From Job Descriptions to Skill Descriptions: Updating HR Basics for an AI-Shaped Labour Market

Traditional job descriptions were never designed for a world where tasks can shift quickly between humans and machines.

They tend to list fixed responsibilities (“owns monthly reporting pack”) and specific tools (“advanced Excel user”) as if the work will stay the same for years. When AI enters the picture, that assumption breaks: what was once a major part of a role can become partially automated in a matter of months.

To stay competitive, organisations are beginning to focus less on rigid jobs and more on the underlying skills people bring. That shift sounds abstract, but it has very practical implications for hiring, development and reward.

Why job descriptions are under strain

You may already be feeling some of these tensions:

• Roles that looked similar on paper now vary hugely depending on which AI tools teams use.
• Candidates arrive with AI‑enhanced capabilities that are not captured in your existing templates.
• Managers struggle to explain how a role will evolve as automation increases.

Updating job descriptions one by one is slow and often political. A skills‑first approach can give you a more flexible foundation.

What we mean by “skills”

Skills are not just technical qualifications or tools. Think of three layers:

1. Foundational skills: communication, problem‑solving, collaboration, digital fluency.
2. Domain skills: knowledge of your products, markets, processes and customers.
3. Enabling skills for AI: things like data literacy, prompt design, critical thinking about AI outputs and change leadership.

Any given job is simply a bundle of these skills applied to a specific context.

Moving from jobs to skills in practice

You do not need to rebuild your entire HR architecture overnight. Start with a few concrete steps.

1. Build a simple skills language

Working with a manageable list of skills is better than aiming for a perfect taxonomy.

• Start by analysing a handful of critical roles and identifying the skills that truly drive performance.
• Add a small set of AI‑related skills that are relevant across many roles.
• Use plain language so managers and employees can recognise themselves in the descriptions.

This becomes the shared vocabulary you use across HR processes.

2. Tag roles with skills, not just tasks

For each priority role, ask:

• Which skills are essential on day one?
• Which can be learned in role within 6–12 months?
• Which are becoming less important as AI automates certain tasks?

Adjust your job descriptions to emphasise these skills and how they might evolve.

3. Connect skills to learning and progression

Once you know which skills matter, you can:

• Offer targeted learning pathways rather than generic training catalogues.
• Make internal moves easier by showing employees how their existing skills transfer to new roles.
• Reward people not just for tenure, but for building scarce, high‑impact skills.

AI learning programmes fit naturally into this model as a way to grow enabling skills across the workforce.

Implications for AI in hiring

When you hire with skills in mind, your AI questions become more precise.

Instead of asking “Do you use AI?”, you can explore:

• “Tell me about a time you used AI to improve a process or solve a problem.”
• “How do you check AI‑generated outputs for quality and bias?”
• “How have you helped others in your team use AI effectively?”

You can also be clearer about what the role will look like as AI matures: which tasks may shrink, which new responsibilities may grow and how you will support that transition.

Avoiding common pitfalls

A skills‑based approach is powerful, but there are traps to avoid.

• Over‑engineering the framework: a 200‑page skills taxonomy that no one uses is worse than a simple list that people can remember.
• Ignoring managers: they are the ones who will need to apply the framework in hiring and development conversations, so involve them in the design.
• Treating skills as static: review and refresh your skills language regularly as AI reshapes work.

A more honest conversation about work

Ultimately, moving from job descriptions to skill descriptions is about having more honest conversations:

• With candidates: about what will be expected now and how the role may evolve.
• With employees: about which skills they have, which they need and how you will help them grow.
• With leaders: about where to invest in people versus where to automate.

AI is accelerating the pace of change-but a skills‑first approach gives you a stable way to navigate it. Start small, learn fast and your HR basics will be ready for whatever the next wave of automation brings.