{"id":2255,"date":"2026-08-13T11:10:30","date_gmt":"2026-08-13T09:10:30","guid":{"rendered":"https:\/\/jsoncrew.com\/?p=2255"},"modified":"2026-09-08T18:55:07","modified_gmt":"2026-09-08T16:55:07","slug":"wdrozenia-ai-w-firmach-porazka-jak-zrobic-skutecznie","status":"publish","type":"post","link":"https:\/\/jsoncrew.com\/en\/2026\/08\/13\/wdrozenia-ai-w-firmach-porazka-jak-zrobic-skutecznie\/","title":{"rendered":"Why do most AI implementations in companies fail? How to do it effectively?"},"content":{"rendered":"<p class=\"wp-block-paragraph\">According to a report by McKinsey Global Institute, <strong>over 78% AI projects do not progress beyond the pilot phase<\/strong>. The IBM Institute for Business Value estimates that only 15% AI implementations achieve their business goals. The scale of failures is alarming, especially as companies invest more and more in AI and the competitive pressure for digital transformation does not decrease.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Where is the problem? Usually not in the technology itself. Algorithms work. Language models are getting better and cheaper. The problem lies elsewhere, in the way organizations approach implementation. And that's the good news: the way can be changed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why do most AI implementations fail?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">We have analyzed several dozen AI projects in Polish and European companies, from SMEs to enterprise organizations. The pattern of failure is surprisingly repeatable. Implementations fail for the same reasons.<\/p>\n\n\n\n<div class=\"wa-bars\">\n<style>\n.wa-bars{font-family:system-ui,sans-serif;background:linear-gradient(135deg,#0d1b3e,#1a1045);border-radius:16px;padding:2.5rem 2rem 2rem;margin:2.5rem 0;color:#fff}\n.wa-bars__title{margin:0 0 .3rem;font-size:1.05rem;font-weight:700;color:#f87171;text-transform:uppercase;letter-spacing:.08em}\n.wa-bars__sub{margin:0 0 2rem;font-size:.76rem;color:#fca5a5;opacity:.8}\n.wa-bars__row{display:flex;align-items:center;gap:.9rem;margin-bottom:1.25rem}\n.wa-bars__row:last-child{margin-bottom:0}\n.wa-bars__label{font-size:.78rem;color:#cbd5e1;width:210px;flex-shrink:0;line-height:1.35}\n.wa-bars__track{flex:1;height:10px;background:rgba(255,255,255,.08);border-radius:5px;overflow:hidden}\n.wa-bars__fill{height:100%;border-radius:5px;width:0;transition:width 1.25s cubic-bezier(.4,0,.2,1);background:linear-gradient(90deg,#dc2626,#f87171)}\n.wa-bars__pct{font-size:.84rem;font-weight:800;color:#f87171;min-width:36px;text-align:right}\n@media(max-width:500px){.wa-bars__label{width:120px;font-size:.7rem}}\n<\/style>\n<p class=\"wa-bars__title\">Why do AI implementations fail?<\/p>\n<p class=\"wa-bars__sub\">Main causes of AI project failures in B2B companies (McKinsey AI Survey \/ IBM IBV 2024)<\/p>\n<div class=\"wa-bars__row\"><span class=\"wa-bars__label\">Lack of clear business purpose<\/span><div class=\"wa-bars__track\"><div class=\"wa-bars__fill\" data-w=\"72\"><\/div><\/div><span class=\"wa-bars__pct\">72%<\/span><\/div>\n<div class=\"wa-bars__row\"><span class=\"wa-bars__label\">Insufficient data quality<\/span><div class=\"wa-bars__track\"><div class=\"wa-bars__fill\" data-w=\"65\"><\/div><\/div><span class=\"wa-bars__pct\">65%<\/span><\/div>\n<div class=\"wa-bars__row\"><span class=\"wa-bars__label\">Team resistance, lack of change management<\/span><div class=\"wa-bars__track\"><div class=\"wa-bars__fill\" data-w=\"58\"><\/div><\/div><span class=\"wa-bars__pct\">58%<\/span><\/div>\n<div class=\"wa-bars__row\"><span class=\"wa-bars__label\">No sponsor on the business side<\/span><div class=\"wa-bars__track\"><div class=\"wa-bars__fill\" data-w=\"51\"><\/div><\/div><span class=\"wa-bars__pct\">51%<\/span><\/div>\n<div class=\"wa-bars__row\"><span class=\"wa-bars__label\">Scope too ambitious to start with<\/span><div class=\"wa-bars__track\"><div class=\"wa-bars__fill\" data-w=\"44\"><\/div><\/div><span class=\"wa-bars__pct\">44%<\/span><\/div>\n<div class=\"wa-bars__row\"><span class=\"wa-bars__label\">No measurement of ROI during the project<\/span><div class=\"wa-bars__track\"><div class=\"wa-bars__fill\" data-w=\"38\"><\/div><\/div><span class=\"wa-bars__pct\">38%<\/span><\/div>\n<\/div>\n<script>\n(function(){\n  var io=new IntersectionObserver(function(es){\n    es.forEach(function(e){\n      if(!e.isIntersecting)return;\n      e.target.querySelectorAll('.wa-bars__fill').forEach(function(el){el.style.width=el.dataset.w+'%';});\n      io.unobserve(e.target);\n    });\n  },{threshold:.3});\n  var el=document.querySelector('.wa-bars');\n  if(el) io.observe(el);\n})();\n<\/script>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\">1. Lack of clear business purpose<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">\u201eWe want to implement AI\u201d is a technological goal, not a business one. Effective implementations start with a precise question: <em>what specific problem are we solving and how will we measure success?