Tag: artificial-intelligence

The AI Boundary Most Professionals Haven’t Drawn – But Should

Most conversations about AI at work focus on what it can do. Danielle Farage – a leading voice on Gen Z in the workplace, a future of work speaker, and my co-host on the intergenerational podcast From X to Z – was asking a different question in her recent webinar.

This question arose from her attending an event where a panel of neuroscientists discussed cognitive atrophy from AI use, and her hearing them say that Gen Z – the so-called digital native generation – is statistically the least likely to use AI at work. This led to her to partnering with QuestionPro to survey 1,000 Gen Z professionals about how they actually use these tools – and what’s making them hesitate.

The findings are worth thinking about.

75% of Gen Z are not enthusiastic AI adopters, even though the majority use it every week. 67% deliberately protect certain skills from AI.

And this caution, it turns out, is not a sign of being behind. It’s a sign of calibrating – which is exactly what the most sophisticated AI users are doing.

Microsoft research has found that the top 16% of AI users do some work without AI on purpose. 43% of regular AI users actively refrain from using it for certain tasks specifically to maintain their own skills in those areas. And an MIT Professional Education study found that 83% of people who used AI to write an essay couldn’t quote – or even recognise – their own work afterwards.

That last stat explains everything about why the most intentional professionals are drawing lines.

The Three-Zone Framework

Danielle’s practical response to all of this is a framework built around three questions she applies to any task before deciding how AI belongs in it.

The first zone is delegate – work where doing it yourself would teach you nothing you still need to know. Administrative systems, tracking, data organisation, scheduling. Hand it over. Your brain has better things to do!

The second zone is collaborate – work where AI can draft, frame or accelerate, but where your judgment is required to make it right. Most content creation sits here. AI generates a version; you rewrite it until it sounds like you. The output needs your fingerprints on it to actually work.

The third zone is protect – and this is the one most people haven’t named clearly enough. This is where your edge lives. Your strategic instinct. Your distinctive voice. Your ability to ask the hard question in the room. Your creative process. Your relationships. The moment you start outsourcing your zone three, the premium that makes you worth choosing goes with it.

Her test is blunt: if you couldn’t walk into a room tomorrow and fluently discuss what you just created, you’ve handed too much over.

Top AI users don’t outsource their thinking. They use tools to enhance it.

Why This Matters Beyond Individual Practice

When everyone has access to the same tools, individual output rises – but collective novelty can fall. Content starts to sound the same. Presentations share the same structure. Emails lose their personality. And LinkedIn posts can blur into each other.

The people who cut through this environment are not the ones using AI most – they’re the ones who have protected what makes them distinctively themselves. People don’t follow content. They follow people. They follow the voice they recognise, the perspective they trust, the thinking they can’t get anywhere else.

If you quietly outsource your zone three, your audience – and your contacts and collaborators – are likely to notice before you do.

The One Thing She Refuses to Delegate

Beyond creativity,  Danielle was honest about the one category she wouldn’t hand to AI under any circumstances: her relationships. Automated messages, AI-drafted personal communications, mass texts designed to maintain the appearance of connection – these don’t sit right with her.

And if you’re not sure about your own messaging it’s also worth finding out whether they sit right with the people receiving them, regardless of how seamlessly they’re generated.

The Takeaway?

Delegate the chore.

Protect the craft.

Collaborate on everything in between.

It’s a deceptively simple framework. The hard part is being honest about which category your most important work actually falls into – and whether you’re protecting it, or quietly letting it slip into the collaborate pile because it’s easier that way.

(Danielle Farage is a leading voice on Gen Z in the workplace and co-host of the From X to Z podcast, and the HR Morning Show on Purple Acorn. Keep following her LinkedIn to keep up to date with her research into Gen Z and AI at work)

Experience Should Make Us More Questioning, Not More Certain

There’s a particular kind of insight you only get when someone who knows an industry inside out finds themselves navigating it as a candidate.

Ken Ward has spent three decades in Talent Acquisition – agency recruitment, search, RPO, embedded talent, his own business. He understands recruitment from the architecture down. And for the last year or so, he’s been looking for his next senior role while simultaneously completing a counselling qualification.

The combination makes for one of the most honest conversations I’ve had on the HR Means Business podcast about what the senior job market actually feels like right now.

The market is buying certainty. It should be buying judgment

Ken’s read on what organisations think they’re looking for at senior level is sharp and possibly a little uncomfortable. Over-specified job descriptions. Industry silos that treat sector experience as a proxy for capability. A preference for candidates who feel like a known quantity over candidates who might genuinely change how things work.

What he argues organisations should be buying – and what he rarely sees being rewarded – is judgment. The ability to challenge role definitions before recruitment starts, question assumptions hiring managers have baked in, and help organisations make better decisions upstream of the process.

That’s where he believes Talent Acquisition can genuinely operate as a strategic function. And it’s the work that energises him. Not the title. Not the level. Whether he’s employed, contracting, or advisory is secondary to whether the organisation actually trusts TA to influence decisions that matter.

