7 min read<\/p>\n
A significant portion of documented AI implementations in companies do not deliver the promised results. The number varies depending on the source, but whether you read from MIT or industry analysts: Between 80 and 95 percent of projects<\/strong> stagnate, are quietly abandoned, or never reach productive operation. Meanwhile, most executives are increasing their AI budgets for 2026. This is not a contradiction \u2013 it is a warning signal. Because hope is not a strategy.<\/strong><\/p>\n The automotive industry provides vivid examples. Volkswagen’s software subsidiary Cariad burned through billions before the company pulled the plug \u2013 not because the technology did not exist, but because the organization had not defined a clear, prioritized problem<\/strong>. Instead, everything was supposed to be transformed at once. The result: lots of infrastructure, little output. Cariad is not an isolated case. It is the pattern.<\/p>\n The error begins with the framing. \u201cAI-first\u201d sounds like determination and a willingness to innovate. But in operational reality, it usually means: A company buys AI tools and then looks for problems that fit them. This is the reverse of any functioning innovation logic<\/strong>. Technology does not solve problems that no one has formulated.<\/p>\n The first symptom is the solution without a problem. Departments receive budgets to \u201cdo something with AI.\u201d They evaluate tools, build demos, present at internal innovation days. But no one has asked beforehand: Which specific process do we want to improve? By how much? And how do we measure that? The result: Pilot projects that work technically but do not generate any measurable business value<\/strong>.<\/p>\n The second symptom is the horizontal PoC flood. Many companies start ten, twenty, or more proofs of concept in parallel in different departments. Each team works with different data, different providers, different success criteria. The result is a portfolio of half-finished experiments, none of which clears the scaling hurdle. Not because the technology fails, but because no one has done the organizational groundwork \u2013 data infrastructure, governance, change management.<\/p>\n The third symptom is the lack of real success metrics. \u201cWe are now using AI\u201d is a press release. As long as companies measure the success of AI projects by their mere existence rather than by measurable improvements<\/strong> in throughput time, error rate, or cost reduction, the investments remain an act of faith. And faith does not scale.<\/p>\n The few companies whose AI projects demonstrably work share a characteristic: they don’t treat AI as a strategy, but as an engineering discipline<\/strong>. The difference sounds subtle, but it’s fundamental.<\/p>\n Vertical prioritization instead of horizontal dispersion.<\/strong> Instead of introducing AI everywhere at once, these companies identify a specific process with high pain potential and high data availability. They solve this one problem completely \u2013 from data cleansing to model training to integration into the operational workflow. Only then comes the next use case.<\/p>\n Clear business cases before the first prompt.<\/strong> Before even a single API is connected, there’s a business case that quantifies the expected benefit. Not in vague categories like “efficiency improvement,” but in concrete numbers: reduction of processing time from 48 to 12 hours. Lowering the error rate from 8 to 2 percent. Saving 200,000 Euro per quarter. Anyone who can’t name these numbers doesn’t have an AI project \u2013 they have a hypothesis.<\/p>\n Augmentation instead of replacement.<\/strong> This ties in with the Klarna debate, which sparked waves again in mid-2025. Klarna had chosen “AI-first” as its company motto and consistently cut jobs. The result: declining service quality, growing customer frustration, a CEO who had to publicly admit that the math didn’t add up. The counter-model is companies that use AI to augment human work. A clerk who processes twice as many cases with AI support generates more value than a bot that answers every second case incorrectly.<\/p>\n Anyone who wants to reduce the error rate of their AI initiatives doesn’t need a new tool or another consultant. They need discipline with three questions<\/strong> that must be answered before every project start.<\/p>\n Question one: What specific, measurable problem does this project solve?<\/em> If the answer is “we want to use AI,” that’s not a problem, but a means. Back to the start.<\/p>\n Question two: What is the current baseline value, and what’s the target?<\/em> Without a baseline, there’s no measurable progress. If you don’t know how long a process takes today, you can’t judge whether AI makes it faster. Establishing the baseline is often more laborious than building the model \u2013 and that’s exactly why it’s skipped. Fatal error.<\/strong><\/p>\n Question three: What happens if the AI is wrong?<\/em> Every AI system has an error rate. The question isn’t whether errors occur, but whether there’s a fallback position. In insurance claims processing, a misclassified case might be an annoyance. In medical diagnostics, it’s a catastrophe.