SME AI Transformation: From Adoption to Measurable Impact and Scale
Session Overview
AI transformation for small and medium-sized enterprises is not simply a matter of introducing new technology. It begins by defining operational pain points and inefficiencies, validating results through small-scale data and rapid proof-of-concept projects, and then expanding proven outcomes across the organization.
Drawing on practical cases from Panasonic, Sephora Global, and Interz, this session demonstrates that successful transformation depends on data accumulation, executive commitment, active ownership by frontline teams, and clear problem definition.
The session introduces a staged AI transformation model spanning awareness, adoption, integration, scaling, and innovation. It also explains how organizations can define the responsibilities of each role and function, and use reusable AI modules to extend validated results across workflows, processes, departments, and business models.
Key Takeaways
- Why SME AI transformation should begin with operational problems, not technology
- How small datasets and rapid PoCs can validate measurable business outcomes
- The role of leadership commitment and frontline ownership in successful implementation
- A practical maturity model from awareness to innovation
- How reusable AI modules can scale proven results across functions and business models
Speaker
Younghwan Lee is Director of the Digital Innovation Research Center at Korea University's Convergence Research Institute. Drawing on extensive experience in manufacturing, he leads research and policy implementation in AI and digital transformation across the public and industrial sectors.
He has conducted more than 100 policy and consulting projects for local governments and public institutions, and leads national and regional research initiatives in industrial data, manufacturing AI, tourism data quality validation, smart mobility, and disaster safety.
As a member of the KOIIA Industrial Data Space Technical Committee, he has built industry-academia-research collaboration networks with organizations including KT, Kakao Mobility, KETI, and TTA, supporting the practical adoption and expansion of AI across industrial and public services.