{"id":16810,"date":"2025-01-27T08:01:53","date_gmt":"2025-01-27T16:01:53","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=16810"},"modified":"2025-06-13T16:36:22","modified_gmt":"2025-06-13T23:36:22","slug":"2025-enterprise-ai-predictions","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/2025-enterprise-ai-predictions\/","title":{"rendered":"Previs\u00f5es de IA empresarial para 2025: Quatro mudan\u00e7as proeminentes que est\u00e3o remodelando a infraestrutura e a estrat\u00e9gia"},"content":{"rendered":"

As organiza\u00e7\u00f5es est\u00e3o indo al\u00e9m da experimenta\u00e7\u00e3o inicial para implementar estrat\u00e9gias de IA mais sofisticadas e pr\u00e1ticas. A tend\u00eancia de simplesmente adotar solu\u00e7\u00f5es de IA prontas para uso est\u00e1 dando lugar a uma abordagem mais diferenciada, em que as empresas est\u00e3o equilibrando cuidadosamente o poder de grandes modelos de linguagem com a precis\u00e3o de solu\u00e7\u00f5es especializadas, ao mesmo tempo em que preparam sua for\u00e7a de trabalho e infraestrutura para essa transforma\u00e7\u00e3o. Essa mudan\u00e7a se alinha com as previs\u00f5es de IA para 2025, marcando um momento cr\u00edtico na ado\u00e7\u00e3o da IA empresarial. As organiza\u00e7\u00f5es devem tomar decis\u00f5es estrat\u00e9gicas sobre sua arquitetura de IA, desenvolvimento de aplicativos, gerenciamento de dados e desenvolvimento de talentos para permanecerem competitivas em 2025 e nos anos seguintes.<\/span><\/p>\n

Quatro previs\u00f5es de IA para 2025: Como a estrat\u00e9gia empresarial evoluir\u00e1<\/h2>\n

As empresas adotar\u00e3o modelos h\u00edbridos de IA, combinando LLMs e modelos menores e espec\u00edficos de dom\u00ednio, para proteger os dados e maximizar os resultados<\/b><\/p>\n

As empresas adotar\u00e3o uma abordagem h\u00edbrida para a implementa\u00e7\u00e3o de IA que combina grandes modelos de linguagem com modelos menores, mais especializados e espec\u00edficos de dom\u00ednio para atender \u00e0s demandas dos clientes por solu\u00e7\u00f5es de IA que sejam privadas, seguras e espec\u00edficas para eles.<\/span><\/p>\n

Embora os grandes modelos de linguagem ofere\u00e7am recursos gerais poderosos, eles n\u00e3o est\u00e3o equipados para responder a todas as perguntas relacionadas ao dom\u00ednio de neg\u00f3cios espec\u00edfico de uma empresa. A prolifera\u00e7\u00e3o de modelos especializados, treinados em dados espec\u00edficos do dom\u00ednio, ajudar\u00e1 a garantir que as empresas possam manter a privacidade e a seguran\u00e7a dos dados e, ao mesmo tempo, acessar o amplo conhecimento e os recursos dos LLMs.<\/span><\/p>\n

Os usos desses LLMs for\u00e7ar\u00e3o uma mudan\u00e7a na complexidade t\u00e9cnica das arquiteturas de dados para as arquiteturas de modelos de linguagem. As empresas precisar\u00e3o simplificar suas arquiteturas de dados e concluir seus projetos de moderniza\u00e7\u00e3o de aplicativos.<\/span><\/p>\n

A IA impulsionar\u00e1 a reescrita completa de aplicativos, \u00e0 medida que as empresas v\u00e3o al\u00e9m das solu\u00e7\u00f5es complementares<\/b><\/p>\n

Embora haja agora uma onda de empresas adicionando recursos de IA aos aplicativos existentes, principalmente na gera\u00e7\u00e3o de conte\u00fado e no marketing, setores como o de sa\u00fade, com grandes quantidades de dados inexplorados, precisar\u00e3o ir al\u00e9m de simples aprimoramentos de IA. As empresas perceber\u00e3o que o simples uso da IA para melhorar os aplicativos existentes \u00e9 insuficiente e precisar\u00e3o reescrever completamente seus aplicativos para capitalizar totalmente o potencial da IA.<\/span><\/p>\n

O futuro a longo prazo \u00e9 uma transforma\u00e7\u00e3o abrangente em que todos os aplicativos - pequenos, m\u00e9dios e grandes - ser\u00e3o revisados e reescritos usando IA. Esse movimento abrangente marcar\u00e1 uma mudan\u00e7a fundamental de solu\u00e7\u00f5es complementares para redesenhos b\u00e1sicos, \u00e0 medida que as organiza\u00e7\u00f5es reconhecerem os benef\u00edcios de criar aplicativos verdadeiramente voltados para IA que possam aproveitar totalmente os recursos da tecnologia.<\/span><\/p>\n

As arquiteturas de dados ser\u00e3o redesenhadas para dar suporte \u00e0 integra\u00e7\u00e3o de IA e garantir a transpar\u00eancia<\/b><\/p>\n