AI is a hammer looking for a nail

Ken’s take on AI in recruitment is characteristically direct. He’s optimistic about where it’s heading but sceptical about where it currently is.

The problem, as he sees it, isn’t the technology. It’s the sequencing. Organisations are asking where they can apply AI rather than first asking whether their decision-making processes are worth automating at all. The result is what he calls industrialising the mess – taking broken processes and running them faster and at greater scale.

His framing is one worth remembering: judgment doesn’t compete with AI. Judgment determines whether AI creates value. If the judgment behind a process is sound, AI amplifies it. If it isn’t, AI amplifies the dysfunction instead.

He also makes a pointed observation about the CV problem. The TA industry has spent years demanding bullet-pointed achievement statements – measurable, formatted, uniform. AI language models learned from those CVs. Now the industry is complaining that every application looks identical and reaching for new tools to sort the problem. The tools didn’t create the uniformity. The industry did.

LinkedIn, visibility, and the limits of content

Ken started writing on LinkedIn as a job search strategy. What he found was something more useful: a thinking tool. Writing to think, as he puts it, rather than writing to be seen.

He’s sceptical of what he calls “serendipity on demand” – the idea that increasing your LinkedIn visibility is a reliable route to a senior role. His view is that organisations at that level are buying judgment, not content. Social media can help people discover you. It can’t substitute for what they find when they do.

His posts reach around 500 people a week. He’s made peace with that by reframing the metric. If 500 people showed up to an event he’d organised, would that feel like a failure? The algorithm isn’t the point. The thinking is.

What counselling taught a recruiter about recruitment

The most unexpected thread in the conversation is what Ken has taken from his counselling training into his professional thinking.

The core shift is this: Talent Acquisition rewards reaching conclusions. Counselling rewards delaying them. The discipline of sitting with uncertainty rather than resolving it immediately has, by his own account, made him a better recruiter and a more useful thinker.

He uses ChatGPT not as a sourcing tool or an application accelerator but as what he calls a Socratic reflection tool – a way of interrogating his own assumptions. Why did I react that way? Why do I think this is wrong? Is that judgment or bias?

The biggest thing the last year has taught him, he says, is that experience shouldn’t make us more certain. It should make us more questioning.

Authenticity and integrity have always been his stated values. He’d now add curiosity.

For anyone navigating a senior job search, managing a TA function, or just thinking about what good recruitment actually looks like – it’s a perspective worth thinking about.

Check out the full podcast conversation here – https://www.hrhappyhour.net/episodes/how-ai-and-assumptions-are-shaping-hiring-in-2026/ – or through the image below

What Holds the Workplace Together When Everything Else Is Changing

I’ve been on a fair few podcasts over the years – as well as hosting my own HR Means Business – but it’s often the conversations that end up somewhere unexpected that are the ones that stay with me. My recent appearance on The DEX Show with Thomas McGrath was one of those.

We started – as these things often do – with career history. Mine is not a straight line. Trainee accountant. Tax specialist. Recruiter. Early social media adopter at a time when most people in my world thought blogging was a hobby for people with too much time. Writer, speaker, analyst, researcher, podcaster.

The thread connecting it, if there is one, is restlessness – not the restless looking for what’s next, but the restless curiosity about whether there’s a better way of doing what’s in front of us. Every new technology, every new platform, every new way of sharing thinking raises the same question: how can we actually make the best of this? Not how the people who built it say we should use it, but what it might genuinely enable if we approach it with curiosity rather than compliance.

That instinct, it turns out, is exactly what I think the current moment demands – from everyone, at every career stage.

Nobody Planned for This

One of the threads I keep returning to in my writing and speaking is that nobody really designed for longer lives. The UK state pension age was set when average life expectancy was 68. You’d get roughly three years of it on average, if you were lucky. Now average life expectancy is 81 or 82 for men, 84 or 85 for women, and people remain active, mentally sharp, and genuinely motivated to keep contributing well beyond what previous generations would have considered working age.

The result is five generations in the workforce simultaneously – not by plan, but by accumulation. Organisations are still finding their way through what that means structurally. The culture, the career paths, the management models – none of them were designed for this configuration. We’re effectively building the plane while flying it.

And layered on top of that, for the youngest cohort entering work, is a housing market that has effectively broken the traditional social contract. In the UK (and much of the West) Property prices have risen roughly 40 times over the past four decades – whilst salaries have risen around nine or ten times. The model that worked for previous generations – work hard, save, buy a home, build stability, progress – doesn’t function the same way anymore. Academic Dr. Eliza Filby has written a best selling book about the result, called the Inheritocracy – a generation more likely to move forward through inheritance than through career progression – which I think is a must read for HR and managers in general, as it shapes how young people show up at work, what they expect from it, and how much faith they’re willing to place in conventional career structures.

The AI Assumption that Doesn’t Hold

There’s a lazy assumption running through a lot of AI and workplace conversations that digital natives will simply absorb AI fluency. They’ll be the ones who figure it out. ‘Find a young person who understands this’ as the joke often goes.

