Tag: Technology

Hybrid Working: Why Policy Isn’t the Problem – Leadership Is

It’s become a familiar story within organisations. A CEO or senior leader returns from a conference or forum convinced that really getting everyone back to the office will fix culture, collaboration, and performance.

But as recent research from MIT Sloan Management Review involving Brian Elliott, Nick Bloom and Prithwiraj Choudhury has highlighted, this focus is misplaced. Hybrid work isn’t a policy problem – it’s a leadership capability problem.

The most successful organisations aren’t arguing about office attendance. They’re building the skills, systems, and trust to help people work effectively – wherever they are. Here are some of my thoughts on the research and, in particular, what they mean for HR and business leaders.

1. The Policy Trap: RTO Mandates Miss the Point

Many leaders are treating hybrid work like a compliance issue: set a rule or guidelines, communicate it, and measure adherence. Yet despite the rise in return-to-office mandates – up 12% last year alone – actual attendance increased only by 1-3%.

Managers are quietly prioritising performance over presence. Faced with losing high performers or bending rigid rules, most choose results. The real cost of these mandates isn’t absenteeism – it’s the wasted leadership energy spent enforcing policies that don’t solve the real challenge: how to enable effective collaboration across distance.

2. What the Research Actually Shows

Despite much debate and rhetoric – in magazines, books, forums and from the conference stage – the evidence doesn’t support rigid office mandates. Peer-reviewed studies have shown:

  • Hybrid work does not reduce productivity and can lower attrition by a third, saving millions in turnover
  • Remote work can increase productivity (by up to 10% in call-centre studies) and broaden workforce diversity
  • When teams spend 23% – 40% of their time together, they perform best. What matters isn’t where people work – it’s how purposefully they connect.
  • Hybrid arrangements consistently boost engagement and innovation by expanding perspectives and increasing psychological safety.

The conclusion is clear: effectiveness comes from flexibility and clarity of purpose, not from counting badge swipes.

3. The Four Capabilities That Define Hybrid Success

The companies thriving in flexible work environments share four core leadership capabilities – none of which depend on fixed policies.

Know Your Talent Edge

Start with strategic clarity. Hybrid work should serve the organisation’s competitive advantage – whether that’s attracting hard-to-find talent, fostering creativity, or offering flexibility competitors can’t.

Allstate, Airbnb, and the European Central Bank have each tailored hybrid models to suit their unique needs. The best approach isn’t uniform; it’s fit for purpose.

Measure Results, Not Presence

This is the leadership mindset shift. High-performing companies judge employees on outcomes, not hours.

Synchrony and Atlassian are two organisations that have used transparent goal-setting systems so everyone can see progress and impact. This approach strengthens trust, reduces bias, and helps retain diverse talent – particularly women, who are disproportionately penalised by rigid in-office demands.

Let Teams Lead the Way

The most effective hybrid models are designed at the team level. Teams know their collaboration rhythms better than executives do. Atlassian empowers teams to agree on shared norms – like guaranteed overlap hours or quarterly in-person sprints. Microsoft and Teradyne are businesses that use similar flexibility within a broad corporate framework.

Uniform policies flatten nuance. Empowered teams create alignment and accountability.

Invest in Getting Better

Hybrid work isn’t a one-time policy shift – it’s an ongoing capability build. Leading companies are investing in:

  • Spaces: redesigned for collaboration, not occupancy
  • Resources: budgets for purposeful team gatherings, not daily commutes
  • Skills: manager training and playbooks for leading distributed teams

Hybrid success depends less on where people work and more on how leaders build trust, alignment, and capability across boundaries.

4. The Leadership Imperative

Research consistently makes it clear: hybrid is here to stay – and it’s working. The organisations moving ahead today are those that stopped treating flexibility as an HR issue and started treating it as a core leadership discipline.

The real question isn’t ‘how many days in the office’ – it’s ‘how effectively do we create connection, clarity, and accountability across teams’?

Leaders who master that shift will build organisations that can flex with whatever comes next. Because the future of work isn’t about place – it’s about how we work together to create value.

(This post originally appeared in my twice-weekly newsletter HR Means Business – subscribe to make sure you don’t miss my latest conversations, thoughts and writing)

Rethinking HR – Building a New Operating Model for the 2020s

For more than two decades, HR has been guided by variations of the Ulrich model – a structure that helped professionalise the function, but one that now struggles to keep pace with today’s complexity. I recently interviewed Perry Timms, Founder and Chief Energy Officer of People & Transformational HR Ltd, on my HR Means Business podcast, to talk about the concepts outlined in his new book The HR Operating Model.

Perry believes that the HR profession needs a blueprint built for the world we actually work in – not the one we used to. “It still feels like HR is an order-taking, administrative function. We’ve been knocking on the strategic door for a long time. It’s time to redesign the house.”