<\/p>\n These three questions aren’t an innovation framework for workshops. They’re a filter. If you answer them honestly, you’ll find that out of ten planned AI projects, maybe three pass the test<\/strong>. That’s exactly the point. Three well-thought-out projects that go into production beat thirty pilots that end in slide presentations.<\/p>\n Five questions for an honest assessment in two minutes:<\/p>\n Anyone who answers at least four of these questions with yes is already working problem-first. Everyone else should not expand their AI roadmap, but sharpen it. Fewer projects, more impact.<\/strong> Less vision, more engineering. Less “AI-first,” more “problem-first, AI-enabled.” That’s not a brake on innovation. It’s the prerequisite for innovation to actually land.<\/p>\n The most common cause is a lack of problem definition. Companies acquire AI tools without first formulating a concrete, measurable use case. This is compounded by poor data quality, lack of governance, and unclear success metrics.<\/p>\n<\/details>\n Before every project starts, three questions must be answered: which concrete, measurable problem is being solved, which baseline value serves as the starting point and which fallback position exists if the AI is wrong. Anyone who answers these questions honestly will find that of ten planned AI projects perhaps three pass the test.<\/p>\n<\/details>\n Augmentation means that AI supports and enhances human work \u2013 for example, through faster data analysis or suggestions. Replacement completely replaces human labor with AI. Studies and practical examples like Klarna show that Augmentation often delivers better results.<\/p>\n<\/details>\n A sound business case quantifies the expected benefit in concrete figures, such as reducing processing time from 48 to 12 hours or saving 200,000 euros per quarter. Anyone who cannot cite such figures has only a hypothesis. The business case must be ready before the first prompt.<\/p>\n<\/details>\n Data quality is the crucial foundation. Without cleaned, structured, and complete data, no AI model can work reliably. Establishing a data baseline and building data infrastructure is often more labor-intensive than the actual model training.<\/p>\n<\/details>\n Klarna had chosen AI-first as its company motto and consistently cut jobs. The result was declining service quality and growing customer frustration, so the CEO had to admit publicly that the math does not add up. The counter-model is companies that use AI to augment human work.<\/p>\n<\/details>\n Less is more. Successful companies prioritize vertically: they solve one problem completely before tackling the next use case. Three well-executed projects with measurable impact are more valuable than thirty parallel proof-of-concepts.<\/p>\n<\/details>\n Source of the title image: Unsplash \/ Scott Graham<\/em><\/p>\n Image source: AI-generated (November 2026)<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" 7 min read Key Takeaways Between 80 and 95 percent of all AI projects in companies never reach productive operation \u2013 despite increasing budgets. \u201cAI-first\u201d systematically leads companies to buy technology and then search for suitable problems. Successful companies focus on vertical prioritization: solving one concrete problem completely before moving on to the next use […]<\/p>\n","protected":false},"author":114,"featured_media":36350,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_focuskw":"ai-first","_yoast_wpseo_title":"AI-First: Better strategies than the hype cycle","_yoast_wpseo_metadesc":"AI-first strategy fails without clear problem definition. Focus on real business problems first, then apply AI. Discover the right approach\u2014learn more now.","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_opengraph-image-id":0,"_yoast_wpseo_twitter-image":"","_yoast_wpseo_twitter-image-id":0,"featured_post_sortierung":0,"featured_post":0,"pre_headline":"","bildquelle":"","teasertext":"","language":"de","_evm_slot_owner":"","_evm_translation_lang":"","_wp_old_slug":[],"footnotes":""},"categories":[694,673],"tags":[],"class_list":["post-14744","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-work-leadership","category-reboot-germany","entry"],"wpml_language":"en","wpml_translation_of":11359,"yoast_head":"\nWhy \u201cAI-first\u201d Systematically Fails<\/h2>\n
What successful companies do differently<\/h2>\n
The problem-first framework: Three questions before every AI project<\/h2>\n
Checklist: Ready for AI projects or just for AI announcements?<\/h2>\n
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Frequently Asked Questions<\/h2>\n
Why do so many AI projects fail in companies?<\/strong><\/summary>\n
Which three questions must be clarified before an AI project starts?<\/strong><\/summary>\n
What is the difference between Augmentation and Replacement in AI?<\/strong><\/summary>\n
How concrete does a business case for an AI project have to be?<\/strong><\/summary>\n
What role does data quality play in AI implementation?<\/strong><\/summary>\n
What were the consequences of the radical AI course at Klarna?<\/strong><\/summary>\n
How many AI projects should a company pursue simultaneously?<\/strong><\/summary>\n
Read more<\/h3>\n
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