It doesn’t hold. Being comfortable with technology generally doesn’t mean being equipped for the specific disruption that AI represents. If anything, entering the workforce at exactly the moment when entry-level roles are being most directly affected by AI – when the learning pathway that used to exist is being compressed or potentially removed – may be more destabilising at the start of a career than at any other point. Gen Z are digital natives. They are not AI natives. And that distinction matters.

Where HR and Technology Converge

Thomas asked me where the traditional silo between HR and IT is heading. My answer: it has to dissolve, because it no longer reflects how work actually functions.

HR started in my personal work experience as timesheets, contracts, learning and professional development, a focus on progression, and personal issues that might be impacting work. Personnel, not people. How it’s evolved – employee experience, engagement, development, retention, skills progression, creating the conditions that allow people to bring their best selves to work – is inseparable from technology.

Screening and hiring. Skills matching. Predictive analytics that can begin to forecast turnover or identify where skill gaps are emerging. Personalised learning pathways. Tools that can help managers lead across generational lines they don’t always understand.

The data and analytics layer is where I think it gets really interesting. Not automating what exists, but enabling organisations to ask questions they couldn’t previously ask – and act on the answers before the cost of not acting becomes visible.

The Thing That Holds it all Together

Thomas ended the conversation with a question about employee experience as a metric in the age of AI transformation. It was the right question to end on, because it’s the one I keep coming back to regardless of what else changes.

How organisations treat their people determines how those people perform, whether they stay, and whether they help the business grow. That’s been true since I started work as a 19-year-old trainee accountant in a firm where the two oldest people were 52 and 55. It will be true when whatever comes after AI has reshaped work again.

Technology changes what’s possible. Experience determines what people do with it.

In an era of AI transformation, measuring how people experience work isn’t a soft metric sitting at the edge of the business case. It’s the signal that tells you whether everything else is actually working.

Always be learning. Always be connecting. Be genuinely curious about people – what drives them, what they’re trying to build, what they need to do their best work.

That’s the advice I gave technology professionals navigating AI transformation on The DEX Show. It’s also, I think, the most durable career philosophy I’ve found across a career that has taken more turns than I ever planned!

You can listen to the whole podcast conversation here : https://www.linkedin.com/posts/the-dex-show_what-does-a-great-employee-experience-look-activity-7493368503358550016-4beq?utm_source=share&utm_medium=member_desktop&rcm=ACoAAABRMBEBEmfX44AFqq97vuDd1uRv9jtNDKY

Is Your Culture Ready for AI? The Question Most Organisations Haven’t Asked

Mervyn Dinnen

Mervyn Dinnen

Writer / Speaker / Podcast Host / Analyst / Top 100 HR Tech Influencer / co-author of the books Digital Talent & Exceptional Talent

The questions most organisations are asking about AI is the wrong one:

What is AI doing to our culture?

How do we manage the impact?

How do we bring our people with us through the change?

These are reasonable questions. But, according to Nick Holmes – a year into a PhD on organisational culture and one of the more original thinkers I’ve spoken to on this subject on the latest episode of the HR Means Business podcast – they all start from the wrong premise.

AI is a tool. And when you put a powerful tool into an environment, the environment determines what happens next.

The tool doesn’t.

The better question is not what AI is doing to your culture. It’s whether your culture is designed to make use of it at all.

The Familiarity Trap

Nick’s PhD has surfaced a finding that should give every HR leader a pause. There are 164 different definitions of organisational culture and 70 different measurement tools – and none of them have reached consensus. Culture is, as he puts it, treated atmospherically: like the weather, ever-changing, hard to pin down, something everyone references and almost nobody can truly explain.

This is not an academic problem. It is a practical one. Leaders who cannot explain how culture actually works cannot design it, cannot measure whether it’s changing, and cannot create the conditions AI requires to deliver value.

The zip analogy makes it concrete. Everyone can use a zip. Almost nobody can explain the mechanism – how the teeth engage, how the lock works. The moment you ask, confidence collapses. AI is amplifying exactly this effect inside organisations. People feel more familiar with subjects after a ChatGPT query. They don’t understand them more deeply. And leaders are making AI strategy decisions – agreeing to roadmaps, signing off on investments – based on familiarity rather than a genuine understanding.

The Engagement Coincidence

Gallup has tracked employee engagement for nearly 26 years. When they started, it sat at 23%. Today it remains at 23%. In that same window, and specifically over the last five years, AI adoption has accelerated while engagement has declined.

Nick was careful not to claim causation. But he doesn’t dismiss the coincidence either. His explanation is structural: organisations are no longer running large, discrete redundancy exercises. They are cutting continuously – a few roles here, a few there, constantly.

The psychological research is clear that it takes around six months to recover from the impact of a layoff round. When cuts are continuous, organisations are permanently in psychological deficit. Trust never fully rebuilds. And employees completing engagement surveys while that cloud hangs over them will answer negatively, even when the work itself is unchanged.

The Sabotage Nobody is Talking About

I think there’s one topic from our conversation that deserves to be taken seriously at leadership level – according to the Workplace Intelligence Report 2026, 29% of employees admit to actively undermining their employer’s AI strategy. Among Gen Z, that figure rises to 44%.

