HR is becoming less about administering employment and more about designing the conditions in which humans and machines work together well.

For years, the future-of-work conversation was dominated by a fairly predictable set of questions. Would people work from home? What did Gen Z want from employers? How could organisations attract scarce talent? And how much flexibility was too much flexibility?

Those questions haven’t disappeared, but they are being overtaken by a more consequential one: what happens when the technology changes at the same time as the workforce using it?

That is the challenge now facing HR and people leaders. AI is moving into recruitment, performance management, training, employee support and everyday workflows. Employers are beginning to expect AI literacy rather than merely encourage it. At the same time, a generation shaped by remote learning, social media, economic uncertainty and very different expectations of work is moving deeper into organisations. The result isn’t simply a new technology cycle or a generational shift. It is both at once.

My sense is that this changes the people agenda in a fundamental way. The organisations that navigate the next few years well won’t be those that automate the most HR processes or bend furthest to every new employee preference. They will be the ones that become much more deliberate about what machines should do, what humans still need to learn to do well, and what good leadership looks like when both are changing.

AI fluency is becoming part of employability

One of the clearest shifts in the research is how quickly AI capability is moving from an optional advantage to an expected workplace skill. The Wall Street Journal reported that Accenture had trained around 70% of its roughly 779,000 employees in generative AI fundamentals, with CEO Julie Sweet saying employees for whom reskilling was not viable would be exited. McKinsey & Company has begun considering how consultants make decisions with AI in performance reviews, KPMGassesses collaboration with AI in wider evaluations, and PwC has introduced AI training for new graduate hires.

That changes the psychology of AI adoption. For the first few years of generative AI, employees could reasonably treat tools such as ChatGPT as an experiment. Increasingly, that is no longer the case. LinkedIn job postings requiring AI literacy skills grew 70% in the 12 months to July 2025, according to data cited by the Journal. The emerging message from employers is not simply that AI may change your job. It is that your willingness and ability to work with AI may increasingly affect whether you get the job, how you are assessed once you have it and which opportunities come your way.

But there is an important trap here. Mandating AI use is much easier than building AI capability. The same Journal reporting noted that more than 40% of US workers who didn’t use AI said the main reason was that they didn’t believe it could help their work. The issue, in other words, isn’t always resistance to change. Sometimes people have been given a tool without a convincing use case.

For people leaders, this means AI literacy can’t be reduced to prompt training. Employees need permission to experiment, examples of where AI genuinely improves work, clarity about where it should not be trusted, and time to redesign workflows rather than simply bolt a chatbot onto the old ones. The skill that matters may be less ‘Can you use AI?’ and more ‘Can you make good decisions about when and how to use it?’

Recruitment is becoming an arms race

Nowhere is the tension clearer than hiring. Candidates are using AI to write CVs, tailor applications, prepare for interviews and, increasingly, assist them during interviews themselves. Recruiters are using AI to screen, rank and communicate with those candidates. In some parts of the market, it can feel as though the bots are applying to be interviewed by other bots.

There are real benefits. Axios reported SHRM data showing that 65% of HR professionals surveyed were using AI at work, compared with 45% of the broader workforce. Among HR professionals using AI, nine in ten were relying on it to generate job descriptions or screen applicants. Australian recruiters interviewed by the Sydney Morning Herald similarly described processes that once took days being completed in minutes.

There is also emerging evidence that AI can sometimes outperform traditional approaches. In research highlighted by The Wharton School professor Ethan Mollick, an experiment involving around 70,000 applicants for customer-service roles in the Philippines found an LLM voice recruiter produced 12% more job offers and 18% more starts, with one-month retention 17% higher. That is significant. But it doesn’t settle the question of whether machines should run recruitment.

The risks run in the opposite direction. Automated screening can struggle with unconventional career paths, transferable skills and people whose experience doesn’t happen to match the language in a job description. Australian reporting has also highlighted concerns about opacity and discrimination in automated hiring. And as candidates use more AI themselves, recruiters face a different problem: a polished application is becoming a weaker signal of actual capability.

That may produce an unexpected outcome. The more automated recruitment becomes, the more valuable genuinely human evidence may become. Employers will need better ways to see how someone thinks, communicates, responds to ambiguity, builds rapport and exercises judgement. The CV may become easier to produce. The human behind it may become harder, and therefore more important, to assess.

The next skills gap may be surprisingly human

This matters because some of the research on younger workers points to a second challenge. A 2026 report on workplace anxiety cited almost half of Gen Z respondents experiencing anxiety around workplace interactions such as meeting new people, making small talk and picking up the phone. A separate Wall Street Journal report described parents becoming involved in adult children’s job searches and workplace issues; in a Zety survey of more than 1,000 Gen Z respondents, 20% said a parent had attended a job interview with them.

It would be easy to turn those findings into another round of Gen Z bashing. That would miss the more useful point. Many younger workers spent formative years studying remotely, interviewing over Zoom and entering workplaces where face-to-face interaction was less frequent. At the same time, digital communication has made it possible to avoid many of the uncomfortable interpersonal moments through which previous generations developed confidence.

If that is even partly true, employers face a choice. They can interpret a lack of confidence as a character flaw, or they can recognise that some capabilities now need to be developed more deliberately. Giving difficult feedback. Handling disagreement. Speaking to a stranger. Reading a room. Asking for help. Recovering after an awkward conversation. These have always been workplace skills, but we rarely called them skills because people acquired so much practice almost by accident.

AI raises the stakes further. If a machine can draft the email, prepare the presentation, summarise the meeting and suggest the answer, employees may get fewer repetitions of the very tasks through which judgement is built. The leadership challenge is therefore not simply to make people more productive. It is to ensure productivity gains don’t quietly remove the experiences people need in order to become capable.

The office argument is becoming less binary

The same nuance is needed in the debate about where people work. The old argument tended to divide people into pro-office and pro-remote camps. The evidence is becoming messier. Employers are increasing pressure for attendance, sometimes through what the Wall Street Journal calls ‘hybrid creep’: linking visibility to advancement, improving attendance tracking and allowing unwritten expectations to do some of the work of formal mandates.

At the same time, younger workers aren’t uniformly demanding full-time remote work. Other research in the source material suggests Gen Z can place considerable value on being around colleagues, even while wanting flexibility and strong boundaries. That makes sense. The office can be inconvenient and socially demanding, but it is also where a great deal of tacit learning happens: overhearing how an experienced colleague handles a client, watching a manager navigate conflict, or being pulled into a conversation you didn’t know you needed.

The more useful question for leaders is therefore not ‘How many days should people be in?’ It is ‘What is worth coming in for?’ If the office simply recreates a day of individual video calls at a different desk, attendance will feel performative. If it provides access to coaching, collaboration, social connection, visibility and learning that are genuinely harder to reproduce remotely, its value becomes easier to understand.

Employee experience is becoming observable data

Another shift deserves more attention: AI isn’t only helping employees do their jobs. It is increasingly watching, interpreting and coaching how those jobs are done.

Burger King, for example, has been developing an AI assistant called Patty that sits inside employee headsets. It can answer operational questions, draw on information across restaurant systems and assess whether customer interactions include markers of ‘friendliness’, such as saying please and thank you. The company describes it as a coaching tool. Meanwhile, Axios reported on HR platforms capable of joining meetings, taking notes, drawing on workplace data and nudging managers about goals and performance.

There is obvious potential here. A manager can’t observe every customer interaction or remember every commitment made in every meeting. AI can surface patterns that humans miss. But the line between coaching and surveillance can become thin very quickly. If employees know every interaction may become a data point, leaders need to be exceptionally clear about what is being collected, why it is being collected, who can see it and what decisions it can influence.

This may become one of HR’s defining governance responsibilities. The technical question is whether an AI system can measure something. The leadership question is whether it should.

Gen Z is also changing what employees can contribute

There is a tendency in conversations about younger workers to focus on what they lack. The research also points to capabilities organisations can use more intelligently.

The Wall Street Journal reported that Starbucks, Delta Air Lines and other employers are recruiting staff to create social-media content about life at work. Starbucks selected 53 baristas for its Green Apron Creators program, while Delta brought a group of frontline employees to its Atlanta headquarters to develop employee-generated content. What was once the kind of behaviour employers worried about is, in some organisations, becoming a recognised capability.

That is an instructive shift. Younger employees don’t merely arrive with different expectations. They arrive with different native skills, networks and cultural instincts. The challenge for leaders is to distinguish between behaviours that need development and capabilities that should be harnessed. Good people leadership has always involved both.

