AI is no longer merely simple automation or a technological component; it has become a core driver of fundamental innovation in businesses and organizations. As evidenced by basic work assistance examples such as automated email drafting and document summarization, AI has already become widespread across many sectors. However, AI's true value extends far beyond these applications. A recent groundbreaking report from the MIT Sloan School of Management, titled 'Chaining Tasks, Redefining Work: A Theory of AI Automation,' reveals AI's potential to revolutionize entire workflows, not merely enhance the efficiency of individual tasks. A core insight from this study is that while most organizations approach AI primarily as a tool to boost productivity for individual tasks, such as drafting emails or summarizing documents, this 'task-by-task mindset' can actually restrict AI's true value. MIT researchers emphasize that AI can dismantle boundaries between work streams, optimize entire processes, and profoundly influence productivity and organizational culture. A particularly significant insight from this report is that assigning an entire 'task chain' to AI can generate greater value, even if AI does not surpass human performance in every individual task. This finding necessitates a fundamental re-evaluation of existing AI adoption strategies. According to the report, AI can accelerate the overall pace of work by eliminating the review and adjustment steps typically required in each task previously performed by humans. MIT researchers specifically highlighted the concept of 'coordination costs.' They argue that the review, verification, and adjustment processes occurring during task handoffs between humans and AI are a primary factor in slowing down the entire workflow. The study indicates that when AI-friendly tasks are grouped together, they can be executed as a single, continuous flow, thereby eliminating friction, reducing handoffs, and accelerating output. For example, consider a customer service process. In a traditional approach, humans and AI alternate involvement at various stages: customer inquiry reception (AI), classification and prioritization (human), initial response drafting (AI), review and revision (human), and dispatch (AI). Each handoff point necessitates review and coordination, consuming time. However, the approach suggested by the MIT report is to assign this entire chain to AI, with humans intervening only in exceptional or complex cases. This method allows most routine inquiries to be processed seamlessly, significantly reducing coordination costs and drastically improving the overall processing speed of the system. This not only implies technological efficiency but also suggests the potential for fundamental changes in organizational design and business models. Researchers at the MIT Sloan School emphasize, "For companies to extract the greatest value from AI, they must go beyond technology adoption and redesign their entire organizational structure and work processes," advocating for AI to be approached as a broad organizational design challenge rather than an individual technological decision. The study also adds, "Realizing AI's full potential requires organizations to be patient in redesigning workflows and building sufficient capabilities." This advice reminds companies of the necessity to view AI as more than just a mere tool. This holds significant implications, particularly for Korean companies. While Korea possesses world-class competitiveness in specific fields such as semiconductors and 5G technology, its approach to AI utilization remains cautious and somewhat conservative. Many Korean companies are adopting AI, but often remain confined to the 'task-by-task mindset' highlighted by the MIT report. Most small and medium-sized enterprises (SMEs) limit themselves to passive methods such as email filtering and consumer responses via chatbots, hesitating to embrace actual workflow changes. This stems from their approach of 'adding' AI to existing work processes. However, what the MIT research emphasizes is not 'adding' AI, but 'redesigning' the entire workflow around AI. This difference may seem minor, yet it has an enormous impact on an organization's competitiveness. Nevertheless, positive changes driven by AI are emerging in leading Korean companies. Some large corporations are experiencing productivity improvements by adopting AI-based predictive maintenance and quality control systems in manufacturing sites, while digital platform companies are enhancing personalized services and recommendation algorithms using AI. However, a closer look at these cases reveals that most focus on optimizing individual tasks and have not yet progressed to the level of redesigning entire workflows. AI Utilization Levels and Challenges for Korean Companies Compared to global leaders, Korean companies still exhibit a gap in the scope and depth of actively utilizing AI. In particular, examples of act
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