This isn’t resistance. It’s sabotage. And the generation most assumed to be AI-native is the most likely to engage in it – precisely because they are the most aware of what it can do and the most anxious about what it might mean for their futures. A survey of 17 and 18 year olds found that 70% would prefer to have grown up without social media and AI entirely.

The implications are significant. No AI strategy survives 44% of its youngest employees actively working against it. The response cannot be enforcement. It has to be trust – and trust is a cultural outcome, not a communications campaign.

The Cognitive Load Problem

The promise of AI has always been that it would free people to do more human, creative, and meaningful work. The data suggests something different is happening. 77% of employees say AI has increased their workload. Frequent AI users report 45% higher levels of burnout.

The reason, Nick argues, is that organisations are using AI to do things faster without stopping doing the things underneath. Work accelerates. More gets added. Zombie processes survive because nobody asked what should stop. The organisations genuinely extracting value from AI ask a different question: not what can we do faster, but what can we stop doing entirely?

What Good Looks Like

The organisations that Nick sees getting this right share one characteristic – they don’t lead with efficiency. They lead with purpose.

Their message to employees is not “AI will help us work faster” – nobody gets out of bed for efficiency. It is: “How do we use these tools to enhance what you already do brilliantly, and change what our organisation is capable of?”

That distinction may sound small. But its consequences are not.

Action Points for HR Professionals

Based on my conversation with Nick, here are five practical starting points:

Audit cultural readiness before AI readiness. Before your next AI investment, assess the environment it will land in. Do your people have psychological safety? Is trust between managers and teams strong? Is change communicated well or poorly? The answers determine AI outcomes more than the technology choice does.

Apply the McKinsey ratio. For every pound invested in AI tools, plan to invest five in your people – in upskilling, change readiness, adoption support and manager capability. Most organisations are spending the inverse of this.

Take the sabotage data seriously. If a significant proportion of your workforce – and particularly your younger employees – are actively working against your AI strategy, a communications push will not fix it. Understand the specific fears driving it and address them directly.

Ask what stops, not just what starts. Before implementing AI in any workflow, ask explicitly: what existing process or task will this replace entirely? If the answer is nothing, you are adding cognitive load, not reducing it.

Train managers first. Managers are the lynchpin between AI strategy and employee experience. If they are not equipped to use these tools themselves, cannot answer their teams’ questions about them, and are not modelling their use confidently, adoption will stall at every level below them.

You can check out my full conversation with Nick Holmes here – https://www.hrhappyhour.net/episodes/how-culture-can-make-or-break-ai-adoption/ – or through the image below

Making AI Matter in HR: Why the Gap Between Hype and Reality Is Still Enormous

Every HR and Talent Acquisition conference right now seems to give roughly the same messaging – AI is transforming recruiting. AI is revolutionising HR. AI is everywhere, doing everything, and the organisations that haven’t fully embraced it are already falling behind.

I recently interviewed my friend, co-author, and podcast host Matt Alder, on the HR Means Business podcast as he has been speaking to practitioners, vendors and thought leaders about the future of talent for over a decade. His honest assessment of where we actually are is rather different from the narrative being sold.

“It’s a very mixed picture,” he told me “There are certain sectors where it’s full speed ahead – principally front-line volume hiring – but the rest of the talent space is still largely at the pilot and experimentation stage. The bulk of organisations haven’t moved beyond the most simple use cases”

Interview scheduling. Rewriting job descriptions with an LLM. Things that, as Matt points out, aren’t really AI at all in any meaningful sense.

The Jagged Frontier Problem

Matt borrowed a phrase from Professor Ethan Mollick to describe where AI actually is right now: a jagged frontier. Remarkable at some things. Surprisingly poor at others. And often dangerously confident about the things it gets wrong.

The organisations making real progress, he argues, share a common starting point – they began with the problem, and not the technology. They find out what was broken in their process first – and then worked out how AI could help fix it.

The companies that simply plugged AI tools into existing processes found that speeding up something that wasn’t working just made it worse faster.

This distinction matters enormously. And it leads directly to Matt’s framework for what AI readiness in talent acquisition actually requires: five things that need to work together rather than in isolation.

Process architecture – genuinely rethinking how recruiting works, not just automating the current version.

Decision design – being explicit about where human judgment sits in the process and why.

Team capability – understanding what skills recruiters actually need in an AI-enabled world, which involves more bottom-of-funnel relationship work and less top-of-funnel screening.

Data and measurement – moving beyond the metrics that were easy to capture to the ones that actually matter to the business.

Governance and trust – both the regulatory dimension and the equally important question of whether candidates trust the process they’re going through.

“People talk about governance all the time,” Matt observed. “Data and measurement get discussed occasionally. Capability, decision design and process architecture – hardly at all.”

The Candidate Side Nobody is Talking About

For Matt, the most underreported story in recruiting right now isn’t what employers are doing with AI – it’s what candidates are doing.

Application volumes are surging. Resumes are being auto-generated and customised at scale. And while the industry conversation frames this almost entirely in terms of cheating or gaming the system, Matt sees it differently.

