Competition over artificial intelligence is intense at companies everywhere. With AI agents now emerging beyond generative AI, efforts to apply the technology to workflow automation, cost cutting, customer service and new product development are accelerating. For companies, AI is no longer an optional new technology but something closer to a matter of survival and competitiveness.
Yet amid all the praise for AI, we need to ask whether it may also create new crises for companies.
There is no question that AI raises productivity and opens new business opportunities. But the story changes when technology spreads faster than an organization's capacity to manage and control it, because an error by AI can quickly become an error by the company.
The case of Air Canada illustrates this well. In 2022, a customer who had just learned of his grandmother's death asked the airline's chatbot about a special discount for attending a family funeral. The chatbot told him that bereavement fares were available for about 800,000 won, and that he could buy a ticket at the full price of about 1.6 million won and then apply for the discount within 90 days to receive a refund of the difference. The customer bought the ticket on that basis, but the airline refused when he later requested the refund. In 2024, a Canadian tribunal ruled that Air Canada was responsible for the chatbot's incorrect guidance, finding that the company could not escape liability by saying it was "what the chatbot said." To a customer, an answer from AI is an answer from the company.
Inside a company, it is possible to distinguish between information generated by AI and information written by an employee. But that distinction does not matter to customers. The moment it is delivered through a corporate website or app, what AI says becomes the company's official voice.
The risks of AI do not stop at customer service. Amazon once developed an AI-based recruiting system and discovered a problem it had not anticipated. Trained on a decade of hiring data, the system reflected the male-dominated character of the organization and showed a tendency to rate female applicants unfavorably. Amazon eventually scrapped the system.
The two cases raise the same question in different areas. The concept worth noting here is automation bias — the phenomenon in which people place excessive trust in the judgments of automated systems or algorithms and neglect their own judgment and verification.
The most dangerous thing about AI is not that it gives wrong answers. It is that even its wrong answers sound highly plausible.
Consider a marketing manager who uses AI-written ad copy with little verification, a human resources manager who accepts an AI analysis of applicants as an objective assessment, and a call center worker who passes along an AI-generated response as is. The work moves faster. But the moment verification disappears, small errors can spread rapidly across the entire organization.
AI can also learn the biases embedded in an organization's past data. As the Amazon case shows, that is why the expectation that AI will be more objective than humans can itself become dangerous.
This connects to confirmation bias as well. People tend to accept information that fits their existing judgments and beliefs more readily. And when the authority of "an AI analysis" is added, doubting the result becomes even harder.
That is why "the AI analyzed it that way" must not serve as a kind of absolution inside a company.
AI should be a tool that supports decision-making, not one that replaces it. Human judgment and verification are especially necessary in areas where individual rights and corporate reputation are at stake, such as hiring, customer compensation, pricing and credit reviews. The core capability of a company in the age of AI, therefore, does not lie in how much AI it uses.
It lies in the ability to decide how far to trust AI and where to doubt it. Companies need to settle three questions at the same time they adopt AI.
First, in which areas must AI's judgments be verified by a human? Second, who bears ultimate responsibility when AI gets it wrong? Third, how will the company explain an error to customers and the market and restore trust?
This is not a task for technology departments alone. It is a management issue that the CEO and the board, along with every corporate function including human resources, marketing, public relations and legal, must weigh together.
That is why this question is worth raising now, at the height of the AI boom. Until now, competitiveness in digital transformation has rested on how quickly a company adopts. In the age of AI, raising the speed of adoption and the capacity for control together matters more.
AI is a technology that amplifies a company's capabilities. In innovative organizations, it builds productivity and creativity. But in organizations with weak verification systems, it can amplify errors, bias and reputational risk as well.
In the end, technology will not determine whether AI saves a company or pushes it into crisis. The humans and organizations that use AI will. The real competitive edge in the age of AI will come not from the company that adopts it first, but from the company that makes the best use of its power while also managing its risks.