From Function to System: A Holistic Redesign

Perry’s new model doesn’t simply tweak existing structures – it reimagines HR as an interconnected system, built around four intersecting circles – Systems, Products, Science, and Technology – reflecting how people, processes, and performance actually interact inside modern organisations.

At its heart sits People Experience – a concept that connects every stage of the employee journey, from candidate to alumni. It’s not about engagement as a “soft” idea, but about creating value at every touchpoint. As Perry puts it, “People experience isn’t a supercharged version of wellness. It’s high performance, but sustainable — evidence-led, inclusive, and grounded in business outcomes.”

From Data to Decision

One of the model’s key intersections is People and Performance Analytics. HR leaders have talked about data for years, but Perry’s approach brings it into the operating system itself. The idea isn’t to build separate analytics teams, but to embed insight everywhere – translating data into business intelligence that guides decisions in real time.

This shift moves HR beyond reporting on “what happened” toward understanding “why it happened” — and ultimately shaping “what happens next.”

Redefining High Performance

In the previous model, high performance would often meant results at any cost. Perry argues for a more complete definition – one that includes social, human, and intellectual indicators.

“High performance shouldn’t be one-dimensional,” he says. “It’s about thriving teams who deliver results and sustain themselves through learning, balance, and purpose.”

The outcome is regenerative performance – where success fuels energy and capability, rather than exhaustion and turnover.

Technology as a Collective Responsibility

Technology, in Perry’s view, must move from being an HR project to being an HR practice. The most effective organisations he studied didn’t silo innovation; they built open, participatory infrastructures that tested tools in real use cases, gathered feedback, and aligned technology with the data they actually needed.

AI plays a role here too – not as a disruptor, but as a smoother of operations. When automation handles routine, rule-based work, HR professionals can focus on relationships, creativity, and context – the human edge that technology can’t replicate.

The Meaning Maker: HR’s New Purpose

One of the model’s most powerful ideas is the Meaning Maker role — a function dedicated to ensuring organisational purpose remains alive and visible.

“The purpose of an organisation ought to make people think, ‘I’m glad I’m here because I believe in this’” Perry explains. That sense of meaning is the new psychological contract.

In a world of automation and flux, meaning-making becomes HR’s bridge between strategy and soul – helping employees understand why their work matters.

Getting Started: From Products to Purpose

For HR teams wondering where to begin, Perry offers some clear guidance – start small, but start differently

“Think of your HR services as products,” he says. “Ask: if people had to buy this service for the value it provides, would they?” Then build it to that standard.

This product mindset signals seriousness, relevance, and innovation – and begins to shift how the rest of the business perceives HR.

HR at the Front, Not the Back

Perry’s challenge to boards is equally direct: Let HR Lead. “You can’t ask HR to deliver what you want when you don’t know what you need,” he says. “In uncertain times, HR should be out front – interpreting signals, shaping workforce strategy, and helping organisations prepare for what’s next.”

The Future: Adaptive, Human, and Purposeful

The new HR operating model isn’t just a redesign of roles and processes. It’s a cultural transformation – one that blends systems thinking, human science, and technology to build organisations that are fit for the 2020s and beyond.

And the journey doesn’t end here. “If HR can get ahead of the curve for a change,” he says, “we can help build organisations that are adaptive by design — not just efficient, but alive.”

You can listen to my full conversation with Perry Timms here – https://www.hrhappyhour.net/episodes/creating-a-new-operating-model-for-hr/ – or through the image below:

Designing Job Search Tools That Work for Neurodivergent Candidates (and Everyone Else)

AI is transforming the job search, but without intentional design, it can just as easily raise barriers as remove them. If you want to attract – and fairly evaluate – neurodivergent talent, the mandate is simple: teach people how to use AI well, make the journey accessible, and build trust at every step.

At the HR Technology Conference & Exposition I was at a session run by my friend Crystal Lay, MBA MScIOP – an award winning Global Employer Brand & I/O psych executive – in which she talked through her research, and shared what AI reveals about hiring, bias and belonging. She highlighted a study with over 450 participants. Some of the key findings included men’s higher confidence in technology, women’s underestimation of their skills, and the importance of familiarity over gender in AI adoption. Neurodivergent individuals, particularly women, showed higher AI usage and developed skills.

Here are my main takeaways:

Start with skills, not slogans

Many candidates already use AI multiple times a day. Help them to use it well. Publish a plain-language “AI starter kit” on your careers site with:

  • Prompt guides for common tasks (CV tailoring, cover letters, portfolio curation, transcript summaries)
  • When/why to use AI for each role type and task
  • Advice and guidance on how to verify facts and use personal evidence
  • Personalisation tips (always begin with your own information, achievements, and voice)

When we show candidates how to work with AI – not like Google, but like a conversational partner – we lift the quality for everyone and reduce anxiety for people who benefit from structure and scaffolding.

Designing for neurodivergent accessibility

Language and layout matter. Use conversational copy, clear headings, white space, and short blocks that are easy to scan. Offer modal choices for high-stress steps:

  • If AI video interviews feel impersonal or confusing, provide equivalent alternatives: typed responses, audio-only, or off-camera options.
  • For screening tasks, let candidates choose between written or recorded submissions.

