Korea is betting on artificial intelligence (AI) to solve its productivity problem, but it is more likely to make the problem worse. The country’s AI push is undeniably ambitious and well-funded, with the government aiming to rank among the world’s top three AI powers. Major firms like Samsung, SK Hynix and Kakao are investing aggressively, and enterprise adoption already exceeds 25 percent, placing Korea among global leaders. However, none of this addresses the core issue: Korea’s productivity problem is structural rather than technological.

Measured as GDP per hour worked, Korea remains in the lower third of OECD economies at roughly $44 per hour. This gap has persisted despite world-class R&D investment, the highest robot density in the OECD and one of the most highly educated workforces in the world. The explanation lies in how productivity is distributed.

Korea’s headline productivity figure reflects two fundamentally different economies operating in parallel. At the top are globally competitive conglomerates that are capital-intensive, technologically advanced and already positioned to absorb AI at scale. Below them sits the rest of the economy. While small and medium-sized enterprises (SMEs) account for 99.9 percent of firms and over 80 percent of employment, they produce only a fraction of the output of large firms, with OECD data indicating their productivity levels are closer to one-third. At the sector level, the imbalance is even more pronounced: Service sector productivity in Korea is roughly half that of manufacturing, compared to near parity in many advanced economies. This is not a marginal gap, but a deeply embedded structural divide spanning sectors, firm sizes and labor allocation.

AI does not operate independently of this structure; rather, it actively reinforces it. Across advanced economies, new technologies are adopted fastest by firms that already possess the requisite capital, data infrastructure and specialized talent. Because these capabilities are heavily concentrated — far more so in Korea than in most OECD countries in terms of both firm productivity and employment distribution — the result is not simply uneven adoption, but asymmetric impact.

While large firms can easily integrate AI into existing systems to automate high-value processes and scale productivity gains quickly, smaller firms face a starkly different reality. Without data integration, managerial bandwidth or the skilled labor to deploy AI effectively, their adoption remains shallow or purely cosmetic. The technology may be present, but the productivity effect is not. This distinction matters deeply, as superficial adoption creates the illusion of progress without changing underlying performance. A firm that licenses an AI tool without reorganizing workflows, upgrading management practices or retraining its workforce does not become more productive — it merely becomes marginally more digital. Across Korea’s SME base, this pattern of adoption without transformation is already visible. In this environment, AI does not function as a bridge to the frontier — it becomes a marker of just how far behind much of the economy still is.

In an economy where the productivity frontier is both highly advanced and deeply isolated, this dynamic will not resolve naturally over time. Instead, it will intensify. The root problem is not a lack of access to technology but the absence of pressure to use it productively. As of 2023, Korea operated 1,646 SME support programs spanning credit and tax incentives to public procurement. While these programs provide stability, they simultaneously insulate firms from competition. A business protected by preferential financing and guaranteed demand has little incentive to restructure operations, invest in skills or adopt AI in ways that fundamentally improve productivity. This is precisely where the system breaks down.

AI is not a plug-in solution but a force multiplier that amplifies existing capabilities and exposes inherent weaknesses. Consequently, efficient firms become even more efficient, while lagging firms do not automatically catch up . In many cases, they fall further behind as the gap between frontier and non-frontier firms expands. Korea’s current approach mistakenly treats AI adoption as the ultimate objective when the true goal should be productivity. Counting how many firms adopt AI, particularly when those businesses are already among the most advanced, says little about whether the broader economy is becoming more productive. The relevant question is whether firms with 10 to 49 employees — the SMEs where most Koreans actually work — are increasing their output per hour. On that measure, current policy is unlikely to deliver meaningful change.

Closing this gap requires moving beyond expanding access to technology and focusing on changing the incentives that shape behavior. Support for SMEs can no longer remain unconditional; it must be tied to measurable improvements in productivity, management practices and workforce capability so that firms refusing to upgrade are not perpetually sustained without consequence. Similarly, the service sector — long treated as a secondary concern — must become central to this effort. It is where the majority of workers are concentrated and where the productivity deficit is most severe. Without structural transformation in services, national productivity will remain permanently constrained, regardless of technological advancements at the top.

Korea has built an economy where gains on the frontier are strong and consistent but diffusion to the rest of the system is dangerously weak. This is a failure of structure, not technology, and artificial intelligence will only accelerate the imbalance rather than correct it. While Korea’s AI strategy is already assured for its largest firms, the pressing question is whether it will succeed for the economy as a whole. Unless policy shifts from protecting firms to upgrading them by moving away from subsidizing participation toward enforcing performance, AI will only deepen the structural divide that has defined Korea’s productivity problem for decades. That is the risk Korea is now taking.

Charles Chang is a PhD candidate in AI Convergence and a security resilience consultant based in Seoul, with extensive experience spanning government and corporate leadership. Any views, thoughts and opinions expressed in this article are solely his own and do not reflect the views, opinions, policies, or position of his employer.

Source: Korea Times News