AI & Digital Economy

Korea’s high rate of AI use should not be confused with economy-wide productivity transformation. Time savings become output only when firms redesign workflows, targets, authority, measurement, and accountability around the technology. The missing input is organizational capital rather than access to another general-purpose AI tool. The Korean Policy Question Korea provides an unusually clear test of the distinction between adoption and value.

Korea’s AI strategy cannot be reduced to the supply of software engineers because AI makes empirical and decision claims. Correct implementation does not guarantee representative data, valid inference, appropriate objectives, or safe deployment. National capability requires mathematics, statistics, domain knowledge, governance, and organizational judgment alongside engineering. The Korean Policy Question Korea’s industrial success gives engineering a deservedly high status in technology policy.

Korea’s AI labor market often combines software delivery, model development, domain integration, and supervision under one title. These roles require different evidence because they carry different forms of responsibility for data, inference, and decisions. A clearer occupational taxonomy would improve education policy, recruitment, procurement, and professional accountability. A Title That Conceals the Production System Korean employers and training providers frequently use ‘AI expert’ for people pe