Split View: SI 산업의 AI 대전환: 삼성SDS·LG CNS·SK C&C의 AX 전략과 SI 엔지니어 생존 가이드

SI Industry AI Transformation: Korean Tech Giants AX Strategy and Engineer Survival Guide

• 1. The Current State of the SI Industry and AI Transformation
  • 1-1. Global SI Market Size and Growth Outlook
  • 1-2. Korea's IT Services Market Structure
  • 1-3. From DX to AX: What the Shift Means
  • 1-4. Why AI Matters for SI
• 2. Deep Dive: AI Strategies of Korea's Top 3 SI Companies
  • 2-1. Samsung SDS: Full-Stack AI Strategy
    • Business Overview (2024)
    • AI Strategy: From Infrastructure to Consulting
    • Key Product Analysis
    • Hiring Trends
  • 2-2. LG CNS: IPO and AI Coding Platform
    • Business Overview (2024-2025)
    • AI Strategy: Software Productivity Revolution
    • Focus Industries
  • 2-3. SK C&C / SK Group: AI Infrastructure and Agents
    • Business Overview
    • AI Strategy: Infrastructure + Agent Dual Approach
    • SK's Differentiator
• 3. AI Application Patterns in SI Projects (Real-World Cases)
  • Pattern 1: Existing System + AI Feature Addition
  • Pattern 2: AI-Based Business Automation (RPA to AI Agent)
  • Pattern 3: Data Analytics and Prediction
  • Pattern 4: Conversational AI Services
  • Pattern 5: Multi-Agent Workflow
• 4. AI Skill Transformation Required for SI Engineers
  • 4-1. Traditional SI Skills vs AI SI Skills
  • 4-2. Essential AI Tech Stack
  • 4-3. Soft Skill Changes
• 5. Career Strategies for SI Engineers in the AI Era
  • 5-1. Survival Strategy (Current SI Professionals)
  • 5-2. Career Transition Strategy (To AI-Focused Companies)
  • 5-3. Strategy by Career Level
• 6. The Future of SI: 2026-2030 Outlook
  • 6-1. How AI Changes SI
  • 6-2. Multi-Agent SI
  • 6-3. Three Characteristics of Surviving SI Engineers
• 7. 15 Interview Questions + Learning Roadmap
  • 15 Interview Questions
  • Learning Roadmap
• Quiz
• References

1. The Current State of the SI Industry and AI Transformation

1-1. Global SI Market Size and Growth Outlook

The global Systems Integration (SI) market is valued at approximately $553 billion in 2025 and is projected to grow to$764 billion by 2030 (CAGR 6.7%). The core growth drivers are AI and cloud.

1-2. Korea's IT Services Market Structure

Korea's IT services market has a unique chaebol-affiliated SI structure.

Tier 1 - Chaebol-Affiliated SI

• Samsung SDS: Samsung Group IT services + external business expansion
• LG CNS: LG Group IT services + accelerating independence through IPO
• SK C&C: SK Group IT services + AI/DT business expansion

Tier 2 - Mid-sized SI / Specialized Companies

• POSCO DX, Hyundai AutoEver, Hanwha Systems
• KT DS, NHN Cloud, Kakao Enterprise

Tier 3 - Global SI Korea Offices

• Accenture, Deloitte, IBM, Infosys

Tier 4 - SMB SI / Startups

• Thousands of small SI firms (subcontracting structure)

1-3. From DX to AX: What the Shift Means

DX (Digital Transformation) was about digitizing existing business. The focus was on cloud migration, mobile app development, and legacy system modernization.

AX (AI Transformation) goes a step further. It means integrating AI as a core business capability.

DX (2015-2023)                     AX (2024-2030+)
─────────────────                  ─────────────────
Cloud migration          →        AI-native architecture
Mobile apps              →        AI agent services
Data warehouse           →        AI/ML pipelines
RPA automation           →        Agentic AI automation
Dashboard reporting      →        Predictive/generative AI analytics

1-4. Why AI Matters for SI

According to the McKinsey 2024 Global AI Survey:

• 88% of organizations use AI in at least one business function
• 65% of organizations have adopted generative AI in one or more functions
• 25% of AI adopters confirmed cost reduction benefits

Gartner predicts that by 2028, 70% of AI applications will be built as multi-agent systems.

