[One Sip of a Book] AI Adoption: This Question Is More Important Than Solving Deficiencies

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Artificial Intelligence (AI) is deeply penetrating our daily work. There are continuously emerging cases worth referencing, such as data-driven decision-making, work efficiency improvement, and new business model development. While opinions vary on the claim that AI will replace existing personnel, there is little disagreement that work performance can significantly differ depending on the level of AI utilization. However, many companies that lack sufficient AI adoption capabilities often find themselves at a loss about where to start. Professor Tae-Sung Yoon of KAIST Graduate School of Technology Management presents step-by-step strategies to consider when introducing AI.

For AI to evolve, data is essential. Data and AI operate like two sides of the same coin. Accumulated data is necessary to use for AI training or prediction. The more diverse and voluminous the data accumulated, the higher the level of AI that can be expected. Some even give up on AI system development at the stage of collecting data needed for AI training. From a long-term perspective for AI management, the most fundamental solution is digital transformation.

Once the scope of AI management is defined, the outline of the investment scale can be grasped. The investment method and timing can also be determined. If the scope of AI management changes, the investment scale changes as well. However, defining the scope of AI management is not easy. AI management starts small and expands significantly. Initially, it is executed focusing on improving peripheral tasks, and as the introduction effect is confirmed, the application scope gradually expands to core tasks. If you cannot decide where to start AI management, you cannot even outline the investment amount. Managers should not hastily decide on large-scale investments just because they hear news that other companies have adopted AI management.

Companies belonging to the first group implicitly cooperate and aim to gain profits at an appropriate level. Corporate behavior is determined by industrial structure, and cases where performance is generated as a result mainly appear in industries with little market change. For example, the beer industry or cola industry, which form an oligopoly and realize stable profits. The first group uses a competitive strategy that monopolizes the market by utilizing intellectual property rights, including not only standard patents but also essential patents and know-how that are not standards. The scarcer the resources a company holds, the more advantage it gains in competition. The more difficult it is for competitors to imitate or substitute resources, the more the company maintains a sustainable competitive advantage. Regardless of which company leads the first group and dominates the market, the goal is market monopoly. To this end, they create a closed ecosystem. After forming the first group centered on their own company, companies within the group establish partnerships and cooperate. They build entry barriers to exclude competitors and compete only among companies belonging to the same group in the market.

AI management inevitably involves a three-dimensional strategy of technology, economy, and security. Management strategies must be established targeting all three dimensions simultaneously. On the technology dimension, autonomy and intellectual property packages are central. On the economic dimension, value is created together with customers based on personalization and prediction. On the security dimension, the impact of decoupling led by the U.S. on the global supply chain must be absorbed. The manager is positioned at the origin of the three-dimensional strategy. Each dimension’s responsible party seeks partial optimization, but the overall optimization integrating the three dimensions must be designed as the manager’s responsibility. The manager must stand at the forefront of AI management, look to the future first, and decide the speed and direction of management. It is an era that requires the wisdom of managers more than ever.

Few managers believe they have sufficiently secured the technology, data, resources, and talent necessary for AI management. Most managers complain about deficiencies, saying they have not yet prepared all the prerequisites. Efforts to resolve deficiencies are important for introducing AI management. However, before addressing deficiencies, managers must first answer the question: “What kind of company do we want to become?” Managers must answer this question from their managerial perspective.

AI Management | Written by Tae-Sung Yoon | Secret House | 316 pages | 25,000 KRW