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’s AI transformation journey.
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.
The issue is not a lack of investment but a lack of readiness. Many organisations continue 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 Disconnected data environments and outdated systems reduce the accuracy and trustworthiness of AI outputs, making it difficult to scale beyond pilot projects.
- Only one-third of enterprises have AI-ready infrastructure.
- Legacy systems and data silos remain the most significant barriers.
- Leading sectors include financial services, healthcare, and telecom,
- Governance and data consistency are critical for scaling AI.
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.
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.
Quelle:
