For all the breathless headlines about robots replacing workers, the reality of automation—and its effect on labor—unfolds in slow, uneven waves. It isn’t a matter of robots suddenly flooding every shop floor, but a patchwork of change that plays out sector by sector, year by year. If you want to see what’s really happening beneath the surface, you need to get specific, and that’s where the ISIC codes come into their own.
ISIC codes give us the language to talk about which slices of the economy are most exposed. Take ISIC 2811, for the manufacture of engines and turbines—prime territory for robotics and machine tools. Or ISIC 6201, covering computer programming and consultancy, where “automation” looks less like metal arms and more like code that writes itself or platforms that replace human middlemen. With this structure, it becomes possible to move beyond generalizations and actually see where automation is advancing and how it’s affecting real people.
The approach starts with data collection. Over the last decade, industry associations, technology vendors, and national statistical offices have all tracked robotics adoption in manufacturing, as well as software automation in services. Linking this adoption data to ISIC-coded sectors lets analysts spot which areas are leading the way. Sometimes, the results align with intuition—automotive plants or electronics assembly. Other times, they reveal quieter revolutions, like in logistics, agriculture, or back-office finance.
But measuring robots alone is not enough. The bigger question is what happens to jobs and wages as automation ramps up. For that, you need to correlate robotics adoption with employment and wage trends by ISIC code, not just at a national level, but as granularly as the data allows. Some sectors see jobs disappear, others shift or specialize, and still others actually add employment as productivity boosts output or new roles emerge to support and maintain the machines.
Patterns can be surprising. In some manufacturing segments, a surge in robotics corresponds to falling headcount but rising average wages—a sign that routine tasks are being automated while skilled roles become more valuable. In sectors like ISIC 6201, employment may grow even as automation expands, because the demand for digital skills outpaces what automation can replace. The story isn’t simple, and it rarely fits a single narrative of job loss or gain.
One concrete example: in a recent analysis of ISIC 2811, the number of robots per 1,000 workers doubled in just five years, but total employment dropped only slightly, while the share of technical and supervisory jobs increased. Meanwhile, ISIC 6201 saw a proliferation of automated testing and deployment tools, yet the total number of programmers continued to rise, driven by the relentless need for new applications and services.
Of course, data is never perfect. Company-level reporting can lag, sector definitions evolve, and many forms of automation—especially software—don’t get neatly tracked in the same way as hardware. Analysts have to be careful, triangulating findings with industry surveys, wage data, and, when possible, interviews with firms and workers.
But even with these limitations, the exercise matters. Mapping automation’s impacts through the discipline of ISIC coding brings much-needed clarity. It makes it possible for policymakers, educators, and businesses to focus reskilling programs where they’re actually needed, to anticipate which communities might be at risk, and to think creatively about where the next wave of opportunity might emerge.
In the end, the story of automation isn’t one of inevitability, but of choices—made more visible when we take the time to see the data in all its sectoral specificity. ISIC codes, though never perfect, offer the scaffolding for that vision, turning vague anxieties about the future of work into something that can be measured, debated, and, ultimately, managed.