“Candidates are just using the tools available to them. The CV is the wrong format for the age we’re living in – and candidate AI is exposing that.”

The real innovation in talent acquisition, he believes, is going to be driven from the candidate side, not the employer side. Candidates move faster. They adopt tools faster. And the organisations responding intelligently – moving skills assessment earlier in the process, designing experiences that are genuinely agent-resistant – are the ones that will pull ahead.

The Ownership Vacuum

Focus on this picture for a moment. Most organisations have no clear owner for AI strategy. In some it sits with technology. In others, legal or compliance. In many, nobody owns it at all – it’s just noise. In very few organisations does HR or Talent Acquisition take the lead.

The consequences of that vacuum are significant. Employees are using AI for work without telling their employers – and the regulatory and bias risks that creates are substantial. Simply banning AI use, as some organisations have tried, doesn’t work. People use it anyway, just less transparently.

“The risks of doing nothing, or of banning it, are enormous,” Matt says. “Neither of those things reflects the reality of what’s actually happening.”

What Good Leadership Looks Like Here

It comes down to two things. A clear vision – not just for the technology, but for what you actually want TA or HR to achieve, and how AI fits into that journey.

And psychological safety – creating an environment where people can talk honestly about how they’re using these tools, where pilots are allowed to fail without blame, and where the organisation learns collectively rather than hiding its experiments.

The technology is moving faster than any previous wave of change. But the fundamentals haven’t changed at all.

  • Start with the problem
  • Design the process
  • Build the capability
  • Be honest about where you are

The organisations that do those four things will look very different in 18 months time from the ones still debating whether AI is ready for them.

AI IS ready for them. The question is whether they’re ready for AI?

Check out the full podcast conversation here – https://www.hrhappyhour.net/episodes/making-ai-matter-leadership-culture-and-the-future-of-hiring/ or through the image below

AI Adoption Is Not the Goal. Better Work Is.

We know that there is no shortage of big claims about AI.

Economic growth. Productivity gains. Transformed industries. Smarter services. New opportunities. Fewer inefficiencies.

The final panel session I attended at the Institute for the Future of Work‘s conference on Making the Future Work bought many of these themes together. The headline figures were certainly eye-catching: AI could add as much as £40 billion to UK GDP by 2030, and around three quarters of companies surveyed say they are already seeing productivity improvements from adoption.

That sounds impressive. And it is.

But the conversation was not just about the scale of the opportunity – it was the repeated reminder that AI adoption is not the same thing as successful transformation.

And that distinction matters. Because right now, much of the AI story is still concentrated in relatively narrow areas. Marketing and administration are among the most common functions where AI is being used, which tells us two things. First, adoption is happening. Second, it is still patchy. The benefits are not yet flowing evenly across sectors, workplaces, or regions.

This is important because when people talk about AI as a national productivity solution, it is easy to assume that adoption will somehow spread naturally and that value will automatically follow. In reality, the barriers are much more familiar: regulation, access to data, access to finance, organisational inertia, and the simple difficulty of changing how work gets done.

In other words, the challenge is not just technological. It is operational, structural, and human.

That came through clearly in some of the examples shared. One of the most interesting was healthcare. AI tools are being used in the NHS to support diagnostics, including lung cancer detection, while ambient voice technologies are helping reduce admin by capturing clinical notes in the background. Yet the point made was not that AI is replacing clinicians. In fact, in some cases it is increasing demand for skilled professionals, because better detection and faster insight generate more need for expert interpretation and treatment.

That is a useful corrective to some of the loud narratives around job displacement.

The message here was not that AI is removing work wholesale, but that it is reshaping work. It changes the mix of tasks, the pace of workflows, and the kinds of expertise that become more valuable. It can take friction out of the system, but it can also expose where systems, processes, or skills are not ready.

Which brings us to workforce transformation.

The UK’s ambition to up-skill 10 million workers by 2030 through the AI Skills Boost programme is a significant one, and the fact that more than 1 million courses have already been delivered shows that this is moving beyond just rhetoric. But the conversations also made clear that skills cannot be treated as a side issue. If AI changes work, then skills policy, workforce planning, and management capability have to move with it.

And that means thinking about inclusion as well as scale.

The Women in Tech Task Force was highlighted as an important example of this. If AI is going to shape the future economy, then the people designing, deploying, and governing it need to reflect society more broadly. Otherwise, we risk reproducing old inequalities inside new systems.

The same is true geographically. AI adoption could easily become another force that strengthens existing regional divides if investment, infrastructure, and innovation remain concentrated in the same places. That is why local examples matter.

They show that AI can be embedded in communities in ways that are practical, grounded, and relevant to local needs, rather than being treated as something that only happens in major tech hubs or policy circles. Which is a real takeaway.

The success of AI should not be measured only by how widely it is adopted, or how many tools are introduced, or even how much productivity improves in a narrow sense. It should also be measured by whether it helps people do better work, whether it opens up opportunity more broadly, and whether the gains are being shared fairly.

