This article is part of a second chapter in the series — Thai Society in the Intelligent Age — examining how Thai cultural characteristics may shape the way we adopt, interpret and live with artificial intelligence.
During the first phase of the digital era, inequality was often described through access. Who had a computer? Who had Internet connectivity? Who could afford a smartphone?
The intelligent age creates a deeper divide. Two people may have access to the same AI tool but receive very different value from it.
One knows how to frame a question, verify an answer, combine information and apply the result. Another accepts the first response without understanding its limitations. One organisation possesses clean data and trained employees. Another has access to software but lacks the systems needed to use it responsibly.
This is the difference between access to AI and capacity with AI.
Thailand's national strategy recognises that AI infrastructure must be accompanied by human capability and an effective ecosystem. Recent development assessments similarly emphasise the importance of digital skills, data infrastructure and trustworthy adoption.
Without deliberate action, AI may increase existing inequalities. Large corporations can invest in data, cybersecurity, specialised talent and governance. Small businesses may rely on consumer tools without understanding where their data goes. Urban schools may experiment with personalised learning while rural schools continue to face more basic constraints.
The intelligent divide may also exist within the same organisation. Senior executives may receive strategic AI training while operational employees are expected simply to follow new systems. Younger workers may be comfortable experimenting but lack business judgement. Older workers may possess deep experience but feel excluded by unfamiliar interfaces.
This is not only a technology problem. It is a social design problem. Thailand should define AI inclusion as more than providing tools. People need the ability to question outputs, protect information, recognise manipulation and know when human expertise remains necessary.
Training should be practical and connected to real life. A farmer does not need to become a data scientist to benefit from AI. But the system must communicate in an accessible form and reflect local conditions. A small merchant does not need to understand model architecture. But they should understand which customer information must not be uploaded. An older employee does not need to imitate a younger employee's way of learning. Training should respect existing experience and build from it.
The most dangerous divide will not be between people who use AI and people who do not. It will be between those who can use intelligence to expand their choices — and those whose choices are increasingly made by intelligent systems they do not understand.
Equal access to AI does not create equal opportunity unless people also have the capability to use it with confidence and judgement.