As 2025 passed, the questions surrounding AI for South Korean companies fundamentally changed. The focus shifted from "Should we adopt AI?" to "How can we operate it efficiently?" According to the McKinsey Global AI Survey (2025), 88% of companies worldwide are utilizing AI in at least one business function.
However, behind these impressive figures lies a harsh reality. Only one-third of companies have scaled AI across their entire organization, while the remaining two-thirds remain stuck in the experimental or pilot phase. Gartner has warned that by the end of 2025, 30% of generative AI projects will be abandoned after the proof of concept (PoC) stage, with actual abandonment rates exceeding this prediction. This phenomenon is referred to as "AI pilot fatigue."
The essence of the question has now changed. It is no longer about whether to adopt AI, but rather about how to operate it efficiently to enhance productivity and reduce costs.
Many companies fail to implement AI or stop at the PoC stage not due to technological issues, but because of a lack of strategy and execution methodology. Through my experience with various companies' AI transitions, I have identified a clear principle: Think big, start small, and scale fast.
The first step in AI transformation should begin not with technology, but with business. The question should be, "Where can we apply AI to create the most value for our business?" rather than "Which AI model should we use?"
This involves identifying business outcome-driven areas and conducting feasibility analyses. Companies should first pinpoint AI applications linked to clear performance indicators such as revenue growth, cost reduction, risk mitigation, and enhanced customer experience. Decision-making should start from a return on investment (ROI) perspective, rather than technical curiosity or trends.
Thinking big is not merely optimism. It involves strategic thinking about how AI will reshape our business model and operations in three to five years, and then working backward to determine what actions to take now.
In a rapidly changing environment, making large upfront investments and following lengthy development cycles is the most dangerous approach in the AI era.
Define minimum viable products or minimally operable agents for individual tasks and validate them in real work environments within short cycles. A minimally operable agent is not just a simple demo; it is a functioning AI agent that operates in real work settings and delivers measurable results. Lessons learned from small failures become assets for future expansions.
Once performance is proven in individual tasks, the key is to rapidly disseminate these successes throughout the organization. However, many companies fail at this stage by attempting to simply copy and paste successful PoCs.
Rapid scaling means building an AI platform that considers common environments, internal work processes, and the organization and its employees simultaneously. According to S&P Global Market Intelligence (2025), the percentage of companies that abandoned AI initiatives surged from 17% in 2024 to 42% by mid-2025. Those companies did not just lose their investments; the real loss was the time gap that occurred while competitors improved productivity through AI.
Companies that successfully enter the operational phase of AI will widen their gap in productivity and cost structure compared to competitors. While rivals are stuck in repeating PoCs, those already on the operational track are preparing for the next phase.
The conclusion is clear. Think big to envision the overall business outcomes first. Start small to validate minimally operable agents. Then, embed rapid scaling across the organization, focusing on platforms, processes, and employees. The foundation of this journey lies in process definition and AI-ready data, and its execution should be accelerated through strategic collaboration with AI specialists. AI transformation is not a technological issue but a matter of strategy and execution. The clock is ticking even now. Time is money.
* This article has been translated by AI.
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