{"id":6369,"date":"2026-07-21T15:28:41","date_gmt":"2026-07-21T15:28:41","guid":{"rendered":"https:\/\/ceo.com.pl\/en\/?p=6369"},"modified":"2026-07-21T15:52:57","modified_gmt":"2026-07-21T15:52:57","slug":"more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568","status":"publish","type":"post","link":"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/","title":{"rendered":"More Than 5 Million Polish Jobs Are Exposed to Generative AI"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">According to the most comprehensive study to date, by NASK and the International Labour Organization (ILO), 30.3 percent of jobs in Poland \u2013 about 5.08 million positions \u2013 show some degree of exposure to automation or transformation driven by generative AI. The authors are explicit: this is not a forecast that these jobs will disappear, but that their task content will change. A narrower group of 817,500 positions carries genuinely high exposure.<\/p>\n\n\n\n<div class=\"aig26en-article\">\n<style>\n.aig26en-article{--navy:#131F49;--amber:#e67a2d;--bg:#f7f8fb;font-family:Georgia,'Times New Roman',serif;color:#232323;line-height:1.68;max-width:900px;margin:0 auto;}\n.aig26en-article h2{color:var(--navy);font-family:Arial,Helvetica,sans-serif;font-size:28px;font-weight:800;margin:36px 0 14px;border-left:6px solid var(--amber);padding-left:14px;}\n.aig26en-article h3{color:var(--navy);font-family:Arial,Helvetica,sans-serif;font-size:21px;font-weight:700;margin:28px 0 10px;}\n.aig26en-article h4{color:var(--amber);font-family:Arial,Helvetica,sans-serif;font-size:17px;font-weight:700;margin:20px 0 8px;}\n.aig26en-article p{font-size:17px;margin:0 0 16px;}\n.aig26en-article .aig26en-headline{font-family:Arial,Helvetica,sans-serif;font-size:32px;font-weight:900;color:var(--navy);line-height:1.2;margin:0 0 10px;}\n.aig26en-article .aig26en-lead{font-size:19px;font-weight:600;color:#3a3a3a;margin:0 0 18px;}\n.aig26en-article .aig26en-dateline{display:inline-block;background:var(--navy);color:#ffffff !important;font-family:Arial,Helvetica,sans-serif;font-size:13px;font-weight:700;letter-spacing:.02em;padding:6px 14px;border-radius:3px;margin:0 0 22px;}\n.aig26en-article .aig26en-callout{background:var(--navy);border-radius:8px;padding:22px 26px;margin:22px 0;}\n.aig26en-article .aig26en-callout *{color:#ffffff !important;}\n.aig26en-article .aig26en-callout h4{color:var(--amber) !important;margin-top:0;}\n.aig26en-article .aig26en-callout p{font-size:16px;margin-bottom:8px;}\n.aig26en-article table{width:100%;border-collapse:collapse;margin:18px 0 26px;font-family:Arial,Helvetica,sans-serif;font-size:15px;}\n.aig26en-article th{background:var(--navy);color:#ffffff !important;text-align:left;padding:11px 14px;font-weight:700;}\n.aig26en-article td{padding:10px 14px;border-bottom:1px solid #e0e2ea;}\n.aig26en-article tr:nth-child(even) td{background:var(--bg);}\n.aig26en-article .aig26en-chartbox{background:#ffffff;border:1px solid #e0e2ea;border-radius:8px;padding:20px;margin:20px 0 28px;}\n.aig26en-article .aig26en-chartbox canvas{max-height:340px;}\n.aig26en-article .aig26en-caption{font-family:Arial,Helvetica,sans-serif;font-size:13px;color:#6a6a6a;text-align:center;margin-top:10px;}\n.aig26en-article .aig26en-flag{font-weight:700;color:var(--navy);}\n.aig26en-article .aig26en-note{font-family:Arial,Helvetica,sans-serif;font-size:14px;color:#5a5a5a;background:var(--bg);border-left:4px solid var(--amber);padding:12px 16px;margin:16px 0;}\n.aig26en-article .aig26en-sources{font-family:Arial,Helvetica,sans-serif;font-size:13px;color:#6a6a6a;border-top:2px solid var(--navy);padding-top:14px;margin-top:34px;}\n.aig26en-article .aig26en-grid2{display:grid;grid-template-columns:1fr 1fr;gap:20px;}\n@media (max-width:640px){.aig26en-article .aig26en-grid2{grid-template-columns:1fr;}}\n<\/style>\n\n<div class=\"aig26en-headline\">5.08 Million Jobs in Poland Are Exposed to GenAI. These Occupations Face the Biggest Changes<\/div>\n<div class=\"aig26en-dateline\">COMPILED: JULY 20, 2026 \u00b7 OCCUPATIONAL EXPOSURE DATA: NASK\u2013ILO, 2025 \u00b7 EMPLOYMENT AND UNEMPLOYMENT DATA: GUS\/LFS, 2024\u20132026<\/div>\n\n<p>In 2025, Poland received its most detailed assessment yet