Tag: ai

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 :

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!

Creating the Future of Talent Acquisition and Hiring

In the rapidly evolving landscape of talent acquisition and hiring, there are a number of key trends emerging that will re-shape how we attract, hire and develop our future talent. Some of these trends could be game-changers for business leaders and HR professionals, as they represent a shift in workforce dynamics, whilst shaping the future of work and potentially leading to a rethink of traditional HR practices.

My close friend, co-author and co-collaborator Matt Alder has been writing and documenting these shifts through our co-authored books, and his research for podcast interviews and online courses, and I recently had a conversation with him on my HR Means Business podcast to better understand how some of these emerging trends will impact the future of hiring and retention.

We identified and discussed five main trends.

1. Skills-Based Hiring

Hardly a brand new development, but skills-based hiring is definitely a growing trend amongst Talent Acquisition and HR teams as organisations increasingly recognise the limitations of traditional hiring practices that prioritise previous experience over potential. Skills-Based hiring focuses on hiring individuals based on their current skills, and on their ability to develop new ones, rather than rely on their past job titles or educational background. There are 3 key benefits to this approach:

  • Broader Talent Pools: By focusing on skills, organisations can tap into a more diverse talent pool, including candidates from different industries and backgrounds
  • Enhanced Diversity: Skills-based hiring promotes diversity by reducing biases associated with traditional hiring criteria
  • Future-Proofing Workforce: This approach aligns closely with the evolving nature of work, which we see as increasingly valuing skills and competencies over specific experiences

2. Total Talent Thinking

Total talent thinking is about breaking down the silos within HR departments and approaching talent management holistically. It involves integrating talent acquisition, talent management, and learning and development, to create a cohesive strategy that comprehensively addresses an organisation’s skills needs. There are certain key components that you need for a Total Talent Thinking approach:

  • Collaborative HR Functions: This approach relies on different HR functions working together seamlessly
  • Strategic Workforce Planning: Identifying and planning for the skills that will be needed across the organisation to achieve commercial goals
  • Flexibility and Adaptability: Building a workforce that can adapt to changing business needs and technologies

3. Impact of Generative AI

Whatever you think about Generative AI there is little doubt that it’s transforming Talent Acquisition by both automating various aspects of the hiring process, and also providing new tools for both employers and job-seekers. Generative AI’s full potential is still developing and unfolding, but its current applications are already making significant impacts:

  • Increased Efficiency: AI can streamline the recruitment process by automating routine tasks such as CV screening and initial candidate assessments
  • Enhanced Decision-Making: AI-powered tools can provide insights and analytics to help HR professionals and hiring managers to make better hiring decisions
  • Job Seeker Empowerment: Candidates are using AI to craft tailored CVs and applications, which can increase their chances of getting noticed, provided they use it as a tool to help support their job applications, rather than rely on it to be the application

4. Future-Casting and Strategic Foresight

Matt and I have been talking about Future-casting for a few years. Basically it involves anticipating and planning for future trends and disruptions in the workforce. It requires HR and Talent professionals to adopt more strategic foresight tools and methodologies to try and predict – and so be prepared for – changes that might shape their organisation’s future talent strategies. There are 3 component parts:

  • Trend Analysis: Identifying and analysing the macro and the micro forces that are driving change in the workforce
  • Scenario Planning: This calls for HR and Talent professionals to develop – and prepare for – multiple future scenarios so they can remain agile and responsive
  • Embracing Uncertainty: Recognising and planning for unknowns and uncertainties, such as technological disruptions, economic shifts or – as in the case of Covid – factoring unforeseen epidemics

5. Smart Automation

Automation, powered by AI and other technologies, is set to redefine many aspects of work – not least in the attraction, hiring and onboarding of talent. Smart automation goes beyond simple task automation and includes more complex processes and decision-making functions:

  • Redefining Job Roles: Automation will change the nature of many jobs, requiring employees to adapt and develop new skills, and HR to develop enhanced role profiles
  • Efficiency Gains: Automated processes can lead to significant efficiency improvements and cost savings
  • A Focus on Higher-Value Work: As routine tasks are automated, employees can focus on more strategic, creative, and value-added activities and processes.

You can find out more about how we see the future of Talent Acquisition – and our approach to Total Talent Thinking – on this episode off the HR Means Business podcast –

HR’s Role in Embracing the Future of Work

How is work evolving? What roles will Generative AI, taskification, the skills agenda and job disruption play in reshaping the future talent market? How can we create high value work? And what might future workforce dynamics look like?

Last year I was involved in a simulation run by business consultancy Wikistrat for Upwork in which I joined with a number of analysts, practitioners and consultants to map out a number of potential scenarios for the future of work – taking into account what we know about emerging technology, evolving trends and the preferences and priorities of the current and future workforce.

We came up with a number of potential outcomes using different frameworks and in a recent podcast chat I had with Kelly Monahan, Ph.D. Managing Director of Upwork‘s Research Institute, we talked about the the various trends and HR’s potential role in guiding the reinvention of work.

Accelerated Pace of Business and the Impact of Gen Z

It’s hard to look at how the future of work may develop without acknowledging the current exponential acceleration of all business operations fuelled by evolving tech. This pace of change needs real-time data insights to help inform decision-making, particularly within the HR team. The entry of Gen Z into the workplace will lead to further change – with Kelly Monahan anticipating that they will challenge traditional business norms and practices, and question the purpose of work, which could lead to fundamental shifts in organisational values.

Generative AI and Job Disruption

The advent of generative AI has been transformative but has capabilities taking us way beyond automation. Kelly emphasised how AI really acts as a catalyst, accelerating the development of allied technologies like IoT and 5G, which will require a re-evaluation of job roles, skills and work processes.

Taskification has emerged, and organisations need to start deconstructing their job roles into specific tasks and skills, potentially promoting more fluid work arrangements. Addressing the exponential growth in the number of tasks and skills needs to be high on HR’s learn ing and development agenda.

Up-skilling and Multiplexing Workforces

Responding to these evolving job landscapes will require continuous up-skilling to help effectively navigate any disruption bought about by technology or social change. This will lead to more ‘multiplexing’ – enabling workers to apply diverse skills across departments and tasks, that can boost organisational agility and resilience.

Community Formation and Digital Identities

As organisations evolve in this way, workers are likely to find identity and belonging through digital communities rather than traditional organisational structures. This could have a big impact on engagement, retention and experience as these communities, or digital hubs, are likely to provide cross-functional collaborative opportunities that transcend company, geographical and industry boundaries.

AI’s Impact on High-Value Work

One consequence of Generative AI is the elevation of the complexity and value of work. Kelly Monahan, Ph.D. talked about an increase in high-value projects, for which skilled workers will look for rewards and wage premiums. However, this shift also emphasises the need for continuous skill development if organisations are to remain competitive, which requires support for learning and development as well as recognition and financial rewards.

Grey Rhino vs. Black Swan: Proactive Adaptation

We talked about whether AI was ’a ’Black Swan’ or a ’Grey Rhino’ event. Ultimately it is a Grey Rhino – having a gradual impact that needs proactive adaptation rather than reactive responses. The emphasis is on organisations – particularly HR leaders – to prioritise understanding the evolving challenges their businesses face and navigate future workforce challenges effectively by restructuring work processes, and facilitating upskilling.

In essence, it’s essential for organisations to embrace agility and up-skilling, and transform job structures, in a landscape of ever evolving technology. This can then give HR leaders the platform to help ensure their businesses remain competitive and successful in navigating the future of work, whilst negating the potential for insecurity and instability in future talent markets.

You can listen to my full conversation on this episode of the HR Means Business podcast: