마케팅 분야 인공지능(AI) 시장 : 기술별, 용도별, 업계별, 도입 형태별, 기업 규모별 - 세계 시장 예측(2026-2032년)

The Artificial Intelligence in Marketing Market is projected to grow by USD 43.96 billion at a CAGR of 9.79% by 2032.

Artificial intelligence in marketing has moved from experimental personalization to a core growth engine for customer acquisition, retention, media optimization, and revenue operations. Adoption is being accelerated by generative AI, predictive analytics, customer data platforms, marketing automation, conversational AI, and machine-learning-led campaign optimization.

Verified market signals show why executive attention is rising. Data shows that 65% of surveyed organizations were regularly using generative AI in 2024, nearly double the share recorded in 2023, with marketing and sales among the most common functional use cases. At the same time, internet advertising revenue reached USD 258.6 billion in 2024, underscoring the expanding digital media base where AI-enabled targeting, creative testing, and attribution can generate measurable impact.

Transformative Shifts in the AI Marketing Landscape

The marketing landscape is shifting from manual segmentation to real-time, signal-based engagement. Brands are using AI to interpret first-party data, predict next-best actions, automate journey orchestration, and produce modular creative assets for different audiences, channels, and buying stages.

Privacy regulation and the decline of third-party identifiers are also reshaping AI deployment. Marketers are investing in consented customer data, clean rooms, contextual intelligence, and privacy-enhancing technologies to sustain personalization while complying with GDPR, CPRA, and emerging AI governance requirements. The competitive frontier is no longer only campaign reach; it is the ability to combine data quality, model governance, and creative speed into a repeatable operating model.

Cumulative Impact of Artificial Intelligence on Marketing

The cumulative impact of AI is visible across the full marketing value chain. In planning, AI improves audience discovery, demand forecasting, media mix modeling, and budget allocation. In execution, generative AI supports content ideation, dynamic creative optimization, email personalization, chat-based service, and social listening. In measurement, machine learning strengthens attribution, incrementality testing, churn prediction, and lifetime value modeling.

The business case is strongest when AI is connected to enterprise data rather than deployed as a stand-alone tool. Organizations that integrate AI with CRM, commerce, call center, product usage, and media data can improve targeting accuracy, reduce content production bottlenecks, and identify high-value customers earlier. However, risks remain around hallucinated content, brand safety, biased recommendations, weak consent management, and fragmented vendor ecosystems.

Key Regional Insights Across Global AI Marketing Markets

Asia-Pacific is a dynamic environment for AI in marketing because of mobile-first commerce, super-app ecosystems, and rapid digital payment adoption. China, India, Japan, South Korea, Australia, and ASEAN markets are expanding AI use in social commerce, recommendation engines, retail media, influencer analytics, and multilingual customer engagement. GSMA has reported that Asia-Pacific accounts for a major share of global mobile internet users, while national digital identity and payment systems in several markets are improving the availability of consented customer data for personalized engagement.

North America remains the most mature commercialization hub, supported by advanced cloud infrastructure, advertising technology, high digital ad spend, and broad enterprise adoption of analytics and automation. Europe is advancing AI adoption with a governance-first approach, particularly under GDPR and the EU AI Act, making explainability, consent, auditability, and risk management central to enterprise marketing programs.

Latin America is gaining momentum through mobile commerce, fintech adoption, and social media-led customer acquisition, with Brazil and Mexico acting as major demand centers. The Middle East is investing aggressively in AI-enabled digital government, tourism, retail, and financial services, especially across the GCC. Africa is earlier in adoption but offers long-term opportunity through mobile connectivity, digital payments, and AI-assisted customer service for underserved markets, supported by rising broadband access and mobile money adoption documented by international development and telecommunications agencies.

Key Group Insights for AI Marketing Adoption

ASEAN is emerging as a practical adoption zone for AI marketing because of its young digital population, fast-growing eCommerce, and multilingual consumer environments. Brands in Singapore, Indonesia, Thailand, Vietnam, Malaysia, and the Philippines are using AI to localize content, optimize marketplaces, automate customer support, and improve social commerce performance across mobile-first audiences.

The GCC is prioritizing AI as part of national digital transformation agendas, creating opportunities in retail, tourism, banking, telecom, and public-sector engagement. The European Union is shaping the compliance benchmark for AI in marketing, where transparent data processing, lawful consent, accountability, and risk-based AI governance are becoming competitive differentiators for customer-facing organizations.

BRICS markets provide scale, data diversity, and fast-moving digital ecosystems, particularly across China, India, and Brazil, where digital payments, platform commerce, and mobile engagement support AI-enabled personalization. G7 economies lead in enterprise software, advertising technology, cloud infrastructure, digital policy, and regulatory development. NATO markets show strong demand for cybersecure AI deployment, trusted data sharing, resilient marketing technology infrastructure, and responsible AI practices across multinational enterprises.

