Don’t Look for AI Geniuses—Put Leaders in Charge of Designing the Playing Field [Kim Moon-kyung’s Leadership Tech] (26)

[Financial News] "Wouldn’t the problem be solved if we recruited just a few AI geniuses from Silicon Valley?"
That was the question I heard most often from C-suite executives while conducting a 10-month, company-wide work-design consulting project for Company S this year. The discussions focused on "AX transformation and organizational innovation." As announcements of competitors’ AI adoption pour out day after day and the performance metrics of new models are broken every week, organizations naturally begin looking for an elite outside savior when no clear breakthrough is visible internally. Their sense of urgency is entirely understandable. But they are clearly aiming at the wrong target.
Only a handful of companies worldwide can develop and train massive foundation models from the ground up. The overwhelming majority subscribe to finished tools offered as services by OpenAI, Google, and Anthropic. The real battleground, then, is not the ability to develop foundational technology directly. It is who can more nimbly turn the same available tools into a competitive advantage for their own business. Tom Monahan, CEO of global leadership advisory firm Heidrick & Struggles (H&S), made precisely this point in a recent interview with Korean media.
Only a small number of big tech companies build AI themselves, while most others use tools developed elsewhere. The decisive question is therefore whether a leader can turn the market’s AI into a business weapon. In a survey of Korean C-suite executives conducted by H&S, nearly nine out of 10 said they had already introduced generative AI into their work. By contrast, only four out of 10 leaders in the 2026 global CEO and board survey said they were confident their organizations could take control of the changes AI would bring. AI tools have already entered the workplace, but the leadership needed to wield them in competition is still unprepared.

This enormous disconnect is not historically unfamiliar. Paul A. David, an economic historian at Stanford University, found that although factories replaced steam engines with the latest electric motors in the late 19th century, it took decades for productivity to visibly surge. Electricity’s disruptive potential did not explode simply when motors were installed in factories. It emerged only after factories lowered their ceilings and completely redesigned machine layouts and labor processes around continuous production lines.
Erik Brynjolfsson, a professor at the same university, describes this as the “productivity J-curve” of general-purpose technologies. Immediately after investing in an innovative technology, companies must spend heavily on complementary investments such as process redesign and learning, so productivity stagnates or even declines. Only when those complementary investments accumulate beyond a critical threshold does performance begin to rise vertically. Technology can be purchased, but the complementary investments needed to absorb it can never be bought from outside. That is why leaders inside the organization must directly change how work is done.
The point where countless AI investments fail in industry is also not the technology itself, but the absence of these complementary investments. Even after spending hundreds of millions of won to install an enterprise-wide solution, reports still circulate through old-fashioned, analog approval chains, while decisions continue to rely on experience and convention rather than precise data.
Under leaders who step back from technology and say, "We installed an expensive tool, so now innovate on your own," costly tools can become little more than flashy decorations in the corner of a monitor. Engineers recruited from Silicon Valley at enormous salaries likewise become exhausted and leave amid the invisible territorialism of existing domain experts and bureaucratic procedures. They fail not because there are no geniuses, but because there is no playing field for them to perform on.
There are also successful cases in Korea where companies redesigned the way work was organized before introducing technology. According to a report by IDC Korea analyzing the state of generative AI adoption among Korean organizations, LG Electronics built an internal AI system that automatically generates SQL code, allowing product planners and business developers to analyze hundreds of terabytes (TB) of data themselves without specialized coding knowledge. Rather than hiring and assigning large numbers of outside data analysts, the company fundamentally changed the sequence of work and the distribution of authority so that business planners—who had previously requested data analysis and waited indefinitely—could work with the data directly. As the report points out, successful organizations shared a common trait: instead of launching showy projects that rushed to attach the latest general-purpose model, they first carefully designed the work process around the question, “Which data should be connected to relieve which bottleneck?”

The question executives should ask is now clear. They must move from the technology-procurement question, "Where can we recruit an AI genius?" to the leadership-systems question, "Are our leaders ready to design the playing field on which we will compete with AI?" What organizations urgently need is not a technician who writes code, but a “work architect” who finds the intersection of technology, people, and business and redefines how work flows.


Leaders acting as architects need three dimensions of precise design capability.
First is process-design capability: the ability to break apart and rearrange the business value chain. Leaders must clearly distinguish and reconnect areas AI handles exceptionally well—such as data collection, pattern analysis, and drafting—with areas for which humans must remain accountable, including complex contextual judgment, ethical verification, and emotional communication.
Second is environment-design capability: the ability to reduce employees’ psychological friction. Introducing new technology inevitably creates job insecurity and fatigue. Voluntary experimentation in the workplace can take root only when leaders define AI not as a blade for evaluation and layoffs, but as an intellectual partner that amplifies capabilities—and when they do not blame employees for mistakes made during bold experiments with new tools.
Third is cultural-integration and onboarding design capability: the ability to bring together external technology talent and internal business experts. When the language of technology collides with the language of business, leaders must interpret between the two and build a bridge that enables both sides to unite around shared business results. Talent cannot truly take root in an organization if it never experiences the satisfaction of seeing a model it painstakingly built translate into actual sales and customer satisfaction.
Talent strategy in the AI era cannot be solved by purchasing finished components from outside. No matter how talented someone is, without the business context and organizational soil in which to exercise that talent, the organization will simply pay an expensive tuition fee. The people ultimately responsible for proving that externally sourced tools can create real business impact are not outside geniuses, but internal leaders who understand the context of work on the ground.
This week, before ordering another grand new technology, ask team leaders one question.
"Among our team’s tasks, which process needs to be completely redesigned around AI, and which leader will be responsible for designing that work?"
An organization that cannot provide a clear answer to this question has no playing field to build, even if it brings in all the geniuses of Silicon Valley. Unless leaders redesign the way work is organized, AI will remain nothing more than an expensive cloud-services bill deducted from the company’s bank account every month.

/Kim Moon-kyung (Adjunct Professor, Kookmin University; Vice President, Korean Leadership Association)


 


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