What does this mean for SI companies?

1. AI extension of existing projects: Exploding demand for AI features in all enterprise systems
2. New project types: AI agents, RAG systems, MLOps pipelines
3. Workforce restructuring: Pressure to transition from traditional developers to AI engineers
4. Pricing model changes: From man-month billing to AI performance-based models

2. Deep Dive: AI Strategies of Korea's Top 3 SI Companies

2-1. Samsung SDS: Full-Stack AI Strategy

Business Overview (2024)

AI Strategy: From Infrastructure to Consulting

Samsung SDS's AI strategy takes a full-stack approach, covering everything from infrastructure to platform, applications, and consulting.

[Samsung SDS Full-Stack AI Architecture]

Layer 4: AI Consulting / Business Application
  └── Industry-specific AI solutions (manufacturing, finance, logistics)
  └── AI adoption consulting / PoC services

Layer 3: AI Applications
  └── Brity Copilot (enterprise AI assistant)
  └── Brity Automation (RPA + AI)
  └── ChatGPT Enterprise (exclusive Korean reseller)

Layer 2: AI Platform
  └── FabriX (AI/data integration platform)
  └── Brity Works (workflow automation)

Layer 1: AI Infrastructure
  └── Samsung Cloud Platform (SCP)
  └── National AI Computing Center contract
  └── GPU cluster operations

Key Product Analysis

FabriX - AI/Data Platform FabriX is Samsung SDS's enterprise AI platform. It provides an integrated environment for enterprises to train and deploy AI models using their own data.

Key features:

• Data collection/preprocessing pipelines
• AI model training/deployment management
• RAG (Retrieval-Augmented Generation) pipelines
• Prompt management and A/B testing
• Model monitoring and cost optimization

Brity Copilot - Enterprise AI Assistant Brity Copilot is an enterprise AI assistant already deployed at scale within Samsung Group.

Functional areas:

• Document summarization/generation (reports, emails, meeting notes)
• Code generation/review (for developers)
• Data analysis (SQL generation, chart creation)
• Internal knowledge search (RAG-based)

OpenAI ChatGPT Enterprise Exclusive Korean Reseller Samsung SDS exclusively distributes OpenAI's ChatGPT Enterprise in the Korean market. This is not simple reselling but an enterprise package combined with Samsung SDS's security/compliance solutions.

Hiring Trends

Samsung Group announced a plan to hire 60,000 people over 5 years, with AI/software talent as the core focus. Key hiring areas at Samsung SDS:

• AI/ML Engineers
• Cloud Architects
• Data Engineers
• AI Solution Consultants

2-2. LG CNS: IPO and AI Coding Platform

Business Overview (2024-2025)

AI Strategy: Software Productivity Revolution

LG CNS's AI strategy focuses on AI-ifying software development itself — automating the core SI processes of development, testing, and deployment with AI.

DevOn AI - AI-Based Development Automation Platform

DevOn AI is an ambitious platform that transforms the entire SDLC (Software Development Life Cycle) with AI.

[DevOn AI SDLC Automation]

Requirements Analysis  →  AI requirement organization/classification
Design                 →  AI architecture suggestion/review
Development            →  AI code generation (boilerplate automation)
Testing                →  AI test case generation/execution
Deployment             →  AI-based CI/CD optimization
Operations             →  AI monitoring/failure prediction

Key features:

• Customized code generation through corporate codebase learning
• Legacy code analysis and modernization suggestions
• Automated code review and quality checks
• Automatic test code generation

Focus Industries

LG CNS concentrates AI transformation on three industries:

1. Financial AI

• Banking/insurance/securities AI consultation systems
• AI-based risk analysis
• Automated document processing (OCR + LLM)

2. Manufacturing AI

• Smart factory AI (quality prediction, predictive maintenance)
• Supply chain AI optimization
• Digital twin + AI simulation

3. Retail AI

• AI-based demand forecasting
• Personalized recommendation systems
• AI unmanned store solutions

