AI Agents Proliferate While Companies Struggle to Keep Up

While enterprise adoption of AI agents is accelerating, many companies lack the operational infrastructure required to run them effectively. Specifically, the establishment of business processes, data management, cost controls, and appropriate governance frameworks are all lagging behind the pace of proliferation.

AI agents refer to AI systems that autonomously perform tasks when given a goal, without requiring humans to provide step-by-step instructions. Because they can handle a wide range of work—from sending and receiving emails to information gathering and data analysis—many companies have begun exploring their use. In particular, driven by improvements in large language models (LLMs), agent technology has been advancing rapidly toward practical implementation.

However, a significant gap exists between technological progress and corporate preparedness. To deploy AI agents across an entire organization, companies need mechanisms to maintain data quality and consistency that agents reference, monitoring systems to detect errors and misinterpretations, and operational management frameworks to prevent unexpected cost escalation. All of these demand preparation qualitatively different from traditional software deployment, and many companies are encountering considerable difficulty.

Cost management presents another critical challenge. Because AI agents execute multiple tasks sequentially, API call volumes and cloud computing resource consumption accumulate, creating the risk of unforeseen cost inflation. While small-scale pilot implementations may pose no problems, scaling across the entire organization reveals challenges that appear suddenly, creating a structural difficulty.

From a governance perspective, AI agents present new challenges to enterprises. Since agents make judgments and take action autonomously, it is crucial to establish mechanisms through which humans can understand the agent's decision-making process and intervene when necessary. Particularly when handling sensitive data or customer information, access control design that clearly defines who has authority over agents and within what scope is essential.

This situation can be understood as a "gap between technology supply and corporate demand and readiness." While AI vendor technology development is accelerating, the infrastructure, organizational culture, and talent development on the receiving corporate side are falling behind. Similar gaps emerged when cloud and mobile technologies proliferated in the past, but AI agents pose greater risk due to their greater complexity and autonomy, making preparation shortfalls more consequential.

Going forward, the key for companies lies not in the speed of technology adoption but in the mindset of "expanding steadily within manageable scope." Enterprises will likely diverge based on whether they systematically establish processes, ensure data quality, gain cost visibility, and design governance frameworks incrementally while scaling, or fail to do so. Beyond technology selection alone, the corporation's "readiness to adopt" itself is emerging as an element that will determine competitive advantage.