<\/em> Without this, even the best language model will not bring value, because there is nothing to relate it to. The project goes around in circles, consumes the budget and ends with an \u201einconclusive results\u201d report.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Overestimating the quality of data the company already has<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Most companies know that AI needs data. Few realize that they need it <strong>good data<\/strong>, complete, consistent, up-to-date and appropriately described. In practice, the data is in five different systems, in different formats, with gaps dating back several years. A data audit should be step zero, not a task to be dealt with after the project starts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. The project lives in IT, not in business<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When AI implementation takes place only in the IT department or with an external integrator, it loses contact with operational reality. End users, sellers, customer service, logistics, are not asked for their opinion and then boycott a system that \u201edoes something but interferes with work.\u201d Change management and business involvement from the beginning is not a soft matter. This is a condition for technical success.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Scope too ambitious to start with<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">\u201eBig bang\u201d implementations, where AI is supposed to automate several processes at once over the course of a year, rarely end up as planned. Priorities change, the budget is exhausted before the first results are seen, and enthusiasm fades. Companies that win with AI do it differently: small projects, quick results, gradual scaling.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What does it look like in practice? Three real-life scenarios<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The cases below are scenarios based on projects that we have had direct or indirect contact with while working with companies in the manufacturing, B2B services and e-commerce sectors. The details have been anonymized, but the mechanism of failure is in each case identical to what global reports describe.<\/p>\n\n\n\n<div class=\"wa-cases\">\n<style>\n.wa-cases{font-family:system-ui,sans-serif;margin:2.5rem 0}\n.wa-cases__title{font-size:1rem;font-weight:700;color:#1a1a2e;margin-bottom:1.25rem;text-align:center}\n.wa-cases__grid{display:grid;grid-template-columns:repeat(3,1fr);gap:1rem}\n@media(max-width:700px){.wa-cases__grid{grid-template-columns:1fr}}\n.wa-case{background:#fff;border:1px solid #e5e7eb;border-radius:12px;overflow:hidden;box-shadow:0 2px 8px rgba(0,0,0,.06)}\n.wa-case__top{padding:.75rem 1rem;background:#fef2f2;border-bottom:1px solid #fecaca;display:flex;align-items:center;gap:.5rem}\n.wa-case__icon{font-size:1.25rem;line-height:1}\n.wa-case__label{font-size:.72rem;font-weight:700;color:#dc2626;text-transform:uppercase;letter-spacing:.05em}\n.wa-case__body{padding:1rem}\n.wa-case__sector{font-size:.7rem;font-weight:700;color:#6b7280;text-transform:uppercase;letter-spacing:.06em;margin-bottom:.35rem}\n.wa-case__head{font-size:.88rem;font-weight:700;color:#1a1a2e;margin-bottom:.5rem;line-height:1.35}\n.wa-case__desc{font-size:.79rem;color:#4b5563;line-height:1.55;margin-bottom:.6rem}\n.wa-case__lesson{font-size:.76rem;color:#15803d;font-weight:600;background:#f0fdf4;border-left:3px solid #22c55e;padding:.4rem .6rem;border-radius:0 6px 6px 0}\n<\/style>\n<p class=\"wa-cases__title\">Three scenarios we see too often<\/p>\n<div class=\"wa-cases__grid\">\n\n  <div class=\"wa-case\">\n    <div class=\"wa-case__top\"><span class=\"wa-case__icon\">\u26a0<\/span><span class=\"wa-case__label\">Wrong assumption<\/span><\/div>\n    <div class=\"wa-case__body\">\n      <div class=\"wa-case__sector\">Production, 180 people<\/div>\n      <div class=\"wa-case__head\">\u201eWe have data for years, so AI is about to work.