Accessibility isn’t about removing standards; it’s about providing multiple, comparable paths to demonstrate the same capability.

Put psychological safety on the record

Trust is earned, so state – explicitly – where ethical AI assistance is allowed (and where it isn’t), and describe your own use: how your teams rely on AI, how you review outputs, where humans stay accountable. Then maintain regular transparency updates: what bias tests you ran, what you found, and what you changed. When candidates see you’re on top of risk, they’re more willing to engage honestly.

Use AI where it helps – not everywhere

Not every step needs a bot. Prioritise bias-tested tools that add value at the right moment (e.g. prompt helpers embedded in the application form). Be cautious with practices candidates commonly flag as alienating – like automated video interviews – and make sure there’s a true opt-in alternative.

Fix the plumbing or don’t ship the chatbot

Your chatbot is only as good as the content you feed it. If your careers site or your knowledge base is thin, the bot will guess – and candidates will lose trust. Invest in a robust content layer (policies, FAQs, job frameworks) before you turn on AI. Screen vendor tools against your content footprint and accessibility requirements.

Co-design, don’t guess

Build with neurodivergent job seekers and other marginalised groups. Run iterative tests with mixed methods: qualitative sessions to hear what works and why, plus quantitative surveys to see patterns at scale. Test over time – accessibility is about repeatable ease, not a one-off demo.

Handle AI-written CVs thoughtfully

Yes, AI is in many applications. Treat detection signals as conversation starters and not auto-rejects – especially when writing isn’t the job’s core competency. For roles where original writing matters, be clear in the posting and request a supervised work sample. Blanket bans will disproportionately harm neurodivergent candidates for whom AI is a vital organisational support.

Keep supporting after day one

Onboarding is where equity becomes habit. Provide short trainings on ethical AI use, team norms, and verification practices. Create clear routes for reporting data or bias issues – frontline employees will spot problems faster than pre-scheduled audits – and close any loopholes with updates.

The Future of Work: From Jobs to Meaningful Work in a Tectonic Era

Work has always been one of the three pillars of human fulfilment – alongside relationships and health. When it’s organise and done well, meaningful work doesn’t just pay the bills; it keeps us healthier, helps us live longer, and fuels a sense of purpose. But the way we define, organise, and experience work is shifting faster than ever.

At O.C. Tanner‘s Influence Greatness conference this week I sat in a session by Rishad Tobaccowala, from the The Rethinking Work Platform, which got me thinking. The session was billed as ‘How to Lead in the Age of AI’ but his observations and research went much deeper than that.

He started with the observation that between 2019 and 2029, work has been – and is currently – projected to change more than it did over the previous 50 years. This is not just evolution – it’s a tectonic shift. Yet many leaders are still looking backward, focusing on office returns and outdated structures, metrics and roles, while the ground beneath them is moving. How forward thinking are todays business managers and leaders whilst the world of work around them shifts?

The Five Shifts Reshaping Work

  1. Demographics: Populations are shrinking in many developed countries, birth rates are falling, and societies are aging. This will create pressure to keep older workers engaged and to design flexible arrangements for those balancing work with caregiving. At the same time, generational attitudes toward work and capitalism are diverging sharply. Gen Z, in particular, wants independence, flexibility, and purpose – and not to replicate the lives of their parents.
  2. Technology: AI is widely misunderstood. Far from just hype, it will quietly strip the value of “knowledge for knowledge’s sake.” Around 20% of current work tasks can already be automated, saving up to 40% of time. This won’t eliminate work; it will change how we create and measure value. The winners will be those who redeploy saved time into innovation and new ideas.
  3. Marketplaces: Platforms like Uber, Etsy, Upwork, and Shopify are normalising side hustles and gig work. Increasingly, people will hold both W2 (earned income in the UK) jobs and 1099 income (unearned income in the UK) streams. Work is diversifying beyond traditional employment.
  4. COVID’s Legacy: The pandemic didn’t just change where we work – it changed why. Employees no longer want “bosses.” They want leaders, mentors, and guides. The authority of command-and-control is fading fast.
  5. Declassification of Work: Perhaps the most profound shift: jobs and work are not the same thing. There will be fewer jobs, but no shortage of work. Systems built around employment – healthcare, pensions, identity – must evolve as people assemble income from multiple streams.

A New Worker Ecosystem

The workforce of the future will be more diverse than ever, with five types of workers:

  • Full-time employees
  • Contract workers
  • Freelancers
  • Fractionalized employees (working 60–80% of the time with prorated pay and benefits)
  • Agentic employees (self-directed workers who leverage AI and platforms for autonomy)

This new mix will make “headcount” a less meaningful measure. Instead, revenue per worker will become the key performance metric. Agility – through more flexible, ‘plug-and-play’ teams – will separate resilient organisations from those still organised around outdated hierarchies.

