The Real Reason AI Projects Fail After the Pilot: Lessons From Korea’s Industrial Deployments - KoreaTechDesk | Korean Startup and Technology News
Summary
The article explains that AI projects often fail not because the model is weak, but because organizations lack the data, infrastructure, and operating discipline needed to run AI in production. It highlights Korea’s manufacturing and enterprise push into AI and shows how legacy systems, fragmented data, and limited readiness create deployment bottlenecks. It also warns that AI-assisted or vibe-coded MVPs can hide scaling, security, and reliability problems that only surface after launch. The piece frames AI success as an execution challenge that depends on integration, governance, and cost control. It also points to a clear opportunity for vendors that help companies modernize data, security, and infrastructure before scaling AI.
Classifications
industries
No industries detected
applications
Audit
AskAI Classifications
Labels
Application Security Software
Software as a Service (SaaS)
Application Risk Management