of how generative artificial intelligence may affect employment. NASK \u2013 Poland&#8217;s National Research Institute \u2013 together with the International Labour Organization (ILO), estimated how many jobs in Poland are exposed to GenAI, using the ILO\u2013NASK index based on task-level analysis of 427 occupations under the ISCO-08 classification. At the same time, humanoid robots in China are moving from demonstrations to early production and warehouse deployments, while the EU AI Act is introducing phased obligations for transparency, governance and human oversight of high-risk systems. Below is a breakdown of the numbers, timelines and mechanisms \u2013 based on published data and sourced analysis.<\/p>\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#1_How_many_jobs_in_Poland_could_GenAI_change\" >1. How many jobs in Poland could GenAI change?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#2_Women_vs_men_%E2%80%93_who_is_more_exposed\" >2. Women vs. men \u2013 who is more exposed?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#3_How_often_Polish_companies_actually_use_AI\" >3. How often Polish companies actually use AI<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#4_How_quickly_could_the_changes_happen\" >4. How quickly could the changes happen?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#5_A_separate_front_of_automation_robots_in_factories_and_warehouses\" >5. A separate front of automation: robots in factories and warehouses<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#Concrete_deployments_from_China_2026\" >Concrete deployments from China, 2026<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#6_Does_the_law_actually_ban_replacing_humans_with_AI\" >6. Does the law actually ban replacing humans with AI?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#7_What_new_occupations_will_emerge\" >7. What new occupations will emerge<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#Occupations_directly_tied_to_AI\" >Occupations directly tied to AI<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#Occupations_supported_by_demographic_and_technological_shifts\" >Occupations supported by demographic and technological shifts<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#8_Unemployment_what_the_hard_GUS_data_shows\" >8. Unemployment: what the hard GUS data shows<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/ceo.com.pl\/en\/more-than-5-million-polish-jobs-are-exposed-to-generative-ai-53568\/#Takeaways_for_business_and_investors\" >Takeaways for business and investors<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"1_How_many_jobs_in_Poland_could_GenAI_change\"><\/span>1. How many jobs in Poland could GenAI change?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>According to the report \u201eGenerative Artificial Intelligence and the Polish Labor Market\u201d (NASK, ILO, June 2025), based on Labor Force Survey (BAEL) microdata for Q4 2023 and the first three quarters of 2024, <span class=\"aig26en-flag\">about 30.3 percent of all jobs in Poland \u2013 roughly 5.08 million positions<\/span> \u2013 show at least some exposure to automation or task transformation by GenAI.<\/p>\n\n<p>The highest-exposure group is far narrower: about <span class=\"aig26en-flag\">817,500 people (4.9 percent of the employed)<\/span> work in occupations where most tasks could potentially be performed or substantially transformed by generative AI. The report&#8217;s authors explicitly caution that task exposure does not mean the automatic elimination of an occupation: more often, it points to changes in job content rather than disappearance from the labor market.