\u201d<\/div>\n      <div class=\"wa-case__desc\">The company wanted to predict machine failures. After the start, it turned out that data from the sensors was collected irregularly, in various formats, and some halls were not registered at all. The project stopped at the data preparation stage. Total cost: 8 months and budget consumed in 70% by data cleaning, not AI.<\/div>\n      <div class=\"wa-case__lesson\">Lesson learned: audit your data before signing a contract with an AI vendor, not after.<\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"wa-case\">\n    <div class=\"wa-case__top\"><span class=\"wa-case__icon\">\u26a0<\/span><span class=\"wa-case__label\">Incorrect project structure<\/span><\/div>\n    <div class=\"wa-case__body\">\n      <div class=\"wa-case__sector\">B2B services, 60 people<\/div>\n      <div class=\"wa-case__head\">\u201eWe outsource AI to an external company and wait for the result\u201d<\/div>\n      <div class=\"wa-case__desc\">Lead qualification system ready after 9 months. It worked technically, but the sales department didn't use it because no one asked the salespeople for their opinion during construction. They evaluated leads \u201ethe old way\u201d in parallel with the system because \u201eit's faster this way.\u201d The project was shelved 3 months after implementation.<\/div>\n      <div class=\"wa-case__lesson\">Lesson: End users must contribute to the system, not just receive it.<\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"wa-case\">\n    <div class=\"wa-case__top\"><span class=\"wa-case__icon\">\u26a0<\/span><span class=\"wa-case__label\">Scope too wide<\/span><\/div>\n    <div class=\"wa-case__body\">\n      <div class=\"wa-case__sector\">E-commerce, 40 people<\/div>\n      <div class=\"wa-case__head\">\u201eWe automate everything at once\u201d<\/div>\n      <div class=\"wa-case__desc\">Plan: AI for product recommendations, customer service, inventory forecasting and mailing personalization, in parallel. After 14 months and exceeding the budget by 60%, only recommendations were implemented, working in half of the cases. The rest of the modules did not go beyond the internal testing phase.<\/div>\n      <div class=\"wa-case__lesson\">Lesson: one process, proven result, then scale to others.<\/div>\n    <\/div>\n  <\/div>\n\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">How to implement AI effectively? Two approaches<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The causes of failure are known and repeatable, which means they are also avoidable. Below are the key differences between an approach that fails and one that actually delivers value. If you see a description of your current project in the left column, it's a good time to do so <a href=\"https:\/\/jsoncrew.com\/en\/kontakt\/\">talk to us<\/a> before the project enters the implementation phase.<\/p>\n\n\n\n<div class=\"wa-cmp\">\n<style>\n.wa-cmp{font-family:system-ui,sans-serif;margin:2.5rem 0}\n.wa-cmp__title{font-size:1rem;font-weight:700;color:#1a1a2e;margin-bottom:1.25rem;text-align:center}\n.wa-cmp__grid{display:grid;grid-template-columns:1fr 1fr;gap:1rem}\n@media(max-width:620px){.wa-cmp__grid{grid-template-columns:1fr}}\n.wa-cmp__col{border-radius:12px;overflow:hidden;box-shadow:0 2px 12px rgba(0,0,0,.08)}\n.wa-cmp__head{padding:.875rem 1.25rem;font-weight:700;font-size:.9rem;display:flex;align-items:center;gap:.5rem}\n.wa-cmp__col--bad .wa-cmp__head{background:#dc2626;color:#fff}\n.wa-cmp__col--good .wa-cmp__head{background:#15803d;color:#fff}\n.wa-cmp__body{background:#fff;border:1px solid #e5e7eb;border-top:none;border-radius:0 0 12px 12px;padding:.75rem 1.25rem}\n.wa-cmp__item{padding:.55rem 0;border-bottom:1px solid #f3f4f6;font-size:.82rem;color:#374151;display:flex;gap:.5rem;align-items:flex-start;line-height:1.45}\n.wa-cmp__item:last-child{border-bottom:none}\n.wa-cmp__arrow{color:#9ca3af;flex-shrink:0;font-style:normal}\n<\/style>\n<p class=\"wa-cmp__title\">Two approaches to implementing AI: what makes the difference?<\/p>\n<div class=\"wa-cmp__grid\">\n  <div class=\"wa-cmp__col wa-cmp__col--bad\">\n    <div class=\"wa-cmp__head\">\u2717 An approach that fails<\/div>\n    <div class=\"wa-cmp__body\">\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> \u201eWe implement AI because the competition does too\u201d<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Start with the biggest, most difficult problem<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Project only in IT, business finds out at the end<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Success = system startup<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> The data will \u201eget sorted\u201d after the