\u00c0 medida que a IA se torna mais integrada aos aplicativos, as arquiteturas de dados ser\u00e3o fundamentalmente redesenhadas para dar suporte \u00e0s cargas de trabalho de IA. As empresas implementar\u00e3o novas arquiteturas de dados que v\u00e3o al\u00e9m do simples armazenamento de registros para capturar o \"hist\u00f3rico de intelig\u00eancia\" e os processos de pensamento dos sistemas de IA. Elas precisar\u00e3o simplificar arquiteturas complexas, incluindo a consolida\u00e7\u00e3o de plataformas, e eliminar silos de dados para criar dados confi\u00e1veis.<\/span><\/p>\n

Essas arquiteturas evolu\u00eddas incorporar\u00e3o medidas de seguran\u00e7a robustas para dados e comunica\u00e7\u00f5es de IA. Elas priorizar\u00e3o a transpar\u00eancia e a governan\u00e7a, permitindo que as organiza\u00e7\u00f5es rastreiem como seus dados foram usados no treinamento de IA, monitorem os processos de tomada de decis\u00e3o dos sistemas de IA e mantenham registros detalhados dos insights gerados pela IA e seu racioc\u00ednio subjacente.<\/span><\/p>\n

Os aplicativos de IA ser\u00e3o criados mais pr\u00f3ximos das fontes de dados para reduzir a lat\u00eancia, melhorar a privacidade, reduzir os custos, lidar com as restri\u00e7\u00f5es de largura de banda, aumentar a efici\u00eancia energ\u00e9tica e permitir a escalabilidade. Essa tend\u00eancia inclui tecnologias como IA de borda, IA no local e aprendizado de m\u00e1quina federado. Embora ofere\u00e7a benef\u00edcios significativos, ainda existem desafios como limita\u00e7\u00f5es de hardware, otimiza\u00e7\u00e3o de modelos e complexidade de integra\u00e7\u00e3o.<\/span><\/p>\n

As empresas que n\u00e3o priorizarem a prepara\u00e7\u00e3o da for\u00e7a de trabalho para a IA enfrentar\u00e3o desafios significativos<\/b><\/p>\n

As organiza\u00e7\u00f5es precisar\u00e3o desenvolver planos abrangentes para melhorar as habilidades e treinar a for\u00e7a de trabalho existente para garantir Integra\u00e7\u00e3o perfeita com recursos de IA<\/strong><\/a>. Novas fun\u00e7\u00f5es criativas e estrat\u00e9gicas devem ser desenvolvidas para complementar os recursos de IA, em vez de substituir humanos por sistemas de IA. Os agregadores desempenhar\u00e3o um papel fundamental para ajudar as empresas a identificar e implementar as solu\u00e7\u00f5es corretas de IA.\u00a0<\/span><\/p>\n

As empresas tamb\u00e9m devem preparar sua for\u00e7a de trabalho para gerenciar com efic\u00e1cia as regulamenta\u00e7\u00f5es governamentais de IA, garantindo que permane\u00e7am adapt\u00e1veis e flex\u00edveis, pois essas regulamenta\u00e7\u00f5es provavelmente exigir\u00e3o atualiza\u00e7\u00f5es cont\u00ednuas nos sistemas organizacionais e de IA.<\/span><\/p>\n

Ao olharmos para as tend\u00eancias de 2025, as empresas enfrentam um momento crucial na ado\u00e7\u00e3o da IA. As previs\u00f5es de IA para 2025 indicam que o sucesso depender\u00e1 de planejamento, inova\u00e7\u00e3o e implementa\u00e7\u00e3o cuidadosos para obter opera\u00e7\u00f5es verdadeiramente integradas e que priorizem a IA. As organiza\u00e7\u00f5es devem combinar com efici\u00eancia solu\u00e7\u00f5es de IA especializadas e de uso geral, garantindo a prontid\u00e3o da for\u00e7a de trabalho e uma infraestrutura robusta. Aquelas que equilibrarem a inova\u00e7\u00e3o com a implementa\u00e7\u00e3o pr\u00e1tica - mantendo a seguran\u00e7a, a privacidade e a transpar\u00eancia durante todo o processo - obter\u00e3o uma vantagem competitiva significativa.<\/span><\/p>\n

 <\/p>","protected":false},"excerpt":{"rendered":"

Organizations are moving beyond initial experimentation to implement more sophisticated and practical AI strategies. The trend of simply adopting off-the-shelf AI solutions is giving way to a more nuanced approach, where businesses are carefully balancing the power of large language […]<\/p>","protected":false},"author":85567,"featured_media":16813,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[10122,3917,3919,9973,9937],"tags":[9837],"ppma_author":[10083],"class_list":["post-16810","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","category-company","category-executive","category-generative-ai-genai","category-vector-search","tag-strategy"],"acf":[],"yoast_head":"\nAI Predictions 2025: Four Trends in Enterprise AI Strategy<\/title>\n<meta name=\"description\" content=\"Discover four key AI predictions for 2025 that will shape enterprise strategy, including the rise of hybrid AI models and evolving data architectures. 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Leia mais!","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.couchbase.com\/blog\/pt\/2025-enterprise-ai-predictions\/","og_locale":"pt_BR","og_type":"article","og_title":"2025 Enterprise AI Predictions: Four Prominent Shifts Reshaping Infrastructure and Strategy","og_description":"Discover four key AI predictions for 2025 that will shape enterprise strategy, including the rise of hybrid AI models and evolving data architectures. 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