<\/p>\n\n<div class=\"aig26en-chartbox\">\n<canvas id=\"aig26en-chart1\"><\/canvas>\n<div class=\"aig26en-caption\">Number of jobs in Poland exposed to GenAI, in millions (NASK\u2013ILO, LFS microdata Q4 2023 \u2013 Q3 2024)<\/div>\n<\/div>\n\n<table>\n<tbody><tr><th>Level of GenAI exposure<\/th><th>Main occupational groups<\/th><\/tr>\n<tr><td>Very high<\/td><td>Clerical support workers<\/td><\/tr>\n<tr><td>High<\/td><td>Professionals<\/td><\/tr>\n<tr><td>Moderate<\/td><td>Technicians and other mid-level staff<\/td><\/tr>\n<tr><td>Low<\/td><td>Service and sales workers<\/td><\/tr>\n<tr><td>None or minimal<\/td><td>Industrial workers, craftspeople, farmers, gardeners, forestry and fishery workers<\/td><\/tr>\n<\/tbody><\/table>\n\n<p>Clerical support workers remain the most exposed group \u2013 more than 71 percent of them perform tasks susceptible to GenAI, and nearly half (47.2 percent) fall into the very-high-exposure category. Specific occupations cited in the report include data-entry clerks, bookkeeping staff, administrative secretaries, proofreaders, telemarketers, financial analysts, mobile app developers, and web designers and editors. The ILO notes that while clerical occupations remain the most exposed, exposure is also rising among many specialist and highly digitized professions.<\/p>\n\n<div class=\"aig26en-note\">An earlier 2024 analysis by the Polish Economic Institute (PIE), using a different methodology (AIOE), found 3.68 million workers in the 20 most-exposed specialist occupations, concentrated mainly in the Mazowieckie, Dolno\u015bl\u0105skie, Ma\u0142opolskie and Pomorskie regions. Because the two studies use different counting methods, their results should not be added together or directly compared \u2013 it is cited here as an independent reference point, not as an update of the same figure.<\/div>\n\n<h2><span class=\"ez-toc-section\" id=\"2_Women_vs_men_%E2%80%93_who_is_more_exposed\"><\/span>2. Women vs. men \u2013 who is more exposed?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>GenAI&#8217;s impact is not evenly distributed. According to NASK\u2013ILO, about 39.1 percent of employed women work in occupations exposed to automation or transformation, versus 22.8 percent of men \u2013 equivalent to roughly 3.02 million positions held by women and 2.05 million held by men.<\/p>\n\n<div class=\"aig26en-chartbox\">\n<canvas id=\"aig26en-chart2\"><\/canvas>\n<div class=\"aig26en-caption\">Share of employed women and men in occupations exposed to GenAI (NASK\u2013ILO, 2025)<\/div>\n<\/div>\n\n<p>The gap is especially pronounced among the youngest workers \u2013 in the 15\u201324 age group, the report finds that 47.8 percent of employed women work in GenAI-exposed occupations, versus 22.7 percent of men. The disparity holds across all age groups, which the authors attribute to employment structure: women work more often in clerical, administrative and service roles, and less often in physical occupations.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"3_How_often_Polish_companies_actually_use_AI\"><\/span>3. How often Polish companies actually use AI<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Potential task exposure to automation is far greater than the current scale of deployment. In the NASK\u2013ILO survey (December 2024), only 9.4 percent of workers said their employer had officially rolled out GenAI tools; 19 percent cited plans to do so, while 44.5 percent said their company had no such plans. Guidelines for safe use are also scarce \u2013 68.1 percent of workers said they had received no guidance on GenAI use, and only 8.1 percent confirmed such rules existed at their company.<\/p>\n\n<p>More recent, harder data comes from Eurostat: in 2025, 8.4 percent of Polish companies with at least 10 employees used AI technology, up from 5.9 percent a year earlier. The EU average reached 20 percent in the same period, with leaders Denmark and Finland at around 40 percent. Poland remains among the EU countries with the lowest level of corporate AI adoption.<\/p>\n\n<div class=\"aig26en-note\">NASK\u2013ILO and Eurostat figures are not directly comparable: the former is based on workers&#8217; self-reported use of GenAI tools, while the latter covers various AI technologies as reported by companies themselves.<\/div>\n\n<h2><span class=\"ez-toc-section\" id=\"4_How_quickly_could_the_changes_happen\"><\/span>4. How quickly could the changes happen?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>None of the cited studies points to a single date when AI would trigger mass job losses. NASK\u2013ILO does, however, show how workers themselves assess the outlook over the next five years: among those in highly exposed occupations, 29.8 percent expect significant or complete transformation of their job, versus 17.1 percent among those in occupations with low GenAI exposure. A third of workers in highly exposed occupations believe they will need to adapt their skills, and 58.4 percent of all working Poles say they are willing to learn AI-related skills.<\/p>\n\n<table>\n<tbody><tr><th>Source<\/th><th>What it shows<\/th><th>Horizon<\/th><\/tr>\n<tr><td>NASK\u2013ILO<\/td><td>30.3 percent of jobs (5.08 million) exposed to GenAI, including 817,500 in the very-high-exposure category<\/td><td>current status, 2025<\/td><\/tr>\n<tr><td>NASK\u2013ILO<\/td><td>29.8 percent of workers in highly exposed occupations expect significant job transformation<\/td><td>5 years<\/td><\/tr>\n<tr><td>World Economic Forum, \u201eFuture of Jobs 2025\u201d<\/td><td>Globally: 170 million new jobs and 92 million displaced \u2013 a net balance of 78 million (driven jointly by technology, demographics, the economy and climate, not AI alone)<\/td><td>by 2030<\/td><\/tr>\n<\/tbody><\/table>\n\n<div class=\"aig26en-note\">Earlier estimates circulating in market commentary, claiming outright that 30 percent of occupations will be \u201ereplaced\u201d by 2030, currently rest on weaker evidence than the NASK\u2013ILO indicators and are not repeated here as established fact.<\/div>\n\n<h2><span class=\"ez-toc-section\" id=\"5_A_separate_front_of_automation_robots_in_factories_and_warehouses\"><\/span>5. A separate front of automation: robots in factories and warehouses<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>GenAI and physical robotics are two distinct automation mechanisms. NASK\u2013ILO data concerns primarily software capable of generating and processing text, images, code and information. Humanoid robots, by contrast, affect physical work in manufacturing and logistics \u2013 figures from the two areas should not be directly combined. Here, Asia \u2013 and China in particular \u2013 currently leads.<\/p>\n\n<p>CES 2026 in Las Vegas was described as a turning point: humanoid robots stopped merely demonstrating walking and acrobatics and began performing real production and warehouse tasks.<\/p>\n\n<div class=\"aig26en-callout\">\n<h4><span class=\"ez-toc-section\" id=\"Concrete_deployments_from_China_2026\"><\/span>Concrete deployments from China, 2026<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u2022 UBTech Robotics \u2013 its Walker S2 model can autonomously swap its own batteries; the company says production capacity for its industrial humanoid robots is expected to exceed 10,000 units in 2026.<\/p>\n<p>\u2022 Xpeng \u2013 the electric-vehicle maker is preparing large-scale production of its Iron humanoid robot, with a wider global rollout planned for 2027.<\/p>\n<p>\u2022 Agibot \u2013 presented a full humanoid robot lineup at CES and announced its entry into the U.S. market.<\/p>\n<p>\u2022 The Asia-Pacific region accounted for 42.6 percent of the global humanoid robot market in 2025, representing approximately $1.91 billion; its value is forecast to reach about $2.68 billion in 2026.<\/p>\n<\/div>\n\n<p>According to Andreas Brauchle of consulting firm Horv\u00e1th, cited by CNBC, China is currently ahead of the United States in the early commercialization of humanoid robots \u2013 both countries will eventually build comparably large markets, but China is scaling production much faster at the outset. Behind this is a political decision in Beijing: developing humanoids is meant to address an aging population and a shrinking labor force, while giving China a technological edge.<\/p>\n\n<p>Market estimates vary considerably by methodology, but business interest in physical AI is already substantial. According to the Capgemini Research Institute report \u201ePhysical AI: Taking Human-Robot Collaboration to the Next Level,\u201d two-thirds of organizations regard physical AI as a high priority for the next three to five years, while 79 percent are already exploring, piloting or deploying it. Humanoid robots remain a longer-term proposition: respondents estimated an average of about seven years before they can be scaled broadly.