Companies across Europe are racing to deploy generative AI. Yet most are confusing access with readiness – and paying the price.<\/p>\n\n\n\n
Redesigning Work for the AI Era<\/strong><\/p>\n\n\n\n According to Deloitte’s State of AI in the Enterprise 2026<\/em>, 84% of companies have not redesigned jobs or work processes around AI capabilities, despite recognising that insufficient worker skills are the single biggest barrier to integration. Meanwhile, McKinsey’s Superagency in the Workplace<\/em> (2025) found that while 92% of companies plan to increase AI investment, only 1% consider themselves mature in AI deployment. The reality is that while the tools are everywhere, the internal capability is still catching up.<\/p>\n\n\n\n The root cause is a fundamental misunderstanding of what AI training actually requires. Most organisations focus on tool access and basic awareness – what McKinsey calls “AI literacy” – while neglecting the harder work of embedding AI into actual workflows and decision-making. As McKinsey’s\u00a0Redefine AI Upskilling as a Change Imperative<\/em>\u00a0(2025) illustr<\/span>ates, one company ran an AI literacy course for all employees, yet a month later, adoption was minimal because workflows, incentives, and management behaviours remained unchanged.<\/p>\n\n\n\n The Real Driver of Performance<\/strong><\/p>\n\n\n\n It is rarely a motivation problem. Research published by ATD (2026) shows that individual motivation accounts for just 7% of performance outcomes, while organisational learning systems account for over 23% – more than three times the impact. Essentially, employees aren’t failing AI training; rather, the systems around them are.<\/p>\n\n\n\n The answer lies in treating GenAI literacy as a structured, designed competency rather than a one-off workshop or self-directed experiment. As the HBR report (2026) argues, organisations that embed learning into real workflows, redesign roles around AI outputs, and create feedback loops for experimentation are the ones building durable capability. The EY Work Reimagined Survey 2025<\/em> confirms this: the biggest driver of AI adoption value is skillset, accounting for 49% of an employee’s AI adoption score.<\/p>\n\n\n\n GenAI literacy is now the new baseline, and the challenge today for organisations is ensuring that the training offered is actually designed to deliver results.<\/p>\n\n\n\n _____________<\/p>\n\n\n\n Sources<\/strong><\/p>\n\n\n\n Yritykset ymp\u00e4ri Eurooppaa kilpailevat generatiivisen teko\u00e4lyn k\u00e4ytt\u00f6\u00f6notosta. Useimmat kuitenkin sekoittavat k\u00e4ytt\u00f6oikeuden valmiuteen \u2013 ja maksavat siit\u00e4 hinnan. Ty\u00f6n uudelleensuunnittelu teko\u00e4lyaikakaudelle Deloitten State of AI in the Enterprise 2026 -raportin mukaan 841 000 yrityst\u00e4 ei ole uudistanut ty\u00f6teht\u00e4vi\u00e4 tai ty\u00f6prosesseja teko\u00e4lyn ominaisuuksien ymp\u00e4rille, vaikka he ovatkin my\u00f6nt\u00e4neet, ett\u00e4 riitt\u00e4m\u00e4t\u00f6n m\u00e4\u00e4r\u00e4 ty\u00f6ntekij\u00f6it\u00e4\u2026<\/p>","protected":false},"author":2,"featured_media":4046,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[12],"tags":[],"class_list":["post-4045","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai-insights"],"acf":[],"taxonomy_info":{"category":[{"value":12,"label":"Generative AI Insights"}]},"featured_image_src_large":["https:\/\/genaisa.eu\/wp-content\/uploads\/2026\/04\/ChatGPT-Image-Apr-22-2026-10_02_01-AM-1024x683.png",1024,683,true],"author_info":{"display_name":"Nikolay Tsolev (RCCI)","author_link":"https:\/\/genaisa.eu\/fi\/author\/ntsolev\/"},"comment_info":0,"category_info":[{"term_id":12,"name":"Generative AI Insights","slug":"generative-ai-insights","term_group":0,"term_taxonomy_id":12,"taxonomy":"category","description":"Articles providing tips, trends, and knowledge on generative AI, its applications, and ethical considerations.","parent":0,"count":12,"filter":"raw","cat_ID":12,"category_count":12,"category_description":"Articles providing tips, trends, and knowledge on generative AI, its applications, and ethical considerations.","cat_name":"Generative AI Insights","category_nicename":"generative-ai-insights","category_parent":0}],"tag_info":false,"_links":{"self":[{"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/posts\/4045","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/comments?post=4045"}],"version-history":[{"count":1,"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/posts\/4045\/revisions"}],"predecessor-version":[{"id":4047,"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/posts\/4045\/revisions\/4047"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/media\/4046"}],"wp:attachment":[{"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/media?parent=4045"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/categories?post=4045"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/genaisa.eu\/fi\/wp-json\/wp\/v2\/tags?post=4045"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n