AI AND THE FUTURE OF WORK

The opportunities ahead for leadership teams ready to rethink how work gets done

The conversation about AI and jobs is dominated by two extremes. Neither is helpful. The evidence points to something far more interesting and more useful.

Last updated: March 2026

Who this guide is for

*Last updated: February 2026*

If you're trying to make sense of what AI means for your career, your team, or your organisation's workforce strategy, you're in the right place. This isn't a doom-and-gloom prediction piece. And it's not a breathless celebration of how AI will set everyone free. It's an attempt to look at the evidence, name the tensions honestly, and help you think about what to actually do.

We've spent three years working with senior leadership teams across New Zealand on AI adoption. And the workforce question comes up in every single conversation. People want to know: will AI take jobs? Create jobs? Change jobs? The honest answer is yes, to all three, depending on context. The organisations getting clear on that context early are the ones positioning themselves well.

What makes this moment interesting is that we're not just watching a technology shift. We're watching the way professional work gets structured start to change for the first time in decades. And for teams that engage with it thoughtfully, the upside is significant.

Contents: what you'll find in this guide

• Chapter 1: Why the replacement narrative is too simple
• Chapter 2: What the research actually shows
• Chapter 3: The amplification effect
• Chapter 4: The skills that matter now
• Chapter 5: Entry-level jobs and the career pipeline
• Chapter 6: What this means for education
• Chapter 7: The human things that still matter
• Chapter 8: How to prepare (whether you're leading a team or building a career)
• Further reading

THE REPLACEMENT NARRATIVE IS TOO SIMPLE

There's a phrase that was everywhere in 2023: "AI won't take your job, but someone using AI will." We used it ourselves. And at the time, it was a useful way to frame the shift.

But we stopped saying it about a year ago because the reality has become more layered. In some roles, AI is replacing tasks entirely. In others, it's amplifying what people can do by an order of magnitude. And in a growing number of cases, it's creating entirely new categories of work that didn't exist three years ago. The picture keeps getting more interesting as the tools mature.

The binary "replace or not replace" framing misses all of this. And it misses the thing that matters most: the growing gap between organisations that are actively adapting to AI and those that haven't started yet. That gap is already significant. And it's widening every month, which means the advantage of moving now only grows.

Here's how we think about it now. AI changes work in three ways simultaneously:

Task elimination. Some tasks that humans used to do are now done faster and better by AI. Data entry, basic research, first-draft writing, routine analysis. These tasks don't disappear overnight, but they're shrinking steadily. And that's not necessarily a bad thing. Much of this work was tedious and low-value for the humans doing it.

Task amplification. Some tasks become dramatically more powerful when combined with AI. Strategic analysis, creative work, complex problem-solving, customer engagement. A person with AI can often produce work that previously required a whole team. This is where the most exciting opportunities sit right now, because the ceiling on what small teams can achieve has shifted upward in a meaningful way.

Task creation. New work emerges that only exists because AI exists. Prompt engineering, AI governance, workflow redesign, quality assurance for AI outputs, AI-augmented decision making. These roles barely existed two years ago and are already becoming established career paths.

The question for any individual, team, or organisation isn't "will AI affect my work?" It will. The question is "am I positioned to benefit from how it's changing things?" And the encouraging news is that positioning well is a choice you can make right now.

WHAT THE RESEARCH ACTUALLY SHOWS

The research paints a picture that's both sobering and encouraging, depending on how you look at it.

Harvard and Procter & Gamble research found that a single person working with AI can match or exceed the output of two human collaborators. That's a finding that opens up real possibilities for how you think about hiring, team structure, and how you allocate work. Smaller teams doing bigger things is not a theoretical concept anymore.

BCG found that consultants using AI completed tasks 25% faster and produced 40% higher quality work. But the gains weren't uniform. People who already had strong domain knowledge got more from AI than those who didn't. The tool amplifies what you bring to it. So the investment in developing your people's expertise pays a double return in an AI-enabled world, because the AI makes their existing knowledge more productive.

And then there's the caution worth noting. A METR study found that developers were 19% slower with AI coding tools while believing they were 20% faster. Perceived impact and actual impact can diverge significantly. This is why rigorous measurement matters, and why organisations that build feedback loops into their AI adoption learn faster than those that just assume it's working.

The Microsoft and LinkedIn Work Trend Index found that 75% of knowledge workers are already using AI tools, many without employer approval. Usage doubled in under a year and it spans every age group. The grassroots AI uprising is already happening, whether organisations are ready for it or not. The energy is there. The question is whether leadership teams channel it or let it happen unguided.

84% of organisations haven't redesigned a single job around AI (Deloitte, 2026). 85% are stuck at task-level use (BCG). So the technology is being adopted fast at the individual level, but most organisations haven't begun thinking about what this means for how they structure work. And that's actually an opportunity. The bar for standing out through thoughtful AI integration is still surprisingly low. The organisations that move now have a genuine first-mover advantage.

THE AMPLIFICATION EFFECT

There's a piece we wrote about moving beyond the AI efficiency obsession that gets at something we think is underappreciated. The dominant narrative frames AI purely through the lens of doing the same work faster and with fewer people. And yes, that's happening. But it's the least interesting thing AI does.

The more interesting thing is what happens when AI amplifies human capability to the point where entirely new kinds of work become possible. A marketing team of three can now produce the output of a team of twelve. But the response to that shouldn't just be "great, reduce the headcount." It should be "what could a team of twelve do if each person had AI amplification?" That reframe changes everything. It moves the conversation from cost reduction to capability expansion, and that's where the real strategic value lives.

The electricity analogy is one we keep coming back to. When factories first got electricity, they used it to power the same machines that steam had powered. Same layout, same processes, just a different energy source. It took decades before manufacturers realised they could redesign the entire factory floor around what electricity actually made possible: smaller machines, flexible layouts, assembly lines, new products that couldn't have existed in the steam era. The businesses that redesigned thrived. The ones that just swapped the power source eventually fell behind.

AI is at that same early stage. Most organisations are using it to power their existing processes a bit faster. The organisations that will pull ahead are the ones redesigning their operations around what AI actually makes possible. And we're already seeing early examples of this: teams that have restructured their workflows around AI are reporting not just efficiency gains but qualitative improvements in the kind of work they're able to take on.

The failed McDonald's AI drive-through experiment is instructive. They bolted AI onto an existing workflow without rethinking the workflow itself. It didn't work. That's what happens when you treat AI as a speed boost rather than a capability shift. Compare that with organisations that have redesigned processes around AI capabilities from the ground up. The difference in outcomes is stark.

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THE SKILLS THAT MATTER NOW

So if the nature of work is changing, what does that mean for the skills people need?

We wrote about what still matters at work when everyone has AI, and the answer is more encouraging than you might expect. The skills that AI makes more valuable, not less, are deeply human skills. And they're skills that can be developed, which is good news for anyone willing to invest in their own growth.

Critical thinking and judgment. AI generates options. Humans decide which options are good. The ability to evaluate, question, and make sound decisions in ambiguous situations becomes more important, not less, when AI is producing vast quantities of analysis and recommendations. This is why experienced professionals often get more value from AI than juniors do. They have the judgment to know which outputs to trust and which to push back on.

Domain expertise. AI amplifies what you know. The BCG research is clear on this: people with deep domain knowledge get dramatically more value from AI than generalists do. Knowing your industry, your customers, your market context remains essential. It's the context that makes AI outputs useful rather than generic. So if you're wondering whether to invest in deepening your team's expertise or broadening their AI tool knowledge, the answer is both, but domain expertise comes first.

Communication and collaboration. As AI handles more routine analytical and production work, the distinctly human work of leading teams, building relationships, persuading stakeholders, and navigating organisational dynamics becomes the work that matters most. These are the capabilities that create trust, alignment, and the conditions for good decisions. AI can draft the presentation. A human has to read the room.

Questioning and curiosity. AI rewards humans with a questioning mindset. The people who get the most from these tools aren't the ones who accept the first output. They're the ones who probe, iterate, challenge, and refine. Curiosity is now a professional superpower. And it's a skill that compounds. The more you develop the habit of pushing AI for better outputs, the better your results get across everything you use it for.

