The share of artificial intelligence (AI·Artificial Intelligence) items on boards of major domestic corporations has been rising lately. Major corporations, including Samsung Electronics, SK hynix and Hyundai Motor, are putting investment in Generative AI (AI technology that creates new content such as images and code using existing data) and organizational transformation at the core of their future growth strategies. AI is no longer a project for the technology division but a management agenda that determines corporate value and competitiveness.
The recent trend has accelerated. After Jensen Huang, Nvidia's chief executive officer (CEO), visited Korea, collaboration talks between major domestic groups and global AI corporations came into focus, and SK Telecom said it would team up with Nvidia to enter a "full-stack AI cloud" business based on an AI factory. As AI infrastructure investment moves into full swing, corporations are facing dual pressure to speed up investment and prove results at the same time. The burden is growing along with the investment fever.
As announcements of AI investments, data center construction and Generative AI service launches continue, market expectations are rising. But the timing for actual monetization remains uncertain. In business circles, the view is spreading that "not doing AI is a risk, but investing in the wrong way is also a risk."
◇ Diverging views between CEOs and boards over the pace of AI adoption
A report released by Boston Consulting Group (BCG), "CEOs and Boards Are Aligned on AI in Theory, but Divided in Practice," illustrates this reality. While CEOs and boards agree on the importance of AI, they show significant differences in how to implement it, the speed, and the responsibility structure.
The biggest gap appeared in the pace of AI adoption. According to the survey, about 60% of CEOs felt their boards were rushing the AI transition too much, while boards thought corporations were not moving fast enough.
BCG describes this as an "AI FOMO (Fear of Missing Out)" phenomenon. The less people understand AI, the greater the anxiety about falling behind, which can lead to more aggressive pressure to invest.
There was also a gap in AI understanding. Seventy-five percent of board members rated their own understanding of AI as similar to or higher than that of their fellow directors. But 40% of CEOs said boards either overestimate what AI can actually do or do not fully understand the impact AI will have on growth strategies.
◇ AI strategy is for management, but the execution burden falls on the CEO
The issue is the responsibility structure. Both CEOs and boards answered that management should lead AI strategy, but in actual execution, the burden was concentrating on the CEO.
In the survey, 47% of CEOs said they were directly leading AI strategy execution. By contrast, the share who said a chief AI officer (CAIO) should lead AI strategy did not reach even 10%.
AI is a management task that simultaneously touches organizational structure, capital allocation, workforce operations and business model transitions. Domestic corporations are increasingly creating new AI units or reorganizing them under the CEO, but the burden of final decision-making still often concentrates on the chief executive.
Perceptions also diverge on AI investment performance. CEOs perceived that an average of 35% of their performance evaluations were tied to AI investment outcomes, while boards put that figure at about 27%. This suggests the pressure CEOs feel over AI results is far greater than boards recognize.
Recently, domestic corporations have likewise experienced rising market expectations after announcing AI investments, building data centers, and launching Generative AI services. But the timing for actual monetization remains uncertain. In the end, from the CEO's standpoint, it amounts to having to manage both the "risk of not doing AI" and the "risk of overinvesting without results" at the same time.
BCG noted that the risks of AI transition can grow more from differences in perception between CEOs and boards than from the technology itself. The greater the divergence in views over the pace of AI adoption, expected return on investment (ROI), and responsibility structures, the more AI strategies can become a source of internal conflict rather than a driver of innovation.
The market has begun to ask not about the size of AI investments, but who is accountable and when results can be delivered. In the AI era, competitiveness is more likely to hinge not on which technologies are adopted, but on the structures that decide where to use them and how to connect them to revenue. What CEOs need is not a declaration about AI, but decision-making that manages investment, execution and outcomes all at once.