Korea's AI Export Training Model

The first cohort used KOTRA's pilot AI Export Secretary system for market research. Participants also produced foreign-language short-form promotional content and product photos using tools such as ChatGPT and Gemini. They registered products on BuyKorea, KOTRA's B2B platform, and conducted social-media marketing aimed at overseas buyers. The China module included Greater China social media and TikTok marketing.

That distinction matters. A lot of AI training still starts at the wrong level. It teaches what a large language model is, gives people some prompt tips, warns them about hallucinations, and then ends. Some of that is useful. But it does not answer the question most SMEs have: what should I do differently next week?

KOTRA's answer is more direct. Use AI to research a market. Use it to produce content. Use it to adapt language. Use it to prepare product listings. Use it to test social campaigns. Use it to make overseas marketing cheaper.

A representative from Edubricks, a toy manufacturer that participated in the trainee matching, said the program helped reduce costs and time while opening new overseas sales channels. KOTRA Vice President Ahn Young-ju said AI is becoming essential for lowering costs and boosting effectiveness in export work. Cost, time, and effectiveness are the right metrics. Transformation and disruption are not.

The clever part is the pairing of young trainees and SMEs. A small manufacturer may know its product, customers, production limits, margins, and quality issues. It may not know how to produce short-form foreign-language content or test a buyer-facing digital campaign. A young trainee may understand AI tools, content formats, and social platforms. But they may not understand export markets, buyer risk, product positioning, or B2B sales.

Put them together and you get something closer to a working adoption model. This is different from hiring consultants to tell SMEs to modernize. It is also different from giving firms a software subscription. The KOTRA model creates a temporary human bridge between AI capability and firm-level use.

That bridge matters because AI tools are getting easier, but the work is not. The firm still has to decide which market to test, which buyer segment to target, what claims can be made, what product information is reliable, and which follow-up is worth doing. AI can produce options quickly. It cannot know the business context unless someone supplies it. This is why training programs aimed only at "AI literacy" may underperform. Literacy is necessary, but not enough. SMEs need applied help inside actual commercial tasks.

KOTRA is not treating this as a one-off course. In August 2025, KOTRA described a three-year plan, from 2026 to 2028, to build AI-based personalized services into its export-support platform. The plan includes AI-based customer-service innovation, a data-centric AI transformation ecosystem, and a governance system. KOTRA said firms would be matched more efficiently with AI-recommended buyers and would be able to use AI-based market analysis drawing on information from 131 overseas trade offices.

That is the more ambitious version of the program. The training cohort creates people who can use AI for export marketing. The platform strategy aims to make KOTRA itself more AI-enabled. KOTRA President Kang Kyung-sung described the agency's role as a "smart assistant and reliable partner" for export companies. That is a good phrase because it does not overstate the case. The agency is still the intermediary. AI changes how the intermediary works.

The next layer is regional. In May 2026, KOTRA and local governments announced plans to more than double joint cooperative export and overseas expansion projects for regional companies, from 54 annually to 110. The mechanism is the AI Trade Support Center network: 20 locations across the country meant to help regional companies use AI for export activity. The centers support work such as buyer discovery, product-marketing content creation, SNS marketing, automated market and buyer recommendations, and feedback. Ahn Young-joo said the centers would act as hubs for regional companies to attempt export projects "at minimal cost."

For economic developers, this is the part that should register. Many jurisdictions have export training. Many have digital adoption programs. Many have regional business centers. Korea is trying to connect the three. The result is closer to an operating system for SME export adoption: local access, central data, public credibility, private tools, and practical work.

Training alone usually does not change firm behavior. It creates awareness, sometimes enthusiasm, and often a list of things the firm has no time to do. Korea's broader AI adoption model deals with that through vouchers.

The OECD's 2026 review of Korea's AI Voucher Program describes a government-led program that helps SMEs, medical institutions, and micro-businesses adopt AI solutions while also creating demand for domestic AI providers. It is administered by the Ministry of Science and ICT through NIPA and uses the SMART platform for supplier registration, demand-supply matching, evaluation, and project monitoring.

The 2025 budget was about USD 20 million. Government covered up to 80% of project costs for SMEs and 70% for mid-sized firms. More than 1,000 projects were supported between 2020 and 2024. This is important because it turns AI adoption into a market-building exercise. On one side are SMEs that need help but do not know which supplier to trust. On the other are AI providers that need customers, feedback, and use cases. Government lowers the risk of the match. It does not have to build every solution itself.

The OECD review cites positive effects on employment, sales, and firm performance, especially in services. It also reports sales growth of around 30% relative to non-beneficiaries in one external evaluation, with productivity effects appearing more clearly after one to two years.

Canada is a good comparison because it has the opposite problem in some respects. Canada has world-class AI research and strong AI institutes, but business adoption remains weak. The federal government's June 2026 AI for All strategy says it wants to raise AI adoption from just over 12% to 60% by 2034 and create 250,000 AI-related jobs over five years.

The strategy includes useful pieces: national AI literacy, post-secondary access to trusted AI agents, work placements, SME adoption support, priority sectors such as health, energy, transportation, agriculture, manufacturing, robotics, and government services, and a stronger sovereign AI foundation. The question is whether this becomes operational enough.

Canada does not need more abstract encouragement for SMEs to "use AI." It needs applied adoption channels. Korea's model suggests what those channels could look like.

For trade and economic development agencies, the lesson is straightforward: do not separate AI adoption from sales, exports, productivity, and market development. Teach SMEs how AI changes the work they already need to do. For a food processor, that might mean multilingual buyer materials, market scans, distributor research, compliance summaries, and trade-show follow-up. For a mining services firm, it might mean proposal drafting, technical translation, procurement monitoring, and maintenance analytics. For an agtech company, investor targeting, overseas partner research, and data-room preparation. The common thread is that the use cases sit close to revenue. That is where SMEs pay attention.

The Korea model is not mainly about AI tools. It is about institutional design. Five moves carry across borders.

None of it requires inventing a new technology. It requires organizing a market: people who understand the tools, people who understand the business, public data that improves targeting, and finance that lowers the cost of trying. Many agencies will struggle on the data point, because their information is scattered, stale, or trapped in individual inboxes. AI will expose that. Good.

The Korean model also points to a problem many countries avoid: SME AI adoption is labor-intensive. It is tempting to imagine that a portal, a chatbot, or a self-assessment tool can solve the adoption gap. They cannot. They can help with triage. They can reduce friction. But they will not, by themselves, change how thousands of firms sell, produce, and manage.

Adoption needs people who understand the tools, people who understand the business, and people who can translate between the two. This is where public agencies have a role: not as software companies or AI consultants, but as market organizers.

KOTRA's approach is not perfect, and the evidence is still early. Some firms will use the training once and revert to old habits. Some AI-generated marketing will be mediocre. Some trainees will be better with tools than with commercial judgment. Some agencies will overcount activity and under-measure results.

But the direction is right. Korea is moving from AI awareness to AI use, and from national ambition to firm-level work. That is the step many countries still need to take.