{"id":48352,"date":"2025-10-08T11:27:18","date_gmt":"2025-10-08T09:27:18","guid":{"rendered":"https:\/\/www.investglass.com\/?p=48352"},"modified":"2025-10-06T11:33:35","modified_gmt":"2025-10-06T09:33:35","slug":"por-que-a-ia-fracassa-principais-motivos-e-estrategias-para-o-sucesso-na-implementacao","status":"publish","type":"post","link":"https:\/\/www.investglass.com\/pt\/why-ai-fail-top-reasons-and-strategies-for-success-in-implementation\/","title":{"rendered":"Por que a IA falha: Principais motivos e estrat\u00e9gias para o sucesso na implementa\u00e7\u00e3o"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Artificial Intelligence promises to reshape industries, yet most companies are still struggling to see results. Despite record investments, nearly all AI projects stall before reaching real impact. Why do so many fail and what separates the few success stories from the rest? This article explores the \u201cGenAI Divide\u201d and shares strategies to help organisations cross it. Here let&#8217;s summarise it 15 points out from this super <a href=\"https:\/\/nanda.media.mit.edu\/\">Relat\u00f3rio do MIT NANDA<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">1. Introdu\u00e7\u00e3o: A promessa e o problema da IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a class=\"wpil_keyword_link\" href=\"https:\/\/www.investglass.com\/pt\/ferramentas-de-automacao\/\" target=\"_blank\" rel=\"noopener\" title=\"Intelig\u00eancia Artificial\" data-wpil-keyword-link=\"linked\" data-wpil-monitor-id=\"5507\">Intelig\u00eancia Artificial<\/a> A intelig\u00eancia artificial (IA) tem sido anunciada como a tecnologia mais transformadora do s\u00e9culo XXI. Com o surgimento do aprendizado de m\u00e1quina, do processamento de linguagem natural e, mais recentemente, da IA generativa, as empresas se apressaram em adotar essas ferramentas; no entanto, a abordagem de uma empresa pode ser a diferen\u00e7a entre o sucesso e o fracasso em obter valor. No entanto, apesar dos bilh\u00f5es investidos em pesquisa, infraestrutura e pilotos de IA, a maioria das organiza\u00e7\u00f5es n\u00e3o consegue ver retornos mensur\u00e1veis.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Uma recente verifica\u00e7\u00e3o da realidade mostra que 95% das organiza\u00e7\u00f5es relatam pouco ou nenhum valor dos projetos de IA generativa, apesar do hype e da ado\u00e7\u00e3o generalizados. A divis\u00e3o n\u00e3o se deve \u00e0 falta de inova\u00e7\u00e3o na tecnologia em si, mas sim \u00e0 maneira como ela \u00e9 aplicada, integrada e gerenciada.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Este artigo explora por que os projetos de IA fracassam, o que a \u201cDivis\u00e3o GenAI\u201d significa para as empresas e quais estrat\u00e9gias podem ajudar as organiza\u00e7\u00f5es a desbloquear o verdadeiro potencial da IA.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"geodir-embed-container\"><iframe loading=\"lazy\" title=\"Por que 95% dos pilotos de IA generativa nas empresas est\u00e3o falhando?\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/ykvwFDWPmhk?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">2. A escala de ado\u00e7\u00e3o da IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Ferramentas de IA generativa, como ChatGPT, Midjourney ou Copilot, tornaram-se nomes conhecidos. Milh\u00f5es de funcion\u00e1rios em todo o mundo est\u00e3o fazendo experi\u00eancias com elas diariamente. As taxas de ado\u00e7\u00e3o em setores como bancos, sa\u00fade e varejo s\u00e3o altas. No entanto, ado\u00e7\u00e3o n\u00e3o \u00e9 o mesmo que transforma\u00e7\u00e3o.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While pilots are easy to launch, turning them into production-ready, value-generating systems is far harder. Many organisations get stuck in pilot purgatory running multiple AI experiments without ever scaling them into business-critical processes.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">3. A divis\u00e3o GenAI explicada<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The \u201cGenAI Divide\u201d refers to the gap between AI adoption and AI transformation. On one side are organisations that treat AI as a shiny experiment, running disconnected pilots that fail to influence core workflows. On the other are the few roughly 5% who successfully integrate adaptive, learning-capable systems that transform operations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Essa divis\u00e3o n\u00e3o tem a ver com o acesso \u00e0 tecnologia. Toda organiza\u00e7\u00e3o pode acessar modelos poderosos atualmente. O verdadeiro diferencial \u00e9 a abordagem e a integra\u00e7\u00e3o.