<\/p>\n\n<div class=\"aig26en-chartbox\">\n<canvas id=\"aig26en-chart3\"><\/canvas>\n<div class=\"aig26en-caption\">Country-level comparison of executives prioritizing physical AI within automation strategies (Capgemini Research Institute, 2026)<\/div>\n<\/div>\n\n<p>An industry caveat is essential: despite the media attention, humanoid robots still operate mainly in pilot programs or limited deployments, perform a narrow range of tasks and generally move more slowly than established fixed-arm industrial robots. Reliability, dexterity, cost and workforce training remain major barriers. Capgemini&#8217;s survey suggests that scaling humanoid robots broadly will take an average of around seven years, meaning widespread deployment is more likely in the early 2030s than within the next two or three years.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"6_Does_the_law_actually_ban_replacing_humans_with_AI\"><\/span>6. Does the law actually ban replacing humans with AI?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The widespread belief that European law bans replacing humans with artificial intelligence is imprecise. The EU AI Act (Regulation 2024\/1689) does not prohibit job automation as such. Instead, it introduces phased obligations concerning transparency, governance, risk management and genuine human oversight, particularly for high-risk AI systems used in areas such as employment. The timetable for the application of the high-risk rules was adjusted in 2026 following a political agreement on the EU&#8217;s Digital Omnibus package.<\/p>\n\n<table>\n<tbody><tr><th>Date<\/th><th>What happens<\/th><\/tr>\n<tr><td>August 1, 2024<\/td><td>The AI Act enters into force; its obligations begin to apply in stages<\/td><\/tr>\n<tr><td>February 2, 2025<\/td><td>Prohibitions on specified AI practices and the AI literacy obligation begin to apply<\/td><\/tr>\n<tr><td>August 2, 2025<\/td><td>Governance provisions and obligations for general-purpose AI (GPAI) models begin to apply<\/td><\/tr>\n<tr><td>August 2, 2026<\/td><td>Most remaining provisions begin to apply, including transparency obligations for certain AI-generated or AI-manipulated content<\/td><\/tr>\n<tr><td>By the end of 2026<\/td><td>The European Commission expects to finalize guidelines on the classification of high-risk AI systems<\/td><\/tr>\n<tr><td>December 2, 2027<\/td><td>High-risk rules apply to systems in areas including biometrics, critical infrastructure, education, employment, migration, asylum and border control<\/td><\/tr>\n<tr><td>August 2, 2028<\/td><td>High-risk rules apply to AI systems embedded in products covered by EU sectoral safety legislation<\/td><\/tr>\n<\/tbody><\/table>\n\n<p>Article 26 of the AI Act will be central once the relevant high-risk rules become applicable. A company deploying such a system must assign oversight to people with sufficient competence, training and authority. Human oversight cannot be reduced to rubber-stamping an algorithmic output: the person responsible must be able to understand the system&#8217;s limitations, monitor its operation and, where necessary, disregard, override or stop it. Employers must also inform workers&#8217; representatives and affected employees before putting certain high-risk workplace systems into use. Under Article 86, a person affected by a decision based on the output of an Annex III high-risk system that produces legal or similarly significant effects may request a clear and meaningful explanation of the role of the AI system and the main elements of the decision.<\/p>\n\n<p>Poland&#8217;s parliament completed work on the national AI systems law on July 3, 2026, after considering amendments introduced by the Senate. The final parliamentary text establishes the Commission for AI Development and Security (KRiBSI) as the national supervisory authority. Until the national oversight structure becomes operational, the provisions of the EU AI Act that have already entered into application remain directly binding in Poland, while the high-risk employment rules will apply according to the revised EU timetable.