APAC's AI adoption gap: the proof-of-concept prison is real and getting worse

In plain terms

Four independent APAC reports this week say the same thing: enterprise AI is failing to leave the pilot phase. e27 flags "most APAC enterprise AI still stuck in the proof-of-concept room." Analysts in Singapore blame weak execution, not worker resistance (e27). Agentic AI ambitions collide with legacy systems and poor data quality (e27). And ITmedia reports 85.7% of Japanese companies where the CEO doesn't use AI have "no policy or governance framework". The story is the same across the region: the model works; the org doesn't.

What the pattern says

This isn't a technology problem. It's a change-management problem wearing an AI hat. The consistent findings, each with its receipts:

• Legacy systems(source: e27, "Agentic AI ambitions in Singapore run into legacy systems and data quality gaps"): agentic AI needs API surfaces that most APAC ERPs simply don't expose without months of middleware work.
• Data quality(same e27 report): model outputs are only as clean as the retrieval layer. Most enterprises overestimated their document hygiene.
• Execution capacity(source: e27, "Singapore's AI adoption problem is not worker resistance, but weak execution"): workers aren't resisting - they simply aren't being told what to automate or why. The Enterprise Singapore study cited by e27 is the sharpest read on this.
• Leadership drag(source: ITmedia, art. 17297490 - "AI活用、最大のボトルネックは「経営層」か"): 85.7% of Japanese firms whose CEO doesn't use AI have no AI policy or governance in place. If the top of the org chart doesn't touch AI, no framework forms and mid-managers won't stake a career on rollouts that lack top cover.

India offers the parallel warning: YourStory reports the country's AI workforce is "growing overnight" while AI governance is not. Talent supply is racing ahead of institutional capacity to deploy it.

Under the hood

What POC-to-production actually breaks on:

• RAG plumbing: retrieval quality is the silent killer. Most stalls happen when the LLM is asked to reason over documents that were never labeled, deduped, or versioned.
• Identity and permissions: agents need to act, not just answer. Enterprise SSO models weren't designed for a non-human principal.
• Evaluation gaps: without an eval harness, "it works in the demo" is the ceiling.

So what

For decision-makers: the ROI on AI is not a modeling problem - it's an integration problem. Budget accordingly: 1x on the model, 3-5x on the plumbing. For builders: pick the two workflows with the cleanest data and the strongest executive backer, and ignore the rest until they clean up. For anyone selling AI into APAC enterprises: your win isn't a better demo; it's a shorter path from POC to a system-of-record write.