start<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> No plan for employee resistance<\/div>\n    <\/div>\n  <\/div>\n  <div class=\"wa-cmp__col wa-cmp__col--good\">\n    <div class=\"wa-cmp__head\">\u2713 An approach that works<\/div>\n    <div class=\"wa-cmp__body\">\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Specific problem: \u201ereduce lead qualification time by 60%\u201d<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Quick win in 4-8 weeks, then scaling<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Business owner, IT and users from day 1<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> KPI defined before the first line of code is written<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Data audit as step zero<\/div>\n      <div class=\"wa-cmp__item\"><i class=\"wa-cmp__arrow\">\u203a<\/i> Change management in parallel with technical aspects<\/div>\n    <\/div>\n  <\/div>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">A framework for successful AI implementation: 6 steps<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Successful AI implementations share a common structure. They are not the result of chance or luck. They are the result of using a proven process that eliminates the most common causes of failure before they occur. We use the following framework for every AI project we implement for our clients.<\/p>\n\n\n\n<div class=\"wa-steps\" id=\"wa-steps-root\">\n<style>\n.wa-steps{font-family:system-ui,sans-serif;margin:2.5rem 0;background:#f8faff;border:1px solid #dbeafe;border-radius:16px;padding:2rem}\n.wa-steps__title{font-size:1rem;font-weight:700;color:#1e3a8a;margin-bottom:1.5rem;text-align:center}\n.wa-step{display:flex;gap:1rem;align-items:flex-start;padding:.9rem 0;border-bottom:1px solid #e0e7ef;opacity:0;transform:translateY(18px);transition:opacity .45s ease,transform .45s ease}\n.wa-step:last-child{border-bottom:none}\n.wa-step.wa-step--vis{opacity:1;transform:translateY(0)}\n.wa-step__num{min-width:38px;height:38px;border-radius:50%;background:#1e3a8a;color:#fff;display:flex;align-items:center;justify-content:center;font-weight:800;font-size:.95rem;flex-shrink:0}\n.wa-step__head{font-weight:700;color:#1e3a8a;font-size:.88rem;margin-bottom:.2rem}\n.wa-step__desc{font-size:.8rem;color:#475569;line-height:1.55}\n.wa-step__tag{display:inline-block;font-size:.67rem;font-weight:700;padding:.15rem .45rem;border-radius:4px;background:#dbeafe;color:#1e3a8a;margin-top:.3rem;letter-spacing:.03em}\n<\/style>\n<p class=\"wa-steps__title\">A 6-step framework for effective AI implementation<\/p>\n<div class=\"wa-step\"><div class=\"wa-step__num\">1<\/div><div class=\"wa-step__body\"><div class=\"wa-step__head\">Define a specific business problem<\/div><div class=\"wa-step__desc\">Not \u201ewe want AI\u201d, but \u201ewe want to shorten the lead qualification time from 48 hours to 4 hours\u201d. The more precise the question, the easier it is to measure success and choose the right tool.<\/div><div class=\"wa-step__tag\">Week 1<\/div><\/div><\/div>\n<div class=\"wa-step\"><div class=\"wa-step__num\">2<\/div><div class=\"wa-step__body\"><div class=\"wa-step__head\">Conduct a data audit<\/div><div class=\"wa-step__desc\">AI is only as good as the data that powers it. Check: what you have, what is missing, whether the data is complete and up-to-date. This is step zero, not a \u201estart-up\u201d task.<\/div><div class=\"wa-step__tag\">Weeks 1\u20132<\/div><\/div><\/div>\n<div class=\"wa-step\"><div class=\"wa-step__num\">3<\/div><div class=\"wa-step__body\"><div class=\"wa-step__head\">Appoint a business sponsor<\/div><div class=\"wa-step__desc\">Every AI implementation needs an owner on the business side, a person with decisive decision-making and a personal interest in the success of the project. Without it, the project drifts.<\/div><div class=\"wa-step__tag\">Day 1<\/div><\/div><\/div>\n<div class=\"wa-step\"><div class=\"wa-step__num\">4<\/div><div class=\"wa-step__body\"><div class=\"wa-step__head\">Deliver quick win in 4-8 weeks<\/div><div class=\"wa-step__desc\">Choose a narrow, solvable problem and deliver the result quickly. The first visible benefit builds the trust of the management board and the team's motivation for the next stages.<\/div><div class=\"wa-step__tag\">Month 1-2<\/div><\/div><\/div>\n<div class=\"wa-step\"><div class=\"wa-step__num\">5<\/div><div class=\"wa-step__body\"><div class=\"wa-step__head\">Measure the ROI of each stage<\/div><div class=\"wa-step__desc\">Set KPIs before launch: time, cost, number of errors, satisfaction. Measure every two weeks. If the indicators are going in the wrong direction, correct the course, do not wait for the end of the project.