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"7_What_new_occupations_will_emerge\"><\/span>7. What new occupations will emerge<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Alongside the transformation of existing jobs, the market is generating new roles:<\/p>\n\n<div class=\"aig26en-grid2\">\n<div class=\"aig26en-callout\">\n<h4><span class=\"ez-toc-section\" id=\"Occupations_directly_tied_to_AI\"><\/span>Occupations directly tied to AI<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u2022 AI model trainer, prompt and agentic-workflow designer<\/p>\n<p>\u2022 Technology ethicist, AI Act compliance auditor<\/p>\n<p>\u2022 AI systems cybersecurity specialist<\/p>\n<p>\u2022 Data analyst, machine learning engineer<\/p>\n<\/div>\n<div class=\"aig26en-callout\">\n<h4><span class=\"ez-toc-section\" id=\"Occupations_supported_by_demographic_and_technological_shifts\"><\/span>Occupations supported by demographic and technological shifts<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>\u2022 Electrotechnology engineer (forecast by the Institute of Labor and Social Studies)<\/p>\n<p>\u2022 Care and medical occupations for an aging society<\/p>\n<p>\u2022 Scientists: mathematics, chemistry, physics, engineering<\/p>\n<p>\u2022 Logistics staff supervising automated supply chains<\/p>\n<\/div>\n<\/div>\n\n<p>The practical challenge is therefore not only to create new job titles, but also to retrain people whose existing roles will change. Employers will need clearer rules for safe AI use, training focused on real workplace tasks rather than generic tool demonstrations, and procedures for documenting meaningful human oversight. Without those measures, the gap between occupational exposure and employees&#8217; ability to work effectively with AI may widen.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"8_Unemployment_what_the_hard_GUS_data_shows\"><\/span>8. Unemployment: what the hard GUS data shows<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The registered unemployment rate, after a record low of 4.9 percent in June and October 2024, rose to 6.1 percent in March 2026, before a cyclical, seasonal spring decline: 6.0 percent in April and 5.9 percent in May 2026 (915,900 registered unemployed, up 17 percent year on year).<\/p>\n\n<div class=\"aig26en-chartbox\">\n<canvas id=\"aig26en-chart4\"><\/canvas>\n<div class=\"aig26en-caption\">Registered unemployment rate in Poland, 2024\u20132026 (GUS \/ Ministry of Family, Labor and Social Policy)<\/div>\n<\/div>\n\n<div class=\"aig26en-note\">The rise in registered unemployment should be read with caution. New rules for registering and recording unemployed people took effect on June 1, 2025, which may limit year-on-year comparability. There is also no basis for attributing the current rise in registered unemployment directly to artificial intelligence \u2013 GUS and the Ministry of Family, Labor and Social Policy point instead to seasonal factors and a decline in job openings reported to labor offices.<\/div>\n\n<p>The internationally comparable LFS (BAEL) unemployment rate stood at 3.3 percent in Q1 2026 \u2013 still one of the lowest in the European Union (Eurostat put it at 3.1 percent in January 2026, tying Poland with Bulgaria for first place in the EU). In the same quarter, however, the number of employed people fell by 109,000 versus the end of 2025, while the economically inactive population grew by 82,000, to 12,538,000. The toughest situation remains among young people: the unemployment rate for the 15\u201324 age group reached 12.5 percent.<\/p>\n\n<p>A structural factor is at work in the background: according to the GUS&#8217;s main demographic forecast, Poland&#8217;s population will shrink from 37.8 million in 2022 to 30.9 million by 2060 \u2013 nearly 7 million fewer people over four decades. A shrinking labor supply could, in practice, partly offset job losses caused by automation \u2013 one of the arguments in the debate over whether AI may turn out to be an answer to labor shortages rather than solely their cause.