2-3. SK C&C / SK Group: AI Infrastructure and Agents

Business Overview

AI Strategy: Infrastructure + Agent Dual Approach

AI Infrastructure Axis

• Ulsan AI Data Center: Large-scale GPU cluster operations
• NVIDIA partnership for AI computing services
• Cloud-based AI infrastructure service (CloudZ)

AI Agent Axis

• SK Telecom "A." Platform: Telecom-based AI agent
• Agent marketplace: Building a diverse AI agent ecosystem
• Enterprise AI agents: Business automation, customer service

Global AI Partnerships

• Microsoft: Azure-based AI service collaboration
• NVIDIA: AI infrastructure/computing partnership
• Anthropic, OpenAI: LLM model partnerships

SK's Differentiator

SK leverages the synergy of Telecom (SKT) + IT Services (SK C&C) + Semiconductors (SK hynix) to cover the entire AI value chain.

[SK AI Value Chain]

SK hynix: HBM (High Bandwidth Memory) → Core GPU component
    ↓
SK C&C: AI infrastructure / data centers
    ↓
SK Telecom: AI services / agent platform
    ↓
SK Affiliates: AI applications (energy, chemicals, bio, etc.)

3. AI Application Patterns in SI Projects (Real-World Cases)

Pattern 1: Existing System + AI Feature Addition

The most common pattern: Adding AI capabilities to enterprise systems already in production.

Representative Case: RAG-Based Internal Knowledge Search

Solving the problem where existing keyword-based internal portal search makes it difficult to find desired information.

# Simple RAG pipeline example
from langchain.vectorstores import Milvus
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

# 1. Vectorize internal documents
embeddings = OpenAIEmbeddings()
vectorstore = Milvus.from_documents(
    documents=company_docs,
    embedding=embeddings,
    collection_name="internal_knowledge"
)

# 2. Configure RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    chain_type="stuff",
    retriever=vectorstore.as_retriever(
        search_kwargs={"k": 5}
    )
)

# 3. Query
result = qa_chain.run("What is our annual leave policy this year?")

Architecture:

[User] → [Existing Portal UI] → [API Gateway]
                                      ↓
                              [AI Service Layer]
                              ├── Embedding Service
                              ├── Vector DB (Milvus)
                              ├── LLM Service (GPT-4o/Claude)
                              └── Prompt Management
                                      ↓
                              [Existing Backend Systems]
                              ├── Document Management System
                              ├── Internal Wiki
                              └── Policies/Manuals DB

Pattern 2: AI-Based Business Automation (RPA to AI Agent)

Overcoming RPA limitations with AI: Traditional RPA is rule-based and fragile to variations. AI Agents understand context and respond flexibly.

# AI Agent-based document processing example
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.tools import tool

@tool
def extract_invoice_data(document_path: str) -> dict:
    """Extract key data from invoice."""
    # Parse invoice with OCR + LLM
    ocr_text = run_ocr(document_path)
    extracted = llm.extract_structured(
        text=ocr_text,
        schema=InvoiceSchema
    )
    return extracted

@tool
def validate_against_po(invoice_data: dict) -> dict:
    """Validate extracted invoice data against purchase order."""
    po = fetch_purchase_order(invoice_data["po_number"])
    discrepancies = compare(invoice_data, po)
    return {"valid": len(discrepancies) == 0, "issues": discrepancies}

@tool
def submit_for_approval(invoice_data: dict, validation: dict) -> str:
    """Submit validated invoice to approval process."""
    if validation["valid"]:
        return approval_system.submit(invoice_data, auto_approve=True)
    else:
        return approval_system.submit(invoice_data, flag_review=True)

# Agent autonomously decides which tools to call in sequence
agent = create_openai_tools_agent(
    llm=ChatOpenAI(model="gpt-4o"),
    tools=[extract_invoice_data, validate_against_po, submit_for_approval],
    prompt=agent_prompt
)

Pattern 3: Data Analytics and Prediction

AI-Based Demand Forecasting and Anomaly Detection

# Demand forecasting + LLM interpretation example
import pandas as pd
from prophet import Prophet
from langchain.chat_models import ChatOpenAI