A Crisis of Leadership, Not Culture

Many leaders talk about “bringing people back to the office for culture.” But culture has never been confined to an office. Collaboration, learning, and relationship-building often happened elsewhere – off-sites, conferences, even restaurants. The water cooler myth has long been just that.

What’s really at stake is leadership. Bosses who allocate, monitor, and control are out of sync with today’s workforce. Leadership in the future looks more like jazz than a classical orchestra: improvisational, responsive, and collaborative. Leaders must create conditions for excellence, growth, and trust – not try to control every note.

How We Adapt: New Mindsets and Practices

Rishad was clear – the future won’t adapt to us; we must adapt to it. That means upgrading our “mental operating systems.” Just as our smartphones update every year, we must commit to learning daily – at least one hour a day – to stay relevant and resilient.

Equally important is adopting what he calls the ‘immigrant mindset’:

  • Think like outsiders, questioning assumptions and seeing opportunities others miss
  • Act like underdogs, prepared to disrupt established “castles and moats”
  • Invest long-term, trading short-term comfort for long-term gain

The Human Centre of the Future

For all the talk about AI and automation, the future of work isn’t really about technology. It’s about people. Companies don’t transform – people do. Organisations that invest in leadership, flexibility, and meaning will thrive.

Work, in its essence, is not disappearing – it is being redefined. The challenge for leaders, workers, and society alike is to uncouple work from jobs, embrace new worker types, and design systems that give people both purpose and flexibility.

The future is an undiscovered country. To navigate it, we’ll need adaptability, courage, and above all, leadership that sees people as the centre of every transformation.

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The AI Effect on Entry-Level Jobs and Career Progression

“Using ChatGPT might make you stupid.” That bold statement – based on a study – appeared on a number of news sites and in business journals recently. The article was accompanied by brain scan images suggesting that AI erodes critical thinking.

It’s the kind of story guaranteed to spark outrage – particularly among older generations who see technology as a shortcut rather than a skill. Needless to say it was a topic ripe for discussion between me and Danielle Farage on our #FromXtoZ podcast!

And also needless to say – the truth is far more complex, and raises bigger questions about how AI is reshaping not just how we work, but how we learn and progress in our careers.

The Disappearing Entry-Level Job

For decades, entry-level jobs were designed around repetitive, and often quite menial, tasks. Interns summarised files, created reports, and performed groundwork that provided valuable context and an understanding of how things fit together. While boring at times, those tasks were the building blocks for developing judgment and critical thinking. They helped you learn how to spot patterns, understand stakeholders, and prepare for more senior responsibilities.

Today, those very tasks are being done by AI in seconds. Need a summary? ChatGPT delivers one instantly. Need a cover letter? AI can generate multiple versions faster than you can type your name. For employers, this is a productivity boost. For graduates, juniors and interns, it means fewer “easy” tasks to start with – and potentially fewer opportunities to learn by doing.

Learning Gaps and Lost Context

One of the risks we talked bout is that when AI handles entry-level tasks, people may lose valuable context. The act of digging through files, for example, could teach you how information is structured, help to learn what’s important, and why things are done a certain way.

Without these experiences, new hires may have less foundational knowledge – and therefore slower long-term development opportunities – which echoes a common complaint among Gen Z workers that either they have little to do, or they are immediately thrown into complex tasks without the understanding that entry-level work used to provide.

That jump can accelerate learning for some, but for others, it can create stress and lead to potential skill gaps.

Shifting Skill Priorities

If AI can handle repetitive tasks, what skills will matter more?

Soft skills are rapidly rising to the top of the list – communication, collaboration, creativity, and emotional intelligence. Critical thinking is still essential, but it may shift away from basic data gathering and toward making strategic connections and asking better questions.

For example, instead of summarising a document, a junior analyst might now be expected to analyse AI’s summary and extract what’s missing or misleading. Instead of drafting a cover letter from scratch, they might focus on personalising and contextualising AI’s output in a way that resonates with their employers.

Changing Brains, Changing Learning

Our conversation also touched on how our brains – and our learning habits – are changing. Gen Z (and AlphaGen) have been exposed to technology and gamified learning from childhood so have different cognitive expectations. Tasks requiring deep focus and delayed gratification (like writing reports or doing long-form research) can feel more challenging when our brains are wired for quick dopamine hits from apps, games, and social media.

This is more than just a workplace issue; it’s a societal one. As technology accelerates, how we teach, train, and even design work needs to adapt to different cognitive baselines. Should we be worried about critical thinking decline? Or should we embrace the fact that tools like ChatGPT free up mental energy for deeper and more analytical thinking? The answer likely depends on how organisations and educators adapt.

Rethinking Entry-Level Work

The old career ladder was built on predictable steps: you start with basic tasks, learn the ropes, then climb upward as you gain experience. AI is dismantling some of those steps. That’s not necessarily bad – many interns now handle complex projects far earlier in their careers than previous generations ever did – but it requires intentional design. Employers need to:

  • Redefine entry-level roles to focus on applied problem-solving, creativity, and human interaction.
  • Provide context in new ways—mentorship, job shadowing, and structured learning can fill gaps left by disappearing grunt work.
  • Invest in soft skill development as AI takes over technical routine tasks.

A Transitional Phase

Ultimately, we’re currently in a transitional phase. Entry-level jobs are not disappearing, but they are transforming. The work experience of someone starting out today looks nothing like it did even five years ago. That can feel unsettling, but it’s also an opportunity – to design jobs, education, and career pathways that prepare people not just to survive in an AI-driven workplace but to thrive.

The big question is not whether AI is making us “stupid” – it’s how we will redefine learning, working, and progression in a world where machines handle the basics and humans focus on what truly requires a human touch.

You can check out the full podcast conversation here : https://www.youtube.com/watch?v=cu6W-UqLj2Q

Or through the image below

And let us know what you think in the comments…..

A Potential Framework for Mitigating AI Bias in Talent Acquisition

In a recent newsletter I wrote about some of the takeaways from my interview with Heidi Barnett, President at isolved Talent Acquisition (formerly ApplicantPro), at the Unleash conference about the evolution of Talent Acquisition. The integration of AI and advanced analytics in candidate profiling presents us with both a tremendous opportunity and also significant risk. While these technologies can enhance efficiency and improve matching accuracy, they also have the potential to perpetuate or amplify existing biases in hiring practices.

In this – the second part of my interview with Heidi – I’m specifically looking at some of the ways in which TA professionals can proactively address these challenges.

Understanding the Sources of AI Bias

AI bias in TA typically stems from three primary sources: historical data, algorithmic design, and implementation choices. Historical hiring data can often reflect previous discriminatory practices, unconscious biases, or systemic inequalities that existed in previous recruitment decisions. When AI systems learn from this data, they can inadvertently replicate these patterns.

Algorithmic design bias can occur when the parameters and weightings built into AI systems favour certain demographic groups or characteristics. For example, if an algorithm heavily weights specific educational institutions or previous company experiences, it may systematically exclude qualified candidates from underrepresented backgrounds.

Implementation bias happens when organisations fail to properly configure, monitor, or maintain their AI systems. This can include using inappropriate data sets, failing to regularly oversee and audit decision outcomes, or not accounting for changing market conditions and organisational needs.

Establishing Frameworks for Bias Detection

TA professionals must start taking a more systematic approach to identifying bias before it impacts hiring decisions. Start by conducting regular audits of your AI system’s outputs, and analysing hiring patterns across different demographic groups. This should help identify any statistical disparities in screening rates, interview invitations, and final hiring decisions.

Another way is to create baseline metrics that track diversity at each stage of the recruitment funnel, and then compare these metrics before and after AI implementation to help identify any trends that may give cause for concern. Pay particular attention to how multiple identity factors might compound bias effects.

It’s also key to establish feedback loops with hiring managers, candidates, and internal diversity teams to gather qualitative insights about any potential biases. Sometimes bias manifests in subtle ways that statistical analysis might miss, such as the language used in AI-generated communications or the types of questions prioritised in screening processes.

Implementing Technical Safeguards

It’s key to work with your technology vendors to understand how their algorithms function and what safeguards they’ve built in. Demand transparency about training data sources, algorithmic decision-making processes, and bias testing procedures. Reputable vendors should be able to provide detailed documentation about their bias mitigation efforts.

Also important to implement human oversight checkpoints at critical decision stages. While AI can handle initial screening efficiently, human reviewers should still be involved in final candidate selections. Train these reviewers to recognise potential bias indicators and provide them with diverse candidate profiles for consideration.

You can also consider using multiple AI tools or approaches for candidate evaluation, comparing results to identify potential bias blind spots. If different systems consistently exclude similar demographic groups, this may indicate systemic bias that requires investigation.

Building Inclusive Data Practices

Audit your historical hiring data before using it to train AI systems. Remove or adjust data points that reflect past discriminatory practices. This might include eliminating certain educational requirements that weren’t truly necessary for job success or adjusting for historical underrepresentation in specific roles.

Expand your data sources to include more diverse talent pools. If your historical data primarily reflects candidates from certain networks or sources, actively seek data from underrepresented communities, alternative education pathways, and non-traditional career backgrounds.

Regularly refresh your training data to reflect current market conditions and organisational values. AI systems trained on outdated data may not align with current diversity and inclusion goals or may miss emerging talent sources.

Creating Accountability Structures

Establish clear governance structures for AI bias monitoring and mitigation. Assign specific team members responsibility for conducting regular bias audits and create procedures for addressing findings that give rise for concern. This accountability should extend to senior leadership, ensuring that bias mitigation receives appropriate organisational priority.

Document your bias mitigation efforts thoroughly. This documentation can serve multiple purposes: it demonstrates due diligence in legal contexts, provides learning opportunities for continuous improvement, and creates institutional knowledge that survives personnel changes.

