Domestic companies are currently the most proactive globally in adopting artificial intelligence (AI). According to an OECD survey, the AI a...
Domestic companies are currently the most proactive globally in adopting artificial intelligence (AI). According to an OECD survey, the AI adoption rate among South Korean companies stands at 28%, the highest among member countries. The country also leads in the adoption of the Internet of Things (53%) and big data analytics (40%). However, a concern observed through interactions with numerous companies is that many still approach AI adoption primarily through isolated use cases or technology-centric methods. As a result, despite high adoption rates, few companies have successfully achieved full-scale digital transformation (transformation) through AI or actively communicate such a vision.
In fact, Cisco’s “2024 AI Readiness Index” report reveals that only 13% of South Korean companies prioritized AI adoption as their top budgetary focus. Additionally, 35% reported that their infrastructure lacks the scalability and flexibility required for AI expansion, while 77% assessed their readiness as “intermediate level or lower.” In other words, a significant gap exists between high adoption rates and actual preparedness.
The Turning Point of Operational Innovation Brought by AI
Improving corporate performance and achieving sustainable growth have always hinged on operational innovation. While markets and customers operate beyond a company’s control, operations are a domain that businesses can design and improve. Enhancing profitability and differentiating competitiveness through optimized operations aligns with a company’s core mission.
Yet, even in the seemingly controllable realm of operations, companies vary widely in the speed and outcomes of innovation. Despite shared goals, differences in human resources, processes, and change management capabilities determine results. This is where AI—particularly generative AI—is gaining attention. Its applications are rapidly expanding across customer service, supply chain planning, quality control, and back-office automation. Consequently, many executives expect AI to “perfectly control efficient operations by replacing human labor.”
Technology Alone Is Not Sufficient
However, BCG’s “2024 AI Adoption Trends Report” shows that 70% of operational innovation outcomes still stem from “people and processes.” Algorithms contribute 10%, while technology and data account for 20%. In global corporate cases collaborated on by BCG, productivity improvements remained around 20% when only AI technology was applied. In contrast, companies that combined AI with process redesign, organizational restructuring, and role redefinition saw productivity gains exceeding 30%.
This pattern is consistent with BCG’s domestic project experiences. The common thread among successful AI-driven operational innovations is that companies invested more time in standardizing work methods and managing change before AI adoption. AI transformation is not a sum of isolated use cases but a journey toward redefining the entire operational model. Only this approach enables reduced workloads, optimization across the value chain, and productivity improvements that surpass initial targets.
Five Execution Conditions to Make AI Transformation a Reality
Several principles must guide executives to successfully drive AI transformation. First and foremost is establishing clear goals and a centralized execution framework. AI adoption is not a technological experiment but a strategic investment directly tied to financial performance. Companies should set specific targets—such as “a 30% cost reduction within three years”—and create a dedicated AI transformation organization to concentrate responsibility and authority. This prevents fragmented proofs of concept (PoC) and enables scaling.
Second is transforming the operational model itself. AI is not merely an efficiency tool but demands a redefinition of work methods. For instance, customer service agents should evolve from responders to problem solvers, while employees must undergo reskilling to handle analysis, decision-making, and customer management. Key performance indicators (KPIs) must also shift from volume-based metrics to qualitative ones like quality, customer satisfaction, and recurrence prevention rates.
Third, the essence of AI adoption lies not in technology but in people. According to BCG, only 30% of managers and 28% of frontline employees have received education on AI’s impact. Lack of training and anxiety slow adoption. Therefore, reskilling, clear usage guidelines, psychological safety nets, and appropriate incentive designs must be implemented in parallel.
Fourth is the strategy for scaling AI. While small pilot projects are easier to execute, enterprise-wide scaling is far more challenging. Companies should work backward from targeted financial outcomes to determine which processes and organizations to transform, when, and how. Simultaneously, they should pursue “lighthouse projects” by selecting a few representative processes to demonstrate quick wins, while developing a systematic scaling roadmap to expand these successes across other processes and sites. A global tech company, for example, initially saw only improved response speeds after piloting AI-based customer support summaries. However, after redesigning the entire process and establishing a dedicated AI operations team, productivity increased by over 10%, with company-wide cost savings confirmed.
Finally, the critical factor determining success is change management. To mitigate AI risks and accelerate adoption, companies must design human-in-the-loop verification and oversight systems. This requires structured guidelines, transparent disclosure of decision-making rationale, tiered supervision based on risk levels, and management of adoption-related metrics. Additionally, companies must establish and embed “Responsible AI” principles into their operational models.
The Golden Time Must Be Seized
Whether AI will fully replace humans remains uncertain. However, its role as a key to corporate productivity is undeniable.
Foxconn Chairman Liu Yangwei’s recent remarks are telling: “AI will not replace you. It will only replace employees who do not utilize AI with those who do.”
South Korea’s manufacturing sector faces a turning point amid population decline, an aging workforce, and high-cost structures. Now is the golden time to secure new competitive advantages through AI-driven operational model redesign. The starting point is not technology but transforming people and operational models.
※ This article has been translated by Upstage Solar AI.
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