Series Order
Part 1 | What Should Humans Do When AI Expands the Limits of Thought?
Part 2 | Companies That Adopt AI and Companies That Create with AI Are Different
Part 3 | In the AI Era, Corporate Competitiveness Depends on the Speed of Organizational Change
By Im Byung-min (IBM), Columnist
Most companies today are adopting AI. Generative AI has become a hot topic in meeting rooms, employees receive AI training, AI chatbots are deployed in customer service centers, and coding AI is introduced to development teams. On manufacturing floors, vision AI inspects quality; marketing departments use AI to craft ad copy; and executives demand AI-related strategies and roadmaps. On the surface, it appears that corporate AI transformation is already underway.
However, we need to ask ourselves a critical question. Have we simply adopted AI, or is the company itself changing through AI? These are two entirely different matters. AI adoption is a technological issue, whereas AI transformation is a management issue.
It is not difficult for a company to purchase an AI solution or build a single system. Selecting a consulting firm, implementing a platform, and running a few pilot projects are also feasible tasks. The real challenge lies in what follows. Is AI actually changing work processes, the speed of decision-making, organizational roles, and the way people work? Ultimately, is it transforming the value delivered to customers and the company’s competitive approach?
This is where a vast gap emerges between AI adoption and AI transformation. AI adoption involves layering a new tool—AI—onto existing workflows. AI transformation, by contrast, entails redesigning existing work processes with AI as the fundamental premise.
Consider, for instance, providing a sales representative with generative AI. If the AI summarizes customer meeting details, drafts proposals, and searches for market information, that certainly constitutes AI adoption. However, the situation changes if sales operations—including workflows, evaluation systems, customer management methods, and the way sales data is accumulated—are redesigned with AI at the core. That constitutes AI transformation.
Similarly, a manufacturing company does not automatically become an AI-transformed enterprise simply by installing an AI-based vision inspection system. True transformation begins only when AI goes beyond merely detecting defects to linking production conditions with quality data, automatically adjusting processes, and even redefining the role of the quality management organization. Ultimately, what matters is not merely the presence of AI, but how deeply it is integrated into the company’s operating system.
In the AI era, a company’s competitiveness will likely hinge not on the number of AI projects it has, but on how quickly it can scale AI adoption across the entire organization. Some companies run dozens of AI projects. They generate countless reports, conduct proof-of-concept (PoC) projects, and hold seminars and training sessions. Yet, even after a year, the actual way work gets done on the front lines remains largely unchanged.
In contrast, other companies may start with only a few AI projects but quickly validate them, decisively halting those that fail while immediately scaling successful ones to other parts of the organization. When an individual becomes proficient with AI, their know-how is codified into the organization’s workflows. AI-driven methods developed in one department are promptly adopted by others. This triggers a chain reaction of changes across workflows, systems, data structures, and evaluation frameworks. The difference between these two companies lies not in their level of AI technology, but in the speed of their transformation.
In the AI era, this gap widens over time because AI transformation does not proceed linearly. It evolves from an individual using AI to a team adopting it, eventually changing the team’s working methods and establishing them as the standard for the entire organization. As standards shift, systems evolve and data accumulates. The level of AI utilization then rises, ultimately leading to increased productivity.
Furthermore, companies that transform quickly build a structure that enables even faster future transformation. Conversely, slow-moving companies may find that adopting AI actually increases complexity. Departments use disparate AI tools, data remains siloed, and new AI-driven processes clash with legacy workflows. Despite the adoption of AI, employee workloads increase, leading to the inevitable question: “Why hasn’t productivity improved?” This is not a failure of AI itself, but a failure of the transformation strategy.
When formulating AI strategies, many companies begin by asking what technology to implement. However, the order of questioning must change. What is the first task within our company that needs to change? How can AI redesign that task? And how quickly can we scale that change across the entire organization?
These questions must take precedence. In the AI era, a leader should not necessarily be the person with the most technical knowledge of AI, but rather someone who manages the pace of change. Instead of focusing solely on where to apply AI, leaders must decide which tasks to eliminate, which decision-making structures to alter, and how to redefine organizational roles. Above all, it is crucial to abandon perfectionism. Traditional corporate decision-making typically involves a sequence of careful review, approval, securing a budget, system development, and extensive testing before implementation.
Consequently, the most dangerous state for a company is the illusion that it is adopting AI while its fundamental nature remains unchanged. AI strategies may exist in boardrooms and AI roadmaps in reports, with AI projects underway across the organization. Yet if employees continue to work in the same old ways, decisions are made at the same old pace, and key performance indicators (KPIs) remain unchanged, it is difficult to call this AI transformation.