# 1. Time series forecasting (Prophet)
model = Prophet(yearly_seasonality=True)
model.fit(sales_data)
forecast = model.predict(future_dates)

# 2. LLM interprets forecast results and generates report
llm = ChatOpenAI(model="gpt-4o")
report = llm.invoke(f"""
Analyze the following demand forecast results and write an executive report:
- Forecast period: Next quarter
- Key figures: Average {forecast['yhat'].mean():.0f} orders/day
- Upper bound: {forecast['yhat_upper'].mean():.0f} orders/day
- Lower bound: {forecast['yhat_lower'].mean():.0f} orders/day
- YoY change: +12%
Derive 3 key insights with business context.
""")

Pattern 4: Conversational AI Services

Customer Support Chatbot / Internal Helpdesk

[Conversational AI Service Architecture]

Customer/Employee  →  [Chat Interface (Web/App/Teams)]
                              ↓
                       [Conversation Management Layer]
                       ├── Intent Classification
                       ├── Conversation History (Memory)
                       └── Multi-turn Context Maintenance
                              ↓
                       [AI Processing Layer]
                       ├── RAG (Knowledge-based responses)
                       ├── Function Calling (System integration)
                       └── Human Handoff (Agent transfer)
                              ↓
                       [Backend Systems]
                       ├── CRM / ERP
                       ├── Knowledge Base
                       └── Ticket System

Pattern 5: Multi-Agent Workflow

Multi-agent system automating from document processing to approval

[Multi-Agent Document Processing Workflow]

[Document Intake Agent]
  |  Document classification, metadata extraction
  ↓
[Data Extraction Agent]
  |  Extract key data with OCR + LLM
  ↓
[Validation Agent]
  |  Compliance check, data consistency verification
  ↓
[Approval Routing Agent]
  |  Determine approver based on amount/type
  ↓
[Notification Agent]
     Notify results, trigger follow-up actions

Each agent operates independently and receives the previous agent's output as input. On errors, it automatically retries or escalates to a human.

4. AI Skill Transformation Required for SI Engineers

4-1. Traditional SI Skills vs AI SI Skills

4-2. Essential AI Tech Stack

Tier 1: Must Learn Within 6 Months

Python + FastAPI
├── Standard for AI service backends
├── Java developers will be amazed by FastAPI's simplicity
└── Async processing, Pydantic models essential

LangChain / LlamaIndex
├── De facto standard for RAG pipeline construction
├── Integration with various LLMs and vector DBs
└── Understanding Agent, Chain, Tool patterns essential

Vector DB
├── Milvus: Large-scale enterprise environments
├── pgvector: PostgreSQL-based, integrates with existing RDB
└── Understanding embedding generation/search/indexing principles

Prompt Engineering
├── System/User/Assistant role separation
├── Few-shot, Chain-of-Thought techniques
├── Prompt version management and A/B testing
└── Structured output (JSON mode) usage

Tier 2: Skills for Project Leadership (Within 12 Months)

MLflow / Weights & Biases
├── Experiment tracking and model version management
├── AI project reproducibility
└── Team-level collaboration workflows

Docker + Kubernetes (AI Workloads)
├── GPU node scheduling
├── AI model serving (TorchServe, vLLM, TGI)
├── Auto-scaling configuration
└── Model rolling updates

LangGraph / CrewAI
├── Multi-agent orchestration
├── State machine-based workflows
└── Human-in-the-Loop patterns

AI Security/Governance
├── Prompt injection defense
├── Data privacy (PII masking)
├── AI output filtering
└── Audit logging and monitoring

Tier 3: Skills for Architects and Leaders

AI System Architecture Design
├── LLM routing (cost/performance optimization)
├── Caching strategy (semantic caching)
├── Multi-model orchestration
└── Failure response/fallback design

Cost Optimization
├── Token usage optimization
├── Model selection strategy (small vs large models)
├── Batch processing vs real-time processing
└── GPU resource efficiency

AI Project Management
├── AI PoC → Pilot → Full rollout roadmap
├── AI project risk management
├── Data quality management framework
└── Client expectation management

4-3. Soft Skill Changes

SI engineers in the AI era need new soft skills beyond technical capabilities.

