• The gap between India’s deployment of agentic AI and the rest of the region is wider than headlines suggest.
• Global report finds India ahead, Australia and Japan are still finding their footing.

A new global survey of 1,879 IT leaders captures how unevenly agentic AI adoption is moving in Asia. The OutSystems 2026 State of AI Development report, released today, finds that 97% of organisations globally are exploring agentic strategies – but how far along they actually are depends heavily on where they sit.

In APAC, the split is sharp. India reports the highest concentration of “expert” agentic AI users of any country surveyed. Australia and Japan are at an intermediate stage, still moving initiatives from pilot toward production. The difference shows up directly in where returns are landing.

Where agentic AI adoption is paying off in Asia

India leads all surveyed countries in IT development and productivity ROI realisation at 50%, meaning half of Indian respondents are seeing returns specifically from agents assisting with code generation and workflow automation. Japan leads in operational efficiency ROI at 37%, a different profile, but a real one.

The report finds that 49% of enterprises globally have moved more than half of their agentic AI projects from pilot to full enterprise production. Brazil and India post the highest rates of this transition in all markets surveyed, suggesting that both markets have developed the internal muscle to take agents out of controlled environments and into live operations.

That practical momentum matters more than a self-assessment of ability. Any team can describe itself as advanced. The pilot-to-production rate is the metric that reflects actual organisational commitment.

The trust picture in the region

Globally, 73% of respondents express high or moderate trust in agents acting autonomously, up from 40% who said last year they mostly trusted generative AI to write code without human assistance. That’s a change in twelve months.

India again leads in APAC, with “high trust” ranking as the top response among Indian respondents. Every other country surveyed, including Australia and Japan, lists “moderate trust” as their primary position. The distinction is meaningful: high trust tends to correlate with willingness to deploy agents in mission-critical processes not productivity tools alone.

The sector breakdown adds context. Financial services globally lead on complete trust in autonomous agents, at 12%.

The barrier Asia shares with everyone else

Where APAC aligns with the global picture is on the barrier side. Legacy fragmentation and integration difficulties are the top blockers to AI development success, cited by over 40% of surveyed leaders regardless of seniority. Some 38% list legacy systems as the primary reason agentic AI projects have stalled out entirely.

In Japan, where legacy enterprise infrastructure runs deep, 44% of respondents cite insufficient internal skills as a barrier, the highest of any country surveyed. That skills gap is the structural reason why a market with real operational efficiency returns is still sitting at intermediate agentic maturity.

The governance picture is equally challenging. Only 36% of organisations globally have a centralised approach to agentic AI governance. Most are running agents in fragmented environments, relying on project-level rules not any overarching framework. In markets scaling fast, that gap accumulates quickly.

What the APAC divergence means for enterprise strategy

The report’s APAC data describes two different adoption stories running in parallel. India’s trajectory – high ability self-assessment, leading production deployment rates, and the highest IT productivity ROI of any country – suggests a market that has moved from experimentation to execution and is now consolidating returns.

The challenge there is governance catching up with deployment speed. Australia and Japan reflect a more cautious posture, building ability at an intermediate pace with a stronger emphasis on governance before scale. Japan is the only country in the survey where generative AI-assisted development did not rank first. Traditional code and outsourced vendor-built solutions took the top spots instead – a revealing sign of where risk appetite sits.

India’s deployment pace and Japan’s caution are both rational responses to their respective enterprise environments. The question for multinationals operating in both is whether their AI governance frameworks are built to handle the difference.