Adaptability. The pace of change means that specific tool knowledge has a short shelf life. The ability to learn new tools quickly, adapt to new workflows, and stay comfortable with uncertainty is more valuable than mastery of any single platform. The good news is that the core principles of working with AI transfer across tools. Once you've learned to work well with one platform, picking up the next one is significantly faster.

Is AI shrinking our brains or enhancing our thinking? It depends entirely on how you use it. If you outsource your thinking to AI, you'll atrophy. If you use AI to extend your thinking, to challenge your assumptions, to explore options you wouldn't have considered, it makes you sharper. The tool doesn't determine the outcome. How you approach it does. And the people who approach it as a thinking partner rather than a replacement for thinking are consistently getting the best results.

ENTRY-LEVEL JOBS AND THE CAREER PIPELINE

This is the part of the future of work conversation that deserves the most careful attention.

The vanishing generation of entry-level jobs is a real phenomenon. Much of what junior employees traditionally did, basic research, data compilation, first-draft reports, scheduling, inbox triage, is exactly what AI does well. And organisations are starting to notice that they can give those tasks to AI instead of hiring a graduate.

The challenge is that those "boring" junior tasks were never just about the output. They were the training ground. That's where people learned how organisations work, built professional relationships, developed judgment, and earned their way into more complex work. Remove the entry-level rung from the career ladder and you've got a pipeline issue that takes years to materialise but is very hard to fix once it does. The organisations thinking about this now will be the ones with strong leadership benches in five and ten years' time.

But there's a positive angle here too. AI also creates new entry points. Roles in AI quality assurance, prompt development, workflow design, and AI-assisted customer experience didn't exist a few years ago and are growing fast. The career landscape isn't simply shrinking. It's reshaping.

The answer isn't to preserve junior roles artificially. The work is changing. But organisations need to think carefully about how they develop talent in a world where AI handles the tasks that used to develop people. Mentorship, structured learning, exposure to complex work earlier in careers. These become more important, not less. And the leadership teams that build intentional development pathways now will have a real competitive advantage in talent.

For people early in their careers, the implication is clear: proficiency with AI isn't optional. It's as foundational as literacy and numeracy. The professionals who will thrive are the ones who learn to work with AI tools early and develop the human skills that AI can't replace. That combination of AI fluency and human capability is a powerful career foundation.

WHAT THIS MEANS FOR EDUCATION

The workforce implications of AI extend all the way back to how we educate people. And the decisions being made in education systems right now will shape the workforce we have in ten years.

America has put AI into every school. The US has made AI literacy mandatory in schools, meaning American teenagers will be AI-literate before they're old enough to drive. That's a signal that should matter to every leadership team in New Zealand. The next generation of employees will arrive expecting to use AI in their work. If your organisation isn't ready for that, you'll look outdated before they finish onboarding. But the flip side is exciting: imagine the capability of a graduate cohort that's been using AI tools throughout their education. The potential contribution of those new hires goes up significantly.

What does AI mean for universities? is a question we've explored in some depth. The short answer is that universities need to rethink what they're teaching and how they're assessing. Critical thinking, research methodology, and the ability to evaluate AI-generated content become core skills. Rote memorisation and formulaic assessment become less relevant. The institutions that adapt fastest will produce graduates who are ready for AI-augmented work. And there are already universities making bold moves in this direction, redesigning curricula around AI-assisted learning rather than trying to police AI use out of existence.

For employers, this means that the quality of new hires will increasingly vary depending on where and how they were educated. AI literacy will need to be assessed during hiring and developed through onboarding, regardless of what someone's degree says. The organisations that build strong AI onboarding programmes will attract better talent and get new hires productive faster.

Leading a team through this transition?

THE HUMAN THINGS THAT STILL MATTER

I want to end the substantive sections with something that gets lost in all the discussion about productivity gains and workforce transformation. And it might be the most important part.

Reclaiming time and meaning in the AI age is something we wrote when thinking about what happens when AI takes over the tedious parts of knowledge work. In theory, it frees people up to do more meaningful, creative, strategic work. In practice, that only happens if organisations and individuals are intentional about it. Without intentionality, the time AI saves just gets filled with more tasks, more meetings, more busyness. But with intentionality, the shift can be genuinely positive. People spend less time on drudgery and more time on the work that drew them to their profession in the first place.