Set specific, measurable goals for bias reduction and diversity improvement. Regularly track progress against these goals and adjust your approaches based on results. Consider tying these metrics to team performance evaluations and organisational success measures.

Continuous Learning and Adaptation

The landscape of AI bias is constantly evolving as technology advances and our understanding deepens. Stay current with research, best practices, and regulatory developments in AI ethics and employment law. Try and participate in industry forums and professional development opportunities focused on responsible AI implementation.

Regularly reassess bias mitigation strategies as your organisation grows and changes. What works for a small company may not scale effectively, and what’s appropriate for one industry may not apply to another. Be prepared to adapt your approaches based on new insights and changing circumstances.

Foster a culture of continuous improvement around bias mitigation. Encourage team members to raise concerns about potential bias and create safe spaces for discussing these sensitive topics. The most effective bias mitigation happens when entire teams are engaged and committed to the effort.

Moving Forward Responsibly

Addressing AI bias in talent acquisition isn’t a one-time project – it’s an ongoing commitment that requires vigilance, resources, and organisational support. The goal isn’t to eliminate all AI tools due to bias concerns, but rather to implement them responsibly with appropriate safeguards and oversight.

By taking proactive steps to understand, detect, and mitigate bias, TA professionals can harness the power of AI while maintaining fair and inclusive hiring practices. This balanced approach will ultimately lead to better hiring outcomes, stronger organisational diversity, and reduced legal and reputational risks.

The future of Talent Acquisition depends on our ability to leverage technology while preserving human values of fairness and inclusion.

Check out my full interview conversation with Heidi here :

Why Starting a Career Feels Tougher Than Ever for Young Professionals

Today’s emerging workforce are facing challenges that previous generations didn’t. Entry-level opportunities – and other early career pathways – are getting fewer, and those that exist might seem harder to access. Traditional routes such as trainee roles, apprenticeships and trial periods appear to be getting harder to access. For many of the younger Gen Z group starting a career, or even finding interesting or challenging work, is becoming harder.

I discussed this with Danielle Farage during one of our recent From X to Z podcast chats. Despite what some more senior level professionals might think, this isn’t a problem bought about by a lack of ambition or drive amongst the emerging workforce, but instead a result of the way businesses now tend to be structured, resulting in four main challenges that early career workers now face:

➡️ Fewer career levels: There just aren’t as many steps to climb. An increase in flatter organisational structures means reduced opportunities for progression or promotion

➡️ Rising pressure to move fast: Digital channels and social media platforms fuel comparison and motivation to move ahead quickly, but then so do real economic pressures – like stagnant salaries in a time of rising inflation, and increased housing costs.

➡️ Wages that don’t reflect reality: Salaries for early-career roles haven’t kept up with inflation, meaning companies are offering less than they paid for the same entry level roles a few years ago.

➡️ Fewer entry-level roles: Some companies are cutting back on junior roles, or beginning to replacing them with AI. On top of that, many young professionals complain of poor management and limited mentorship opportunities, which can further stall development.

The result? A generation hungry to grow, but often stuck without support.

You can listen to our conversation or watch it below, and let me know what you think and how you’re seeing businesses support the emerging workforce:

Rethinking the Future Workforce: AI, Work Design, and the Human Element

The evolution of how, when and where we work continues apace – driven by technological innovation, changing worker expectations, and an increasingly decentralised workforce that wants agency in how, when and where they work, and access to information and tech support as and when they need it.

To try and make sense of the many of shifts happening in the workplace, I recently invited Andrew Spence – a workforce futurist and author of the weekly Workforce Futurist newsletter in which he shares the latest research and thinking around the world of work – on to the HR Means Business podcast for a conversation to unpack what the future may look like, and which current trends leaders should be tracking.

1. AI Is Useful — But Let’s Not Get Carried Away

AI is no longer new, but the recent explosion in natural language tools like ChatGPT has made it feel fresh, and potentially game-changing. even magical. Andrew’s view is that whilst AI is incredibly useful (acting as a solid “6 or 7 out of 10” assistant) it’s not necessarily intelligent in the human sense. We’re often seduced by the friendliness of the interface and assume these tools are more capable than they are. In reality, he sees them as very fast data processors.

The risk in overhyping AI is it can give the impression that entire professions and specialisms might disappear overnight. He points out that tools still need human oversight, creativity, and interpretation. AI can certainly enhance productivity, but won’t replace the value humans bring to the table – especially when that value lies in empathy, context, or nuance.

2. HR Needs to Own the Work Design Agenda

Today’s workforce includes a wide range of permanent employees, freelancers, fractional workers, along with increasing automation and AI agents. In this new reality, the real opportunity for HR lies in designing how work gets done — not just who does it.

This means thinking beyond organisational charts and job titles to start looking at which outcomes are necessary, and how best to achieve them. Could a blend of full-time hires, freelance consultants, and AI tools deliver more value than a conventional team?