1. Ability to Explain AI Limitations to Clients

Client: "Make the AI give 100% accurate answers" Wrong response: "Yes, it's possible" Right response: "Current AI technology can achieve approximately 95% accuracy, and for the remaining 5%, we propose a Human-in-the-Loop approach where humans review the output"

2. AI Project Scoping (Avoiding Over-Promise)

The biggest risk in AI projects is over-promising. In traditional SI, you could say "This feature will take 3 months." But with AI, results vary dramatically based on data quality and model performance.

3. Data Governance Understanding

80% of AI projects is data preparation. Why SI engineers need to understand data governance:

• PIPA (Personal Information Protection Act) compliance
• Data quality management
• Training data bias verification
• Data pipeline design

4. Ethical AI Deployment Awareness

• Fairness verification of AI outputs
• Understanding Explainable AI (XAI) requirements
• AI usage disclosure and transparency
• Clear accountability assignment

5. Career Strategies for SI Engineers in the AI Era

5-1. Survival Strategy (Current SI Professionals)

If you're currently working in the SI industry, here is a step-by-step strategy.

Step 1: Python + AI Fundamentals (3 Months)

Month 1: Python Basics
├── Python syntax (Java developers can pick up quickly)
├── FastAPI basics (REST API development)
└── Project: Build a simple API server

Month 2: AI/ML Fundamentals
├── OpenAI API usage
├── Prompt engineering basics
├── LangChain introduction
└── Project: Build a simple chatbot

Month 3: RAG Fundamentals
├── Understanding embeddings and vector DBs
├── LangChain RAG pipeline
├── Basic prompt optimization
└── Project: FAQ chatbot (RAG-based)

Step 2: RAG Pipeline Construction Practice (2 Months)

Month 4: Advanced RAG
├── Chunking strategies (Semantic, Recursive)
├── Hybrid search (keyword + vector)
├── Reranking (Reranker models)
└── Project: Internal document search system

Month 5: Agents + Tool Use
├── Function Calling patterns
├── Multi-step agents
├── LangGraph basics
└── Project: Business automation agent

Step 3: Lead an Internal AI PoC (3 Months)

This is the most important step. Learning alone is not enough — you need real experience solving actual business problems with AI.

Recommended PoC topics:

• Internal document search AI (RAG)
• Code review automation
• Test case auto-generation
• Failure log analysis AI
• Customer inquiry auto-classification

Step 4: Secure an AI Project Lead Role

Leverage your PoC success to take on an actual AI project lead role.

[SI Engineer AI Transition Roadmap]

Current State     3 Months      6 Months       9 Months       12 Months
────────────────────────────────────────────────────────────────
Java Developer → Python      → RAG Build    → Internal PoC → AI Project Lead
Infra Engineer → K8s GPU     → MLOps Learn  → AI Infra PoC → AI Ops Engineer
PM/PL          → AI Literacy → AI Planning  → AI PoC PM    → AI PM/Consultant
QA Engineer    → AI Testing  → LLM Eval     → AI QA PoC    → AI Quality Eng.

5-2. Career Transition Strategy (To AI-Focused Companies)

Path A: SI to AI Startup

Strengths: Domain expertise (finance, manufacturing, retail, etc.) + AI skills Weaknesses: Startup culture adaptation, fast pace requirements

Preparation:

• AI project portfolio on GitHub (minimum 3 projects)
• Domain-specific AI solution experience
• AI-related blog posts/presentations

Path B: SI to Big Tech Solutions

Strengths: Client-facing experience + technical skills Weaknesses: High technical interview bar

Target companies and roles:

• AWS Solutions Architect
• Google Cloud Customer Engineer
• Microsoft Azure Specialist
• Salesforce AI Consultant

Path C: SI to AI Consulting

Strengths: Business understanding + project management + AI capability Weaknesses: English proficiency required (for global firms)

Target companies:

• McKinsey (QuantumBlack)
• BCG (BCG X)
• Accenture AI
• Deloitte AI Center of Excellence

5-3. Strategy by Career Level

Junior (1-3 Years)

