One theme ran through HRcoreNordic 2026 louder than any other: uncertainty. Here is how HR can lead through it instead of bracing against it.
TL;DR: Macro uncertainty is near historic highs, the half-life of skills is shrinking, and almost every HR team is being asked to steer transformation it cannot fully predict. The lesson emerging from the field is counterintuitive: getting value from AI depends less on the technology and more on the people leading it. For HR, leading well in uncertainty means swapping prediction for readiness, and readiness starts with seeing what your workforce can actually do. That is what AI-powered skills intelligence delivers: a live view of capability that lets you make decisions on capabilities, not on org-chart boxes.
Why is uncertainty the defining challenge for HR right now?
Because it is measurable, and it is rising. The World Uncertainty Index, built by Hites Ahir, Nicholas Bloom and Davide Furceri from the frequency of the word "uncertainty" in Economist Intelligence Unit country reports across 143 countries, has spiked around almost every major shock of the last two decades: the financial crisis, the Euro debt crisis, Brexit, the 2016 US election, US-China trade tensions, and the pandemic. Recent readings put global uncertainty back near the top of that range.
This is also the frame PXO Culture chose for its 2026 HR & People Priorities Report, themed around fundamental uncertainty. And it lands on top of a workforce already in flux: the World Economic Forum's Future of Jobs Report 2025 finds that 39% of the skills workers use today will be transformed or outdated by 2030, with 59% of the workforce needing reskilling or upskilling over the same period.
So HR is being asked to make long-range people bets (succession, mobility, the green transition, AI adoption) in conditions where the ground keeps shifting. The old reflex, treating change as a one-off project with a start and end date, no longer fits. As we argued in our piece on why the author of Agile HR called it dead, the goal is no longer episodic change. It is building for ongoing uncertainty.
Does navigating uncertainty depend on AI, or on people?
Both, but not in the order most teams assume. The organizations getting the most out of AI tend to succeed because of the people leading the change, not the sophistication of the tools they buy. Technology is necessary. It is rarely the deciding factor.
The leadership writer Simon Sinek frames the human side as a duty of care: leadership is less about being in charge than about looking after the people in your charge, and that demand is at its sharpest precisely when things are uncertain. Put the two ideas together and a clear principle emerges for HR. In uncertainty, the leaders who win are the ones who can see their people clearly and act on that understanding. AI is what makes that possible at the scale of a modern workforce. It does not replace the judgement; it equips it.
What does it mean to lead transformation with AI rather than be led by it?
Being led by AI looks like buying tools and hoping outcomes follow. Leading with it looks like deciding what human question you need answered first, then using AI to answer it. For most HR teams under pressure, that question is the same one executives keep asking: what can our workforce actually do, and where can it go next?
Answering it is not a matter of more dashboards. It is a matter of turning the data you already hold into capability you can act on. That is the difference between technology that sits in your stack and technology that changes your decisions.
How does skills intelligence help HR navigate uncertainty?
Skills intelligence is the layer that converts scattered HR data into a live, decision-ready picture of workforce capability. It reads what already exists across your systems (roles, career histories, training, certifications) and structures it around skills, in one consistent language across teams, sites and countries. We unpack the mechanics of that in detail in our companion article on the skills activation problem, and the systemic logic behind it in product-led HR and treating talent as a system.
In conditions of uncertainty, that layer does three things no static process can:
- It replaces proxies (job titles, tenure, manager perception) with evidence of what people can do now.
- It keeps pace with change, because skills data is continuously refreshed rather than captured once a year and left to go stale.
- It connects capability to action through internal mobility, so people can move toward where the work is going.
The point is not to predict the future perfectly. It is to be ready for whichever future arrives.
How do you make workforce decisions when you can't predict what's next?
You stop trying to forecast headcount and start modelling capability. When a reorganization lands, you can ask which skills carry across and which need building, rather than just who sits in which box. When the green transition reshapes a job family, you can see the adjacent skills already in your workforce. If AI changes what work gets done, you can identify who is positioned to move with it and who needs support to get there.
This is the shift from prediction to readiness. Readiness does not require certainty about what is coming. It requires clarity about what you have. With skills gap analysis and internal mobility running on a live capability view, HR can respond to change in weeks rather than reorganizing blind and hoping the talent fits.
Where should HR leaders start?
Three practical moves to begin, none of which require a multi-year programme:
- Activate the data you already hold, rather than launching another collection exercise. Most of the raw material for a capability view is already sitting in your HRIS.
- Anchor the work to a real decision your executive team is facing in the next eighteen months, so skills intelligence proves its value against something concrete.
- Open internal mobility early, so the capability view is immediately useful to employees and they have a reason to keep it current.
The constraint here is rarely the technology, which is mature and integrates with the systems you already run. The constraint is whether HR treats workforce clarity as a strategic priority now, or leaves it on the "someday" list while the workforce keeps changing underneath.
To see what a live capability view looks like in your own organization, explore Skills View, our guide to map your workforce skills without employee engagement.
What does the 365Talents skills intelligence platform do?
365Talents is an AI-powered skills intelligence platform built for exactly this problem: giving HR a live, decision-ready view of workforce capability without launching another data-collection programme. A few things make it suited to uncertainty:
- It reads and structures the skills data already held across your systems, connecting to more than 100 HR tools including SAP SuccessFactors, Workday and Oracle HCM, so it strengthens your existing stack rather than replacing it.
- Its AI maintains a living skills framework that updates as roles and business needs evolve, and works across languages, which matters for organizations operating in several countries at once.
- It turns that capability view into action: personalised recommendations for jobs, projects, internal mobility and learning for employees, and skill-gap analytics for HR and workforce planning.
- It is built on responsible-AI foundations. 365Talents was the first HR tech company to achieve the ISO 42001 standard for AI management systems, which matters when capability data starts informing decisions about people.
The approach is proven at scale
Global organizations including Société Générale, Alstom, Veolia, TotalEnergies, Orange, SNCF use 365Talents to become skills-based organizations, with employee activation rates that reach 81% at RTE and 85% at SNCF. The distinction that underpins it is simple: skills intelligence is the foundational layer, an accurate view of who has which skills, and talent intelligence is the decision layer built on top, using that data for mobility, succession and workforce planning. You cannot have the second without the first.
It is the use of AI to turn the workforce data an organization already holds into a structured, continuously updated view of what its people can do, so HR and the business can make decisions based on capability rather than job titles or assumptions.
Because uncertainty rewards adaptability over prediction. When you cannot forecast exactly what is coming, the most valuable thing you can have is clarity about your current capability and the ability to redeploy it quickly.
No. The evidence points the other way: the value of AI in transformation depends heavily on the people leading it. AI scales human judgement and visibility; it does not substitute for leadership.
People analytics typically reports on headcount, attrition and demographics. Skills intelligence reports on capability and capacity, which is what most workforce decisions in a transformation actually depend on.
Faster than most expect. The technology integrates with existing HR systems, and capability views can be operational in weeks rather than quarters when the work starts from data that already exists.
It connects to more than 100 HR systems to activate the skills data you already hold, maintains a living skills framework that updates as needs change, and turns capability into action through internal mobility and gap analysis, all on an ISO 42001-certified responsible-AI foundation.