The bargain between employee and employer is being renegotiated

This brings us to perhaps the most important change. The psychological contract around work is shifting.

Deloitte’s 2025 global Gen Z and Millennial Survey found that long working hours and inadequate recognition were among the strongest contributors to workplace stress for younger employees. Randstad research in the source material found 44% of Gen Z workers said their current role didn’t align with their dream career, 37% regretted their sector choice and 54% regularly browsed for new roles. At the same time, three in five said they would take a job that didn’t align with their personal values if the pay and benefits were strong.

That last finding is worth noticing because it complicates the stereotype of Gen Z as purely purpose-driven. Economic pressure matters. Career opportunity matters. Recognition matters. Flexibility matters. Values matter too, but not in isolation. Younger workers are making trade-offs just as every generation has, only under a different set of economic and technological conditions.

This is why simplistic generational labels are such a poor management tool. Leaders need to understand cohort-level patterns without turning them into assumptions about individuals. A 24-year-old employee may want flexibility and more face-to-face mentoring. They may care about purpose and still take the better-paid job. They may be highly capable with AI and deeply uncomfortable making an unexpected phone call. Contradictions aren’t evidence that the research is wrong. They are evidence that people are complicated.

The job of HR is moving upstream

Taken together, these trends suggest that HR is becoming less about administering employment and more about designing the conditions in which humans and machines work together well.

Recruitment becomes a question of how to combine algorithmic efficiency with human judgement. Learning becomes a question of building AI capability without outsourcing thinking. Hybrid work becomes a question of designing environments where people actually learn and connect. Performance management becomes a question of deciding which new data is useful and which is intrusive. Generational leadership becomes less about accommodating preferences and more about developing capability while recognising that the workforce has changed.

None of this makes the human side of leadership less important. I suspect the opposite is true. As more of the mechanics of work become automated, the quality of judgement around those mechanics becomes more consequential. Leaders will need to be clearer about expectations, better at coaching, more thoughtful about development and more transparent about how technology is being used.

For a long time, HR technology promised to make people management more efficient. AI can certainly do that. But efficiency is only part of the brief. The bigger opportunity is to use this moment to become more intentional about the capabilities, experiences and relationships that make people valuable in the first place.

The workplace of the future won’t be built by choosing between technology and humanity. It will be built by getting much better at knowing where each belongs.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: canva.com

How do we harness the power of technology, without allowing it to reshape young brains and change the way we learn?

For more than a decade, the narrative in education seemed straightforward. More technology meant better learning. Classrooms filled with tablets, laptops and digital dashboards were seen as symbols of progress.

Schools proudly announced one-to-one device programs and parents were assured that coding lessons and AI tools would prepare children for the future.

But something interesting is happening.

Across the world, educators are beginning to quietly ask a difficult question. What if the tech revolution in classrooms has gone too far?

From Scandinavia to Australia, schools are rebalancing their approach. Devices are being limited. Handwriting and printed books are making a comeback. Even some of the most enthusiastic technology advocates are beginning to argue that learning might require more analogue experiences than we assumed.

This shift is not about rejecting innovation. Rather, it reflects a deeper rethink driven by new research, rising concerns about child development and the rapid arrival of artificial intelligence in the classroom. The result is a growing movement toward what might be called “mindful technology” in education.

For leaders, parents and professionals watching these changes unfold, the implications are significant. The classroom is becoming a testing ground for a broader societal question. How do we harness powerful technology without allowing it to quietly reshape our brains, behaviour and expectations in ways we never intended?

Here are five emerging trends shaping the new balance between technology and analogue learning.

1. The Analogue Comeback

For years, many education systems raced to digitise classrooms. Tablets replaced textbooks and interactive screens replaced whiteboards. But a growing body of evidence suggests the learning benefits were far less impressive than promised.

In fact, large international studies from the OECD found that countries investing heavily in classroom technology often saw declines in literacy, numeracy and science outcomes rather than improvements.¹

These findings have prompted some governments to rethink their approach. Sweden, once one of the earliest adopters of digital learning tools, has begun reversing course by reinvesting in printed textbooks and traditional learning materials after concerns about declining academic performance.²

The shift is also happening at the classroom level. Teachers increasingly report that students concentrate better when reading physical books and writing by hand. Education researchers argue that digital devices, when overused, can fragment attention and reduce the sustained focus required for deep learning.³

The implication is not that digital tools are useless. Rather, they are simply not the silver bullet many once believed.

For schools and organisations, the lesson is broader than education. Technology should enhance human capability, not replace the cognitive processes that build it. Sometimes the smartest innovation is reintroducing what worked before.

Professionals working in education or learning design should ask a simple question when evaluating new tools. Does this technology deepen thinking, or merely make the process faster?

2. The Screen-time Generation Arrives at School

Teachers are noticing something else that would have been almost unimaginable a decade ago. Many children arriving at school struggle with basic physical and cognitive skills.

A January 2026 survey of 1,000 primary school staff in England found that 28 per cent of five-year-olds could not use books correctly, with some even attempting to swipe pages like a phone screen. More than half of the teachers surveyed said excessive screen time played a significant role in declining school readiness.⁴

This observation aligns with a growing body of neuroscience research examining how early screen exposure shapes developing brains.

A 2025 longitudinal study in Singapore found that higher screen exposure before the age of two predicted accelerated but inefficient development in brain networks associated with visual processing and cognitive control. Children exposed to greater screen time showed slower decision making at age eight and higher anxiety by adolescence.⁵

Even three to four hours of daily screen exposure was enough to produce measurable effects.

A broader meta analysis of 34 studies also found links between excessive screen use and reduced attention span, impaired executive function and observable changes in brain regions associated with learning and memory.⁶

None of this means screens are inherently harmful. But it does suggest that developing brains need a diverse range of experiences including reading, movement, play and face-to-face interaction.

The practical takeaway for educators and parents is surprisingly simple. Old-fashioned habits still matter. Frequent parent child reading has been shown to counter some of the negative cognitive effects linked to early screen exposure.⁵

In other words, sometimes the most advanced strategy for cognitive development looks suspiciously like what grandparents used to recommend.

3. The AI Homework Dilemma

If screens created one challenge for schools, artificial intelligence has introduced a far bigger one.

AI chatbots have quickly become a standard part of student life. A Pew Research study released in February 2026 found that 54 per cent of American teenagers now use AI chatbots for schoolwork, with one in ten saying they rely on them for most or all of their assignments.⁷

Perhaps more concerning, 59 per cent of teens believe AI cheating is a regular occurrence at their school, rising to 76 per cent among students who already use chatbots for homework.

The impact on learning itself may be even more significant.

Research highlighted in recent reporting on AI and education suggests that heavy reliance on AI writing tools can significantly reduce cognitive engagement during learning tasks.⁸ Students who outsource thinking to algorithms may complete assignments faster but retain far less understanding.

Yet despite these concerns, AI tools are rapidly being integrated into education systems. Technology companies are introducing AI assistants into classroom platforms, while governments are beginning to include AI literacy as part of standard curricula.⁸

The challenge is not access to AI. It is ensuring that AI assists learning rather than replacing it.

One education commentator captured the dilemma with a striking analogy. Asking students to self regulate AI use during homework may be a little like asking a gambling addict to make wise choices inside a casino.

The solution likely lies in redesigning assessments and classroom practices so that AI becomes a thinking partner rather than a shortcut.

For educators and professionals designing learning experiences, the key question becomes this. How do we use AI to expand curiosity rather than outsource cognition?

4. The Rise of “mindful tech” Classrooms

Faced with these challenges, many schools are not abandoning technology. Instead they are adopting a more deliberate and balanced approach sometimes described as “mindful tech”.

This philosophy recognises that digital tools can be valuable when used intentionally but harmful when used automatically.

Some schools are introducing device-free periods during the day to encourage deep reading, conversation and collaborative problem solving. Others are using digital tools selectively for tasks such as simulations or research rather than continuous screen exposure.⁹

At the same time, educators and parents are becoming more cautious about the influence of large technology companies within education systems. Critics argue that classrooms have become a testing ground for digital platforms whose long-term impact on learning remains uncertain.¹⁰

This caution is also emerging in policies around student safety. For example, St Hilda’s Anglican School in Western Australia recently removed identifiable student images from public social media due to growing concerns about AI generated deepfakes and image manipulation.