Core Goal: Build AI foundation + participate in hybrid projects

Recommended Activities:
├── Python + AI fundamentals learning (online courses)
├── Join/lead internal AI study groups
├── Volunteer for projects with AI components
├── Obtain AI-related certifications
│   ├── AWS Machine Learning Specialty
│   ├── Google Cloud Professional ML Engineer
│   └── Microsoft Azure AI Engineer
└── Publish personal AI projects on GitHub

Mid-Level (3-7 Years)

Core Goal: Lead AI-specific projects + establish specialization

Recommended Activities:
├── AI project sub-lead experience
├── Position yourself as domain AI expert
│   ├── Financial AI (AML, risk, underwriting)
│   ├── Manufacturing AI (predictive maintenance, quality)
│   └── Public Sector AI (administrative automation)
├── Accumulate AI architecture design experience
├── Present at internal/external AI seminars
└── Maintain a tech blog

Senior (7+ Years)

Core Goal: Choose AI Architect / AI PM / AI Consultant track

Track A - AI Architect:
├── Large-scale AI system design
├── Multi-model orchestration
├── AI infrastructure design (GPU clusters, MLOps)
└── Lead technical decision-making

Track B - AI PM/Director:
├── AI project portfolio management
├── AI business development (Pre-sales)
├── C-Level AI strategy advisory
└── AI team building and development

Track C - AI Consultant:
├── AI adoption strategy development
├── AI ROI analysis and business case
├── AI governance framework design
└── Industry-specific AI best practices

6. The Future of SI: 2026-2030 Outlook

6-1. How AI Changes SI

Gartner predicts that by 2028, 80% of coding tasks in SI projects will be automated by AI.

This does not mean SI engineers will disappear. Their roles will transform.

[SI Engineer Role Evolution]

2020-2024: Coder (writing code is the core job)
    ↓
2025-2027: AI-Augmented Developer (boosting productivity with AI tools)
    ↓
2028-2030: AI Orchestrator (designing and coordinating AI systems)

6-2. Multi-Agent SI

Future SI projects will have multiple AI agents collaborating to build systems.

[2028 SI Project Vision]

[Requirements AI Agent]
  |  Analyze client interviews, auto-generate requirements specs
  ↓
[Design AI Agent]
  |  Requirements-based architecture design, ERD generation
  ↓
[Development AI Agent]
  |  Design-based code auto-generation (80% automated)
  ↓
[Testing AI Agent]
  |  Test case generation, automated execution, bug reports
  ↓
[Deployment AI Agent]
  |  CI/CD pipeline execution, monitoring setup
  ↓
[SI Engineer (Human)]
     Supervise entire process, client communication, exception handling
     = "AI Orchestrator"

6-3. Three Characteristics of Surviving SI Engineers

1. Domain Expertise Even if AI generates code well, you need financial domain expertise to instruct "Design a system that complies with this financial regulation." AI is a tool, and knowing where to use the tool is a human skill.

2. AI Literacy The ability to use AI tools proficiently. Prompt writing, AI agent design, AI pipeline construction, etc. This is a different dimension of capability from coding.

3. Client Communication The area AI cannot replace. The ability to understand client business problems, translate them into AI solutions, and explain results. The essence of SI is "the bridge between technology and business," and only humans can play this bridge role.

7. 15 Interview Questions + Learning Roadmap

15 Interview Questions

Technical Questions (7)

1. Explain the components of a RAG pipeline and the role of each stage. Key Points: Be able to explain the full flow of document loading - chunking - embedding - vector storage - retrieval - context injection - LLM generation.

2. Explain the principle of similarity search in vector DBs, and the difference between cosine similarity and Euclidean distance. Key Points: Geometric meaning of embedding vectors, HNSW/IVF indexing, curse of dimensionality, etc.

3. Suggest 3 engineering methods to reduce LLM hallucinations. Key Points: RAG, prompt engineering (Chain-of-Thought), temperature adjustment, fact-checking tool integration, structured output.

4. What is prompt injection attack and how do you defend against it? Key Points: Direct vs indirect injection, input filtering, role separation, output validation, guardrail setup.