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">4. Raz\u00f5es comuns para o fracasso de projetos de IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Por que a maioria dos projetos de IA fracassa? Surgem v\u00e1rios temas recorrentes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Falta de objetivos claros: Muitos projetos come\u00e7am sem objetivos comerciais definidos.<\/li>\n\n\n\n<li>Expectativas irrealistas: As empresas superestimam o potencial de curto prazo da IA.<\/li>\n\n\n\n<li>Baixa qualidade dos dados: A baixa qualidade dos dados pode fazer com que um modelo de IA produza resultados tendenciosos ou incorretos, levando ao fracasso do projeto quando os modelos s\u00e3o treinados em conjuntos de dados tendenciosos, incompletos ou irrelevantes.<\/li>\n\n\n\n<li>Lacunas de integra\u00e7\u00e3o: Os pilotos funcionam isoladamente, mas n\u00e3o s\u00e3o dimensionados para sistemas ativos.<\/li>\n\n\n\n<li>Resist\u00eancia cultural: Os funcion\u00e1rios geralmente n\u00e3o t\u00eam treinamento ou desconfiam dos resultados da IA.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Estudos do MIT e da McKinsey sugerem que at\u00e9 80% de pilotos de IA nunca chegam \u00e0 produ\u00e7\u00e3o, destacando que a execu\u00e7\u00e3o, e n\u00e3o a ambi\u00e7\u00e3o, \u00e9 o principal gargalo.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">5. O papel dos dados: Entrada de lixo, sa\u00edda de lixo<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A IA \u00e9 t\u00e3o boa quanto os dados que consome. Dados de alta qualidade e bem administrados s\u00e3o essenciais para o sucesso, mas muitas organiza\u00e7\u00f5es subestimam esse requisito. Conjuntos de dados mal rotulados, valores ausentes e falta de diversidade nas amostras de treinamento geralmente prejudicam as iniciativas de IA. Pr\u00e1ticas inadequadas de dados s\u00e3o uma das principais causas de falha da IA em implementa\u00e7\u00f5es no mundo real.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Strong data management practices covering collection, governance, cleansing, and labelling are not optional extras. Without them, AI projects collapse under the weight of bad inputs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">6. Pilotos que n\u00e3o s\u00e3o escalonados<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Os pilotos de IA s\u00e3o sedutores porque s\u00e3o r\u00e1pidos de lan\u00e7ar e f\u00e1ceis de mostrar. Mas os pilotos sem uma estrat\u00e9gia de escalonamento est\u00e3o condenados. Muitos executivos comemoram as demonstra\u00e7\u00f5es de prova de conceito que nunca se transformam em fluxos de trabalho empresariais.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A pergunta principal deve ser: <em>\u201cComo esse piloto se integrar\u00e1 \u00e0s nossas opera\u00e7\u00f5es, sistemas e KPIs di\u00e1rios?\u201d<\/em> Se a resposta n\u00e3o for clara, o projeto j\u00e1 est\u00e1 caminhando para o fracasso. O gerenciamento eficaz de projetos \u00e9 essencial para garantir que os pilotos sejam transformados com sucesso em sistemas de produ\u00e7\u00e3o.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">7. Casos de uso desalinhados<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As iniciativas de IA geralmente perseguem o hype em vez de resolver problemas urgentes. Por exemplo, 50% dos or\u00e7amentos de IA generativa s\u00e3o canalizados para vendas e <a class=\"wpil_keyword_link\" href=\"https:\/\/www.investglass.com\/pt\/ferramentas-de-marketing\/\" target=\"_blank\" rel=\"noopener\" title=\"marketing\" data-wpil-keyword-link=\"linked\" data-wpil-monitor-id=\"5508\">marketing<\/a> projetos, principalmente porque eles produzem resultados vis\u00edveis. No entanto, estudos mostram que a automa\u00e7\u00e3o de back-office geralmente oferece um ROI melhor.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Successful projects start with real pain points processes where automation, prediction, or insight can dramatically improve efficiency or customer experience. Identifying the actual use case guides the selection of the most effective solution, ensuring that the chosen approach truly addresses the underlying business problem.