The broader implication is that digital literacy increasingly includes understanding when not to use technology.

For organisations outside education, this trend mirrors a wider cultural shift. As technology becomes more powerful, intentional use becomes more important.

The real competitive advantage may lie in knowing when to unplug.

5. The Rediscovery of Focus

Perhaps the most important trend emerging from this classroom reset is the rediscovery of something modern life has quietly eroded: Focus.

Many educators believe that the rapid stimulus switching common in digital content may be conditioning young brains to expect constant novelty. Fast cuts, notifications and algorithmic feeds reward brief attention rather than sustained concentration.

But deep learning requires exactly the opposite. Reading complex material, solving difficult problems and writing thoughtful arguments all depend on the ability to maintain focus for extended periods. It is a skill that must be practised and strengthened.

This is one reason printed books, handwriting and extended discussion are returning to classrooms.

The concern among some researchers is that the next generation could become the first in modern history to display lower cognitive capability than their parents. Some analysts argue that cognitive performance has stagnated or declined in parts of the developed world since around 2010, coinciding with the rise of smartphones and always-on digital environments.³

The good news is that attention is trainable. Activities such as reading, discussion, outdoor play and time in nature help rebuild cognitive endurance and restore the mental stamina required for learning.

For professionals across industries, the lesson extends far beyond classrooms. In a world of constant distraction, the ability to focus deeply may become one of the most valuable skills of all.

Conclusion: Finding Balance in the Digital Classroom

Technology is not disappearing from classrooms. If anything, it will continue to expand.

Artificial intelligence, immersive simulations and adaptive learning platforms will all play an increasing role in education over the coming decades. But the enthusiasm that once surrounded classroom technology is now being tempered by experience and evidence.

The emerging lesson is not that technology is harmful, but that learning is deeply human.

Children need conversation, movement, reading, play, curiosity and sustained attention. Digital tools can support these experiences, but they cannot replace them.

The schools now leading the way are not the ones with the most screens. They are the ones asking smarter questions about when technology helps and when it quietly gets in the way.

For parents, educators and leaders watching this shift unfold, the message is clear. The future of education will not be fully digital or fully analogue.

It will be deliberately balanced.

And in an age obsessed with innovation, that might turn out to be the most innovative idea of all.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: canva.com

For generations, the career advice seemed straightforward: get educated, learn to think, and try to make a living with your brain rather than your back.

Knowledge work was the safe end of the labour market. The plumber did something useful, certainly, but the accountant, analyst, lawyer or coder had the more future-proof career.

Artificial intelligence is beginning to scramble that logic. Generative AI lives in the environment we have spent decades making machine-readable: documents, databases, spreadsheets, code, email and screens. A plumber, nurse or electrician still has to navigate an unruly physical world full of awkward spaces, unpredictable people and endless edge cases. In some settings, it may be easier to automate the first draft of a legal memo than to replace a leaking pipe behind a wall.

That does not mean the white-collar job apocalypse has arrived. The evidence so far is much more complicated. But it does suggest we may be asking the wrong question when we obsess over which jobs AI will take. The more useful question is this: what becomes valuable when intelligence gets cheap?

Capability is not the same as displacement

There is no shortage of dramatic predictions. AI companies are scenario-planning for unemployment levels that would once have sounded unthinkable. Business leaders have warned of enormous disruption. At the other extreme, optimists point to every previous technological revolution and argue that new jobs will emerge as old ones disappear.

At this point, neither camp has enough evidence to declare victory.

One of the more useful pieces of research in the material I have been studying came from Massachusetts Institute of Technology researchers who tried to move beyond conventional AI benchmarks. They identified more than 11,500 workplace tasks, ran workplace-style versions of those tasks through more than 40 AI models, and had people in the relevant occupations judge whether the output was actually usable. By 2025, AI could complete roughly 65 per cent of text-based tasks to a minimally acceptable standard, up from around half the year before.

That is extraordinary progress. It is also easy to misread. ‘Good enough’ is not the same as reliable, error-free or ready to run unsupervised inside a real organisation. Legal work, for instance, performed relatively poorly because precision, judgement and strategic guidance matter. Management tasks could often be accelerated, but coordination and decision-making remained harder.

This is the distinction that gets lost in much of the jobs debate. A technology being capable of performing a task does not mean an organisation will immediately deploy it, that customers will accept it, regulators will permit it, or that the surrounding job disappears.

Jobs are bundles of tasks. AI can remove some, accelerate others and increase the value of the remainder. We may therefore see a great deal of task displacement before we see equivalent job displacement.

The safest jobs may not be where we thought

For years, automation anxiety focused heavily on factories, warehouses and transport. Robots were coming for physical labour first, or so the story went.

Physical automation is certainly advancing. Humanoid robots can now run, kick footballs and perform eye-catching demonstrations. Yet the World Humanoid Robot Games also test machines on wonderfully mundane activities such as connecting cables, handling warehouse goods and completing industrial assembly. The reason is revealing: the physical world is messy.

Molly Kinder at The Brookings Institution has described this as the ‘messy middle’. Hospitals, restaurants, construction sites, airports and nursing homes require dexterity, situational awareness, trust and constant adjustment to circumstances nobody anticipated in advance. Cognitive work conducted inside a computer is often much more structured.

There is an irony here. The economy spent decades telling young people that manual work was vulnerable and knowledge work was secure. AI may make that distinction considerably less useful.

NVIDIA chief Jensen Huang has even argued that the AI infrastructure boom will create huge demand for electricians, plumbers, carpenters and construction workers. Someone still has to build the data centres, factories, power systems and physical infrastructure behind all this supposedly immaterial intelligence.

None of this makes trades immune from automation. It simply reminds us that technological exposure is not distributed according to old assumptions about occupational status.

When Abundance Arrives, Scarcity Moves

Economics has a habit of rewarding scarcity. For much of modern working life, competent intellectual output was scarce enough to command a premium. Research took time. A polished first draft took skill. Code required specialists. Analysis depended on people who had spent years learning a field. AI is beginning to make some of those outputs abundant.

Organisational psychologist Adam Grant recently put the creative version of this idea neatly: when ideas become abundant, taste and tenacity matter more. Good judgement and execution become scarce. I think the same principle extends far beyond creativity.

Researchers Roberto Rigobon and Isabella Loaiza at MIT Sloan School of Management have developed a useful framework for the human capabilities that remain particularly complementary to AI. They call it EPOCH: Empathy and emotional intelligence; Presence, networking and connectedness; Opinion, judgement and ethics; Creativity and imagination; and Hope, vision and leadership.

What makes their work interesting is not merely the list. In an analysis spanning 19,000 tasks across 950 occupations, they found that newly emerging workplace tasks showed higher levels of these human-intensive capabilities than tasks that had disappeared. In other words, work may already be evolving towards some of the things humans remain unusually good at.

This leads to what I think of as the Scarcity Shift. Whenever technology makes a capability abundant, look for where scarcity moves.

When answers become abundant, good questions appreciate. When content becomes abundant, taste appreciates. When synthetic communication becomes abundant, trust appreciates. When competent analysis becomes abundant, judgement appreciates. When execution gets easier, deciding what deserves to be executed becomes more important.

The point is not that these qualities are uniquely or permanently human. It is that their relative economic value can rise as other capabilities become easier to access.

The Apprenticeship Paradox

There is one part of this transition that worries me more than most: entry-level work.

Junior roles have always contained a fair amount of low-value activity. Graduates research, summarise, prepare first drafts, build presentations, check documents and perform routine analysis. Organisations tolerate the inefficiency partly because this is how people learn. You do the basic work, watch more experienced colleagues, make mistakes, develop judgement and gradually earn responsibility for harder decisions. AI is becoming very good at the bottom rungs of that ladder.

Randstad’s Gen Z Workplace Blueprint, drawing on more than 11,000 workers and an analysis of 126 million job postings globally, found postings for roles requiring zero to two years’ experience had fallen 29 percentage points since January 2024. Technology, logistics and finance recorded particularly large falls. Randstad does not attribute that decline solely to AI, and neither should we. Economic conditions and hiring cycles matter too. But the direction deserves attention.

It creates an apprenticeship paradox: if AI removes the work beginners used to do, how do beginners become experts?

Hybrid work adds another wrinkle. Research on remote work increasingly describes the office not simply as a place to complete tasks but as social infrastructure: a place where younger workers overhear conversations, observe how experienced colleagues handle ambiguity, build networks and absorb tacit knowledge that is difficult to put in a manual.