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"Takeaways_for_business_and_investors\"><\/span>Takeaways for business and investors<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Poland&#8217;s labor-market exposure to GenAI is currently concentrated in clerical work and information-processing tasks rather than simple physical labor \u2013 reversing the traditional automation pattern of previous decades. At the same time, actual corporate adoption in Poland, at 8.4 percent, remains well below the EU average of 20 percent. For employers, the main near-term issue is not a legal ban on automation, but the need to introduce transparent procedures, effective human oversight and documented accountability when AI systems influence employment decisions. For investors tracking physical automation, the key reference point remains the pace of humanoid robot commercialization in China, where production scale \u2013 not just technological capability \u2013 is increasingly shaping competitive advantage.<\/p>\n\n<div class=\"aig26en-sources\">Sources: Troszy\u0144ski M., Berg J., Gmyrek P., Kami\u0144ski K., Konopczy\u0144ski F., \u0141adna A., Nafradi B., Ros\u0142aniec K., \u201eGenerative Artificial Intelligence and the Polish Labor Market,\u201d NASK\u2013PIB \/ ILO, 2025; Gmyrek P. et al., \u201eGenerative AI and Jobs: A Refined Global Index of Occupational Exposure,\u201d ILO Working Paper 140, 2025; Eurostat (isoc_eb_ai dataset, 2026 edition); GUS, Ministry of Family, Labor and Social Policy; World Economic Forum, \u201eFuture of Jobs Report 2025\u201d; Capgemini Research Institute, \u201ePhysical AI: Taking Human-Robot Collaboration to the Next Level,\u201d 2026; Fortune Business Insights, \u201eHumanoid Robot Market,\u201d 2026 edition; European Commission, AI Act implementation timeline following the Digital Omnibus political agreement; Sejm of the Republic of Poland, AI Systems Act of July 3, 2026 (print no. 2443); Polish Economic Institute, \u201eAI on the Polish Labor Market,\u201d 2024 (cited only as an earlier comparative study); Reuters and other cited business and technology reporting. Own compilation.<\/div>\n<\/div>\n\n<script nowprocket=\"\" data-cfasync=\"false\" src=\"https:\/\/cdnjs.cloudflare.com\/ajax\/libs\/Chart.js\/4.4.1\/chart.umd.min.js\"><\/script>\n<script nowprocket=\"\" data-cfasync=\"false\">\n(function(){\n  var aig26enAttempts = 0;\n  var aig26enTimer = setInterval(function(){\n    aig26enAttempts++;\n    if (typeof Chart !== 'undefined') {\n      clearInterval(aig26enTimer);\n      aig26enInitCharts();\n    } else if (aig26enAttempts >= 100) {\n      clearInterval(aig26enTimer);\n    }\n  }, 100);\n\n  function aig26enInitCharts(){\n    var navy = '#131F49';\n    var amber = '#e67a2d';\n    var navyLight = '#4a5580';\n\n    try {\n      var c1 = document.getElementById('aig26en-chart1');\n      if (c1) {\n        new Chart(c1.getContext('2d'), {\n          type: 'bar',\n          data: {\n            labels: ['Positions exposed to GenAI (total)', 'of which very high exposure'],\n            datasets: [{\n              label: 'million jobs',\n              data: [5.08, 0.8175],\n              backgroundColor: [navy, amber]\n            }]\n          },\n          options: {\n            responsive: true,\n            plugins: { legend: { display: false } },\n            scales: { y: { beginAtZero: true, title: { display: true, text: 'million people' } } }\n          }\n        });\n      }\n    } catch(e) { console.error('aig26en chart1 error', e); }\n\n    try {\n      var c2 = document.getElementById('aig26en-chart2');\n      if (c2) {\n        new Chart(c2.getContext('2d'), {\n          type: 'bar',\n          data: {\n            labels: ['Women', 'Men'],\n            datasets: [{\n              label: '% of employed in occupations exposed to GenAI',\n              data: [39.1, 22.8],\n              backgroundColor: [amber, navy]\n            }]\n          },\n          options: {\n            responsive: true,\n            plugins: { legend: { display: false } },\n            scales: { y: { beginAtZero: true, max: 50, title: { display: true, text: '% of employed' } } }\n          }\n        });\n      }\n    } catch(e) { console.error('aig26en chart2 error', e); }\n\n    try {\n      var c3 = document.getElementById('aig26en-chart3');\n      if (c3) {\n        new Chart(c3.getContext('2d'), {\n          type: 'bar',\n          data: {\n            labels: ['Japan', 'South Korea', 'China', 'USA'],\n            datasets: [{\n              label: '% of executives planning humanoid