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">8. Colabora\u00e7\u00e3o entre humanos e IA: N\u00e3o \u00e9 substitui\u00e7\u00e3o, mas parceria<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Ao contr\u00e1rio dos temores populares, a IA n\u00e3o substitui os seres humanos em sua totalidade. Em vez disso, os projetos mais bem-sucedidos projetam sistemas human-in-the-loop em que a IA aumenta, e n\u00e3o substitui, a tomada de decis\u00e3o humana.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Por exemplo, a IA pode fazer a triagem das consultas dos clientes, sinalizando as mais simples para automa\u00e7\u00e3o e escalando os problemas complexos para agentes humanos. Esse modelo h\u00edbrido gera confian\u00e7a, reduz os riscos e obt\u00e9m melhores resultados do que a IA ou os humanos isoladamente. A forma\u00e7\u00e3o de uma equipe qualificada para gerenciar e supervisionar a colabora\u00e7\u00e3o entre humanos e IA \u00e9 essencial para garantir que esses sistemas operem de forma eficaz e forne\u00e7am resultados ideais.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">9. A economia sombria da IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">One striking trend is the rise of shadow AI employees using generative tools unofficially to boost productivity. Whether writing reports, summarising meetings, or automating spreadsheets, these personal AI hacks often deliver better ROI than formal initiatives. Often, it is the choice of the right tool for the task that drives these unofficial successes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Em vez de ignorar ou punir a IA invis\u00edvel, as organiza\u00e7\u00f5es com vis\u00e3o de futuro estudam e aprendem com ela. Os padr\u00f5es de uso n\u00e3o oficial podem informar a estrat\u00e9gia oficial, ajudando os l\u00edderes a entender onde a IA realmente agrega valor.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">10. A import\u00e2ncia da adaptabilidade nos sistemas de IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Generic, static models quickly reach their limits. Learning-capable systems that adapt to feedback and context are the future. Without adaptability, AI becomes brittle useful in a demo, but useless in complex, changing workflows.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Startups crossing the GenAI Divide tend to build narrow but highly adaptive systems. They prioritise domain fluency deep knowledge of a specific industry or process over broad general-purpose capability. These adaptive systems are treated as living products: dynamic, operational entities that are continuously monitored, versioned, and improved through real-time feedback and human oversight, ensuring ongoing business impact and seamless integration into enterprise workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">11. Compreens\u00e3o dos modelos e solu\u00e7\u00f5es de IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The critical factor that separates your successful AI initiatives from total failures? Deep, practical understanding of AI models and solutions. In your rush to adopt artificial intelligence, you&#8217;re overlooking the complexities that drive effective AI projects. This oversight is your leading cause of AI project failure you&#8217;re underestimating the importance of high quality data, robust training data, and the nuances of machine learning models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In today&#8217;s business world, your AI pilots fail to deliver measurable return. This &#8220;GenAI Divide&#8221; isn&#8217;t just about your access to the latest AI tools or recent software updates it&#8217;s about whether you truly grasp how AI systems work, what their limitations are, and how to align them with your real business needs. Your inflated expectations, driven by hype, lead you to invest in AI features that look impressive in demos but fall short in production, especially when you ignore edge cases and integration challenges.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A ci\u00eancia de dados e a experi\u00eancia de seus cientistas de dados est\u00e3o no centro de todos os projetos de IA bem-sucedidos. Esses profissionais garantem que seus modelos de IA sejam treinados com dados de boa qualidade, testados rigorosamente e projetados para reter feedback e se adaptar a novos cen\u00e1rios. Sem essa base, at\u00e9 mesmo suas tecnologias de IA mais avan\u00e7adas produzem resultados n\u00e3o confi\u00e1veis, levando a um retorno mensur\u00e1vel zero e a um investimento desperdi\u00e7ado.