We could therefore end up removing junior tasks at the same time as we weaken some of the informal mechanisms through which juniors traditionally learned to move beyond them.

That is not an argument for dragging everybody back to a desk five days a week. It is an argument for redesigning early-career development deliberately. If AI does the first draft, a graduate may need earlier exposure to clients, decisions, critique and judgement rather than simply being given less work.

AI Fluency will be Baseline, Not the Differentiator

There is a danger in all this talk about human skills. It can quickly turn into comforting advice to focus on empathy and leave the technology to somebody else. That would be a mistake.

Randstad‘s 2026 Workmonitor surveyed 27,000 workers and 1,225 employers across 35 markets and analysed more than three million job postings. Vacancies asking for ‘AI agent’ skills had surged 1,587 per cent. Governments are responding too. The UK has announced an ambition to provide practical AI skills training to 10 million workers by 2030.

AI fluency is rapidly becoming part of basic professional literacy. The advantage will not come from choosing between technical and human capability. It will come from combining them.

The person who can use AI well and exercise sound judgement over its output is more useful than the person who does only one of those things. The manager who can automate routine analysis but still read a room has an advantage. The marketer who can generate 100 ideas in minutes but has the taste to discard 97 of them has an advantage. The adviser who can retrieve an answer instantly but knows which question the client is really asking has an advantage.

Being human is not, by itself, a career strategy. Human capabilities have to be developed with the same seriousness we once reserved for technical expertise.

Organisations Will Move More Slowly than the Technology

Another reason to resist precise predictions about mass unemployment is that organisations are stubborn things.

AI models can improve in months. Companies do not. They have legacy systems, budgets, risk committees, regulators, customers, employment laws, managers, incentives and habits. A technically possible automation can take years to become an operational reality.

Australian middle-market research from Pitcher Partners illustrates the gap. In its 2025 Business Radar survey, 72 per cent of leaders said they were actively using AI tools, yet only 13 per cent had made AI a genuine strategic priority with dedicated budgets and plans to scale. Adoption is widespread; transformation is not. That gap buys people time. But it would be foolish to confuse slow organisational change with low eventual impact.

Walmart offers a more plausible picture of the next phase than either utopia or apocalypse. CEO Doug McMillon has said AI will change essentially every job in the company. Walmart has been automating warehouse and back-of-store work while creating roles such as ‘agent builders’ and continuing to need people in high-touch roles. The company has indicated its overall workforce could remain broadly flat while the business grows, meaning the composition of work changes even when total employment does not collapse.

That may be the pattern to watch: not jobs vanishing in neat occupational blocks, but organisations progressively unbundling work and redistributing tasks between people and machines.

The Repricing of Human Capability

Nobody can responsibly tell you today whether AI will eventually create more jobs than it destroys. The range of credible possibilities is simply too wide. Anthropic is openly scenario-planning for everything from modest disruption to a world in which demand for human labour is persistently lower. Other researchers point out that we have not yet seen anything resembling that level of displacement in aggregate labour-market data.

Both facts can be true. The job apocalypse is not here, and preparing for significant disruption is still sensible.

What we can see more clearly is a repricing already underway. Some cognitive tasks that once required scarce expertise are becoming cheaper. Some physical skills look more resilient than expected. Early-career pathways need redesign. AI literacy is becoming baseline. And capabilities such as judgement, trust, taste, curiosity, leadership and human connection are becoming more important precisely because information and competent output are becoming easier to produce.

For leaders, that suggests a useful exercise. Don’t only ask which jobs AI could automate. Ask three questions instead:

What just became cheap?
What remains scarce?
What becomes more valuable because of the change?

Those questions will produce different answers in a law firm, hospital, mine, school, retailer or construction company. They will also change as the technology improves.

That is why I suspect the future of work will be less about protecting a fixed list of ‘human jobs’ and more about continually noticing where value has moved.

The future of work won’t simply be shaped by what machines learn to do. It will be shaped by what remains valuable once they can.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: canva.com

For years, shopping online followed a predictable pattern. You searched, compared, skimmed reviews, opened too many tabs, got distracted, then either bought something or gave up.

It was clunky, time-consuming and mentally draining, but at least the rules were clear. Ads were ads. Advice lived somewhere else.

That separation is disappearing fast.

AI shopping assistants are moving from the sidelines into the centre of the buying journey. Tools like ChatGPT, Gemini, Perplexity and Microsoft Copilot are no longer just helping people research purchases. They are recommending products, surfacing prices, inserting “Buy” buttons and, in some cases, letting users complete a purchase without ever leaving the conversation.

This shift is happening faster than most people realise. Advances in generative AI, changing consumer expectations and growing frustration with traditional online shopping are converging. Add in a generation comfortable outsourcing decisions to algorithms, and the result is a retail landscape that looks fundamentally different to the one we grew up with.

For professionals, retailers and leaders, the urgency is real. This is not just a new marketing channel. It’s a rewiring of how trust, influence and decision-making work in commerce.

Trend 1: Advice and Advertising Are Merging

The first shift is subtle, and that’s what makes it powerful. The line between genuine advice and paid promotion is becoming increasingly blurred.

Modern AI assistants don’t just answer questions. They suggest products, rank options and increasingly prompt users to buy. Microsoft Copilot now embeds shopping recommendations directly into conversational responses. Other platforms have experimented with sponsored answers that sit alongside organic suggestions, often without clear visual distinction.

Unlike traditional advertising, these prompts don’t feel like ads. They feel like guidance. When a recommendation arrives in a conversational tone, supported by what looks like reasoning, people lower their scepticism. The persuasive power comes not from interruption, but from integration.

For consumers, this creates a new challenge. How do you tell the difference between impartial advice and a commercial nudge? One practical habit is emerging: ask the AI why it recommended something. Does it explain trade-offs? Does it offer alternatives? Or does it funnel you toward a single “best” option? Real advice explains choice. Advertising pushes outcomes.

For businesses, this trend rewrites influence. Visibility alone is no longer enough. Products must be defensible, explainable and competitive when placed side by side in an AI-generated comparison. If your offering can’t survive transparent scrutiny, AI will expose that quickly.

Trend 2: Transparency Will Be Forced, Not Volunteered

At the moment, regulation in this space is lagging behind reality. There are few clear rules forcing AI shopping tools to disclose what is paid, what is sponsored and what is genuinely impartial.

That lack of clarity matters for two reasons. First, consumers deserve to know when bias or commercial influence is present. Second, accountability becomes murky when AI systems get things wrong.

A recent example highlighted this risk when an AI travel assistant confidently advised tourists to visit a hot spring in Tasmania that simply does not exist. The issue wasn’t just the hallucination. It was the absence of clear responsibility. Who is accountable when an AI invents reality with confidence?

As AI increasingly shapes purchasing decisions, transparency will become unavoidable. Disclosure around sponsorship, data sources and limitations will be demanded by regulators, journalists and consumers alike.

For leaders and organisations, this is an opportunity to move early. Systems that explain recommendations, acknowledge uncertainty and clearly separate advice from advertising will earn trust long before regulation forces compliance.

Trend 3: AI Is Compressing Hours of Shopping Into Minutes

Despite the risks, the consumer upside of AI-powered shopping is undeniable. These tools are dramatically reducing friction.

Tasks that once took hours now take minutes. Comparing prices across retailers. Summarising thousands of reviews. Tracking historical discounts. Monitoring price changes. In some cases, completing a purchase without opening a single browser tab.

Australian consumers are leading this shift. Research shows a significant majority of Australians have interacted with AI while shopping in recent months, well above the global average. More tellingly, more than half of those users have made purchases based on generative AI recommendations.

This is no longer experimental behaviour. It’s habitual. Many households are already using AI to stretch budgets, find better deals and automate parts of the shopping process.

For businesses, this fundamentally changes discovery. Where search engine optimisation once dominated, answer engine optimisation now matters just as much. If an AI model cannot easily surface, explain and justify your product, it effectively disappears from consideration.

Trend 4: The Weekly Grocery Shop Is Being Rewritten

AI-driven shopping is not confined to screens. It is reshaping physical retail too.

Smart carts and supermarket apps already allow shoppers to scan items as they go, track their total in real time, receive personalised discounts and pay without lining up. But the bigger transformation is happening quietly in the background.

Experts predict supermarkets will become smaller and more focused on fresh food. Pantry staples like toilet paper, nappies and cleaning products will increasingly be reordered automatically and delivered to homes without conscious effort.

In practice, in-store shopping becomes about choice, quality and freshness, while AI handles repetition behind the scenes. Two shoppers standing in the same aisle may see different specials because the system understands their habits, preferences and household needs.

AI is also changing how decisions are made in the moment. A shopper considering a new pair of headphones can take a photo, upload it to an AI assistant and instantly receive a comparison across models, prices and stores, including historical data showing whether a discount is likely soon.

For retailers, loyalty will be driven less by location and more by usefulness. The brands that win will be the ones that integrate seamlessly into consumers’ decision-making systems.

Trend 5: Trust Becomes the Ultimate Competitive Advantage

As AI takes on more of the buying journey, trust becomes the scarce resource.

These systems are persuasive by design. They speak with confidence. They explain reasoning. They feel personal. That makes over-reliance a real risk.

Consumers will increasingly judge platforms not by how clever they are, but by how transparent and controllable they feel. Can users understand why something is being recommended? Can they override it? Can they see alternatives?

For professionals building or deploying these tools, the goal should not be to remove humans from the loop, but to keep them meaningfully informed. Trust grows when people feel empowered, not nudged.

Brands that over-optimise for persuasion may win short-term sales, but they risk long-term credibility. Restraint, clarity and honesty will prove more valuable than cleverness.

What This All Means

AI-powered shopping is not a future scenario. It is already reshaping how people research, decide and buy. The five trends are clear. Advice and advertising are merging. Transparency will be demanded. Shopping is becoming faster and more automated. Physical retail is being re-engineered. And trust is becoming the defining differentiator.

The challenge for leaders and professionals is not technical. It is behavioural. Understanding how people make decisions when AI is in the room, and designing systems that support rather than exploit that reality.

The question is no longer whether AI will influence what we buy. It already does. The real question is whether we build a shopping ecosystem that is helpful, honest and human, or one that quietly nudges us while pretending not to.

The difference will come down to the choices being made now, while the rules are still being written.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: Canva

Not long ago, humanoid robots sat firmly in the category of “cool demo, wildly impractical.” They dazzled on conference stages, tripped over their own feet on YouTube, and then quietly disappeared back into research labs. That phase is ending fast.

Humanoid robots are moving from spectacle to systems. From factories and hospitals to aged care facilities and, eventually, our homes, they are inching closer to everyday life. Goldman Sachs estimates there could be more than 13 million humanoid robots in use globally by 2035. That’s less than a decade away. While most of these robots will appear in workplaces first, the ripple effects will be felt across households, cities and entire industries.

The drivers are converging rapidly. Advances in AI vision, balance and hand dexterity are accelerating. Labour shortages are intensifying as populations age and fewer people enter physically demanding roles. Cultural expectations are shifting around convenience, care and the value of time. And younger generations are far more comfortable sharing space with machines than any before them.

For leaders and professionals, the question is no longer whether humanoid robots will matter, but how quietly and quickly they will reshape expectations. 

1. From Sci‑Fi Spectacle to Quiet Utility 

The first major shift is psychological. Humanoid robots are not arriving with dramatic flair or cinematic ambition. They’re slipping in through side doors, doing the dull jobs no one wants to talk about at dinner parties.

We already live with robots, even if we don’t think of them that way. They vacuum our floors, mow our lawns and assist surgeons. In fact, more than 80 percent of prostate surgeries are now performed using robotic systems. COVID accelerated this trend, particularly in agriculture and logistics, where closed borders and labour shortages forced rapid adoption.

Humanoid robots represent the next logical step because they fit into environments built for humans. Factories, warehouses and hospitals don’t need to be redesigned when the robot has two legs, two arms and can use existing tools. That’s why companies like BMW, Hyundai and Tesla are already trialling humanoid robots on factory floors for repetitive and physically demanding tasks. Hyundai has publicly stated it plans to deploy humanoid robots in US factories from 2028.

China offers a glimpse of what early adoption looks like at scale. Humanoid robots are already working as tour guides, retail assistants, warehouse staff and service workers, with some even assisting in policing and security roles. Dedicated robot training centres allow machines to learn by observing humans rather than being painstakingly programmed line by line. 

The implication is clear. Early adoption will be quiet and practical rather than flashy. Organisations that treat humanoid robots as boring infrastructure rather than futuristic mascots will extract far more value from them. 

2. Cobots, Not Job Stealers 

It’s impossible to discuss humanoid robots without confronting workforce anxiety. Elon Musk has said Tesla aims to build up to 100,000 humanoid robots per month within five years. Numbers like that naturally raise concerns about job losses.

But the reality is more nuanced. Humanoid robots are particularly good at jobs humans increasingly struggle to fill. Dirty, dangerous and repetitive work. Heavy lifting. Night shifts. Tasks that lead to injury, burnout or high turnover.

Robots are already being used for warehouse picking, post‑surgery rehabilitation support and repetitive assembly. Deloitte predicts physical AI and humanoid robots will play a major role in addressing labour shortages, especially as populations age and healthcare demand grows. 

Rather than replacing humans, most experts expect robots to change the nature of work. This is where the idea of “cobots” becomes critical. Collaborative robots that work alongside humans, taking on physical or repetitive tasks while people move into supervision, creativity, problem‑solving and decision‑making roles.

For organisations, the real opportunity lies in redesigning jobs, not eliminating them. Professionals who focus on skills like judgement, empathy, oversight and systems thinking will become more valuable, not less.

3. Impressive, Fallible and Still Learning

The technology behind humanoid robots has advanced rapidly, particularly in vision systems, balance and hand dexterity. Some recent demonstrations have been so realistic that audiences questioned whether they were watching a robot or a human in disguise.

At the same time, viral clips of robots face‑planting, freezing mid‑task or dropping objects are not anomalies. They are part of the learning curve. This is what early‑stage intelligence looks like in physical form.

Robots perform best in controlled environments like factories and warehouses. Homes are far more challenging. Pets move unpredictably. Children run. Objects shift. Lighting changes. Most humanoid robots today still rely on some level of human supervision or remote assistance for complex tasks.

This phase closely mirrors the early days of self‑driving cars. Highly impressive in certain contexts, unreliable in others. The risk is not that robots will fail, but that humans will assume they won’t.

Organisations that succeed will design systems that assume occasional failure and build safeguards accordingly. 

4. The Home Robot Will Sell Time, Not Wow 

When humanoid robots enter homes, affordability and accessibility will dominate the conversation. Today, a humanoid robot like Neo costs around $20,000. By 2035, that figure is expected to fall closer to $10,000 as manufacturing scales and components become cheaper. 

But ownership won’t be the starting point for most people. Early home robots will be aimed at wealthy households, aged care facilities and people with mobility needs. LG has already demonstrated prototype home robots capable of folding laundry and preparing simple meals, while projects like Tombot, a robotic puppy designed to support people with dementia, show how emotionally intelligent design can support care settings. 

For most households, the first exposure will likely be shared robots in apartment buildings, hotels or assisted living environments rather than owning one outright. Leasing models and robot‑as‑a‑service offerings will play a significant role in improving accessibility.

The real appeal is not novelty. It’s time. Even saving 30 to 60 minutes a day by offloading repetitive tasks changes how people live, work and rest.

5. Trust Will Matter More Than Life-like Design

Safety, privacy and psychological trust will ultimately determine whether humanoid robots are accepted into daily life. Most are designed to be lightweight, slow and compliant, stopping when they encounter resistance.

Privacy is a genuine concern. Robots rely on cameras and sensors to navigate spaces, raising questions about data storage, access and ownership. There is also the risk of over‑trust. Robots that look human can trigger emotional responses even when people know they are machines.

Experts agree humans will remain in the loop for a long time, particularly in homes and healthcare settings. Acceptance will depend less on realism and more on whether people feel in control of the technology.

There is also a genuine fear response to consider. An estimated 20 percent of the population experiences some degree of robophobia. Ignoring that reality would be a mistake. 

What This All Adds Up To

Humanoid robots are not coming to replace us, impress us or entertain us. They’re coming to quietly reshape how work gets done, how care is delivered and how time is reclaimed.

The trends are clear. Practical utility over spectacle. Collaboration over replacement. Rapid progress with real limitations. Time as the killer feature at home. Trust as the deciding factor everywhere. 

The future won’t arrive with a dramatic unveiling. It will arrive task by task, shift by shift, home by home. The robots are learning fast. We should too.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: Canva

When news broke that Disney had signed a landmark agreement with OpenAI, the reaction across the creative industries was mixed.

