AI Strategy Team
Overview
The AI Strategy Team (AI전략팀) is a dedicated organization established by companies, public institutions, and research institutes to internalize artificial intelligence (AI) as a core driver of organizational strategy. Going beyond a simple technology review department, it binds data, talent, budget, governance, and regulatory response into a single roadmap and serves as a "translator" connecting management decision-making and frontline execution. Since the spread of generative AI, it has become not an option but an essential organization in most large organizations.
Key Content
Definition and Background
The AI Strategy Team originated from data science organizations and digital transformation (DX) task forces in the mid-2010s. Initially, individual departments conducted PoC (proof of concept) independently, but duplicate investments, data silos, and disconnected performance problems accumulated. To solve this, a central organization that sets AI priorities and presents standards from an enterprise-wide perspective became necessary, and after 2022, with the emergence of generative AI and large language models (LLMs), its status rose rapidly.
Organizational Structure and Types
- Centralized (CoE): The AI Strategy Team directly holds budget, talent, and platforms and carries out enterprise-wide projects. It is advantageous for control and standardization, but closeness to frontline operations may decrease.
- Decentralized (Embedded): AI personnel are assigned to each business unit, and the strategy team is responsible only for guidelines and performance management. Execution speed is fast, but duplication and variance occur.
- Hybrid (Hub and Spoke): The hub handles strategy, governance, and common platforms, while spokes handle domain application; this is currently the most common form.
Core Roles
1. Strategy Formulation: AI vision, 3–5 year roadmap, investment priorities, and Build vs Buy decisions.
2. Use Case Discovery and Selection: Evaluate tasks with a Value and Feasibility matrix, and concentrate resources on top tasks.
3. Governance and Risk Management: Operate internal policies and review systems that manage data quality, access rights, model bias, hallucination, copyright, and privacy issues.
4. Talent and Culture: AI literacy education, internal champion development, job redesign, and change management programs.
5. Technology Assetization: Build data pipelines, MLOps/LLMOps, prompt and model asset libraries, and internal retrieval-augmented generation (RAG) platforms.
6. External Cooperation and Performance Measurement: Partnerships with startups, research institutions, and cloud providers; management of adoption rate, automation rate, ROI, and productivity metrics.
Members and Required Competencies
AI researchers, data scientists, machine learning engineers, data engineers, product managers, domain experts, and legal/compliance staff participate. The ability to speak both technical understanding and business language at the same time—i.e., "bilingual capability"—is cited as the team's core competitiveness.
Operating Methodology
The typical flow is problem definition → data diagnosis → PoC → pilot → enterprise-wide scaling → transition to operations. Because most projects stop at the pilot stage, called "PoC hell," the AI Strategy Team makes it a principle to clearly designate the operating owner and performance metrics from the beginning.
Success and Failure Factors
Success factors include top management sponsorship, clear ownership, securing high-quality data, and setting shared goals with frontline operations. Conversely, a technology-centered approach, placing regulatory response on the back burner, absence of performance measurement, and excluding frontline operations are representative causes of failure.
Latest Trends
In 2024–2025, the role of the AI Strategy Team is being reorganized in three directions. First, as the center of gravity shifts from generative AI to agentic AI, the number of tasks involving redesigning business processes themselves, rather than introducing a single model, has increased. Second, institutionalization of regulatory response has progressed. As the EU AI Act took effect in 2024 and obligations by risk level are being applied in stages, and as discussions on the U.S. NIST AI RMF and Korea's AI Framework Act interlock, governance personnel have become a permanent role in the strategy team. Third, optimization of cost and efficiency. GPU and inference costs have emerged as key management items, and hybrid architectures combining small language models (sLM), on-device AI, and retrieval-augmented generation (RAG) have become standard, moving away from an all-large-model approach. In addition, C-level positions such as Chief AI Officer (CAIO) are being newly established, and organizational differentiation—separating strategy functions from platform and engineering functions—is accelerating.
Related Topics
- [[Artificial Intelligence]]
- [[Digital Transformation]]
- [[AI Governance]]
- [[Machine Learning]]
- [[Generative AI]]
- [[Organizational Management]]