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The MIT study and resources like the AI incident database highlight your recurring theme: your AI projects fail most often due to poor understanding of underlying models, insufficient testing, and lack of focus on solving real problems. For your mid market firms and large enterprises alike, the lesson is clear your success depends on more than just deploying AI tools. You need commitment to understanding how these tools function, how they integrate with your existing systems, and how you can adapt them to deliver real value.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Suas organiza\u00e7\u00f5es que priorizam esse entendimento est\u00e3o mais bem equipadas para navegar pelas complexidades da iniciativa de IA. Voc\u00ea reconhece a import\u00e2ncia de enfrentar os desafios de integra\u00e7\u00e3o, planejar casos extremos e garantir que seus modelos de IA evoluam \u00e0 medida que as necessidades de neg\u00f3cios mudam. Essa abordagem n\u00e3o apenas reduz o risco de fracasso do projeto de IA, mas tamb\u00e9m maximiza o retorno sobre o investimento, transformando a IA de centro de custos em um verdadeiro impulsionador do crescimento dos neg\u00f3cios.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In a landscape where you&#8217;re investing millions in AI initiatives, and where the line between your success and failure is razor-thin, your ability to understand and control AI models and solutions is paramount. Your teams and leaders who focus on this understanding rather than simply relying on hype or the latest technology are far more likely to deliver projects that succeed at scale, provide measurable return, and solve your real business problems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Por fim, \u00e9 essencial aprender com os erros do passado. O banco de dados de incidentes de IA oferece insights valiosos sobre onde e por que os projetos de IA fracassam, refor\u00e7ando a necessidade de pesquisa rigorosa, foco e educa\u00e7\u00e3o cont\u00ednua. Ao fazer da compreens\u00e3o a pedra angular de todas as iniciativas de IA que lan\u00e7ar, voc\u00ea poder\u00e1 superar o GenAI Divide e garantir que seus investimentos em intelig\u00eancia artificial proporcionem um valor duradouro e transformador.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">11. Li\u00e7\u00f5es de construtores bem-sucedidos<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As empresas de IA que prosperam atualmente seguem um padr\u00e3o comum:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Eles criam sistemas adaptativos que melhoram com o tempo.<\/li>\n\n\n\n<li>Eles se concentram em casos de uso espec\u00edficos e de alto valor, em vez de conjuntos de recursos extensos.<\/li>\n\n\n\n<li>Eles priorizam a integra\u00e7\u00e3o do fluxo de trabalho, incorporando a IA aos processos comerciais di\u00e1rios.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Isso contrasta com as empresas que criam demonstra\u00e7\u00f5es chamativas sem incorpor\u00e1-las \u00e0s ferramentas reais que os funcion\u00e1rios usam.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">12. Li\u00e7\u00f5es de compradores bem-sucedidos<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Do lado do comprador, as organiza\u00e7\u00f5es mais eficazes tratam a aquisi\u00e7\u00e3o de IA mais como terceiriza\u00e7\u00e3o de processos de neg\u00f3cios (BPO) do que como software como servi\u00e7o (SaaS) tradicional. Elas exigem:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Personaliza\u00e7\u00e3o adaptada a seus fluxos de trabalho.<\/li>\n\n\n\n<li>Resultados baseados em resultados, n\u00e3o apenas em recursos.<\/li>\n\n\n\n<li>Parcerias com fornecedores para o desenvolvimento conjunto de solu\u00e7\u00f5es.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Essa mentalidade faz com que a IA deixe de ser um \u201cproduto que voc\u00ea instala\u201d e passe a ser uma parceria que voc\u00ea desenvolve.