deployment',\n              data: [76, 72, 70, 69],\n              backgroundColor: [navy, navy, amber, navyLight]\n            }]\n          },\n          options: {\n            responsive: true,\n            plugins: { legend: { display: false } },\n            scales: { y: { beginAtZero: true, max: 100, title: { display: true, text: '% of respondents' } } }\n          }\n        });\n      }\n    } catch(e) { console.error('aig26en chart3 error', e); }\n\n    try {\n      var c4 = document.getElementById('aig26en-chart4');\n      if (c4) {\n        new Chart(c4.getContext('2d'), {\n          type: 'line',\n          data: {\n            labels: ['Jun 2024', 'Oct 2024', 'Nov 2025', 'Jan 2026', 'Mar 2026', 'Apr 2026', 'May 2026'],\n            datasets: [{\n              label: 'Registered unemployment rate (%)',\n              data: [4.9, 4.9, 5.6, 6.0, 6.1, 6.0, 5.9],\n              borderColor: amber,\n              backgroundColor: 'rgba(230,122,45,0.15)',\n              fill: true,\n              tension: 0.25,\n              pointBackgroundColor: navy\n            }]\n          },\n          options: {\n            responsive: true,\n            plugins: { legend: { display: false } },\n            scales: { y: { title: { display: true, text: '%' } } }\n          }\n        });\n      }\n    } catch(e) { console.error('aig26en chart4 error', e); }\n  }\n})();\n<\/script>\n","protected":false},"excerpt":{"rendered":"<p>According to the most comprehensive study to date, by NASK and the International Labour Organization (ILO), 30.3 percent of jobs in Poland \u2013 about 5.08 million positions \u2013 show some degree of exposure to automation or transformation driven by generative AI. The authors are explicit: this is not a forecast that these jobs will disappear, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":5707,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_publicize_message":"Artificial intelligence could transform the work of more than 5 million people in Poland.\n\nAccording to research by NASK and the International Labour Organization, 30.3% of Polish jobs show some exposure to automation or task transformation driven by generative AI. However, this does not mean that millions of jobs will disappear. In most cases, AI is more likely to change what employees do and how they work.\n\nClerical and administrative occupations face the highest exposure, while physical jobs in industry, agriculture and skilled trades remain far less affected. The impact is also uneven: women are significantly more likely than men to work in occupations exposed to GenAI.\n\nWhich jobs could change first? How quickly are Polish companies adopting AI? And what does the EU AI Act mean for employers and employees?\n\n#ArtificialIntelligence #AI #Poland #Jobs #LabourMarket #FutureOfWork #Technology","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[21],"tags":[3806,2974,2803,3462,2839,2783,279,369,47,280,4749,2690,2669,2892,3151,2679,4716,3529,64,2931,4133],"class_list":["post-6369","post","type-post","status-publish","format-standard","has-post-thumbnail","category-careers","tag-ai-act","tag-artificial-intelligence","tag-capgemini","tag-cnbc","tag-content","tag-contrast","tag-denmark","tag-european-commission","tag-european-union","tag-finland","tag-fortune-business-insights","tag-gap","tag-gus","tag-las-vegas","tag-machine-learning","tag-media","tag-nask","tag-pie","tag-poland","tag-tesla","tag-world-economic-forum"],"jetpack_publicize_connections":[],"_links":{"self":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts\/6369","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/comments?post=6369"}],"version-history":[{"count":2,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts\/6369\/revisions"}],"predecessor-version":[{"id":6373,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts\/6369\/revisions\/6373"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/media\/5707"}],"wp:attachment":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/media?parent=6369"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/categories?post=6369"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/tags?post=6369"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}