5. Design an architecture for adding AI features to an existing Java/Spring-based system. Key Points: Separate AI service as a microservice (Python/FastAPI), API Gateway integration, async processing, error handling.

6. Explain how to efficiently manage GPU resources in AI model serving. Key Points: Batch inference, model quantization, vLLM's PagedAttention, GPU time-sharing, auto-scaling.

7. Explain state management in a multi-agent system using LangGraph. Key Points: Graph-based state machine, node/edge definitions, conditional routing, checkpointing, Human-in-the-Loop.

Business/Strategy Questions (4)

8. How would you explain the ROI of an AI project to a client? Key Points: Quantitative metrics (processing time reduction, cost savings) + qualitative metrics (customer satisfaction, employee experience) + phased approach (prove in PoC).

9. How would you respond when a client says "Automate everything with AI"? Key Points: Set realistic expectations, identify AI-suitable areas, present phased adoption roadmap, design Human-in-the-Loop.

10. How would you define performance criteria (SLA) for AI models in an SI project? Key Points: Multi-dimensional SLA including accuracy, response time, availability, cost efficiency. Difference from traditional IT SLA (AI is probabilistic).

11. How can AI be leveraged in legacy system modernization projects? Key Points: AI code analysis (understanding legacy code), automated migration, auto-generated tests, auto-generated documentation.

Career/Culture Questions (4)

12. How have you built your AI capabilities as an SI engineer? Key Points: Specific learning path, real project application experience, continuous learning commitment.

13. How would you handle data quality issues in an AI project? Key Points: Data profiling, cleansing pipeline, client negotiation, data governance framework establishment.

14. What are the differences between traditional SI methodologies (Waterfall/Agile) and AI project methodologies? Key Points: Experiment-based approach, data dependency, uncertainty management, iterative performance improvement.

15. How do you expect the role of SI engineers to change in 5 years? Key Points: From coders to AI orchestrators, increasing importance of domain expertise, AI system design/supervision role.

Learning Roadmap

[SI Engineer AI Transition 12-Month Roadmap]

Phase 1 (Month 1-3): Foundations
├── Python programming
├── OpenAI API usage
├── Prompt engineering
└── Simple chatbot development

Phase 2 (Month 4-6): Practical Basics
├── LangChain/LlamaIndex
├── Vector DB (Milvus/pgvector)
├── RAG pipeline construction
└── AI service API design

Phase 3 (Month 7-9): Advanced
├── Multi-agent (LangGraph)
├── MLOps (MLflow, Docker, K8s)
├── AI security/governance
└── Internal AI PoC execution

Phase 4 (Month 10-12): Expert
├── AI architecture design
├── Cost optimization
├── AI project management
└── AI project lead role

Recommended Learning Resources:

Quiz

Test your understanding of the SI industry's AI transformation with these questions.

References

1. Grand View Research, "System Integration Market Size Report, 2025-2030"
2. McKinsey Global Institute, "The State of AI in 2024: Gen AI adoption spikes"
3. Gartner, "Top Strategic Technology Trends 2025: Agentic AI"
4. Samsung SDS, "Annual Report 2024 - Cloud & AI Business"
5. Samsung SDS, "FabriX Platform Documentation"
6. Samsung SDS, "Brity Copilot Enterprise Guide"
7. LG CNS, "IPO Prospectus 2025 - AI Strategy Overview"
8. LG CNS, "DevOn AI Platform Whitepaper"
9. SK Group, "AI/Digital Talent Recruitment Plan 2025"
10. SK Telecom, "AI Agent Platform 'A.' Technical Overview"
11. LangChain Documentation, "RAG Pipeline Best Practices"
12. LlamaIndex Documentation, "Enterprise RAG Architecture"
13. Gartner, "Predicts 2025: AI Will Transform 80% of SI Coding Tasks by 2028"
14. IDC Korea, "Korea IT Services Market Forecast 2025-2029"
15. KOSA (Korea Software Industry Association), "2025 Software Industry Survey"
16. Deloitte, "2025 Global AI Transformation Survey"
17. Accenture, "Total Enterprise Reinvention with AI"
18. Forbes, "How Korean Tech Giants Are Leading Asia's AI Transformation"