It’s time for HR to become architects of work, not just custodians of headcount.

3. Decentralised Workforces Are Here — and Growing

Andrew’s research points to a growing shift toward non-traditional work structures. From gig platforms to fractional executives, people increasingly want to work flexibly and globally. During COVID, millions experimented with side hustles and online platforms – a mix that they kept afterwards.

This rise of the decentralised workforce poses significant challenges for HR. How do you maintain compliance, cohesion, and culture when a manager might hire a freelancer halfway across the world without the organisation even knowing? Our traditional systems and structures aren’t usually built for this – and work tech needs to evolve accordingly.

4. Loneliness at Work Is Real — and Rising

As more people work remotely or flexibly, loneliness is becoming a quiet crisis. Surprisingly, research shows that under-30s (primarily Gen Z) are the loneliest demographic – even more so than the elderly. While being alone doesn’t always mean being lonely (thanks to digital entertainment and online communities) there are real implications for engagement and productivity.

This presents both a challenge and an opportunity for HR. Organisations that can create meaningful, human-centred experiences – offering opportunities for connection, belonging, and purpose – may find a competitive edge in attracting and retaining talent.

People don’t just want a job; they want to feel part of something.

5. The Future HR Function: Smaller, Smarter, and More Strategic

Looking ahead, Andrew envisions a more specialised HR function, consisting of smaller teams, but with deep expertise in compliance, workplace technology, and strategic workforce planning. Much of the traditional ‘people management’ he sees shifting  to team leads, AI agents, and decentralised systems.

The central HR function will focus on ensuring that all these moving parts work together — ethically, efficiently, and in line with business goals.

In other words – HR’s role isn’t disappearing. It’s transforming!

The future of work isn’t just about technology, but is about reimagining how value is created, how work is designed, and how people connect. As AI and decentralisation reshape the landscape, HR has a unique chance to lead – not by holding onto outdated models, but by helping create a new era of work.

It’s not just about adapting to the future. It’s about designing it.

Check out the full conversation for more of Andrew’s thoughts and insights on how work – and HR – is evolving here https://www.hrhappyhour.net/episodes/hrs-role-in-managing-and-developing-the-workforce-of-the-future/

How AI Can Help Create Purpose Driven Work

There can be little doubt that AI has the capability to reshape organisations, giving businesses of all sizes an opportunity to use it not just for efficiency but to help foster a purpose-driven work culture, that can lead to better retention, higher engagement and more meaningful work.  Responsible AI integration will also help to empower employees and enhance collaboration, maintaining ethical and human-centred values within businesses.

In the coming weeks I’ll be speaking at HR Tech Europe, CIPD Scotland, In House Recruitment Expo and the inaugural Employee Xperience Expo and, perhaps unsurprisingly (!), amongst the topics I’ll be talking about will be the need to maintain humanity and human connection in an AI world, how we can harness AI to create meaningful work experiences, and the best ways to leverage AI for better recruitment.

From the various conversations I’ve had and research I’ve seen whilst preparing, I’m putting together some of the ways in which AI can help to create purpose driven work, and I’m sharing a few of them here. Let me know what you think.

Defining Purpose with AI

A strong purpose is the foundation of any thriving and meaningful workplace culture, and AI can help organisations refine their mission by providing data-driven insights into employee engagement, customer needs, and societal impact. AI-powered analytics can be used to align business goals with core values, ensuring that every decision supports a broader mission beyond profits.

For example, AI-driven employee sentiment analysis can gauge how well employees connect with the company’s mission. By tracking engagement patterns, leadership can identify areas where cultural reinforcement might be needed and make real-time adjustments to help strengthen alignment with company purpose.

Enhancing, Not Replacing, Human Work

One of the most recurring significant concerns about AI is job displacement. However, when implemented carefully, AI should be used to enhance rather than replace human work. By automating repetitive and time-consuming tasks, AI allows employees to focus on more meaningful, creative, satisfying and strategic contributions.

One straightforward example comes from customer services and the way AI-powered chatbots can handle routine customer inquiries, freeing up time for customer service representatives to engage in more personalised interactions that can lead to better outcomes and customer retention.

Similarly, AI-driven project management tools can streamline workflows, allowing employees to concentrate on innovation and problem-solving rather than administrative tasks.

Promoting Human-AI Collaboration

Rather than fully automating processes, AI should function as more of an assistant or support to the human workforce. The best AI applications can enhance decision-making, and offer insights that complement and support human judgment.

One example from hiring is that AI-driven recruitment tools should be able to help HR teams identify the best candidates, leaving human recruiters to assess cultural fit and emotional intelligence. AI-powered data analytics can provide business leaders with real-time insights, but final decisions should take into account human expertise and perceptions, and ethical considerations.

Improving Employee Wellbeing and Experience

Important ways in which AI can help promote employee wellbeing and help improve the employee experience are by optimising workloads, personalising career development, and identifying burnout risks. AI-driven HR platforms can recommend learning opportunities specifically tailored to an individual’s career aspirations and skills, which would help drive a culture of personal and professional up-skilling and growth.

