By
Stuart Kerr, Technology Correspondent,
LiveAIWire
A measurable AI workplace divide is opening in the global workforce, and the line runs not between industries but within them. Workers in the same organisation, performing related functions, are experiencing opposite trajectories depending on one variable: whether their role has been augmented by AI tools or targeted for automation by them.
The World Economic Forum’s Future of Jobs Report 2025 found that 170 million new roles were expected to be created by AI-related demand through 2030, while 92 million existing positions were expected to be displaced, producing a net gain of 78 million jobs globally. That headline figure is frequently cited to argue that AI creates more work than it eliminates.
What it conceals is more instructive: the new roles and the displaced roles are not in the same sectors, do not require the same skills and are not accessible to the same workers. The AI workplace divide is not primarily between people who have jobs and people who do not. It is between people whose skills are becoming more valuable and people whose skills are being repriced downward faster than they can adapt.
Where the AI Workplace Divide Is Already Visible
The clearest current evidence complicates the assumption that generative AI simply favours the already-skilled. Research from MIT Sloan’s Institute for Work and Employment Research found the opposite pattern in at least one important respect: generative AI tools tend to act as a skill leveller, delivering the largest performance gains to previously lower-performing workers rather than to top performers, which could compress wage differences within a given role over time. The mechanism researchers point to is that AI complements the parts of a job that inexperienced workers struggle with most, while experienced top performers already possessed much of what the tool supplies.
That finding does not mean AI is a straightforward equaliser. The same MIT research notes real countervailing risks: some firms will prove far more adept than others at deploying AI, widening gaps between companies and their respective workforces, and increased workplace surveillance enabled by AI could tie wages more tightly to individual output in ways that increase precarity for some workers even as within-role skill gaps narrow. The AI workplace divide, in other words, may run less along the high-skill-versus-mid-skill line the popular narrative assumes, and more along which employers and which workers get access to well-designed AI tools in the first place.
The Geography of the Divide
The AI workplace divide has a geography that public discussion has substantially understated. Within wealthy economies, the divide maps onto educational attainment and urban versus non-urban labour markets. Across economies, it maps onto the existing distribution of digital infrastructure, AI investment and regulatory frameworks. Countries that have invested heavily in digital infrastructure, technical education and adaptable labour market policies are seeing AI generate measurable productivity gains and new high-value role categories. Countries that have not are seeing automation of existing work without the compensating creation of new work, because the investment conditions and skill supply required to generate AI-adjacent roles do not exist at the necessary scale.
The WEF Future of Jobs Report identified a growing skills mismatch as one of the primary drivers of this geographic and demographic polarisation. The skills most in demand by employers deploying AI, which include analytical reasoning, complex problem-solving, creative synthesis and the interpersonal capabilities required to work alongside and oversee AI systems, are not the skills concentrated in the workers most exposed to displacement risk. Closing that mismatch requires investment in education and reskilling at a scale and speed that most institutions and governments are not currently delivering.
What Changes Inside Organisations
Within individual organisations, the AI workplace divide is reshaping reporting structures, performance expectations and compensation decisions in ways that are not yet fully visible in aggregate labour market data. Roles that were previously defined by the volume of information they processed are being redefined around the quality of judgement applied to AI-generated outputs. That redefinition benefits workers who have developed strong contextual and evaluative skills and disfavours workers who were compensated primarily for speed and volume of throughput.
The organisations navigating this transition most effectively are those that have been deliberate about three things: identifying which roles are being augmented versus which are being automated, investing in reskilling for the workers in the second category before the displacement becomes terminal and restructuring compensation to reflect the new value distribution rather than preserving legacy pay structures that no longer map to actual contribution. Most organisations have not yet been this deliberate. The pressure to realise efficiency gains quickly is creating a pattern of fast deployment and slow reskilling that concentrates the benefits of AI in existing high-value roles and concentrates the costs in already-vulnerable positions.
For workers trying to understand where they sit in this reshaping, our analysis of what the 2025 and 2026 employment data actually shows about AI and job displacement examines the evidence more precisely than the broad figures usually cited in public debate. The pattern that emerges is one of accelerating divergence rather than uniform disruption: some roles becoming dramatically more productive and valuable, others becoming progressively less so, at a pace that makes the reskilling window shorter than most workers or their employers have assumed.
The Skills That Will Matter
The most durable finding from the employment research literature on AI and work is that the skills most resistant to automation are the ones that require situational judgement, emotional intelligence, creative synthesis and the ability to navigate novel problems that do not fit patterns from past data. These are not skills that most educational systems teach explicitly or that most employers have invested heavily in developing. They are skills that workers typically develop through sustained exposure to complex, ambiguous work, which is precisely the kind of work that AI is being deployed to assist with rather than replace.
The practical implication is counterintuitive: the most effective preparation for an AI-saturated workplace is not learning to operate AI tools, though that is useful, but developing the contextual and evaluative capabilities that AI tools require human oversight to perform. Workers who understand the domain their AI tools are operating in well enough to catch errors, assess outputs and make judgements that the tool cannot make are positioned very differently from workers who are proficient at using the tools but cannot independently evaluate what they produce.
The organisations and educational systems that understand this distinction will produce workers who are positioned to benefit from AI augmentation. Those that treat AI literacy primarily as tool proficiency will produce workers who are efficiently replaceable by the tools they have been taught to use. Our piece on how AI is simultaneously helping and hurting workers in ways that depend on how it is deployed examines the specific conditions under which augmentation and automation diverge in practice. The AI workplace divide is not predetermined. It is the outcome of decisions being made now by employers, policymakers and workers about which direction to push it. For a sharper view of the institutional obligations involved, our piece on what genuine AI bias guardrails require in practice covers the accountability infrastructure that makes AI deployment equitable rather than simply efficient.
What Comes Next
The AI workplace divide will not close automatically as the technology matures. The evidence from earlier waves of automation suggests the opposite: early divergence tends to become entrenched as capital flows to the roles and sectors where productivity gains are realised and away from the roles and sectors where displacement is occurring. Without deliberate policy and organisational intervention, the divide will harden into a structural feature of labour markets in ways that prove very difficult to reverse.
The interventions that have historically narrowed technology-driven workplace divides share common features: they invest in skills before displacement becomes irreversible rather than after, they distribute the productivity gains from technology broadly enough to sustain consumer demand and political legitimacy, and they adjust the institutional rules governing work fast enough to reflect new value distributions rather than preserving legacy arrangements that no longer match economic reality. All three conditions are currently absent or partial in most major economies. That is the most honest description of where the AI workplace divide stands: visible, widening and not yet addressed with the seriousness its trajectory warrants.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity, and the social impact of emerging technology. He publishes daily at LiveAIWire.com.