APAC Does Not Have an AI Adoption Problem. It Has an AI Evidence Problem.
An expert interpretation of the traceability gap emerging as AI takes a larger role in business decisions and actions.
Sumsub APAC State of Digital Trust: AI Governance Benchmark (2026)
This commentary is Teguh Digital's independent interpretation of selected findings in the report. Sumsub did not endorse Vector5 or Teguh Digital, and the report should be consulted for its complete methodology and conclusions.
The signal is not adoption. It is proof.
The Sumsub benchmark reports APAC governance scores of 69.9 for Autonomy, 70.3 for Responsibility and 61.0 for Traceability. In other words, organizations report that AI is taking on meaningful work and that responsibility is being assigned, but the evidence layer is less mature.
The same study reports that 95% of respondents are confident they can explain an AI decision, while only 50% report the ability to reconstruct the decision pathway and 38% report secure, tamper-proof records providing a permanent audit trail of AI actions.
Explanation is not the same as evidence.
An AI system may be able to describe why an answer was produced. A stronger trust test is whether an independent reviewer can reconstruct what happened from preserved evidence without relying on the same model to explain itself afterwards.
Why this matters commercially
As AI moves from drafting and recommendations into multi-step workflows, approvals and autonomous actions, organizations need more than policy documents. Important outcomes increasingly need a traceable relationship between the source material, the AI-assisted work, the responsible human or organization, the approval and the final artifact.
We call the desired result Portable Evidence for AI: evidence that can travel with an important output and be checked outside the application that created it.
From AI output to portable evidence
Where Vector5 fits
Vector5 is Teguh Digital's architecture for progressively adding this evidence layer to real work. Monday supports productive and governed AI workflows; EviMark is designed to create portable proof around important artifacts; DeepEyes extends protection for intended recipients; and the planned validation layer is intended to support independent verification. The LLM can change. The evidence and accountability architecture should remain.
This is why we see traceability as infrastructure rather than another AI feature. The objective is not to claim that every AI output is true. It is to make important AI-assisted work more reconstructable, attributable, integrity-protected and verifiable.
Can you prove what your AI did?
Discuss how your current AI workflows can be reconstructed, attributed and evidenced.
Reference: Sumsub APAC State of Digital Trust: AI Governance Benchmark, 2026. Statistics above are selectively cited and paraphrased for commentary; readers should refer to the original report for full context, methodology and market breakdowns.