validate them with high-performers who are actually doing the work.

Keep the taxonomy as simple as possible. For example, perhaps setting four (not 10!) proficiency levels: foundational, working, advanced, expert. Or defining skills in three layers:

• Job-critical skills – what you must be able to do to perform the role
• Core enterprise skills – how you work, aligned to values and culture
• Future skills – what will matter more as AI evolves

Finally, connect your taxonomy to real workflows.

A skills taxonomy is only useful if it plugs into hiring and interview guides, internal mobility and talent marketplaces, learning pathways, workforce planning, and performance and development conversations.

If your taxonomy lives in a spreadsheet, it will die there.

Step 2: Identify the Human Skills AI Can’t Replace

Workday research found that 83% of respondents believe AI can elevate the importance of uniquely human skills and enhance human creativity.

This sentiment is at the heart of human-AI collaboration.

AI is good at data crunching, searching, pattern recognition and supporting repetitive tasks. But humans still own judgement, ethics, creativity, trust-building and oversight.

In each job family, ask teams to tell you:

• What tasks are repetitive, administrative or pattern-based?
• Where are people spending time that doesn’t require human judgement?
• Where do we need stronger oversight, review and decision-making?

Getting people involved in this process can help to reduce fear.

Many parents worry about how their children's future will be impacted by AI – particularly if they're already studying for qualifications in engineering and other professions that are rapidly evolving.

That fear is understandable. But it’s also based on a misunderstanding of what technology does.

AI doesn’t remove the need for human work. It changes what humans do, and increases the value of the skills that are hardest to automate: creativity, critical thinking and human connection.

CHROs have a responsibility to help people understand how AI can amplify their impact, how to work alongside AI as a teammate, and which skills they might need to drive that human-AI collaboration.

This is a critical counterpoint to the relentless message coming through social media that 'AI is coming for your job'.

Step 3: Build Universal AI Fluency

Coexistence requires confidence. And confidence requires fluency. But how do we ensure AI fluency across five generations and different cohorts in the workforce?

First, we must distinguish between being AI literate and AI fluent. Literacy is knowing the tool exists; fluency is the confidence to use it to redesign workflows.

For a CHRO, this is a major design challenge. You must ensure that reskilling in the age of AI is equitable and reaches every corner of the organisation.

This prevents a digital divide, where only a small percentage of your workforce captures the productivity gains of the technology.

A Harvard Business Review article noted the higher propensity for women in the workplace not to use generative AI.

That’s not a small issue. If only one cohort is experimenting, then you create unequal capability development and opportunity gaps.

AI fluency training must be mandatory, universal and ongoing.

As well as learning how to prompt and build agents, people need education on governance, bias management, trustworthiness, ethical use, data safety and how to sense-check outputs.

Because if teams don’t understand what they’re putting into tools – and what those tools are doing with the data – risk increases quickly.

Step 4: Drive Adoption With Reverse Mentoring

As organisations work to develop AI fluency, they also have a powerful opportunity to bring generations together to learn from each other.

Organisations will soon employ five generations side-by-side – introducing a 60-year span of lived experience in the workplace.

Gen Z are ideal catalysts of AI adoption – curious, comfortable with technology and willing to experiment.

At the same time, more experienced employees bring strategic context. Together, both groups learn faster.

The experience of teaching others helps Gen Z to deepen their own capability. Explaining a concept to someone else tests their knowledge and increases awareness, strengthening both confidence and real-world application.

A multi-generational and gender-balanced workforce is also significantly less likely to face occupational shortages, because you're able to draw from a wider, more agile talent pool.

Step 5: Use Skills-Based Hiring Models

As Dr. Barney Glover noted in our conversation, we are seeing a shift where skills-first recruitment is no longer a niche strategy, but a worldwide trend.

When you hire for skills rather than credentials, you increase the size – and diversity – of your talent pool.

We’ve seen this firsthand at Workday. When we transformed to skills-based hiring, the outcomes in just 12 months were significant:

• 32% decrease in time to fill
• 11% increase in offer acceptance
• 24% increase in Candidate NPS — even for candidates not hired

This didn’t just improve hiring outcomes. It improved trust, strengthened our employer brand, and unlocked a more diverse pool of talent.

But it required discipline. We had to define job-critical skills and Workday core skills (values and cultural alignment).

And then we had to re-educate hiring managers – because legacy interview habits are deeply ingrained.

Equip Your Workforce for the Future

When digital tools can enter the organisation as easily as any other app, the real differentiator becomes the system you build around them: the guardrails, the fluency, the confidence, and the clarity about what humans own and what technology can support.

This is why the most practical starting point for rearchitecting your workforce in the age of AI isn’t the next platform, pilot or policy. It’s capability.

When you treat skills as your control system, you can redesign work with intent.

You can decide where automation genuinely adds value, where human judgement must stay in the loop, and how to build a workforce that grows stronger alongside technology.

That’s how human-AI collaboration becomes a productivity multiplier.

We are currently in a period of transition. In such times, it is essential to ensure employees have the agility to move into new areas where they can apply both existing and emerging skills alongside the technologies of the future.

Watch the InnovationAus webinar with Jo-Anne Ruhl and Dr. Barney Glover (Commissioner of Jobs and Skills Australia), discussing how Australia’s national productivity depends on building an AI-ready workforce over the next two years.