The harmony of humanity and AI isn't a given. It's a choice. The organisations that get this right will be the ones that use AI to remove drudgery while preserving and elevating the parts of work that give people meaning, connection, and purpose. And the evidence from early adopters is encouraging: when AI integration is done well, people report higher job satisfaction, not lower. They feel like they're doing more meaningful work.

And authenticity matters more than ever. The reason we moved from blog posts to video is directly related to this. When AI can produce competent written content at scale, the things it can't replicate (genuine human presence, unscripted thinking, real personality) become more valuable. That applies to every profession, not just content creation. In a world where AI-generated output is everywhere, the human touch becomes a differentiator rather than a commodity.

The argument that AI leads to the enshitification of human knowledge is one we've pushed back on directly. AI doesn't average down human capability. Used well, it amplifies the best of what humans know. But that "used well" caveat is doing a lot of work. It requires skill, judgment, and the willingness to engage with the tools seriously rather than dismissing them after a single mediocre output. The people and organisations willing to invest that effort are the ones who will see the benefits.

HOW TO PREPARE

So what do you actually do with all this?

If you're leading an organisation: Start with your own AI literacy and your leadership team's. You can't make good decisions about workforce strategy if you don't understand the technology yourself. Then look at your workflows, not just individual tasks. Where could AI change the entire way work gets done, not just speed up one step? And think carefully about your talent pipeline. How are you developing the next generation of leaders when AI is absorbing the tasks that used to develop them? The leadership teams doing this thinking now are building organisations that will be significantly stronger in three to five years.

If you're managing a team: Identify the tasks in your team that AI could handle and start experimenting. But don't just automate and move on. Think about what your team members could do with the time AI frees up. Could they take on more strategic work? Develop new skills? Build relationships with clients that drive long-term value? The manager's role in an AI-augmented world is less about task allocation and more about capability development. And that's actually a more rewarding version of management for most people.

If you're building a career: Get proficient with AI tools now. Not tomorrow, not next quarter, now. Start using ChatGPT, Claude, or Gemini in your daily work. Build the skill of working alongside AI. And invest in the human capabilities that AI amplifies rather than replaces: critical thinking, communication, domain expertise, curiosity, and adaptability. The combination of AI proficiency and strong human skills is the most career-resilient position you can build.

The organisations and individuals that will thrive aren't the ones with the best AI tools. They're the ones who combine AI capability with distinctly human strengths. That combination is where the value sits. And the window to build that combination ahead of your competitors is open right now.

FURTHER READING FROM ACROSS THE SITE

FURTHER READING FROM ACROSS THE SITE

The Changing Nature of Work

• What still matters at work when everyone has AI - The human skills that become more valuable
• Beyond the AI efficiency obsession - Why AI could grow jobs, not just replace them
• One person plus AI can outperform teams - Harvard and P&G research on human-AI collaboration
• Still think AI won't replace you? - Why the old reassurance no longer holds

Skills, Cognition, and Meaning

• Reclaiming time and meaning in the AI age - What happens when AI removes the drudgery
• Is AI shrinking our brains? - Cognition and AI dependency
• The harmony of humanity and AI - A philosophical look at human-AI integration
• The AI enshitification argument - Why the "dumbing down" critique is shallow

Education and Careers

• The vanishing generation of entry-level jobs - Career pipeline disruption
• America put AI in every school - What NZ should learn from US policy
• What does AI mean for universities? - Higher education's AI challenge
• Why I'm moving to video - How AI is reshaping content and trust

Historical Perspective

• Genius Makers book review - The people who built modern AI

ABOUT THE AUTHOR

Mark Laurence

Mark is the founder of Ten Past Tomorrow, an AI consultancy and education business based in New Zealand. A trained futurist (Institute for the Future) and practical AI specialist, he works with senior leadership teams to move organisations from AI curiosity to AI capability.

He has worked with 100+ NZ organisations and leads Rapid AI Traction, a four-week programme for senior leadership teams, and The Path to AI Emergence, a ten-month transformation programme.

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