SPECIAL FEATURE | The new calculator at work: What AI means for jobs, for people
AI is not destiny. It is a tool whose social impact will be shaped by deliberate choices.
Artificial intelligence has moved from the margins of tech labs to the center of workplaces everywhere. For some businesses, it is a productivity multiplier; for many workers, it is a source of anxiety. Employers and economists now offer divergent readings of the same phenomenon. AI will create new kinds of work and enhance many existing roles, they say, yet it will also render certain tasks — and in some cases whole jobs — redundant. That tension is not theoretical. Recent industry and intergovernmental studies show both sizable job creation and significant displacement across sectors and countries.
The World Economic Forum’s Future of Jobs 2025 report, drawing on the plans of more than 1,000 large employers, forecasts that new technologies will create the equivalent of roughly 14% of today’s employment even as tens of millions of roles face displacement; the net picture is one of job growth, but only after large and painful shifts in job tasks and skills. The International Labour Organization’s recent work similarly refines which occupations are most exposed to generative AI and stresses that exposure is not the same as inevitable job loss — but it is a clear warning that many tasks can be automated.
How AI reshapes work, not just jobs
Why the split view? The answer lies in how AI changes work rather than just jobs. When a company deploys a generative AI tool, it rarely swaps human beings for silicon in a single transaction. Instead, it changes the task mix of existing roles: research assistants become report-drafting facilitators, customer service agents become supervisors of AI chatflows, and junior analysts become curators of model outputs. Those shifts can raise productivity and create higher-value roles — but only for workers who can move into them. Employers that adopt AI often report growth, innovation and even hiring in new areas, while the distribution of those gains can be uneven.
“AI will not replace you. But you will be replaced by someone who knows how to use AI.” That aphorism — blunt, imperfect and widely repeated — captures a practical truth of the transition. The most immediate advantage in an AI-enabled economy goes to people who combine domain expertise with AI fluency. Employers increasingly prize workers who can orchestrate models, check outputs for bias and error, and translate a machine’s suggestions into business decisions. Analyses of job postings already show rising demand for AI-related skills across sectors, from marketing and product to legal and finance.
At the same time, broad claims that “AI will create more jobs than it destroys” obscure real distributional risks. Regional studies by the ILO and World Bank estimate several percentage points of potential job elimination in full automation scenarios, with younger workers and women in formal sectors particularly vulnerable. Even if economies add jobs overall, whole communities, age cohorts or industries can still experience net losses. Entry-level positions in customer service, junior administrative work and routine data entry are especially exposed — the very roles that traditionally serve as gateways into longer careers.
Who benefits, who loses, and what choices matter
The second aphorism — “Any job that requires working in front of a computer screen will be replaced by AI” — is both alarmist and partially grounded. Not every screen-based job will disappear, but many cognitive and clerical tasks performed on screens are highly automatable. Pattern recognition, drafting text, summarizing and routine analysis are precisely the tasks generative models do well. Yet historical evidence and newer studies suggest employers often restructure work to complement AI rather than eliminate workers outright, at least initially. That creates a window for reskilling, though it may be brief for some.
What determines whether this transition is manageable or catastrophic is policy and firm strategy. International institutions emphasize active labor-market measures such as targeted reskilling, portable benefits for nonstandard workers, stronger unemployment insurance and tax policies that help share gains from AI-driven productivity. Occupation-level tracking of AI exposure and sustained social dialogue are critical so training and placement efforts match real demand.
Businesses also play a decisive role. Firms that report better outcomes from AI adoption typically pair technology deployment with human-centered implementation: clear role redesign, on-the-job training and incentives that encourage human-AI collaboration. When AI amplifies existing workers’ productivity, firms tend to grow and add roles in higher-value areas. When AI is used mainly to substitute routine labor without redeployment strategies, layoffs become more likely.
For workers, the prescription is increasingly clear. Building “T-shaped” skills — deep domain expertise paired with practical AI and data literacy — offers the best protection against displacement. For managers, auditing roles to distinguish automatable tasks from those requiring judgment is essential. Governments, meanwhile, must lower the cost of retraining and make it easier for firms, especially small and medium enterprises, to invest in people rather than treat labor as expendable.
For the Philippines and Southeast Asia, the stakes are high. Many economies remain reliant on routine and informal work, while training systems struggle to keep pace with technological change. International evidence shows that local policy choices matter. Targeted subsidies, sector-specific training programs and stronger links between employers and educators can soften the shock and widen access to emerging opportunities.
AI is not destiny. It is a tool whose social impact will be shaped by deliberate choices. The evidence points to faster-changing work, uneven gains and real risks of exclusion. Whether AI ultimately expands opportunity or deepens inequality depends less on algorithms than on how societies prepare workers to live alongside them — and how willing institutions are to invest in people, not just machines.
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