At a time when much of Hollywood is resisting artificial intelligence, launching legal challenges, or warning of creative collapse, Disney chose a very different path.

Rather than treating AI as an existential threat, Disney treated it as a question.

How could this technology help us tell better stories? How might it expand creativity rather than diminish it? And how do we engage with it early, thoughtfully, and on our own terms?

The agreement allows OpenAI to work with Disney’s vast library of characters and storytelling assets across emerging AI animation and generative video tools. It is an extraordinary move, not just because of the scale of the deal, but because of what it signals.

Disney is once again choosing partnership over protectionism. Exploration over resistance. Curiosity over certainty.

To understand why this decision matters so much, you need to see it not as a one off, but as the latest chapter in a pattern that stretches back more than a century.

This is not a company dabbling in disruption. Disney is a company that has always believed the future belongs to those willing to rethink first.

Walt Disney built an empire not by predicting the future, but by being relentlessly curious about what might be possible.

Long before Disney became synonymous with global entertainment, Walt Disney was an outsider. He was not backed by powerful studios, nor was he working within accepted industry rules. His success came from repeatedly asking questions others dismissed as impractical or reckless.

What if animation could carry emotional weight? What if stories could live across multiple platforms? What if entertainment could be experienced as a place, not just a performance?

These were not safe questions.

In the 1930s, when Disney decided to create Snow White and the Seven Dwarfs, industry insiders openly mocked the idea. Feature length animation was considered commercial suicide. Cartoons were short, disposable novelties. No one believed audiences would sit through ninety minutes of illustrated storytelling.

Disney nearly bankrupted his company bringing Snow White to life. He invested in new animation techniques, new camera technology, and unprecedented levels of artistic detail. What he was really investing in, however, was a belief that curiosity about emotional realism would be rewarded.

It was. Snow White did not just succeed. It created an entirely new category of cinema.

That same pattern repeated in the 1950s, when television emerged as a disruptive force. Hollywood studios panicked. Cinema attendance declined. Executives treated television as a threat to be resisted.

Disney Again Went The Other Way.

Rather than rejecting television, he partnered with ABC to create a weekly television series. On the surface, it was entertainment. In reality, it was something far more strategic. The show helped finance, promote, and emotionally prepare audiences for Disneyland, a physical place that did not yet exist.

Television was not competition. It was infrastructure.

That deal helped fund Disneyland’s construction and made Walt Disney himself a household name. At a time when business leaders remained largely invisible, Disney became the face of his vision. Not as a corporate executive, but as a guide, a storyteller, and a trusted presence in people’s homes.

Again, Curiosity Reshaped The Rules.

Disneyland itself was another audacious experiment. Walt Disney rejected the idea of building an amusement park. He disliked the chaos, the noise, and the transactional nature of existing parks. Instead, he imagined something different.

A theme park. A place where architecture, psychology, storytelling, and movement worked together to create a coherent emotional experience.

Buildings were deliberately designed to feel welcoming rather than overwhelming. Pathways controlled anticipation and discovery. Attractions were not rides, but experiences. Guests could spend an entire day immersed in story without stepping onto a single attraction.

It was entertainment designed around humans, not hardware.

Since opening in 1955, more than one billion people have visited Disneyland. That number alone tells a powerful story about the enduring appeal of human centred design driven by curiosity.

Crucially, Disney’s culture did not fossilise after Walt’s death.

When the company acquired Pixar in 2006, it faced a choice. Absorb Pixar and impose corporate discipline, or protect the very culture that made Pixar special.

Disney Chose Protection.

Pixar’s creative model rewarded questioning, dissent, and rethinking. Hierarchy mattered less than ideas. Disagreement was not seen as disloyalty, but as a necessary ingredient for excellence. Some of Pixar’s most successful films emerged precisely because creators were encouraged to challenge assumptions rather than conform to them.

This was curiosity embedded at scale.

Under Bob Iger’s leadership, that philosophy continues. Listening to an interview with Iger recently, I was struck by his admission about how Disney evaluates new ideas. The first question is not about cost. It is not about risk mitigation.

The First Question is Simple: Can we make this great?

That mindset explains why Disney’s response to artificial intelligence looks so different to many of its peers.

Where others see loss of control, Disney sees creative leverage. Where others fear displacement, Disney sees expansion. Where others hesitate, Disney experiments.

The OpenAI partnership echoes the ABC television deal of the 1950s. In both cases, Disney encountered a new technology that unsettled an industry. In both cases, it chose engagement over retreat.

This does not mean blind optimism. Disney has learned from missteps, including the difficult early years of Disneyland Paris. But even those moments reinforced a deeper truth. Curiosity must be paired with accountability. Experimentation must be paired with learning.

That balance is what allows curiosity to endure.

Walt Disney once said that Disneyland would never be finished, as long as there was creativity left in the world. He was not talking about a park. He was talking about a posture.

A refusal to believe the story is ever complete. A willingness to revisit assumptions. An understanding that relevance is not preserved through protection, but through exploration.

The OpenAI partnership is not a departure from Disney’s identity. It is proof that the identity still holds.

In a world obsessed with prediction, Disney continues to bet on curiosity. And history suggests that is a very good bet indeed.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: Canva

The future of privacy has arrived quietly, woven into our daily routines so seamlessly that most of us barely notice how much of ourselves we are giving away. Workplaces, cars, shops, neighbourhoods and even our own devices are now part of a growing ecosystem of surveillance reshaping how we live and work.

This shift is being fuelled by rapid advances in technology, a cultural appetite for convenience and personalisation and a regulatory system still scrambling to catch up. For leaders, employers and professionals, the message is clear. These changes are accelerating and recognising them early is essential.

1. Worker Monitoring Is Becoming Everyday

Digital oversight has become surprisingly normal. Employers are now using everything from Microsoft Teams’ new location tracking to keystroke loggers, webcams and GPS tools. Around two-thirds of companies use at least one form of monitoring. What started as a temporary pandemic solution has evolved into an everyday feature of remote and hybrid work.

The issue is not legality. Most of this is legal because Australia’s workplace privacy laws are years behind the technology. The issue is trust. When people feel watched, the home office can start feeling like an open-plan cubicle with better lighting. Some states are finally starting to consider tighter rules around what employers can reasonably track, but the cultural impact is already here. Teams perform better when trust is high and surveillance rarely builds trust.

2. Cars Are Becoming Data Factories

Modern vehicles are now data-hungry machines. A typical car produces about 200GB of data per hour and vehicles with advanced driver-assist or autonomous capabilities can generate up to 1.4TB in the same timeframe. They record where you drive, how you brake, how fast you take corners, the tone of your voice commands and even how you interact with the entertainment system.

Employers are increasingly tracking staff in work vehicles and sometimes after hours. Manufacturers are collecting even more. One major carmaker was caught storing and sharing photos and videos recorded inside customer vehicles. This wasn’t a technical glitch. It was a glimpse into just how much visibility companies now have into our personal lives. Cars used to be private spaces. Now they are mobile data centres and most drivers have little idea what is being captured, where it goes or who can access it.

3. Facial Recognition Is Moving Into Everyday Life

Once a futuristic idea, facial recognition has slipped quietly into daily routines. Several major Australian retailers were found to be scanning customer faces in-store without proper consent. The Privacy Commissioner ruled it illegal, setting a major precedent.

Yet the technology continues to spread. In parts of the United States, you can already board a plane with a face scan instead of a boarding pass. Many stadiums use it to manage crowds and security. One fast-food chain even used facial recognition to identify hungover customers and offer them discounts, which is either clever marketing or a sign we have overcorrected on personalisation.

Public sentiment is shifting fast. More than half of Australians say facial recognition in retail feels like an invasion of privacy. The technology is moving faster than public comfort and far faster than updated regulations.

4. Cameras Are Everywhere and the Idea of Public Space Is Changing

CCTV used to belong to governments and shopping centres. Today, anyone with a doorbell camera or home security system is contributing to a rapidly expanding layer of community surveillance. We are filmed dozens of times a day. Sydney has roughly one camera for every 80 residents. London has one for every 13. In Shanghai and Beijing, it is one for every two people.

Doorbell footage that once lived on a small local device now sits in cloud databases managed by large tech companies. Overseas, police have increasingly requested access to this footage, raising complicated questions about consent, ownership and the rights of people captured incidentally while simply walking past.

