Government Orders ₩200 Billion in AI Public Software Projects to Spur SME Ecosystem
Government Expands Public AI Software Projects, but Bidding Competition Remains Low
The government is actively commissioning public software (SW) projects to revitalize the artificial intelligence (AI) industry and create new growth opportunities for small and medium-sized IT companies.
These initiatives aim to expand the adoption of AI technology across administration, education, and industry sectors, improving public service quality and strengthening global competitiveness through private-sector innovation.
However, as the number of projects has increased, more than half have faced re-bidding due to low competition or participation, prompting calls for institutional improvements. On the 3rd, CLIWANT released a report on the current status of public AI projects, revealing that government-led AI-related public SW projects now total approximately ₩230 billion.
Although the number of public AI projects has rapidly exceeded 1,000, the findings suggest that small and medium-sized IT companies and research institutes are struggling to keep pace.
Industry experts point out that many participants find it difficult to secure profitability compared to their investment, as short-term, task-based orders continue to dominate despite the need for highly skilled talent in AI, cloud, and data analysis.
An official from a university’s industry-academic cooperation foundation stated,
“Although the number of quick, project-based orders has increased, the short duration and low labor cost standards compared to the overall budget make it difficult to allocate qualified personnel. As a result, a structure is emerging where only a few companies and institutions with high technical capabilities repeatedly win projects.”
Experts emphasize that to truly revitalize public AI software initiatives, the government must improve the system to make the ordering process more efficient and expand participation beyond simple short-term contracts.
Given the nature of AI projects—where technology development and data quality assurance require long-term investment—the current framework, which focuses mainly on short-term services, fails to support sustainable R&D structures. In addition, overly restrictive bidding conditions are preventing new and innovative companies from entering the market.
A software industry official noted,
“In the AI sector, innovative technologies from startups and research institutes are emerging rapidly, but the public procurement framework remains outdated. The evaluation criteria and contract models need to be made more flexible to prevent repetitive bidding.”
According to the report, more than 1,000 projects involving over 400 organizations are currently underway, reflecting the government’s focus on digital transformation (DX) and AI adoption as key national priorities.
Participants include numerous university-based industry-academic cooperation groups—such as those from Seoul National University, Pusan National University, KAIST, Hanyang University, and Chungnam National University—as well as private IT firms including LG CNS, KT, DABEEO, TCV, and Amazon Web Services (AWS) Korea.
Universities mainly contributed to AI model development, data construction, and public data analysis projects, while private companies handled implementation-focused projects such as AI platforms, cloud infrastructure, and data hub construction.
Government ministries including the Ministry of Science and ICT, the Ministry of the Interior and Safety, and the Ministry of Trade, Industry and Energy are promoting initiatives such as AI learning data construction, public AI service demonstrations, and AI-based administrative innovation projects. Efforts also include data center advancement, AI model competitions, and pilot projects to encourage broader AI utilization by public institutions.
Industry officials view these efforts as a step beyond basic R&D, representing a direct expansion of the national AI industry base. As large companies like LG CNS, KT, and AWS Korea participate in AI platform and cloud infrastructure projects, public institutions are increasingly transitioning to private-sector technologies.
Yet despite these positive developments, the overall bidding competition remains weak. Analysis shows that over half of the nearly 1,000 AI-related public software projects were re-bid due to insufficient competition or low participation—highlighting an urgent need for system improvements.
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