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The technology does what it is supposed to do. And still, 95% of the organisations<\/strong> in MIT’s 2025 research get zero return on their investment in generative AI. That figure covers the applications companies have built for them or buy in. For general tools such as ChatGPT and Copilot the same research measures something else: over 80% of organisations have explored or piloted them, and nearly 40% report deployment.<\/p>\n Why do AI projects fail then?<\/strong> In the projects we observe, the cause lies not in the model but around it: with the people, the agreements and the organisation. Perhaps you recognise it: there\u2019s a pilot that just won\u2019t finish, or management is asking for “something with AI”. Whether your data is ready for AI <\/a>plays a role, but that’s not what this page is about.<\/p>\n Juvo has been guiding AI and data projects with Belgian organisations for approximately ten years. Below are four pitfalls from these projects<\/strong>, and what they have in common. One nuance beforehand: the MIT report is not a peer-reviewed publication. The direction of the figure holds, but its precision does not.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_code _builder_version=”4.27.8″ _module_preset=”default” custom_padding=”||||false|false” global_colors_info=”{}” raw_content__hover_enabled=”off|desktop”]