The clear message was that AI adoption is not the goal. Better work is.

And if we keep that in mind, then workforce transformation becomes about much more than technology. It becomes about designing systems, skills, leadership, and local ecosystems that allow AI to improve working lives rather than simply accelerate them.

What Economic Shocks and AI Are Really Doing to the European Labour Market

If the European labour market feels confusing right now – that’s because it is. On the surface, the signals seem contradictory – hiring is slowing and budgets are tightening – but at the same time, skills shortages persist, competition for talent remains intense in certain areas, and AI is beginning to reshape roles in real time.

In a recent episode of the HR Means Business podcast, I spoke with Julius Probst, PhD – Senior Economist at Appcast, Inc and Director of Research of Recruitonomics – to try and unpack what’s really happening beneath the headlines – and what HR leaders need to do next.

A slowdown – but not a collapse

Julius was clear from the outset: this is not a structural breakdown of the labour market. However what we are seeing is a cyclical slowdown, driven by external shocks – most notably rising oil prices linked to geopolitical tensions. The impact is familiar: higher inflation, sustained interest rates, reduced consumer spending, and slower job creation.

In the UK, for example, this could push GDP growth down to around 0.5%, bringing the economy close to stagnation. Unemployment has already edged up to 5.2%, and workers have far less freedom to move roles than they did just two years ago.

For employers, that translates into fewer vacancies, more applicants, and reduced wage pressure – at least at headline level.

However this is only part of the story.

The rise of a three-tier labour market

I think one of the most important insights from our conversation is that averages are misleading.

Right now, three very different labour markets are operating simultaneously:

The first is AI talent, where demand is accelerating rapidly. Organisations are competing hard for individuals with the skills to build, implement, or work alongside AI systems.

The second is skilled trades — engineering, construction, and technical manual roles — where shortages remain acute. Brexit has intensified these gaps, and wages are rising accordingly.

The third is routine white-collar work, which is where the pressure is most visible. Administrative, customer service, and back-office roles are increasingly being automated, and hiring has slowed significantly.

In some cases, this is creating an unexpected reversal: skilled tradespeople can now earn more – and enjoy greater security – than graduates entering routine office roles.

AI is already changing the shape of work

AI is often seen as a future trend – but it’s not as it is already showing up in the data.

One of the clearest signals is the decline in graduate hiring. Organisations are using AI to reduce the need for entry-level roles, particularly in areas involving repeatable or process-driven tasks.

Julius was careful not to be alarmist however, as he compared AI to previous general-purpose technologies – from electricity to the railways – which disrupted existing jobs but ultimately created new industries and opportunities.

The reality is that the transition is uneven. Right now, younger workers are bearing the brunt. The people who most need to develop AI-related skills are finding it harder to access the workplace where those skills can be built.

A more cautious workforce

Candidate behaviour is shifting as jobseekers become more cautious – and less willing to move roles in an uncertain market – whilst at the same time, competition for jobs is intensifying, particularly in white-collar roles.

AI is adding another layer of complexity because job candidates can now submit large volumes of applications quickly, which increases noise and makes it harder for recruiters to identify the talent they need.

What should HR leaders do now?

Julius’s advice for HR leaders was pragmatic – and grounded in reality:

First, accept that volatility is the new normal. Economic shocks are no longer rare events. Workforce planning needs to be more dynamic, more responsive, and more frequent.

Second, don’t abandon early-career hiring entirely. Demographic trends are indicating that large numbers of experienced workers will retire in the next three to five years. Organisations that stop building their pipeline now risk a significant talent gap later.

Third, think sectors, not generialist. The impact of economic shocks will vary widely. Hospitality, transport, and energy-intensive sectors will face far greater pressure than technology or financial services.

And finally, treat flexibility as a strategy, not a perk. Hybrid working, for example, isn’t just about employee preference – it enables organisations to access talent beyond expensive urban centres, helping to reduce salary pressures, and improve retention.

Reading the signals

What stood out most from our conversation is that this isn’t a chaotic labour market – but it is a complex one.

The signals are there. But they’re uneven, nuanced, and often contradictory. The organisations that succeed will be those that move beyond headline numbers, understand the specific dynamics in their sector, and make deliberate, forward-looking decisions.

Because the labour market isn’t breaking – it’s evolving.

And the question is whether we’re paying close enough attention.

You can listen to our full podcast chat here – https://www.hrhappyhour.net/episodes/what-ai-and-economic-shocks-mean-for-work-in-europe/ – or through the image below

The Future of Work Isn’t About AI – It’s About How We Redesign Work Around People

There’s a familiar pattern playing out across organisations.

Leaders are investing heavily in AI. The technology is advancing rapidly. The possibilities seem endless. And yet… the results aren’t quite matching the ambition.

That was one of the clearest messages from my recent conversation with Kyle Forrest from Deloitte, discussing their 2026 Human Capital Trends Report. While 78% of executives expect to increase AI spending, many organisations are still struggling to translate that investment into meaningful business and human outcomes.

The issue isn’t the technology. It’s how we’re using it.

