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Alpha Brothers AI Team: 'Good AX Conditions Lie in Tacit Knowledge'
🇰🇷 South Korea /Technology

Alpha Brothers AI Team: 'Good AX Conditions Lie in Tacit Knowledge'

From Dong-A Ilbo · () Korean

Translated from Korean, summarized and contextualized by DistantNews.

At a glance

In-depth Named sources Context piece
  • Many companies struggle to implement automation tools because they fail to capture "tacit knowledge," the unwritten expertise of experienced employees.
  • Alpha Brothers' AI team leader, Choi Chang-wook, explains that this issue has become more pronounced with the rise of Large Language Models (LLMs), which now execute tasks previously done by humans.
  • Alpha Brothers addressed this by shifting from external development to internal construction of tools, enabling employees to build systems reflecting their own tacit knowledge, and by focusing on training AI talent.

The challenge of integrating automation tools often stems from a failure to capture "tacit knowledge" – the intuitive expertise and know-how that experienced employees possess but struggle to articulate. Choi Chang-wook, head of the AI team at Alpha Brothers, identifies this as a root cause for why automation projects, even when technically functional, are often rejected by end-users who feel the tools don't align with their workflow.

The fundamental reason is that tacit knowledge has not been properly reflected.

— Choi Chang-wookExplaining the core issue behind failed automation projects.

Choi explains that the advent of Large Language Models (LLMs) has exacerbated this problem. Previously, even codified knowledge required human execution, allowing for continuous learning and process improvement. Now, AI systems perform many of these tasks, making it harder for new employees to develop the critical judgment needed to assess AI outputs. "How new employees will gain the insight to effectively direct AI is now a core challenge for companies," Choi stated.

How new employees will gain the insight to effectively direct AI is now a core challenge for companies.

— Choi Chang-wookHighlighting the new difficulties posed by AI in skill development.

Alpha Brothers experienced this firsthand when attempting to build an AI-powered financial dashboard for their finance team. Despite the AI team's efforts, the project faced endless feedback loops and communication hurdles. The finance team's tacit knowledge, such as how to interpret tax invoice codes or transfer memos, was difficult to extract and codify.

The communication cost is also another factor. We had to continuously 'dig out' the tacit knowledge from the finance team members because it wasn't their direct job.

— Choi Chang-wookDescribing the difficulties in collaborating with another department on the financial dashboard project.

This led Alpha Brothers to pivot. They realized that with the advancements in LLM performance, many internal tools could be built by the employees themselves using "vibe coding" (intuitive coding). Furthermore, the communication overhead of extracting tacit knowledge from another department was too high. The company shifted its strategy from having the AI team build tools for other departments to empowering employees to develop their own solutions, fostering a culture of "AI-savvy" individuals and focusing on AI talent development.

Tacit knowledge in the mind is only brought out when it aligns with the context of actually doing that work.

— Choi Chang-wookElaborating on the nature of tacit knowledge and its extraction.
DistantNews Editorial

Originally published by Dong-A Ilbo in Korean. Translated, summarized, and contextualized by our editorial team with added local perspective. Read our editorial standards.

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