And AI-powered wellness programs can analyse work patterns and suggest breaks or workload redistribution, which can help employees maintain a healthy work balance.

AI offers a real opportunity to create workplaces that are not only efficient, effective, supportive and also purpose-driven. By using AI to enhance meaningful work, support ethical decision-making, and empower employees, organisations can build cultures that inspire and sustain long-term engagement.

Ultimately, AI should be seen as a tool that amplifies human potential rather than replaces it. When integrated carefully, AI can help companies create work environments where employees feel engaged, valued, supported, and have a real sense of connection with the organisational purpose.

Hope I get to see some of you at the various events I’ve mentioned. Would be great to grab a coffee and find out what you see as the role of AI in creating and enhancing purpose driven work!

HR Challenges and Opportunities for 2025

Organisations are facing growing challenges, all of which are making the role of an HR professional increasingly complex and multi-faceted. As business navigates economic uncertainty, whilst supporting their employees’ mental health and wellbeing, we see almost daily debates online about evolving work models and working arrangements, and how to manage the emerging Gen Z workforce. That’s before we mention the role of AI and how it might impact – positively – the HR workload.

I’ve been looking at some recent research from the team at McKinsey & Company that identified five key areas that are contributing to this workload, but which also present opportunities for a better way of working. With stress on the rise, and some workforces complaining of ‘change fatigue’ it’s time to build trust within our teams and offer real support and enablement for our people.

Increasing Workload and Demands

The responsibilities on HR teams have escalated with modern HR departments not only tasked with traditional hiring, onboarding and compliance but also with addressing the more complicated areas that I outlined in the introduction – mental health and wellbeing support, remote/flexible/hybrid work policies, and a general dissatisfaction with the overall employee experience.

This increase in workload creates a dual tension, with HR teams managing a broader spectrum of demands while ensuring the wellbeing and retention of their people. The need for effective and supportive people management is stronger than ever, but with limited resources and growing tasks many HR teams find themselves stretched thinly.

Pressure of Strategic Involvement Without Necessary Authority

Whilst HR has historically, and wrongly, often been seen as more of a ‘support’ role, today’s organisations increasingly need their HR teams to be strategic partners. However, while they are called upon to influence company culture and policy more strongly, the main question is – do they have the authority to drive real change?

This can leave HR teams in a frustrating position – advisors on transformative strategies but lacking the decision-making authority to oversee their strategies fully realised for real change. HR needs to be part of the strategic conversation from the start, with the authority to influence and make impactful strategic decisions.

Battling ‘Change Fatigue’

Organisations are in a constant state of adaptation be it addressing remote & flexible work transitions, engaging and retaining the Gen Z workforce, building meaningful experiences and implementing AI effectively.

Change is necessary for most organisations, but can also create “change fatigue” among employees and managers if not implemented effectively. For HR teams, who are responsible for implementing and explaining these shifts, it could feel like an endless cycle of implementation without the time to let one change settle before the next is introduced.

Building resilience and carefully pacing transformations are crucial for preventing burnout within HR teams.

Potential Trust Deficits Between HR, Employees, and Leadership

There is often a ‘trust gap’ in organisations between employees, HR, and leadership, with HR teams finding themselves in a delicate balance – representing the company’s priorities whilst advocating for employees. This balancing act could lead to a perception that HR is not genuinely aligned with employees’ needs – thereby damaging trust – or too aligned with senior management.

For HR to be effective mediators between employees and leadership, it is essential that organisations to prioritise transparent, honest communication and to help HR clearly demonstrate the organisational commitment to employee welfare.

Technology’s Role in HR: Efficiency or Extra Burden?

While technology has the potential to streamline HR processes, it sometimes adds additional layers of responsibility. Generative AI, for instance, holds promise for enhancing recruitment, data analysis, and even employee engagement, though has the potential to detract from some of the more creative tasks that our people enjoy doing. Without proper implementation, these technologies can also impose more administrative duties, detracting from the human-centred support HR is meant to provide.

For technology to be effective, it should ease, support and provide smoother experiences for our people, not increase the workload, allowing HR teams to focus more fully on strategic support and enablement to our people.

Turning Challenges into Opportunities

Despite these mounting pressures, HR professionals can take steps to mitigate them and foster a more supportive organisational climate. Prioritising authentic communication and trust-building practices can help bridge any gaps between HR, leadership, and employees. In the long run, enabling HR to work as a full strategic partner – and not be seen as a support or adjunct function – will help to empower the whole organisation to be more adaptive, resilient, and people-focused.

Today’s HR professionals are dealing with some of the most challenging dynamics the workforce has seen. Yet, with the right support and authority they have the potential to transform these into positive experiences, fostering a workplace where employees thrive, organisations adapt smoothly and successfully, and the true value of HR can be recognised and rewarded.