The line between safety and surveillance is getting thinner and many people do not realise how visible their everyday movements have become.

5. The Debate Over Whether Our Devices Are Listening

Ask almost anyone whether their phone is listening to them and you will hear the same story. They mention a product once and find it following them around the internet shortly after. Tech companies insist they are not using phone microphones to target ads. Yet public suspicion is growing and the stories keep multiplying.

Whether microphones are involved or not, voice is becoming the next frontier of data capture. Smart speakers, phones, watches, TVs and cars are equipped with microphones that are always partially awake, listening for wake words. Every new layer of surveillance, from face to movement to voice, generates highly sensitive personal data and individuals have very little control over how long it is kept or who it is shared with.

Hyper-personalisation may be convenient, but many are beginning to question the cost.

The Choices We Make Now Matter Most

Taken together, these five trends show a world moving rapidly toward deeper monitoring and a growing tension between convenience and control. The benefits are real. Better safety, smoother travel, more personalised services and more efficient workplaces. The challenge is finding balance.

Surveillance becomes harmful when it is invisible, unregulated or unchecked. The real issue is consent. Not the tick-a-box kind but genuine, informed consent where people understand what is being collected and why.

This moment calls for curiosity rather than complacency. For leaders, it means being transparent and thoughtful about how monitoring technologies are used. For governments, it means updating laws at the speed of technology rather than the speed of bureaucracy. For individuals, it means paying attention.

The future of privacy will not be defined by the technology itself but by the choices we make about it. We can embrace innovation without surrendering autonomy. We can enjoy convenience without forfeiting trust. The question is not whether surveillance will grow. It is how intentionally we manage it so the future we build still feels like our own.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: Canva

How Agentic Commerce is about to Redefine Retail Forever

Shopping as we know it is about to change – again. The era of agentic commerce is dawning, where AI agents shop, compare, and buy on our behalf. Fueled by generative AI, real-time data, and seamless payment integration, these digital “assistants” are evolving from simple chatbots into autonomous consumers.

The drivers are clear: advances in large language models, the integration of payment systems like PayPal and Venmo within ChatGPT, and growing consumer comfort with automation. Retailers, meanwhile, face both opportunity and existential threat as control over the customer journey begins to shift.

To understand what’s next, let’s explore five key trends reshaping the future of retail and commerce in the age of AI agents.

1. From Search to Conversation: Shopping Becomes Dialogic

Consumers are moving from search bars to conversations. Walmart’s recent partnership with OpenAI epitomises this shift: shoppers can now ask ChatGPT to “plan a taco dinner” or “restock pantry essentials,” receive curated suggestions, and check out – all within the chat.

• Why it matters: Search-based discovery is giving way to contextual dialogue, making shopping more intuitive and personalised. Instead of typing keywords, consumers describe goals or moods and AI agents translate that into purchases.
• Implications: Retailers must now optimise for AEO (Answer Engine Optimization) visibility rather than SEO. The “storefront” of the future may not be a website at all, but a conversation inside an AI ecosystem.
• What to do: Brands should develop “agent-friendly” data schemas and product metadata so their items can be discovered, compared, and purchased via conversational agents.

2. The New Buyer Is a Bot: Agentic Commerce Arrives

Agentic commerce describes a world where AI agents act as autonomous shoppers – buying groceries, managing loyalty points, and even negotiating prices.

• Why it matters: Instead of manually browsing and comparing, users will set rules (“buy detergent when it’s 20% off” or “reorder coffee every three weeks”), and agents will execute these automatically. Research shows that most consumers are interested in AI tools that can make purchases when prices drop.
• Implications: Shopping becomes anticipatory rather than reactive. But as agents gain autonomy, questions of liability, consent, and trust loom large. For instance, who’s responsible if your AI buys something you didn’t intend to or don’t want?
• What to do: Retailers should pilot transparent AI-agent interfaces that let customers review, approve, or override agent decisions to maintain confidence and control.

3. The Frictionless Checkout: Payments Go Invisible

PayPal’s integration with ChatGPT represents a crucial step toward invisible commerce – a world where transactions happen without leaving the conversation. Using OpenAI’s Agentic Commerce Protocol, PayPal now allows direct in-chat payments and merchant listings without extra integration work.

• Why it matters: Payments are no longer the end of the funnel – they’re built into the interaction. This eliminates friction and allows “chat-to-checkout” in seconds.
• Implications: Traditional payment gateways and checkout pages may fade. Trust, fraud prevention, and dispute resolution will instead be embedded directly in conversational layers.
• What to do: Businesses should align with emerging open protocols to make their products agent-accessible and compatible with cross-platform AI ecosystems.

4. AI-Powered Customer Experience: The Age of Anticipation

The customer experience is evolving from reactive to predictive and proactive. McKinsey’s “Next Best Experience” model shows that AI-powered personalisation can boost satisfaction by up to 20% and reduce service costs by 30%. These systems detect when a customer needs help before they even ask.

• Why it matters: The next frontier isn’t personalisation at scale; it’s personalisation in real time, driven by data and generative content. Agentic AI goes further by testing and refining communications autonomously, learning with every interaction. Consider how Yelp’s new AI receptionist and host bots can answer calls, take bookings, and even chat with other bots to coordinate reservations.
• Implications: Companies should integrate predictive and conversational AI tools into their customer service stack—not just to improve efficiency, but to build anticipatory trust.

5. Retail’s Identity Crisis: Losing the Interface

While AI agents simplify the shopper’s life, they threaten to disintermediate retailers. As the Wall Street Journal notes, brands like Walmart and Etsy have jumped onto ChatGPT integration for exposure, but they risk ceding control over customer experience, data, and loyalty.

• Why it matters: When AI assistants become the primary point of discovery, brand identity competes with algorithmic preference. Retailers’ lucrative ad and search ecosystems (which are worth billions annually) could erode as discovery shifts “upstream” into AI chats.
• What to do: The winners will be those who co-create with AI platforms, ensuring their products and brand values are reflected accurately in conversational interfaces. Think API partnerships, branded agent personalities, and data-sharing agreements rather than banner ads.

Some concluding thoughts

The rise of agentic commerce marks a profound shift – from browsing to briefing, from searching to conversing, and from paying to being paid for by your AI.

As consumers hand over more decisions to digital intermediaries, trust, transparency, and control will define the next decade of retail. The question for brands is no longer whether AI will reshape shopping but who will own the relationship when it does.

To stay relevant, leaders must design for an audience of two: the human and the algorithm.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: Canva

The pace of change in artificial intelligence is breathtaking. In just a few short years, AI has shifted from a novelty to an invisible layer in our daily lives.

From workplace emails and meeting notes to wedding vows and condolence messages, the technology is no longer just an office tool – it is shaping how we communicate, connect and even think.

The drivers of this shift are clear: rapid tech innovation, a generation raised on digital tools, and social norms bending around new possibilities. But there is an urgency here. As AI moves from helping us to think to doing the thinking for us, leaders and professionals need to ask a confronting question: what happens to our own cognitive muscles if we stop using them?

Here are 5 trends or implications to consider:

1. Outsourcing Thought: The Rise of ‘Digital Amnesia’

One of the most striking findings comes from a recent MIT study. Students using AI tools to draft essays showed a 47 per cent drop in active brain engagement compared to those writing unaided. Even more concerning, 83 per cent of AI users couldn’t remember what they had written just days later, versus only 10 per cent of students who had done the work themselves. Teachers are noticing it too. A Grade 10 English teacher in Australia observed that students relying on AI for drafts struggled to explain their arguments in class – they had outsourced the thinking, and it showed.

This isn’t limited to schools. Many professionals are letting AI write their emails, summarise meetings and even make decisions. The danger isn’t just the quality of the output, but what is happening in our heads. When we hand over the hard work of thinking, memory and reasoning to a machine, we become passive passengers in our own thought process. Like any skill, cognition is use it or lose it.

2. Homogenised Voices: When Everything Starts to Sound the Same

If you have noticed that social posts, emails and even dating profiles are starting to sound eerily similar, you are not imagining things. When millions of people use the same tools trained on the same datasets, originality gets sanded down.

AI-generated text is often competent, even elegant, but it lacks the quirks and rough edges that make human communication feel real. A University of Arizona study found that when people were told a “thoughtful” message from a friend was AI-written, they felt less connected to the sender even if the words were perfect. In an age where connection is currency, this matters.