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">13. A pr\u00f3xima fronteira: A Web Ag\u00eantica<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Looking ahead, AI is moving towards an agentic web a network of autonomous systems that communicate and coordinate tasks without constant human intervention. These changes are already happening in some industries, where autonomous systems are being integrated into workflows and transforming how work is organized. Emerging protocols such as MCP (Model Context Protocol) and A2A (Agent-to-Agent) are paving the way.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Nesse futuro, os sistemas n\u00e3o gerar\u00e3o apenas texto ou imagens; eles se lembrar\u00e3o, planejar\u00e3o e agir\u00e3o, adaptando-se aos fluxos de trabalho com o m\u00ednimo de supervis\u00e3o. As empresas que se prepararem para essa mudan\u00e7a agora estar\u00e3o mais bem posicionadas para capturar o valor futuro.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">14. Estrat\u00e9gias para superar a divis\u00e3o da GenAI<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Como as organiza\u00e7\u00f5es podem preencher a lacuna entre a ado\u00e7\u00e3o piloto e a transforma\u00e7\u00e3o significativa? As principais estrat\u00e9gias incluem:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Defina objetivos claros: Associe todas as iniciativas de IA a resultados comerciais mensur\u00e1veis.<\/li>\n\n\n\n<li>Investir em dados: Priorize a governan\u00e7a, a diversidade e a relev\u00e2ncia.<\/li>\n\n\n\n<li>Focus on ROI-rich use cases: Don\u2019t just follow the hype automate where it matters.<\/li>\n\n\n\n<li>Apoiar a colabora\u00e7\u00e3o entre humanos e IA: Mantenha as pessoas informadas para que haja supervis\u00e3o e confian\u00e7a.<\/li>\n\n\n\n<li>Aprenda com a IA invis\u00edvel: estude os padr\u00f5es de ado\u00e7\u00e3o n\u00e3o oficiais para orientar a estrat\u00e9gia formal.<\/li>\n\n\n\n<li>Fa\u00e7a parcerias estrat\u00e9gicas: Trate os fornecedores de IA como colaboradores, n\u00e3o apenas como fornecedores.<\/li>\n\n\n\n<li>Escolha sistemas adapt\u00e1veis: Priorize ferramentas com capacidade de aprendizado que evoluem com o uso.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Sem essas estrat\u00e9gias, as organiza\u00e7\u00f5es correm o risco de n\u00e3o ter nenhum retorno sobre seus investimentos em IA.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">15. Conclus\u00e3o: Do fracasso \u00e0 transforma\u00e7\u00e3o<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The story of AI today is one of potential versus practice. While billions are invested, only a small fraction of projects deliver meaningful returns. The GenAI Divide illustrates that technology alone is not the problem it is approach, integration, and execution.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ao aprender com os fracassos, adotar a adaptabilidade e priorizar a integra\u00e7\u00e3o, as organiza\u00e7\u00f5es podem transformar a IA de um centro de custos em um impulsionador de crescimento. O futuro n\u00e3o est\u00e1 nos pilotos, mas nos sistemas que aprendem, colaboram e transformam a forma como o trabalho \u00e9 feito.<\/p>","protected":false},"excerpt":{"rendered":"<p>Artificial Intelligence promises to reshape industries, yet most companies are still struggling to see results. Despite record investments, nearly all AI projects stall before reaching real impact. Why do so many fail and what separates the few success stories from the rest? This article explores the \u201cGenAI Divide\u201d and shares strategies to help organisations cross [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":48330,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[485],"class_list":["post-48352","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article","tag-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.8 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Why AI Fail: Insights from MIT Experts<\/title>\n<meta name=\"description\" content=\"Explore why AI fail in organizations despite high investments. Learn about success factors and the GenAI Divide.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.investglass.com\/pt\/por-que-a-ia-fracassa-principais-motivos-e-estrategias-para-o-sucesso-na-implementacao\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why AI Fail: Top Reasons and Strategies for Success in Implementation\" \/>\n<meta property=\"og:description\" content=\"Artificial Intelligence promises to reshape industries, yet most companies are still struggling to see results. 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