<\/div><div class=\"wa-step__tag\">All the time<\/div><\/div><\/div>\n<div class=\"wa-step\"><div class=\"wa-step__num\">6<\/div><div class=\"wa-step__body\"><div class=\"wa-step__head\">Only scale what works<\/div><div class=\"wa-step__desc\">Only after proving value on a pilot scale do you move the solution to an entire department or organization. Scaling a mistake is much more expensive than starting over.<\/div><div class=\"wa-step__tag\">Month 3+<\/div><\/div><\/div>\n<\/div>\n<script>\n(function(){\n  var io=new IntersectionObserver(function(es){\n    es.forEach(function(e){\n      if(!e.isIntersecting)return;\n      var steps=e.target.querySelectorAll('.wa-step');\n      steps.forEach(function(s,i){setTimeout(function(){s.classList.add('wa-step--vis');},i*110);});\n      io.unobserve(e.target);\n    });\n  },{threshold:.15});\n  var el=document.getElementById('wa-steps-root');\n  if(el) io.observe(el);\n})();\n<\/script>\n\n\n\n<h2 class=\"wp-block-heading\">Where to start? The best processes for the first implementation of AI<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Not all processes are equally good as a starting point. Those that are perfect for the first implementation are those that are available <strong>repetitive and structured<\/strong> (AI is good at routine), they have <strong>lots of historical data<\/strong> (the more examples, the better the model) and where <strong>a mistake is costly, but not catastrophic<\/strong>, you can iterate safely.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Examples that work well as first implementations in B2B companies: <strong>automatic lead qualification<\/strong> based on data from CRM, <strong>categorization and routing of reports<\/strong> to the appropriate departments, <strong>generating first versions of offers<\/strong> based on the client's brief, <strong>churn monitoring and prediction<\/strong> among customers. Each of these processes can be completed in 4-8 weeks and the result can be measured numerically. If you want to check which one suits your company, <a href=\"https:\/\/jsoncrew.com\/en\/kontakt\/\">complete the short contact form<\/a>, we will respond with a specific proposal within one business day.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What do the numbers say after successful implementation?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Companies that have used the described framework report: shortening the time of handling routine inquiries about <strong>40-60%<\/strong>, increase in the precision of lead qualification o <strong>25-35%<\/strong>, reduction of operating costs o <strong>15-30%<\/strong> in the first year. These are not numbers from AI suppliers' presentations, these are results from real implementations in companies employing 50 to 500 people.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Summary: AI works, but it's not magic<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Most AI implementations do not fail due to bad technology. It ends with poor project management, lack of problem definition, poor data quality and missing the human side of change. These are known problems and can be solved. Companies that solve them build a competitive advantage that cannot be quickly made up for by purchasing another license.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At jsoncrew, we help companies go through the entire process: from diagnosing readiness and selecting the first process for implementation, through technical implementation, to measuring results and scaling. If you are considering implementing AI and want to be sure that the project will end with specific results, <a href=\"https:\/\/jsoncrew.com\/en\/kontakt\/\">please contact us<\/a>. We will start with a free diagnosis: what problem is worth solving first and whether you have the data to do it well.<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-left kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;left&quot;,&quot;id&quot;:&quot;2255&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;3&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;3.7&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Oce\u0144 ten artyku\u0142&quot;,&quot;legend&quot;:&quot;3.7 na 5 (3 g\u0142os\u00f3w)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;Dlaczego wi\u0119kszo\u015b\u0107 wdro\u017ce\u0144 AI w firmach ko\u0144czy si\u0119 pora\u017ck\u0105? 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