The same risk exists in workplaces. If every presentation, report and client email starts to carry the same AI polish, it becomes harder to stand out or build trust. Ironically, in a world filled with machine-generated content, the messy fingerprints of a real human voice become a competitive advantage.

3. The Loneliness Loop: AI Companions and Emotional Skills

Seventy-two per cent of teens now use AI for companionship. Apps like Replika, with over 30 million users, offer AI “friends” and even romantic partners. On the surface, it seems harmless – a way to fill the loneliness gap so many young people report. But there is a hidden cost.

Real relationships are hard. They involve compromise, patience, missteps and making up. They require us to grow empathy, negotiation skills and emotional resilience. AI companions offer connection without any of that effort. They never argue, never need forgiveness, never ask for anything in return. They are there to serve you and you alone.

If human relationships are the training ground for emotional intelligence, what happens when a generation learns connection through algorithms that demand nothing of them? For workplaces, this could mean employees entering the workforce with technical brilliance but underdeveloped interpersonal muscles. The skills leaders prize (collaboration, empathy, conflict resolution etc) are forged in the friction of real human interaction.

4. The Education Gap: AI Literacy as a New Divide

Schools are in the middle of a live experiment. Some are embracing AI as a learning tool. Others are banning it entirely, seeing any use as cheating. But even in schools with strict bans, students are finding workarounds. Tools like Quillbot let them rewrite AI-generated content to avoid detection, creating a game of cat and mouse.

The real danger isn’t just plagiarism. It is the widening gap between students who learn how to use AI well and those who don’t. AI literacy is fast becoming as essential as reading and writing. At the same time, those who become over-reliant on AI risk losing the ability to think critically without it.

This divide won’t just shape classrooms – it will flow into workplaces. Organisations will face a new split: employees who can think with AI as a partner, and those who have been shaped by it into passive operators. It is a new kind of literacy gap, and it is opening fast.

5. Communication on Autopilot: When AI Speaks for Us

By next year, forecasts suggest more than 80 per cent of our everyday communication will be AI-assisted. Google’s Gemini platform has already rolled out a feature where the AI will call local businesses on your behalf to book appointments, gather prices and report back. It is convenient and, for many of us, a relief. But every time we hand over a conversation, we lose a little of the social skill it takes to navigate it ourselves.

This is a subtle erosion that mirrors what we have seen with other technologies. Spend years driving a car with cameras and sensors, and you realise how rusty your parking skills are the moment you switch to a basic hire car. Our ability to persuade, negotiate and build rapport is built on hundreds of small, low-stakes interactions. Automate too many of them and those muscles weaken.

AI taking over the grunt work of communication isn’t all bad. It can remove friction, save time and smooth awkward exchanges. But when it handles too much on our behalf, we risk outsourcing not just what we say, but the very human process of learning how to say it.

AI is not the enemy. Used well, it can free us to focus on the parts of work and life that require uniquely human intelligence – creativity, problem-solving, emotional connection. But the line between augmentation and abdication is thin.

The trends are clear: outsourcing thought leads to digital amnesia, homogenised voices dull connection, AI companions risk emotional skill-building, the education gap looms and communication on autopilot weakens our social muscles.

For leaders and professionals, the lesson is simple but urgent. Use AI as a tool, not a crutch. Keep your brain, your voice and your relationships actively engaged. In a world where 80 per cent of communication may soon be machine-assisted, the most valuable thing you can offer is the 20 per cent that is still unmistakably human.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: Canva

Cybercrime is no longer a niche issue. It has grown into a staggering $9.5 trillion economy, making it the third-largest in the world, trailing only behind the United States and China.

Every 11 seconds, a cyberattack occurs, whether through hacking, scams, or ransomware, impacting both businesses and individuals. With technology advancing at breakneck speed, cybercriminals are getting smarter, using artificial intelligence to make scams more convincing and harder to detect. The challenge we face is not just about preventing cybercrime but staying ahead of it.

Businesses and individuals alike are feeling the impact. Last year, nearly 60% of companies fell victim to ransomware attacks, resulting in millions of dollars lost in stolen data and recovery costs. Meanwhile, online fraud is surging, with consumers losing $8.7 billion in the U.S. alone, marking a 14.5% increase from the previous year. These numbers only scratch the surface of an evolving crisis where AI-driven cyber threats are making traditional security measures outdated.

The Evolution of Scams in the Digital Age

As cybercrime evolves, so do the tactics used by criminals. Gone are the days of easily identifiable scam emails riddled with typos. Today’s scams are polished, hyper-personalized, and alarmingly sophisticated, often fuelled by artificial intelligence. Deepfake scams have increased by 700% in the financial sector, with scammers using AI-generated voice clones and realistic video fakes to impersonate CEOs, colleagues, and even family members. Imagine receiving a call from your boss asking for an urgent wire transfer – only it’s not them on the other end of the line.

Phishing scams have also taken a more insidious turn. AI allows scammers to mimic human behaviour, creating emails that sound personal, natural, and tailored to the recipient. They no longer rely on mass email blasts; instead, they invest time building trust over weeks or even months before striking. Another growing threat is QR code fraud, where criminals replace legitimate QR codes with fake ones, directing unsuspecting users to fraudulent websites designed to steal personal data.

This shift in cybercrime means that criminals no longer need to hack into systems. They just need to trick people into willingly giving up their information. In an era where trust is currency, cybercriminals are exploiting human psychology rather than just technical vulnerabilities.

Building Cyber Resilience: Preparing for the Inevitable

With cyber threats becoming more advanced, the focus is no longer just on preventing attacks but on building resilience – the ability to recover quickly when an attack happens. The most secure businesses and individuals take a proactive approach by backing up critical data, detecting threats early, and responding swiftly.

On average, it takes companies 73 days to contain a data breach. That’s more than two months where sensitive information remains exposed, creating opportunities for financial and reputational damage. The reality is that no system is completely secure, which is why preparation is key. Cyber resilience is about being ready, not reactive, ensuring that even if an attack occurs, it doesn’t result in catastrophe.

Practical Steps to Stay Safe Online

Cybercriminals thrive on exploiting weaknesses, and more often than not, their easiest targets are people who fail to take basic precautions. Fortunately, small yet strategic changes can make a big difference in staying protected. Using a password manager like 1Password or Dashlane to create and store complex passwords significantly reduces the risk of account breaches. Avoid reusing PINs, especially common ones like 1234, 1111, or 0000, which are frequently leaked in data breaches.

Another smart precaution is to set up a personal or family codeword for verifying calls or messages that claim to be from loved ones. Scammers often exploit emotions, sending messages that create urgency or panic. Pausing before responding can prevent costly mistakes. Governments are beginning to crack down on cybercrime, with new regulations imposing $50 million fines on companies that fail to protect consumer data. However, personal vigilance remains the best defence against digital fraud.

The Future of Cybersecurity: AI as a Weapon and a Shield

Artificial intelligence is reshaping the battlefield of cybersecurity. On one hand, AI is being weaponized by cybercriminals to automate attacks, create ultra-realistic deepfake scams, and bypass traditional security measures. On Black Friday alone, Visa and Mastercard reported a 200% increase in AI-driven fraud attempts, highlighting just how rapidly these threats are escalating.

At the same time, AI is also being used to strengthen cybersecurity defences. The rise of biometric authentication such as face scans, fingerprints, and even heartbeat recognition is paving the way for a password-free future. While biometrics may offer a more secure alternative to traditional passwords, it also raises concerns about privacy and data security. Are we willing to trade biometric data for convenience, and what happens if that data is compromised?

Meanwhile, industries once thought to be low-risk are now prime targets for cyberattacks. In 2024, a single cyberattack on the UK’s National Health Service delayed over 1,000 surgeries, proving that the consequences of cybercrime go far beyond financial losses. Critical infrastructure, from hospitals to airlines, is now in the crosshairs of cybercriminals, forcing organizations to rethink security at every level.

The Key to Outsmarting Cybercriminals

Cybercrime is evolving fast, but staying safe doesn’t require technical expertise – it requires awareness, adaptability, and action. Questioning unexpected requests, verifying sources, and maintaining strong digital hygiene can prevent most cyber threats from succeeding.

The internet isn’t getting any safer, but by staying informed and taking cybersecurity seriously, we can protect ourselves, our businesses, and our personal data. Cybercriminals are getting smarter so now it’s time for all of us to do the same.

Article supplied with thanks to Michael McQueen.

About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker. His most recent book Mindstuck explores the psychology of stubbornness and how to change minds – including your own.

Feature image: Canva