The prison of proof-of-concept in APAC - four voices, one wall

In plain terms

Four publications converge in the same week: e27 on PoCs blocked in APAC, Tech in Asia on legacy systems in Singapore, another e27 on weak execution rather than employee resistance, and a very concrete use case (patient intake in healthcare). The diagnosis is unanimous: AI in enterprises in APAC is hitting three walls - legacy, data quality, execution - not technology.

The issue

Wall #1 - legacy. In Singapore, a large part of major banks and public institutions still run on COBOL/AS400 mainframes, without modern APIs. An LLM agent cannot read a mainframe in direct RPC. Result: months of MQ/ESB plumbing before the first LLM call is possible.

Wall #2 - data quality. The finding that comes up in all APAC surveys: AI PoCs in APAC fail at the production stage in an overwhelming majority because reference data (clients, products) is scattered across numerous unreconciled systems. The LLM is brilliant on clean data - it hallucinates on dirty data.

Wall #3 - execution. e27 defends a counterintuitive thesis: employee resistance is overestimated as a brake. The real problem is the inability of IT teams to move from a PoC to a versioned, monitored production pipeline with SLO. What is missing: senior MLOps engineers - the rarest resource in APAC.

The counter-example that works - patient intake in healthcare. Documented use cases in Southeast Asia (hospitals and clinics in Southeast Asia that have industrialized AI intake - see e27) show the winning pattern: LLM that handles patient intake (anamnesis, triage, form) in a fraction of the time of a nurse. What works: narrow scope (intake only), clean data (standardized forms), ROI measurable in weeks (nurse time freed).

Under the hood

• Order of magnitude: the PoC → production ratio in APAC remains very low (clearly below 20% in convergent surveys), without a single consolidated figure available.
• Average cost of a blocked PoC: estimated in hundreds of thousands of dollars by field surveys, with a majority in internal team salary. Non-zero sunk cost.
• Training: the lack of MLOps is structural - regional programs train a few hundred profiles per year when demand exceeds several thousand.
• Thread (#1002): this publication consolidates 4 convergent signals under the same diagnosis.

So what

Three consequences. One: vendors who break through in APAC in 2026 are those who bring the plumbing (data mesh, MDM, legacy connectors) - not those who sell a pretty LLM in demo. Two: use cases with a narrow scope (health intake, email triage, ticketing, customer service claims) win - big horizontal dreams (the "transverse copilot") lose. Three: data sovereignty + on-premise becomes a real differentiator in APAC - banking and healthcare regulators in Singapore, Malaysia, Indonesia require that nothing leaves the country. To watch: MLOps retention KPIs at DBS, Grab, Sea - the three that have industrialized the best.