Wonderful Unveils Enterprise AI Expansion Strategy at AI Summit Seoul 2026
Published2026.08.26 09:59
To achieve this, a dedicated local team consisting of field-deployed engineers (FDE) and deployment strategists directly collaborates with customer teams to apply AI to business processes and ensure it can be expanded across the organization.
The company has secured investments from Index Ventures, Insight Partners, IVP, and Bessemer Venture Partners, among others.
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Wonderful Establishes Korea Branch… Jeong Eung-seop Appointed as First Branch Manager
Wonderful, a global enterprise AI agent platform company, has established a Korean subsidiary and appointed Jung Eung-seop, former head of Workday Korea, as the inaugural subsidiary head. Following the company's Series B funding round of $150 million in March, it announced plans to expand operations across more than 30 countries. Jung's appointment, given his track record leading domestic operations at SAP, Slack, and Workday, is interpreted as a move to strengthen local sales and implementation capabilities to penetrate the Korean market. The timing of this expansion is also attributed to the shift in generative AI competition from technology demonstrations to actual operational business transformation.
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The Problem with AI is Not Performance, but Trust
CEO Kang Min-seung emphasized at Snowflake's World Tour Seoul that generative AI has entered a phase where "trust" rather than accuracy becomes the key competitive advantage. As AI is applied to various tasks including consultation, risk management, and operational automation, incorrect data, erroneous responses, and bias are creating actual business risks. He pointed out that enterprises must build a trust layer above AI. To achieve this, accurate data management, semantic structuring, governance, and prompt security are essential. He explained that AI is not merely an automation tool but a technology that reorganizes the entire organization's workflows and decision-making systems. Snowflake provides a platform that supports this trust-based AI operation, emphasizing that enterprises should build "trustworthy AI" rather than "intelligent AI."
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The Decisive Factor for AI Agents in Finance: "Trustworthy Data Infrastructure"
The key to spreading AI agents in the financial sector is establishing a trustworthy data system. KB Kookmin Bank is operating over 100 AI agents by 2025 and is promoting expansion to 300. AI is automating repetitive tasks across various areas such as consultation, risk management, wealth management, and relationship management. However, if data quality, governance, and semantic structuring are insufficient, errors and risks increase. KB is building an AI-Ready organization through a multi-model strategy of GPT, Gemini, and Claude, as well as a Snowflake-based data system. The bank emphasized that AI has entered a phase where it restructures not just simple automation but the entire work process and decision-making system.
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Snowflake Presents Data Platform Roadmap for Agentic AI Era
Snowflake emphasized at the Seoul roundtable that the key to enterprise AI proliferation lies in "Agentic AI" and the integration of data and workflows. Bala Kasivisvanathan stated that AI is evolving beyond simple analytics to become the driver of task automation and operational optimization, and that organizations must strengthen their data quality, processes, and governance as prerequisites for this. CJ OliveNetworks introduced that it is currently expanding AI applications in logistics and operational automation. Snowflake stressed that companies need a strategic shift to connect AI adoption to tangible business impact.
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"All office workers will become agent managers"
At the Dell Technologies Forum 2026, Matt Dunfee, Senior Vice President at Dell Technologies headquarters, forecast that in the AI era, every worker will take on the role of managing dozens to thousands of agents, and emphasized that rather than simply adding AI to existing tasks, processes themselves must be redesigned. He pointed out that the success or failure of AI adoption depends not on technology itself, but on cost efficiency, data preparation, and AI economics, prototyping, and change management.
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