Resources
The writing readersopen most
Collected from what readers actually search for and read. Grouped by theme rather than ranked — every card opens the full post on the blog, in Korean.
Industry adoption reports
Where other companies are actually using AI
The most-read post
How Korea's major companiesare adopting AI
From Samsung, LG and Hyundai down to startups — a survey of how far AI adoption in Korea has actually come. The most-read post on this blog.
Read the full post- Finance
How the big banks use AI
From Shinhan, Hana and NH to Kakao Bank and HSBC — AI in chatbots, risk and wealth management.
Read the full post - AX
The AX era, company by company
What Korean companies have actually changed under the banner of AI transformation.
Read the full post - Manufacturing
AI on Korean factory floors
From smart factories to quality control — where AI has landed in manufacturing.
Read the full post - Construction
AI on the construction site
Hyundai E&C, Samsung C&T, DL E&C — where AI entered design, safety and scheduling.
Read the full post - Global
Walmart to Klarna: AI inside global companies
How global companies wired AI into internal work, traced across 2023–2025.
Read the full post - E-commerce
AI in e-commerce: revenue and efficiency
Ably’s virtual fitting, Musinsa’s image search, Coupang’s demand forecasts — what the platforms actually run.
Read the full post
Practical guides
What you end up looking for once you start evaluating
- SecurityUsing ChatGPT for business where security mattersBusiness vs Enterprise, where leaks happen, and the settings to lock first.
- Public fundingThe 2026 AI voucher, from a buyer's seatChecking suppliers, eligibility, timeline and selection criteria in one pass.
- Closed networksGenerative AI inside an internal networkHow to run generative AI behind network separation, with real rollouts.
- Market mapKorean AI startups, sorted by fieldChatbots, generative, enterprise platforms, industry-specific — what sits where.
- AgentsKinds of AI agents, and how to chooseThey're all called agents but do different jobs. Criteria for picking by situation.
Concepts
Understand the structure,not the buzzword
The most-read concept posts all point at the same thing: the problem is rarely the model, it's the structure between your data. New to this? Start with what an ontology is.
What an ontology is- OntologyWhy an ontology decides whether enterprise AI worksRollouts rarely fail on the model — they fail on the missing decision structure.Read the full post
- LLM × ontologyLLM plus ontology: a blueprint of what the company knowsWhat reduces hallucination isn't a bigger model — it's defined relations between data.Read the full post
- Manufacturing × ontologyNot a way to collect data — a way to connect itIt starts from why production, quality and equipment read the same defect differently.Read the full post
- MCPMCP, explained for everyoneThe protocol that lets AI reach tools and data, unpacked with plain analogies — plus where Korean companies have started using it.Read the full post
This list follows what readers actually visit and search for, and is refreshed periodically.