A reflection on why AI-era inclusion requires seeing — and formally recognising — the informal helpers whose invisible labour makes technology appear to work.
Whenever a new system is introduced, some people learn it more quickly than others. In Thai organisations, the gap is often filled informally. A younger employee helps a senior colleague submit the form. One technologically confident person becomes the unofficial support desk for the department. A family member manages online banking, health applications, government services and device settings for several relatives.
This behaviour reflects generosity and social responsibility. People help because they do not want someone they care about to be excluded. But informal assistance can become invisible labour. The helper receives no formal time, recognition or authority. They remain responsible for their own work while carrying the digital difficulty of everyone around them. If a mistake occurs, accountability becomes unclear — who made the decision, the account owner or the person operating the system on their behalf?
Artificial intelligence may widen this pattern. People who understand how to use AI effectively may begin writing prompts, checking answers, preparing reports or correcting outputs for colleagues whose names remain on the final work. Within families, one person may become responsible for interpreting medical recommendations or detecting scams generated by increasingly sophisticated systems. The organisation or institution sees successful adoption. The hidden helper experiences increasing burden.
My research emphasised that technological environments influence participation and that designers should understand the practical needs of the people expected to use them. This suggests an important principle: a system is not truly accessible simply because someone nearby can help the user survive it.
Thai organisations should identify where informal digital support is occurring. Who is repeatedly asked to fix errors? Who translates the system into language others understand? Who becomes responsible when an executive uses AI without learning how it works? These helpers often possess valuable insight into usability problems, training needs and cultural barriers. Their knowledge should shape redesign.
Formal support can also preserve dignity. Senior employees should be able to ask for assistance without feeling that they are losing status. Training should be connected to real work rather than delivered once and declared complete. AI tools should explain their limits and make review easier for users with different levels of confidence.
At the same time, helping should build capability rather than permanent dependence. Completing every task for another person may feel kind, but it can prevent them from developing the confidence to participate independently. The goal is not equal technical expertise. It is enough understanding to retain judgement and responsibility.
Thailand's culture of helping can make digital transformation more inclusive. But inclusion should not depend on a small number of people quietly carrying everyone else across the gap.
A system has not been successfully adopted when it works only because an invisible helper is continually protecting others from its difficulty.