AI and automation will eliminate 6% of US jobs by 2030, but the real story is more complex

AI And Automation Will Take 6% Of US Jobs By 2030

Don't Confuse Financially-Driven Layoffs With AI Layoffs There's a common impression - among leaders, in the media, and among employees - that AI is already causing widespread unemployment. After all, the US saw well over a million jobs lost to layoffs in 2025. Salesforce CEO Marc Benioff attributed some of the company's layoffs to internal use of its own AI solutions. And, if you walked down the streets of San Francisco, you probably saw Artisan's controversial urban advertisements broadcasting the message, "Stop Hiring Humans." But here's the dirty little secret of AI and layoffs: Every week, we speak to clients telling some version of the following story: "Our CEO said we are laying off 20% of staff and replacing them with AI - how do we do that?" When we ask if they have a mature, vetted AI app ready to fill in those jobs, nine out of ten times the answer is no, and they haven't even started. So, most of the layoffs are financially-driven, and AI is just the scapegoat, at least today. AI And Automation Will Take Some Jobs -- Just Not As Many As You Think Predictions about AI and job losses have run aground on the rocky shores of reality for a long time. In 2016, AI scientist Geoffrey Hinton made an ill-fated job prediction: "We should stop training radiologists now. It's just completely obvious that within five years, deep learning is going to do better than radiologists." Not only has radiology not gone away, it has grown; the Mayo Clinic's radiology staff grew 55% since then. But since our last iteration of this forecast in 2023, the world has changed. Agentic - or, at the moment, more agent-ish - AI has emerged, allowing organizations to create applications that are more accurate and that solve specific problems. We've also seen widespread investments in generative AI, with successes and failures that have taught the market how to get better results. As a result, our new forecast:

Is AI coming for your job? Here's one labor indicator that could soothe your fears

The US's productivity rate could indicate the real impact of AI. AI might not be putting you on the breadline just yet. A new report from research firm Forrester found that despite rampant anxiety around AI-driven job loss, the ubiquitous technology may only replace about 6% of jobs (about 10.4 million) in the US by 2030. The report, which launched at the end of December and is titled "The Forrester AI Job Impact Forecast, US, 2025-2030," highlighted strategies for keeping AI job loss panic on a low simmer and pointed out ways in which hype may be clouding our understanding of what's really happening on the job market. "It's not a small number, and [AI] will influence many more jobs and augment them and change how we work. That doesn't mean it's an apocalypse in the way that many people assume," Forrester VP, Principal Analyst J.P. Gownder told ZDNET. Also: Anxious about AI job cuts? How white-collar workers can protect themselves - starting now The report comes at a time when businesses are grappling with how to effectively implement and scale AI across their workforces, balancing the promises of cost savings and increased productivity with the reality of existing in the early days of buzzy tools like AI agents. Meanwhile, workers are wondering how long they'll be able to keep their jobs. News of layoffs and public comments from some tech leaders often paint a pessimistic picture. Anthropic CEO Dario Amodei said AI could wipe out half of entry-level white collar jobs. However, the firm's findings are in line with other projections, like an August report from Goldman Sachs, which estimated job losses in the 6-7% range, but only if adoption becomes widespread. Forrester identified several ways to keep the fear in check. Those include not confusing financially-motivated layoffs with AI layoffs. Gownder said that some CEOs may take a "brute force" perspective, wanting to cut a certain percentage of jobs in the name of AI, without actually having a mature, vetted AI tool that can do the job. Also: AI will cause 'jobs chaos' within the next few years, says Gartner - what that means "You're not replacing a job with AI," Gownder said. "You're replacing a job for financialized reasons with the vague hope that at some point you may be able to create an AI that does the work, and it is not guaranteed." The report also discussed keeping an eye on the right metrics - like productivity. Gownder explained that a jump in the US's productivity rate could be an indicator of AI's impact. In other words, fewer people doing more because of capital investments in AI. Currently, the rate's been lagging since the 1947-1973 era. "Until you see massive gains, you're not going to see these job losses the way that some believe," Gownder said. Still, Forrester made several revisions to earlier projections from 2023. Among them is the expanded role that generative AI, including agents, will play in job losses. Forrester forecasts that generative AI will be the cause of about 50% of roles lost to automation in 2030, up from 30%. Also: Is AI a career killer? Not if you have these skills, McKinsey research shows The firm also echoed what others have reported, namely that early-career, customer-service, and software jobs may be among the most vulnerable. To be sure, 6% still represents more than 10 million jobs, and trepidation among workers that they may be next to get a pink slip, in and of itself, could be damaging. "If you start coming in with this attitude of, 'we're going to automate as many people as we can and eliminate jobs,' and then you fail at it. Guess what? You still need your human people... with a good attitude, a good intrinsic motivation, or it hurts your business," Gownder said.

AI and automation could erase 10.4 million US roles by 2030

Forrester models slow, structural shift rather than sudden employment collapse AI-pocalypse AI and automation could wipe out 6.1 percent of jobs in the US by 2030 - equating to 10.4 million fewer positions that are held by humans today. This is according to Forrester veep and principal analyst J.P. Gownder, who states in a blog: "To give you a sense of the magnitude, the US lost 8.7 million jobs during the Great Recession. The numbers aren't directly comparable, since jobs lost to AI are structural and permanent while those lost during a recession are cyclical and macroeconomic. But no matter how you view it, the numbers are meaningful and worthy of our attention." The US population has also grown since the early part of this century, further muddying the waters when talking about absolute numbers. The "real but modest impact" that Forrester models between 2025 and 2030 suggests that the replacement of large chunks of the workforce remains unlikely, "as labor productivity would need to accelerate significantly for AI to replace human talent at scale." The more realistic scenario, it says, is that AI will "augment" one in five roles at the end of the forecast period, indicating employers may need to invest in staff training to prepare them for a new age. In the tech world, some of the SaaS providers are already making moves to replace parts of the workforce, including Salesforce, Workday, and ServiceNow. Still, risks remain. Forrester says there is a danger that "over-automating roles" based on the "hype surrounding" AI can result in "costly pullbacks, damaged reputations, and weakened employee experiences." Some businesses - Duolingo and Klarna spring to mind - rowed back their efforts to replace meatbags with AI. Forrester last year said it expects 55 percent of corporations to regret the decision to press ahead with these types of projects and quietly rehire people. Forrester today cites a million US layoffs in 2025 with some attributed to AI. Gownder recalls discussions with clients that want advice on laying off 20 percent of their staff in the lurch toward AI. "When we ask if they have a mature, vetted AI app ready to fill in those jobs, nine out of ten times, the answer is no - and they haven't even started. So most of the layoffs are financially driven and AI is just the scapegoat, at least today." So what has changed since Forrester's previous AI jobs forecast in 2023? Agentic AI has hit the market, and organizations are using it to create applications Forrester claims are more accurate - though not according to everyone - and solve specific problems. Generative AI projects, both successes and failures, are also teaching the market how to get better results, Gownder says. "Where our earlier forecast saw just 29 percent of US jobs lost to automation coming from GenAI, that number is now 50 percent, which accounts for agentic AI solutions that leverage GenAI as well." As for the 20 percent of positions that will be influenced by AI, this "represents a nearly fourfold increase compared with our 2023 forecast." All of which means that while jobs will be going, "we're not heading for an imminent AI job apocalypse." AI might take over some workflows and tasks, but in the next five years, Forrester thinks that work will be mostly undertaken by humans. ®

AI's impact on jobs is set to become more pronounced

Ever since the launch of ChatGPT in 2022, doom-mongers have been predicting a jobs apocalypse. So far, the fears have proved premature: despite chief executives' promises of AI-driven efficiencies, the new technology has not led to widespread lay-offs. But economists expect AI to reshape labour markets in more visible ways over the course of 2026, with some workers taking a hit before productivity gains feed through to wages and living standards. Their concern is that governments intent on winning the AI race have not yet done enough to protect those who may lose out -- in particular, graduates seeking to enter professions once seen as a safe career path. "I am really worried about this," says Molly Kinder, a senior fellow at the Washington-based Brookings Institution. "It is the clear, stated intention of employers and investors to deploy this and create efficiencies with, in many cases, an objective of cutting labour costs . . . we are underestimating in the medium to long term how much transformation could be ahead." Research Kinder published last year in partnership with the Yale University Budget Lab found little evidence that generative AI was, as yet, putting people out of work; or that it was shifting the mix of occupations in the US economy any faster than in previous periods of technological upheaval, after the arrival of computers and the internet. A recent rise in graduate unemployment in the US and Europe chiefly reflects a broader downturn in hiring, economists say, exacerbated by Trump's unpredictable policies, higher UK payroll taxes and a glut of newly qualified graduates in the Eurozone. "We haven't seen convincing signs yet that the rise is structural," says Ben May, director of global macro research at the consultancy Oxford Economics. He thinks big companies are linking lay-offs to AI usage because it "conveys a more positive message to investors" than ascribing them to "other negative factors like weak demand or excessive hiring in the past". But some studies suggest early effects of AI are now adding to young people's difficulties -- with hiring and employment looking weaker in tech and finance-related areas where AI adoption is more advanced, as well as in support and customer service roles already susceptible to automation. Tera Allas, a senior adviser at McKinsey, says analysis she conducted of UK job postings showed a "clear pattern" with sharper declines in occupations more exposed to AI. This does not mean companies had already realised big cost savings or worked out how to deploy AI across their organisation, she says, but "it wouldn't make sense to keep hiring at the same pace". If you ask workers in a company [about their use of generative AI] they are in general very happy with it -- it does the boring part of their work In many teams, AI was unlikely to "do all of one person's job", but managers without technical training might now "build a bot that can do enough of five people's jobs that you don't need to hire a sixth person". These changes may well be positive for many existing employees, as well as creating new opportunities. Stefano Scarpetta, director of the employment directorate at the OECD, cites research by the Paris-based organisation that found small businesses deploying generative AI did not cut jobs. Instead, they were better able to scale up and compete, saw their workload reduced, and became less reliant on external consultants. Evidence so far suggests AI could complement the skills of many workers rather than displacing them or leading to lower overall employment, Scarpetta argues. "If you ask workers in a company [about their use of generative AI] they are in general very happy with it -- it does the boring part of their work," says Sir Christopher Pissarides, a professor at the London School of Economics who led a recent review into the future of work. But he too shares the widespread fears for the prospects of new graduates, especially in an economy like the UK's, driven by professional services. So far, policymakers have been more intent on promoting the development and adoption of AI than on managing the potential fallout for workers. Companies are also behind the curve. Scarpetta says there is not yet enough investment or thought going into training workers in skills that would be complementary to AI, in particular, critical thinking to spot hallucination and use the new technologies effectively. But if the plight of graduates deepens in 2026, it could rise rapidly up the political agenda. In contrast with previous waves of change that destroyed manufacturing jobs, Pissarides says, "now it's graduates, the children of people who have been in professions all their life . . . you are going to hear a lot more about it [as] socially and politically they are more visible." Kinder believes that as AI replaces early career "grunt work", the paths into many professions may need wholesale reinvention. Young people have "done everything they were told to do" to move into professions that offered financial stability, only to find "these are the kind of jobs that are now vulnerable", she says. So far, neither the Trump administration nor any leading Democrat has come out with big ideas to boost early careers, she adds, but if AI-related job losses mount, "I think that's going to change".

Why AI for business means investing in its people

Three priorities are emerging for leaders: redesign roles for an augmented workforce, build human readiness for tools and change the system around the work, such as performance metrics, incentives and expectations. The first promise of the artificial intelligence (AI) revolution sounded straightforward: more time. And to a large extent, that promise is being realized. Recent World Economic Forum data shows that 82% of organizations are actively reinventing themselves with generative AI. At the individual level, the enthusiasm is palpable; in new Workday research, 85% of employees say they're saving between one and seven hours a week using these tools. We are making strides on efficiency. The next horizon is organizational transformation. Yet as we look toward 2026, a paradox is emerging. Individual tasks are getting faster but organizational outcomes are not improving at the same rate. Instead of freeing people to do more meaningful work, a surprising amount of time is spent on rework, oversight and burnout. In our Workday research, we see a hidden productivity drain: for every 10 hours of efficiency gained, employees spend roughly four hours correcting or refining AI-generated outputs. This isn't a failure of AI; it's an important signal. We're investing heavily in tools but not nearly enough in people. We equip employees to "use the tool," but not to apply the uniquely human capabilities that must sit atop it: judgment, creativity and care. The result is what many workers describe as a new treadmill. We're driving higher output, not higher quality outcomes. At the Forum's Annual Meeting 2026 in Davos, Switzerland, with the theme "Spirit of Dialogue," we need to broaden the conversation. This is no longer just about humans talking to machines. It's about rethinking the social contract of work in an AI-powered world. When I talk to chief human resource officers (CHROs) and chief executive officers (CEOs), almost all of them are investing aggressively in AI and training. Yet our data shows a persistent disconnect between executive intent and employee reality. Leaders report robust AI enablement. But the employees who use AI the most tell us they don't feel meaningfully invested in. They know how to prompt the tool but it's less clear on when to trust the output, when to challenge it and where their accountability starts and ends. We've moved fast on AI for content such as drafting and summarizing. The next frontier is AI for context: systems that understand people, skills, organizational policies and risk deeply enough to become trusted delegates. However, even the most context‑rich AI needs a partner in the people who work alongside it. That is the reinvestment imperative. The question for leaders is no longer, "Should we invest in AI?" It's now, "Are we investing in our people along with the right tools?" From our research and conversations with CHROs and CEOs globally, three priorities are emerging. The friction and burnout we feel today are signals that our investments are incomplete. We have poured capital into tools; now we must reach the next level of AI transformation with investment in people. The metric for success cannot simply be hours saved. It must be value created. As leaders gather in Davos, the goal should be to move beyond the question, "What can AI do?" to a more profound one: "Who do we want people to become in an AI-powered world and are we investing accordingly?" That is the real frontier of this revolution and it is, fundamentally, a people question.

The AI apocalypse isn't here yet. It's changing our jobs

Why it matters: The findings land amid a heated debate over whether AI will ultimately eliminate jobs or create new ones. The big picture: The report offers a detailed examination of AI use, looking at an anonymized sample from 2 million real Claude conversations that took place last year on its free and pay-for services. * This is the fourth time Anthropic has released its economic index, focused on understanding AI's role in the job market and economy. Between the lines: Anthropic's own founder and CEO has warned that AI could wipe out half of all entry-level white-collar jobs and push unemployment as high as 10-20% within one to five years. * But the study out today, from his own company, paints a more nuanced picture. And many other researchers find that, like with past technological revolutions, AI is more likely to create jobs than destroy them. * "The future is uncertain," says Peter McCrory, Anthropic's head of economics. Zoom in: AI is reshaping how people work, not if people work. * Put another way: AI takes over parts of people's jobs. * 49% of jobs can now use AI in at least a quarter of the tasks involved -- up from 36% three months ago, Anthropic found. How it works: Researchers used Claude to analyze transcripts of conversations along different dimensions -- was it about work or for educational or personal purposes? * How long would it take to complete a task if they didn't have AI? How many years of education would someone need to understand Claude's response? * They considered whether people were using Claude to fully automate a task for them -- "translate this into French." Or were they augmenting their work -- "let's write this report together." By the numbers: The study found about a 50/50 split between augmentation and automation, with a slight edge to augmentation. * 53% of work, on the free Claude site, involved augmented tasks. That share is down slightly from January of last year when it was 57%. Zoom out: The way AI changes your job depends a lot on what kind of work you do, and broadly speaking the differences can be grouped into two buckets. * Deskilling: AI starts to take on large portions of the roles -- say for data entry workers or IT specialists. This work appears to be more at risk for being automated away -- continuing trends decades-long in the making. * Upskilling: AI takes on some of your more rote work, leaving more time for higher-skill human tasks. As with radiologists or therapists, for example, who can devote more time to interacting with clients and less on back-end, time-intensive work. Friction point: The study finds that AI delivers the biggest productivity gains on complex work -- the same work that most requires human oversight. * It can take Claude minutes to pull together a broad overview of research, says McCrory. * But whether or not that actually generates any real value hinges on your expertise in evaluating that work, he says. * "The most complex tasks that people use Claude for are the ones where Claude tends to struggle most," he says. "Human oversight, direction and iteration is thus that much more valuable." What to watch: The need for humans is either a bottleneck that will slow down any productivity gains brought on by AI, or a force multiplier that will keep us all employed. Reality check: The tech is improving quickly and Anthropic also has an interest in portraying its technology as revolutionary, to draw users and investors. The bottom line: AI is changing the way we work, but the job apocalypse is not here yet.

Is this how to empower people worldwide in the age of AI?

Leaders are gathering at the World Economic Forum Annual Meeting 2026 to ensure that workforces worldwide remain resilient as the global economy undergoes significant change. What if the real promise of generative AI is not efficiency, but the chance to make work more human? As intelligent tools take on routine tasks and deliver clearer insights, they open room for creativity, better decisions and more rewarding work. Leaders play a pivotal role in shaping this transformation, helping to apply AI in a way that elevates human contributions, rather than narrows them. When leaders design and deploy AI with intention, they can amplify the parts of work that help people thrive, creating environments where individuals can engage with meaningful work and ultimately achieve greater success. To understand what is at stake, it helps to look closely at how AI is already changing the structure of work. Headlines about AI and the workforce often overlook the many factors shaping labour markets, including demographics, geography, industry and job design. A clearer view comes from examining how AI changes the structure of work. AI can automate tasks, such as coding or responding to routine inquiries, and it can augment work by giving people better data and sharper insights. AI is reshaping career paths from the bottom up. With younger worker populations shrinking in many industrialized nations, employers cannot afford to lose early-career talent. The priority is equipping these employees to use AI at a higher level, building skills that complement intelligent systems, rather than duplicate them. The future of work depends less on AI replacing people and more on people using AI to amplify the potential of humans and their contributions. As routine tasks fade, employees can focus on work that carries meaning. Intelligent tools spark ideas, support decisions and speed innovation. By moving effort to higher-value activities, AI strengthens the sense of purpose that keeps employees engaged. These changes are redefining leadership. Managers once reacted to problems after they surfaced; now leaders are expected to anticipate risks, recognize emerging patterns and act before challenges escalate. AI supports this new role by enabling early insight. Agentic AI systems at my organization, ADP, for example, can detect payroll inconsistencies weeks in advance, giving managers time to take action. That's the technology side. On the human side, leaders must communicate clearly about the role of AI in their organizations and prepare their employees to use it effectively. These four strategies can guide that process: Set clear expectations about what AI can and cannot do, ground decisions in data, state each tool's purpose and establish a strong governance framework. As roles evolve, give employees new capabilities to pair their judgment and creativity with intelligent tools. Remind employees that AI exists to strengthen their work. Steady communication and thoughtful design can boost confidence and adoption. Leaders need to reinforce that humans still need to apply critical thinking in their roles. In the new world of AI, leaders need to demonstrate deeper empathy for their employees, give them time and space to learn, inspire them and be a role model by showing them how AI can help amplify their potential. The cultural shift behind these strategies matters as much as the technology. Leaders must emphasize that AI is a collaborator that supports employees' growth and purpose, not a replacement mechanism. As AI becomes more deeply embedded in workforce systems, the need for disciplined human oversight grows more, not less. HR and payroll processes sit at the intersection of regulation and employee trust and even small errors can generate outsized consequences. When humans are not consistently involved in AI development, testing and decision pathways, the risks escalate quickly. A common mistake in enterprise AI adoption is treating implementation as a technical rollout from the top. The people closest to the work understand pain points, exceptions and compliance risks far better than anyone else. Their insight is essential to designing purpose-built AI that solves real problems. Rather than delivering finished tools for employees to adapt to, a better model helps organizations build roadmaps tailored to operational realities. An HR professional managing benefits enrollment, for example, could help define how AI agents navigate complex plan rules. This collaborative approach turns users into contributors. Adoption grows because employees trust what they helped design and innovation expands because those closest to the work influence development. A payroll specialist might identify an overtime calculation that creates a compliance risk, making it an immediate priority for agent development. The result is ownership. When employees feel ownership over AI, transformation becomes faster, more effective and more sustainable. As AI becomes more involved in the workplace, trust is crucial. AI can provide important and timely insights on key decisions involving pay, scheduling, benefits and advancement. These decisions have direct consequences for people's livelihoods and their sense of fairness at work. Ensuring that the information supplied by AI is trustworthy is foundational to creating a workplace where people feel valued, respected and supported. Bias detection and mitigation must be built into the design process. ADP's cross-functional AI governance team, for example, reviews every potential AI use case at the ideation phase; one crucial aspect of that review is to confirm that product development begins with ethical and explainable use of data. When AI makes a decision that affects someone's work life, that decision must be understandable and traceable. Technical safeguards alone are not enough. Trust requires consideration during the design of AI tools, vast datasets to effectively train the software and continuous monitoring once systems are deployed. Bias can emerge as systems learn, so ongoing vigilance is essential to ensure AI serves different populations fairly. To build a future of work that is both productive and human-centred, it's important to: AI's true promise lies not in what it automates, but in what it enables people to do. In the age of intelligent tools, the most successful organizations will be those that build technology around people and ensure that every innovation strengthens the human experience at work.

Report: How might AI and automation impact jobs leading up to 2030?

We take a look at some of Forrester's predictions for the US jobs market and explore what this might mean for the wider global landscape. When it comes to the conversation on artificial intelligence (AI), for the most part we all subscribe to a particular point of view. There are those who believe it has the potential to simplify working life, to the point that employees can focus on the aspects of their role that truly motivates them. For others, AI is a slippery slope that exposes the individual and the company to risk, bringing with it the potential to lower skills and make employees surplus to requirement. But, no one technology is inherently good or bad, it is representative of the creator and its user, meaning the real state of AI likely lies in the middle. When it comes to the growing fears that AI could reduce and eliminate jobs, the 'Forrester AI Job Impact Forecast, US, 2025-2030' report shows that, while there is room for concern, only 6pc of roles in the US have the potential to be automated by 2030. Research predicts that "automation and AI will have a real but modest impact on jobs", possibly equating to the loss of 10.4m roles. However, it notes that "widespread AI-driven job replacement remains unlikely, as labour productivity would need to accelerate significantly for AI to replace human talent at scale". Rather than eliminating roles, Forrester's research suggests that AI will impact jobs, with 20pc to be augmented over the course of the next four years. In turn the report theorises that this will enable businesses to further invest in employee training and upskilling. Additionally, the report finds that there is risk in buying into the AI hype as the over-automation of roles has the potential to lead to costly pullbacks, damaged reputations and weakened employee experiences and more than "half of layoffs attributed to AI will be quietly reversed as companies realise the operational challenges of replacing human talent prematurely". Commenting on the report, JP Gownder, the vice-president and a principal analyst at Forrester, said, "We may not be heading for an imminent AI job apocalypse, but how organisations handle AI today will define more than just their future success. Building relationships, creating value Informing, entertaining and connecting the world Join a culture that offers a world of possibilities Join us to start Caring. Connecting. Growing together "To navigate the complexity around the human and AI era, leaders must prioritise governance and invest in their people,  treating AI not as a replacement for human talent but as a tool to enhance it." With this in mind, how might AI and automation impact Europe, over the course of the next four years, as we hurdle towards 2030? Different borders, same challenges Throughout 2025, many organisations cut their numbers in order to prioritise AI adoption and innovation, for example, Indeed, Glassdoor, Workday and Amazon. Some of which gave the excuse that the cuts were necessary to ensure their organisation was keeping pace with competitors in a modernising space. And certainly, there is potential for additional job loss as a result of AI in 2026, as evidenced by a recent Morgan Stanley report published by the Financial Times. The report suggested as many as 200,000 Europe-based banking roles could be eliminated by 2030, as AI fills the spot previously held by a person. Forrester, late last year, released predictions for how AI and automation might further impact Europe in 2026 and similar to the findings of the US-based report, it was discovered that when the hype and initial surge to implement new tech dies down, there could be a a greater focus given to issues such as AI governance, skills development and the use of AI agents. It noted, in the face of rising geopolitical tensions, ongoing uncertainties and new legislative initiatives, which include the European Green Deal and the introduction of the EU AI Act, European countries will be less likely to experiment with high-risk use cases and may instead increase reliance on US hyperscalers such as AWS, Microsoft Azure and Google Cloud. At the end of the day, with The EU AI Act in play, according to experts, companies have an obligation to ensure the workforce is sufficiently skilled in AI literacy, perhaps resulting in greater access to training and subsequent upskilling opportunities. While this won't 'future-proof' an individual's career in the wake of modernisation, it does present employees with the opportunity to get ahead of the wave so to speak. Don't miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic's digest of need-to-know sci-tech news.

AI's $15 trillion prize will be won by learning, not just technology

The DEEP framework embeds learning into workflows for continuous improvement, rather than sporadically engaging workers in piecemeal training. Leaders across business, government and civil society share genuine excitement about AI's potential. Analyses consistently project a multitrillion dollar uplift. The World Economic Forum estimates AI could contribute up to 14% of global GDP by 2030, equivalent to about $15.7 trillion. Yet even as AI tools spread across enterprises, the productivity pay-off remains elusive: The UK's labour‑productivity trend has deteriorated, and US productivity growth has been inconsistent and declining from the highs of the early 2000s. The paradox is clear: Many employees report "time saved" through AI tools, but organizations and economies are not seeing durable, enterprise-level gains. As Chief Technology Officer at Pearson, my conclusion is that while AI investment is accelerating, enterprise productivity remains uneven because there isn't enough focus on the entire system of work: Humans + technology working together. There needs to be greater emphasis on how we reimagine entire workflows afresh, going beyond the notion of implementing technology first, then training humans later. Instead, we need a more integrated approach that puts constant learning by both humans and AI agents at the heart of transformation. Realizing AI's potential requires more than scaling technology; it demands a partnership between people and technology, where learning and adaptation are embedded at every stage. Drawing on deep experience in learning science, we know that navigating this paradigm shift means empowering individuals and teams to thrive; not just through technology tools, but through the continuous development and collaboration that make those tools truly count. This approach ensures that AI serves as a force for good, elevating outcomes for people and organizations alike. The real prize is building an augmented workforce where human expertise is accelerated and amplified by AI in the flow of everyday work, anchored in the authentic context of an individual's skills, and orchestrating the right combinations across workflows. Automation delivers quick, measurable efficiencies; AI-augmented workforces rewire how value is created. New Pearson research shows the greatest productivity gains come when AI is used to codify knowledge, orchestrate multi‑agent work, and embed rapid, continuous learning so people move from "doing" to discernment and creativity. In other words, AI should not replace judgement; it should raise the ceiling of what human judgement can achieve. The biggest obstacle to unlocking this potential is a widening learning gap: While AI capabilities are advancing exponentially, workforce adaptation remains sporadic and superficial. Our research finds that organizations often "tick the box" on AI training initiatives, but fail to rearchitect tasks, roles and skills around human-AI collaboration. This gap explains why individual "time saved" hasn't compounded to enterprise productivity - and is now the key risk to realizing the job transformation the World Economic Forum anticipates. If we close the learning gap and scale augmentation, the economic upside is substantial. Our modeling across more than 300 knowledge‑intensive occupations shows AI-powered augmentation could add between $4.8 trillion and $6.6 trillion to the US economy by 2034 (around 15% of its current size), contingent on broad augmentation adoption. Combined with the Forum's global outlook, the message is consistent: unlock new sources of growth by investing in people. To close the learning gap, we need a fundamentally different approach to upskilling; one that moves beyond legacy training models to AI-enabled, human-centred learning. This means embedding learning directly into workflows, rather than pulling employees away for generic training sessions. To enable this transformation, our research defines four key pillars at the core of a framework we call DEEP: This is how transformation and feedback become the normal cadence of a business strategy; not a pilot programme, but a persistent capability that both prepares workers for future changes and makes transitions faster and more successful. To realize AI's $15 trillion promise, leaders must champion a new learning imperative that is human-centric, iterative and evidence-based. AI investment and learning must go hand in hand; neither can deliver sustainable impact alone. This requires moving beyond the false choice between automation and human workers to embrace workforce augmentation as the path to sustainable productivity growth. C‑suites must invest in learning at scale by reimagining work around human‑AI collaboration, building trusted skills data, credentialling capabilities, and empowering Learning and Development leaders to define the future of work. This future depends on scalable learning systems, measurable skills ecosystems and trusted data to ensure augmentation strengthens, not erodes, human capability.

AI is becoming your new work colleague. But let's not forget your human ones

A collective culture of AI learning will reinstate human agency in the workplace and drive greater productivity. Since entering the mainstream in 2022, AI has begun to reshape the world of work and our understanding of what's possible. Technological advancements and adoption are accelerating at historic rates, driven by proven success and increased productivity. AI has become a true teammate in the modern workforce, solving real problems alongside the worker. As people and technology continue to grow together, the human capital management (HCM) industry faces a critical responsibility: We must ensure AI innovation remains responsible and ethical, while keeping people at the centre. That means finding new ways to amplify human creativity and connection, while prioritizing learning that keeps pace with technology. If we succeed in this commitment, we will help build an AI-powered workforce and a future we all want to live in. Many companies are hyper-focused on AI. According to Stanford University's 2025 ArtificiaI Intelligence Index Report, Generative AI attracted nearly $34 billion in private investment in 2024, up 18.7% from the previous year. And adoption is quickly rising to match. Seventy-eight per cent of the companies surveyed used AI in 2024, reflecting a 55% increase from the year before and outpacing the adoption of the internet in the early 2000s. According to ADP Research, 43% of respondents said they used GenAI frequently at work, with heavy users working in technology or information services. Though still relatively new, AI has already made a profound impact on the world of work. HR technology that leverages AI can solve for real practitioner pain points by automating time-consuming tasks, helping ensure compliance, improving accuracy and providing strategic insights. This will give people time back and allow for more meaningful, strategic and creative work. With the introduction and rapid adoption of agentic AI, the dynamic is further evolving. The workforce is relearning the relationship between people and AI technology. Rather than a tool to be used, AI plays the role of a true teammate, capable of complex workflows, aided decision-making and incredible adaptability. HR leaders also have a role in helping companies navigate this transition to reap the full benefits of their "new hire", from both a customer-facing and talent/HR perspective. A report from MIT Sloan Management Review and Boston Consulting Group found that embracing agentic AI "offers possibilities not only to improve cost-efficiency but also to expand revenue, accelerate innovation, compress learning curves, and restructure (internally)". In fact, as agentic AI reaches the mainstream, employers will face a critical inflection point that will likely determine their long-term success. Agentic AI has created a two-way conversation between workers and technology, both growing in tandem; companies must build the structures and systems needed to support and manage this new relationship. With a thoughtful AI strategy, the possibilities are, quite literally, limitless. To understand our path forward, it is important to identify the challenges of this new dynamic. ADP Research data from more than 30,000 survey respondents in the US revealed that people "who use AI on a daily or near-daily basis report the highest levels of engagement, motivation, and commitment to their work". This result is unsurprising, as top performers tend to be early adopters, and proven results would only increase AI use. More surprising are the survey's additional findings. Those same respondents also reported weaker connections to their co-workers and lower productivity. On closer examination, the findings reveal several key insights on people at work. First, people crave connection. The workplace is full of human moments, conversations and interactions that make us feel like we belong. We create communities of shared experience, and if we aren't careful some of that could get lost as workflows are automated or replaced by AI. Second, there is a gap between output and perceived productivity. As AI replaces easy, repetitive tasks, people take on more high-impact, strategic work. The long to-do lists are replaced with work that is more difficult to measure, which can cause an effort-reward imbalance. Easy tasks mean easy wins, and some workers may be feeling that loss. We must create new opportunities to recognize and reward this harder-to-measure work. The question to answer here isn't just "how do we use AI?" - it's "how do we use AI without losing us?" As AI advancement accelerates, companies must redouble their efforts to upskill and reskill their workforce, starting with their leaders. Workers need to understand when, why and how to use AI in their roles, as well as how to challenge, integrate and manage these systems. Companies can use this as an opportunity for intentional connection, hosting "AI office hours" for people to collaborate, swap tips and feel part of something bigger. Leaders should be trained not just on the tools, but on the mindset needed to drive change and adoption, as well as facilitate intentional connection. By investing in continuous learning, mentorship, training, ride-along and leadership development programmes, companies can build a culture that truly learns out loud. It's also important to note that AI isn't just a tool for productivity; it can be used to amplify culture and fulfillment at work, enhancing human creativity, critical thinking and inclusion. As leaders refocus on building a culture of growth, they may consider redesigning their talent strategy, including their performance management and rewards processes. The world of work has reached a pivotal moment. AI is revolutionizing HR technology, changing both how we work and how we feel about work, as well as the customer experience. People who "learn with AI" will gain a distinct advantage over their colleagues, and the gap between early and late adopters will continue to widen. But as we build this AI-powered future, we must keep people at the centre and prioritize ethical and values-based learning. Workers should feel empowered to ask not only, "Can we automate this?" but, "Should we?" Leaders should focus on culture, invest in upskilling and reskilling, and create opportunities for meaningful connection. We are at a pivotal point in building an AI-powered workforce for the future of work. Because the future of work isn't just easy and smart. It's also wonderfully human.

I'm an AI Architect: You're Not Being Replaced -- You're Being Revalued

Careers built on human connection, presence and experience will outlast purely cognitive roles. It was a very 2024 kind of image: a laid-off tech worker reduced to posting his availability on Manhattan streetlights with a QR code connected to his LinkedIn. "I thought that would make me stand out," Glenn Kugelman told the Wall Street Journal. UC Berkeley computer science professor James O'Brien immediately followed up the WSJ investigation with his own story of students with perfect 4.0 GPAs in their major contacting him worried about having zero offers. "Tech degrees no longer guarantee a job," he wrote on LinkedIn, comparing it to the relatively recent days when Berkeley CS graduates received multiple good offers. As a Berkeley grad myself who has worked in finance, crypto and AI, I have mentored students facing these anxieties about where to take their careers. It's pretty clear that what is happening is very different from the boom-bust cycle of previous tech downturns. IT sector unemployment grew from 3.9% to 5.7% in a single month earlier this year, and Mark Zuckerberg has already said AI will replace mid-level engineers in 2025. Yet we have massively overallocated our youth to roles like software engineering and the diagnostic aspects of medicine that AI will replace en masse. This is just the start of a fundamental restructuring of what human labor is actually worth. With that in mind, we have to think about what work retains value when machines can do almost everything. The jobs that will become more resistant to AI displacement need to be valuable, scalable, ethical and what I call "AGI-resistant." The outcome of that last criterion is the hardest to predict. We are potentially years, not decades, from achieving AGI -- artificial general intelligence that can handle virtually any cognitive task -- but the advancements toward that goal are already changing the labor market. I am concerned about a shock where large segments of the population are made unemployed very quickly. I have serious doubts that governments would adapt quickly enough to issue universal basic income, so we need to think proactively about which roles can actually sustain people through this transition. The principle of AGI-resistant work is that there are tasks, roles and performances where humans are simply preferred, regardless of capability. It's present in human chess tournaments, remaining popular due to the jeopardy of error and chance, despite chess bots being definitively stronger. The Olympics limits performance-enhancing drugs to preserve natural human ability. In both cases, we are choosing the human element over optimal performance. This preference also shows up in community work, where compassion and empathy matter and are essential for the emotional and social development of children. And in the service industry, where it's seen as higher status to have human labor over machines. Entertainment sits at one end of this spectrum, where human preference is strongest and most durable. Entertainment has proven resistant to AI alternatives so far, though sentiment can change over time. The category is actually broader than most people assume. It includes the service industry -- restaurants, hospitality and personal training -- where customers pay a premium for human interaction. The durability stems from three interrelated psychological drivers: boredom, loneliness and scarcity. People crave authentic experiences such as live performances, real connection and moments that feel unrepeatable. They also want to belong to communities and feel valued. None of these can be optimally offered by AI because it's the human element itself that is the product. The work is valuable, scalable and offers sufficient ethical examples. This creates an odd inversion of what we have told young people for decades, pushing them toward "practical" fields like STEM degrees, medical school and corporate law. The calculus is shifting. This isn't a small problem we can address gradually. India's youth population -- 371 million people -- faces an unemployment rate of 16%. In the United States, millions of high earners, including software engineers, will find their work replaced. Over the next three to five years, we will see major shifts as software investments reallocate and AI capabilities expand. Any interim solution needs to employ hundreds of thousands, if not millions, of people, and it needs to happen soon. The next wave of innovation lies in the physical world. The U.S. administration should institute a new New Deal, employing millions of Americans to develop bleeding-edge public infrastructure. This could take two forms: All this work would provide large-scale employment while we navigate toward the longer-term reality where entertainment and human-centered service work become the economic foundation. Students should think differently about skill-building. Knowing what I know now, I would have encouraged my college self to take more probability and statistics classes. Today, quantitative literacy pairs well with the human-centered skills that will only become more relevant like understanding psychology, creating experiences people want and building genuine community. I don't have all the answers, but no one does. Those Berkeley students with 4.0 GPAs and zero offers deserve better than false promises about "practical" degrees. We cannot keep preparing young people for careers that won't exist in five years. In my view, it's wise for us to institute the relevant legislation and large-scale solutions to incentivize a shift toward roles that are sustainable in the medium-term. I'm interested in hearing feedback and connecting with others who are working on this problem because this conversation needs to happen now, before the shock hits.

The great AI job panic may be overblown: Report

While AI is reshaping the landscape of work, it hasn't completely disrupted the job market just yet. Companies are cautiously exploring the potential of AI, and the fear of massive job loss remains largely unfounded. "Beware of AI. AI is coming for your jobs." At least, that's the popular narrative. Ever since ChatGPT burst onto the scene in late 2022, stories about companies quietly replacing entry-level jobs with algorithms have piled up. But according to a new Oxford Economics briefing, the evidence for an AI-led labour market shake-up is, for now, surprisingly thin. That's not to say AI isn't changing how companies work. It clearly is. Businesses are experimenting, reallocating budgets and figuring out where the tech can cut costs or speed things up. Amazon, in late 2025, reportedly began its process to replace 600,000 humans with robots by 2027. Indian IT giant Tata Consultancy Services' CEO K. Krithivasan, in 2024, told the Financial Times that AI will soon kill India's $48.9 billion IT and business process outsourcing industry. Meanwhile, McKinsey & Co. cut about 200 global tech jobs in November 2025 as it shifted more responsibilities to AI. However, Oxford Economics is diving deeper into this trend, asking if this has translated into a meaningful rise in unemployment? The short answer: not really. Take graduates, the group most often said to be under threat. In the US, unemployment among recent graduates rose from 3.9% when ChatGPT launched to a peak of 5.5% in early 2025. This was fuel for claims that AI was eating entry-level jobs. But the The Oxford Economics briefing suggests that rate has since fallen back, and the broader trend looks fairly mild by historical standards. In past downturns, graduate unemployment has almost always risen faster than overall joblessness. This time doesn't look especially different. Zooming out globally tells a similar story. Countries with the biggest jumps in graduate unemployment are also the ones where overall unemployment has risen the most. Places like Japan and South Korea, where labour markets have stayed tight, haven't seen the same graduate pain. That points more to old-fashioned economic cycles than a shiny new AI shock. Also Read: AI-led hiring lifted overall recruitment in 2025: Foundit Insights Tracker What about sectors that are supposedly most "automatable"? Here, too, the picture is nuanced. Some industries with higher AI adoption have seen bigger rises in unemployment. But that doesn't automatically mean workers are being replaced by machines. In many cases, firms appear to be diverting money into AI experiments and trimming other costs -- like hiring -- rather than directly swapping people for software. There's also a simple reality check: if AI were already replacing workers at scale, productivity should be booming. Fewer people, same output, right? Instead, productivity growth in the US and other advanced economies has cooled recently, not surged, the report noted. That suggests AI is still mostly in its trial phase -- interesting, promising, but not yet transformative at the economy-wide level. The raw layoff numbers back this up. AI-related job cuts in the US did rise sharply in 2025, but they still accounted for just 4.5% of total reported layoffs. Job losses blamed on "market and economic conditions" were more than four times higher. In a labour market where up to 1.8 million Americans lose a job every month, AI remains a relatively small player. Bloomberg Intelligence analysts Stuart Gordon and Evgeniy Batchvarov also noted that workers are showing "limited appetite" to switch roles amid geopolitical and economic uncertainty. "Permanent staffing faces a tough 2026 as the segment remains under pressure," they added. Also Read: Is AI helping corporates or taking your job? 6 key takeaways from McKinsey's 2025 AI report Another under-discussed factor is that there are simply more graduates around. In the US and Europe, university-educated young people make up a much larger share of the population than they did a few years ago. When more graduates chase jobs at a time when hiring cools even slightly, unemployment can rise without any help from AI at all. None of this means disruption isn't coming. Oxford Economics is clear that faster AI adoption could still lead to sharper job losses down the line. But that outcome isn't inevitable -- and some early signs suggest the hype may be outrunning reality. Swedish fintech company Klarna, for instance, has already walked back hundreds of AI-linked job cuts after customer service suffered. Surveys in the US hint that AI adoption among big firms may even be plateauing. The takeaway is simply that while AI is changing workplaces, it hasn't yet upended the labour market. For now, the robots are still mostly in the testing phase -- impressive in demos, useful in pockets, but far from staging a full-blown jobs coup.

How businesses can prevent a lost generation in the era of AI

That also presents a risk for businesses, who should take action now to secure their future workforces. Artificial intelligence (AI) is changing the way we work at an unprecedented speed. By 2030, nearly 1.1 billion jobs could be reshaped as technology and global trends open new possibilities across the labour market. Every organization is navigating this transformation, rethinking roles, workflows and strategies in real time. Handled well, this shift has the potential not only to reshape work, but to strengthen how early careers are built and supported. As this shift accelerates, employers have a clear opportunity and responsibility: to help early-career professionals become, and remain, employable. This is especially urgent for a generation whose early work and educational experiences were disrupted by the COVID-19 pandemic. Many young professionals spent critical years in isolation, missing out on in-person learning, mentorship and exposure to complex environments. Now, as organizations contemplate automating entry-level tasks and integrating AI into workflows, important choices come into focus. Efficiency gains are tempting, but the impact on those just starting out is often overlooked. And when early-career pathways disappear, industries lose the pipeline of skilled professionals needed for long-term growth. If automation advances without careful oversight, we could see the emergence of a "lost generation" of early-career talent - an outcome that can be avoided with the right leadership focus and design choices. Early signs of strain are already visible and present a warning sign and a clear moment for action. In the US, unemployment among early-career talent aged 22-27 stands at 7.1%, about three points higher than the overall workforce. This gap is fueling distress among new graduates; 19% of the class of 2026 report feeling "very pessimistic" about the job market. Together, these signals underline why early-career development needs to be more deliberate right now. The challenges facing early-career professionals today are compounded. The pandemic upended experiences that built confidence and judgment. Now, AI threatens to add new pressures on job prospects, mental health and wellbeing. Employers and leaders have the chance to reset how we support this generation, because when we do, we will strengthen both their careers as well as our economy's growth and resilience. Entry-level roles are where many of us learned the ropes, built confidence and found mentors who shaped our careers. When these roles are automated or removed without thoughtful redesign, organizations risk losing the talent who bring energy to teams and ideas that fuel innovation. Longer-term, consequences can include slower skill-building, thinner leadership benches and reduced adaptability. In addition, the absence of a strong pipeline of young talent often forces organizations to rely on hiring experienced employees at a premium, significantly elevating labour costs over time. Early-career erosion also risks undermining overall workforce trust in AI, which stands at 46%. When early-career roles are automated or eliminated, AI is more likely to be perceived as a threat - a view held by 59% of young Americans - rather than a tool for development. When early-career talent is asked to adapt to powerful technologies without confidence or support, trust in both AI systems and leadership decisions weakens. Aon's Resilience Quotient, a collaboration between Aon and Gallup, is yet more evidence of this risk, linking workforce disruption to gaps between the speed of AI adoption and workforce readiness. For entry-level employees still forming their professional identity and judgment, these gaps heighten uncertainty and slow adoption, creating a source of organizational risk that should be an organizational advantage. Organizations that aspire to lead in the age of AI must move beyond the false choice between automating entry-level work and preserving traditional roles at the expense of innovation. The real imperative is to redesign entry-level roles and modernize early-career pathways in ways that strengthen long-term talent pipelines. This means separating routine tasks from development opportunities -- automating repetitive work while preserving and enhancing essential early-career learning. To truly lead, organizations must also re-center early-career development on the human skills that are core to AI-enabled work. As AI takes on more transactional activity, it is essential to emphasize analytical thinking, ethical reasoning, collaboration and accountability. Providing clear guidance on responsible AI use in real-world contexts reinforces trust and ensures that human oversight remains central to the adoption of new technologies. Finally, leadership in this space requires investing in mindsets. Prioritizing and rewarding learning agility, curiosity and adaptability -- traits that are among the strongest predictors of successful AI adoption -- helps create a culture where early-career talent see change as an opportunity for growth. Supporting mental health and wellbeing must also be part of this equation. The pandemic showed us that when organizations prioritize connection, flexibility and access to support, early-career talent is better able to adapt and thrive, even in times of uncertainty. When approached with care, AI can be a catalyst for a broader reset -- one that not only transforms work but also places wellbeing and human connection at the centre of the future workplace. The choices made today will determine whether AI becomes a catalyst for stronger talent pipelines or a force that slows workforce development and long-term economic resilience. In the age of AI, the future of work depends on more than technology. Early-career pathways need to support growth and opportunity. Organizations that invest in reshaping roles and developing talent alongside innovation will be better positioned to sustain growth, resilience and trust, strengthening both their workforce and the broader economy.

Software developers are the vanguard of how AI is redefining work

Self-directed AI upskilling, rather than topdown learning, is fast becoming a key talent strategy in the new era. If we want to understand the future of work, we should look at the people closest to technological change. In 2025, four in 10 developers said AI had already expanded their career opportunities, and close to seven in 10 expect their role to change even further in 2026. Software developers are becoming the first truly AI-native workforce, and they show how every knowledge worker will evolve. Their response has been adaptive rather than defensive; an early lesson in how workers can thrive as responsibilities shift faster than job descriptions. This preparedness matters because work is changing faster than organizations are prepared for. 22% of global jobs are expected to undergo a structural labour-market transformation within five years, driven largely by AI, a pace that outstrips traditional reskilling systems. At the same time, emerging economies will see nearly 800 million young people enter the workforce over the next decade, intensifying the need for accessible paths into AI-aligned work. When rapid job transformation meets massive demographic pressure, the stakes rise: Underemployment, widening inequality and deepening talent gaps at the exact moment when economies can least absorb them. Our observations across more than 400 active projects, the Dev Barometer - surveying more than 1,600 developers in 63 countries in 2025 - and the talent data drawn from more than 2 million yearly applications, all point to the same workforce pivot. Upon embracing AI, developers are already reshaping their roles, skills and value. They may be the earliest signal of how knowledge workers everywhere will adapt next. When AI tools began to mature, developers were expected to be among the first professions disrupted. Instead, 37% say AI has already expanded their career opportunities, a sign of how technology is redefining their work. This evolution is heavily supported by AI, which is strengthening their technical skills and improving both the speed and quality of their work. Rather than automating them out of relevance, AI is shifting their focus toward higher-level problem-solving and design. It demonstrates how AI can elevate knowledge work more broadly. Given this shift, 65% of developers expect their role to be redefined in 2026, moving from routine coding toward architecture, integration and AI-enabled decision-making. And this evolution is not unique to technical roles. Across HR, finance, marketing and operations, AI is already absorbing repetitive, rule-based tasks. It frees professionals to focus on judgement, oversight and strategic direction. Just as developers are moving toward architecture and solution design, other roles are evolving from operators into strategic decision-makers. For leaders, this is the business signal that matters. Teams that embrace AI as a capability-multiplier are climbing the value chain faster and turning that shift into measurable performance gains. Upskilling is often framed as a top-down exercise, with companies diagnosing gaps and designing programmes. Developers reversed that logic. As AI reshapes their work, they're turning to fast, practical, collaborative self-directed learning, and 65% worry about falling behind without it. Most now assess their own gaps and learn through hands-on experimentation or online tutorials, dedicating weekly time to building new capabilities. One engineer said AI helped him level up from junior to near-senior JavaScript skills in just two months - a process that would have taken a company months to formalize. A third of developers (33%) rank GenAI and AI/ML as their top learning priorities for 2026, reflecting a clear shift toward AI-driven roles. But as automation expands, judgement, collaboration and leadership become just as essential. In our teams, Python developers have become AI engineers and backend developers have stepped into AI lead roles, actively identifying and closing their own gaps. This bottom-up adaptability won't remain a developer phenomenon. They're simply the first to show that self-directed learning, rapid skill cycles and AI literacy are now prerequisites for every skilled worker in an AI-driven world. Companies must enable training, supporting peer-led communities that scale expertise internally. This turns individual drive into a collective asset. When employees have the space to learn and teach, self-directed upskilling becomes your strongest talent strategy. Latin America shows what happens when adaptable talent meets accessible AI tools: Geographic barriers become irrelevant, opportunity expands, and countries challenge long-standing productivity barriers. Despite structural limitations, LATAM tech talent is demonstrating how AI-enabled skills create new paths to growth. For many Latin American engineers, remote work unlocked participation in the global digital economy. In our survey, 78% said remote work made their tech career possible because it opened access to global opportunities regardless of geography. AI is amplifying that access. Public models, online tools and accessible training now help workers in emerging economies build in-demand skills quickly. Across Latin America, developers are upskilling faster, taking on higher-value work and often reshaping their communities' economic trajectory. As one developer from El Salvador told us: "Developing countries don't need big infrastructure to leverage AI. With open-source tools, anyone can learn to build effective solutions." It's an approach that emerging markets everywhere can use, making LATAM a template for how adaptable talent can move fast with accessible AI tools. Beyond the noise of breathless AI predictions, a more grounded story is unfolding: Workers are already reshaping their roles and creating new forms of value. Software developers in particular are showing what an adaptive, AI-ready workforce looks like. They're moving toward higher-value work, building new skills quickly and treating change as part of the job. Across hundreds of projects, we're seeing that organizations that empower this behaviour adapt faster, experiment more freely and match talent to strategic priorities much earlier. The lesson they offer leaders everywhere is simple. The future of work will be shaped by people who, instead of reacting to change, anticipate it by building skills, perspective and judgement at a pace that keeps them ahead of the curve.

AI has already added 1.3 million jobs, LinkedIn data says

Rather than costing jobs, AI has been a growth area, creating 1.3 million new roles. Contrary to popular narratives, the labour market hasn't retreated in the way many headlines suggest. In fact, we're seeing the labour market rotate toward a new era of work. A new report on the global labour market from LinkedIn, titled Building a Future of Work That Works, shows we are seeing a slowdown in global hiring, hanging nearly 20% below pre-pandemic levels, largely due to economic uncertainty and monetary policy shifts. Ongoing sluggish hiring set up a labour market dynamic characterized by a restless, underutilized global workforce as just over half (52%) of people say they're job hunting in 2026, while nearly 80% feel unprepared to find a new job. At the same time, the data points to labour market growth rotating toward the current wave of AI investment. While there is evidence that AI is impacting jobs in small pockets, it's also creating demand at scale - including more than 600,000 new, AI-enabled data centre jobs and 1.3 million new roles like AI Engineers, Forward-Deployed Engineers and Data Annotators. Additionally, AI Engineer is one of the fastest‑growing jobs on LinkedIn over the past three years, reflecting sustained demand for AI-centric roles ranging from Directors of AI to Machine Learning Researchers. This mix of uneven hiring and AI-driven job creation marks the emergence of the new-collar era, a workforce that blends knowledge work, advanced technical skills and distinctly human strengths. The message for business leaders is clear: macro volatility isn't eliminating opportunity - it's shifting it. As skills evolve and new roles continue to emerge, here are some of the key trends shaping the labour market in 2026: According to LinkedIn's Executive Confidence Index, business‑leader confidence continues to fall across advanced economies, creating a tense backdrop for a workforce that feels restless and underutilized. According to our data, the pressure is concentrated in advanced economies, where hiring remains 20-35% below pre-pandemic levels, with emerging markets like India (+40%) and UAE (+37%) showing continued momentum. However, despite widespread assumptions, AI isn't driving the hiring slowdown - economic uncertainty, monetary policy shifts and post-pandemic hiring rebalancing are the biggest culprits. In fact, outside of clinical healthcare roles, hiring patterns look the same for jobs with high AI exposure and those with low exposure. These trends also hold true for the hiring slowdown seen among entry level workers. While we've seen a decline in the share of entry level roles over the last three years, the current rates have largely returned to historical norms. These labour market dynamics have placed leaders in a unique position: job seekers are outpacing openings at the highest level since the pandemic, making this an ideal moment to rethink talent strategies and use AI‑powered tools to accelerate hiring and build pipelines for critical new roles. With AI embedded in nearly every job function, the skills landscape is transforming quickly, driving a 70% year‑over‑year increase in US roles that require AI literacy. And this demand is expanding well beyond employer expectations; 53% of US employees said they plan to proactively learn new AI skills within the next six months and 48% believe these skills will help them grow in their career. We're seeing similar increases across LinkedIn with a 92% year-over-year increase in the share of learning time spent watching AI-related courses and a 66% year-over-year increase in posts on our platform about AI-related topics. As AI literacy is now a baseline requirement across many roles, employers are placing even greater value on human capabilities like empathy and personal connection. The real advantage comes from a workforce that blends AI fluency with these uniquely human strengths. To be successful in the current labour market, leaders should make upskilling a core part of their talent strategy to help employees develop the skills needed to thrive in an AI-enabled world of work. AI is accelerating the rise of new‑collar work: the global economy added 1.3 million new AI‑related jobs in just two years, while demand for AI Engineers and data‑centric roles continues to dominate hiring. As demand for AI-engineers continues, it's fuelling new cross-border competition for this talent as well as highly global teams. Furthermore, the surge in Head of AI positions across Australia, Canada, India, Germany, the UK and the US reflects a decisive move toward embedded AI strategy and leadership. These roles require a blend of technical fluency and human adaptability - and they are fast becoming essential infrastructure for competitive organizations. This labour market shift is mirrored in worker behavior. Across major economies, more than half of professionals now prefer trade‑based paths over corporate jobs. Among Gen Z, this preference is even stronger, with nearly 60% viewing technical trades as more meaningful career options. Combined, these data points show a workforce pivoting toward hands-on, skill‑based work as AI continues to redefine job creation and job choice. When you look at the data holistically, four focus areas for leaders emerge: 1. Even in a slower hiring market, competition for business-critical roles will continue to grow as the new-collar era takes shape. As a result, leaders should rethink how they find and deploy talent by expanding candidate pools, creating global teams and using new AI-powered tools. 2. As resumes become easier to embellish and skills evolve rapidly, leaders must verify real capability beyond titles and tenure to assess role fit. 3. At the same time, new AI-driven roles are forming, opening up broader pathways to economic opportunity and making change management essential for executives to lead their teams as they adapt to the evolving world of work. 4. Underpinning it all is upskilling: digital and data literacy are now foundational across nearly every function, and establishing AI fluency as a core competency will be critical to building resilient, competitive organizations. For leaders, acting now by upskilling employees and equipping them with the right AI tools to succeed in the new-collar era is how we build a resilient workforce ready for what's next.