{"id":120287,"date":"2026-04-22T11:00:46","date_gmt":"2026-04-22T04:00:46","guid":{"rendered":"https:\/\/wp.eastgate-software.com\/?p=120287"},"modified":"2026-04-22T11:00:46","modified_gmt":"2026-04-22T04:00:46","slug":"ai-readiness-in-apac-why-adoption-is-outpacing-foundations","status":"publish","type":"post","link":"https:\/\/wp.eastgate-software.com\/jp\/ai-readiness-in-apac-why-adoption-is-outpacing-foundations\/","title":{"rendered":"AI readiness in APAC: why adoption is outpacing foundations"},"content":{"rendered":"<p><span data-contrast=\"auto\">AI readiness in APAC is emerging as a critical barrier to enterprise success, even as adoption accelerates across industries. According to insights from Hitachi Vantara, between 95% and 97% of organisations consider AI a top priority, yet only around one-third believe their infrastructure is mature enough to support it on a large scale. This gap highlights a growing disconnect between ambition and execution in the region\u2019s AI transformation journey.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Across the Asia Pacific, enterprises are moving beyond experimentation into broader deployment, but progress remains uneven. Markets such as Singapore and Australia are advancing quickly, while others lag due to fragmented systems, inconsistent data, and legacy infrastructure. These foundational weaknesses limit the ability of AI systems to deliver reliable, scalable business outcomes.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The issue is not a lack of investment but a lack of readiness. Many\u00a0organisations\u00a0continue to treat AI as a standalone initiative rather than an extension of digital transformation. Disconnected data environments and outdated systems reduce the accuracy and trustworthiness of AI outputs, making it difficult to scale beyond pilot projects.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Only one-third of enterprises have AI-ready infrastructure.<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">Legacy systems and data silos remain the most significant barriers.<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">Leading sectors include financial services, healthcare, and telecom,<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"4\" data-aria-level=\"1\"><span data-contrast=\"auto\">Governance and data consistency are critical for scaling AI.<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">To address these challenges, enterprises must shift focus from tools to outcomes. AI initiatives should begin with clearly defined business problems, supported by unified data platforms and consistent hybrid environments. Governance must also expand beyond data to include AI models, usage policies, and operational controls.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Ultimately, AI readiness in Asia will determine which organisations move from experimentation to measurable value. Companies that invest in data foundations, reduce technical debt, and embed governance early will be better positioned to scale AI and achieve long-term competitive advantage.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;335551550&quot;:6,&quot;335551620&quot;:6}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">\u30bd\u30fc\u30b9\uff1a<\/span><\/b><span data-ccp-props=\"{&quot;335551550&quot;:6,&quot;335551620&quot;:6}\">\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/www.itnews.asia\/news\/ai-readiness-could-be-apacs-biggest-challenge-despite-rapid-adoption-624867\"><span data-contrast=\"none\">https:\/\/www.itnews.asia\/news\/ai-readiness-could-be-apacs-biggest-challenge-despite-rapid-adoption-624867<\/span><\/a><span data-contrast=\"auto\">\u00a0<\/span><span data-ccp-props=\"{&quot;335551550&quot;:6,&quot;335551620&quot;:6}\">\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>AI readiness in APAC is emerging as a critical barrier to enterprise success, even as adoption accelerates across industries. According to insights from Hitachi Vantara, between 95% and 97% of organisations consider AI a top priority, yet only around one-third believe their infrastructure is mature enough to support it on a large scale. This gap highlights a growing disconnect between ambition and execution in the region\u2019s AI transformation journey.\u00a0 Across the Asia Pacific, enterprises are moving beyond experimentation into broader deployment, but progress remains uneven. Markets such as Singapore and Australia are advancing quickly, while others lag due to fragmented systems, inconsistent data, and legacy infrastructure. These foundational weaknesses limit [&hellip;]<\/p>","protected":false},"author":238283293,"featured_media":120289,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","inline_featured_image":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1584],"tags":[1488,1710],"class_list":["post-120287","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-enthusiast","tag-ai","tag-apac"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.4 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>AI readiness in APAC: why adoption is outpacing foundations - Eastgate Software<\/title>\n<meta name=\"description\" content=\"AI readiness in APAC remains a major challenge as weak data, legacy systems, and poor governance limit enterprise AI success and scalability.\" \/>\n<meta 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