Lenovo CIO Playbook: Asia Pacific Enterprises Shift from AI Experiments to Execution in 2026

According to the Lenovo CIO Playbook, enterprises across the Asia Pacific region are entering a new phase of artificial intelligence adoption—one that prioritizes execution over experimentation. Recent data shows that 96% of organizations in the Asia Pacific plan to increase their AI investments in 2026, with spending expected to rise by an average of 15%. This momentum is mirrored across ASEAN+ markets, where enterprises are aligning AI initiatives with clear business outcomes rather than isolated pilots.

For CIOs in the region, AI is no longer just a technology upgrade—it is a strategic growth driver. Revenue growth, improved profitability, and enhanced business and customer experiences have emerged as the top AI-driven priorities. This shift reflects a broader move toward outcomes-led AI adoption, where success is measured by tangible returns. Notably, 88% of organizations now expect positive ROI from their AI investments, with an anticipated average return of 2.8 times their initial spend.

AI adoption is also expanding beyond traditional IT departments. Around 66% of enterprises are already piloting or systematically using AI, and many report that non-IT teams are actively funding AI initiatives. Functions such as marketing, operations, finance, and customer service are increasingly driving demand, accelerating enterprise-wide AI integration.

One of the most significant emerging trends is Agentic AI—systems capable of autonomous decision-making and task execution. Interest in Agentic AI is expected to double over the next 12 months as organizations explore its potential to improve efficiency and responsiveness. However, despite growing enthusiasm, only a small percentage of enterprises feel ready to scale Agentic AI due to persistent challenges around governance, security, data quality, and system integration.

From an infrastructure perspective, hybrid AI has become the default enterprise architecture. Organizations are combining on-premises, cloud, and edge environments to balance performance, regulatory compliance, and security requirements. At the same time, rising inferencing demands are reshaping CIO priorities, with projections indicating that AI compute workloads will increasingly shift toward inferencing by 2030.

Employee productivity is another key focus area. Half of all enterprise PC purchases are expected to transition to AI-optimized devices equipped with on-device AI agents, signaling a move toward more intelligent, assistive work environments.

Despite strong confidence in AI’s value, scaling remains a critical challenge. Only about half of AI proofs-of-concept successfully reach production, highlighting the need for better execution frameworks, governance models, and cross-functional alignment as enterprises move deeper into the AI-driven future.