Too many organisations are still taking a “tech-first” approach – layering AI into existing workflows, automating tasks, and chasing efficiency gains. But as Kyle highlighted, this approach is falling short. The real shift isn’t about humans plus machines. It’s about humans multiplied by machines – redesigning work so that both can operate together to create exponential value.

And that requires something far more complex than a new tool. It requires rethinking how work actually gets done.

The Adaptability Gap

One of the most striking insights from the report is the growing gap between awareness and action.

Around 85% of leaders say adaptability is critical to future success. Yet only a small minority believe their organisations are truly delivering it.

At the same time, employees are experiencing unprecedented levels of change. A decade ago, workers might have dealt with two major organisational changes a year. But today, that number has risen to as many as fifteen.

The impact is predictable: reduced wellbeing, lower engagement, and a lack of clarity about roles and expectations.

The problem is that most organisations are still treating change as something episodic – a programme to be managed, rather than a constant state to be designed for. As Kyle put it, the future isn’t about managing change. It’s about embedding adaptability into the day-to-day flow of work.

The Hidden Risk: Culture Debt

Alongside technical progress, there’s a quieter issue emerging – what Deloitte calls “culture debt.”

Just as technical debt builds up when systems aren’t maintained, culture debt accumulates when organisations neglect the human impact of transformation.

AI is a perfect example. Many employees don’t fully understand why their organisation is investing in AI, how it will affect their role, or what it means for their future careers. If AI creates more productivity, what happens next? More work? More reward? Fewer roles?

When those questions go unanswered, trust erodes. And once trust is lost, it’s far harder – and more costly – to rebuild.

Faster Decisions, Lower Confidence

There’s also a growing tension around decision-making.

Leaders are increasingly using AI to analyse data and inform decisions, often at speed. But with that speed comes a new challenge: accountability. If an AI-supported decision goes wrong, who is responsible?

In many organisations, there is still no clear answer. And that uncertainty creates risk – not just operationally, but culturally. If employees don’t trust how decisions are being made, engagement and confidence begin to decline.

This isn’t a problem HR can solve alone. It requires a co-ordinated, C-suite-level response, with clear frameworks around risk, responsibility, and governance.

Designing Work – Not Just Automating It

Perhaps the most important takeaway from our conversation is this:

AI should not be used simply to make existing work more efficient. It should be used to redesign work entirely.

That means rethinking roles, workflows, and team structures. It means moving away from rigid functional silos – HR, Finance, IT – towards more fluid, cross-functional ways of working. And it means giving people the space and tools to experiment, learn, and adapt.

Some organisations are already beginning to do this well – creating “digital playgrounds” where employees can explore new tools safely, or using AI coaching to help managers communicate and lead more effectively.

These approaches share a common thread: they put people at the centre of transformation, not at the receiving end of it.

The Future Is Still Human

For all the talk of automation, one message came through clearly:

The future of work is not about replacing humans.

It’s about amplifying what humans do best.

AI can process data, identify patterns, and generate outputs. But it cannot replicate human judgment, creativity, or accountability. It cannot make the intuitive leaps that drive innovation. And it cannot take responsibility when things go wrong.

That remains firmly in human hands.

The Real Opportunity

If 2024 was the year of experimentation, and 2025 the year of pilots, then 2026 may well be the year where we start to see real impact at scale. But only for those organisations that get the balance right.

Those that continue to focus purely on technology will struggle to realise value. Those that invest equally in people – in redesigning work, building trust, and enabling adaptability – will be the ones that move ahead.

Because the future of work isn’t being defined by AI alone.

It’s being defined by how we choose to use it.

You can listen to my full podcast conversation with Kyle here: https://www.hrhappyhour.net/episodes/ai-trust-and-the-human-role-in-the-future-workplace/

or through the image below

Enhancing the Human Experience at Work

The pace of workplace technology evolution has never been faster. AI has moved from experimentation to everyday use seemingly almost overnight, promising productivity gains, efficiency, and scale. But as these tools become more powerful, a crucial question sits at the heart of the future of work: how do we ensure technology strengthens rather than replaces the human experience at work?

This question was at the centre of a recent conversation I had with Martin Jackson from Insights, a company long recognised for its work in behavioural science and personality preferences. What emerged was a compelling reframing of what HR and workplace technology should be optimising for.

From efficiency to human effectiveness

For decades, workplace technology has been designed primarily to make things faster – automating tasks and streamlining processes – delivering quicker solutions. But that efficiency-first mindset is increasingly at odds with how people experience work today.

Satisfaction with technology – particularly satisfaction with HR technology – seems to be falling with teams not feeling they are getting the value they were promised. Tools often fail not because they lack features, but because they don’t align with how people think, communicate, or make decisions. When that happens, technology can stop feeling supportive and start feeling intrusive. The opportunity now is to pivot away from pure efficiency and towards human effectiveness – helping people explore problems, communicate with clarity, and exercise better judgment.

Technology should sharpen judgment, not remove it

One of the most important distinctions Martin made was this: organisations shouldn’t try to automate human judgment away. Instead they should be trying to sharpen it.

The most valuable technologies are not those that replace interaction, but those that enhance it – helping people prepare for conversations, understand others’ perspectives, and adapt how they show up at work. This is where personalisation becomes critical.

We’re already seeing this shift amongst major technology players. Personalised assistants, adaptive AI responses, and context-aware tools are becoming priorities. The goal is no longer “more productive humans”, but better humans at work – more aware, more connected, and more effective in the moments that matter.

Behavioural intelligence as the missing layer

Insights’ long-standing personality model is built on a simple but powerful idea: giving people a shared language to understand how they prefer to think, communicate, and decide. That shared understanding can help reduce friction and build patience – especially when the work gets complex or pressured.

What’s changing is how this intelligence can now be used. By digitising the model through the Insights Discovery API, behavioural preferences can be embedded directly into the tools people already use every day.

This unlocks practical, human-centred applications:

  • Coaching nudges that adapt to how someone best receives feedback
  • Onboarding experiences that help new hires communicate more effectively from day one
  • AI-assisted communication that reframes messages so they land better with the recipient
  • Mentor and coach matching based on behavioural compatibility, not guesswork

Rather than generic experiences, work becomes contextual, personalised, and relevant.

Engagement, trust, and change fatigue

Much of what we see as ‘engagement’ really comes down to whether people feel seen and understood. Change fatigue, miscommunication, and disengagement often stem from messages that don’t resonate – not because they’re wrong, but because they’re delivered in the wrong way.

Behavioural intelligence embedded into workflows allows for just-in-time support: prompts before a difficult conversation, guidance before a feedback session, or subtle nudges that help encourage reflection or action depending on the individual.

The result? Fewer misunderstandings, higher relevance, better uptake – and a compounding effect on trust and engagement over time.

Human-centred AI is the real differentiator

AI today can be extraordinarily powerful – but also emotionally clumsy. Large language models can boost productivity, but without behavioural context they often lack empathy, nuance, and emotional intelligence.

Injecting behavioural insight into AI helps to change that dynamic. AI becomes less of a black box and more of a thought partner – something that challenges thinking, improves framing, and supports better decisions without eroding autonomy.

Looking ahead, the most successful workplace technologies won’t win because they’re the fastest or cheapest. They’ll win because they’re the most engaging – the ones that understand users best and adapt accordingly.

Designing technology that earns trust

For HR leaders who worry that new technologies might dilute culture, the answer isn’t to slow down innovation – but to design more deliberately. Test tools in small pilots. Be transparent about what you’re trying, why you’re trying it, and what success looks like. Share what works – and what doesn’t.

Culture only survives if it’s practised. Technology should reinforce that practice, not distract from it.

Enhancing the human experience at work isn’t about resisting AI. It’s about embedding humanity into it – intentionally, ethically, and visibly. When technology supports how people think, communicate, and connect, it doesn’t replace the human experience at work. It elevates it.

You can check out my full conversation with Martin Jackson here https://www.hrhappyhour.net/episodes/enhancing-human-experience-in-the-digital-workplace/

Three Conversations HR and Talent Leaders Can’t Avoid in 2026

The future of work isn’t short of opinions. What it’s often short of is honesty and research-based insight.

As 2026 begins, there are three conversations that I think HR and talent leaders keep circling – not because they’re fashionable, but because they expose where systems, assumptions and practices are under strain. I’ve been writing about these in my Friday newsletters through 2025 and will continue into 2026….and beyond!

1. Rehumanising AI at Work

AI is no longer experimental. It’s embedded. The question is no longer can we use it, but how – and at what human cost. In 2025, I spent a lot of time researching and writing about how AI is reshaping early careers and decision-making, and why governance, trust and human judgment matter more than ever. The most interesting conversations weren’t about tools – they were about accountability.

(“How AI Is Transforming the Early Career Experience”)

2. Workplace Mattering, Not Just Engagement

Engagement remains a priority, but many employees still feel invisible. Recognition exists – yet often fails to translate into motivation, loyalty or sustained contribution.

The organisations that will thrive in 2026 are those that understand mattering: how people experience value, fairness and opportunity day to day, not just in surveys, and how this links to sustainable performance cultures.

(“The Workplace Advantage We’ve Overlooked: Why Mattering Comes Before Performance”)

3. Generational Change as a System Signal

Gen Z continues to be described as difficult, disengaged or demanding. That misses the point.

Whether it’s CV dishonesty, resistance to rigid office mandates, or shifting definitions of success, these behaviours tell us far more about broken hiring and career systems than about attitudes.

(“Why Is Gen Z the Most Miserable Generation?”)

(“What Gen Z & Millennials Want from Work“)

Across all three themes, the common thread is this: people are responding rationally to systems that no longer feel fair, transparent or human.

Throughout 2026, I’ll be exploring these ideas through writing, research, podcasts and conversations with practitioners who’ve learned the hard way. My aim is simple: to reduce noise, challenge lazy narratives, and help leaders talk about the future of work with credibility and clarity.

If you’re grappling with any of these questions, you’re not alone – and these are the conversations I’ll be continuing here.