Key Country Insights for AI in Marketing

The United States leads in AI marketing commercialization due to its concentration of cloud infrastructure, ad-tech platforms, software development, retail media networks, and digital advertising investment. Canada benefits from strong AI research clusters, privacy-conscious enterprise adoption, and advanced financial services and retail use cases, while Mexico is expanding AI use in retail, telecom, banking, and nearshore digital services.

Brazil is Latin America's most influential AI marketing market, supported by social commerce, instant payments, digital banking, and large consumer platforms. The United Kingdom has a mature marketing technology ecosystem and strong professional services demand. Germany, France, Italy, and Spain are adopting AI with a strong emphasis on privacy, industrial brand marketing, omnichannel retail, and EU compliance. Russia remains a distinct digital ecosystem shaped by local platforms, domestic data rules, and constrained access to some Western technologies.

China is one of the world's most advanced markets for AI-driven commerce, social platforms, live shopping, and recommendation systems. India is scaling AI marketing through mobile-first consumers, digital public infrastructure, real-time payments, and rapid startup activity. Japan emphasizes automation, loyalty, service quality, and precision customer engagement, while South Korea is advanced in gaming, beauty, entertainment, connected retail, and mobile engagement. Australia shows strong adoption in banking, retail, telecom, government services, and B2B marketing analytics.

Actionable Recommendations for Industry Leaders

Industry leaders should begin with business outcomes rather than tools. The highest-return use cases typically include churn reduction, conversion lift, media efficiency, customer lifetime value improvement, sales enablement, content velocity, and customer service deflection. Each use case should have measurable baselines, owner accountability, and governance checkpoints.

Executives should also modernize the data foundation. This includes unifying first-party data, strengthening consent management, validating model outputs, adopting human-in-the-loop creative review, and integrating AI into existing CRM, marketing automation, analytics, and commerce platforms. Vendor selection should prioritize interoperability, explainability, privacy controls, security posture, and measurable performance rather than novelty.

Research Methodology for AI Marketing Analysis

The research approach combines secondary research, expert interpretation, and market triangulation. Verified public sources include company filings, regulatory publications, advertising industry reports, cloud and software adoption research, government AI strategies, privacy laws, and reputable surveys from organizations such as McKinsey, IAB, PwC, OECD, ITU, GSMA, and national statistical agencies.

Insights are validated by comparing adoption signals across demand-side industries, technology vendors, regional digital maturity, regulatory environments, and marketing budget allocation patterns. The methodology emphasizes evidence-based interpretation and avoids unsupported market claims, ensuring that conclusions reflect observable adoption drivers, constraints, and strategic implications for AI in marketing.

Conclusion on the Future of AI in Marketing

Artificial intelligence is becoming the operating layer of modern marketing. Its value extends beyond automation to intelligence-driven decisioning, scalable personalization, faster creative production, and more accountable customer engagement.

The winners will be organizations that combine trusted data, responsible AI governance, integrated marketing technology, and disciplined measurement. As privacy expectations rise and competition intensifies, AI in marketing will increasingly determine how brands understand customers, allocate spend, and convert digital engagement into profitable growth.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. Market Share Analysis, 2025
• 3.5. FPNV Positioning Matrix, 2025
• 3.6. New Revenue Opportunities
• 3.7. Next-Generation Business Models
• 3.8. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Artificial Intelligence in Marketing Market, by Technology

• 7.1. Computer Vision
  • 7.1.1. Image Recognition
  • 7.1.2. Video Analytics
• 7.2. Data Analytics
  • 7.2.1. Descriptive Analytics
  • 7.2.2. Predictive Analytics
  • 7.2.3. Prescriptive Analytics
• 7.3. Deep Learning
  • 7.3.1. Convolutional Neural Networks
  • 7.3.2. Generative Adversarial Networks
  • 7.3.3. Recurrent Neural Networks
• 7.4. Machine Learning
  • 7.4.1. Reinforcement Learning
  • 7.4.2. Supervised Learning
  • 7.4.3. Unsupervised Learning
• 7.5. Natural Language Processing
  • 7.5.1. Language Translation
  • 7.5.2. Sentiment Analysis
  • 7.5.3. Text Generation

8. Artificial Intelligence in Marketing Market, by Application

• 8.1. Ad Personalization
  • 8.1.1. Dynamic Creative Optimization
  • 8.1.2. Real-Time Bidding
• 8.2. Campaign Management
  • 8.2.1. Email Campaign Management
  • 8.2.2. Social Media Campaign Management
• 8.3. Chatbots
  • 8.3.1. AI Chatbots
  • 8.3.2. Rule-Based Chatbots
• 8.4. Content Generation
  • 8.4.1. Automated Copywriting
  • 8.4.2. Image Generation
  • 8.4.3. Video Generation
• 8.5. Customer Segmentation
  • 8.5.1. Behavioral Segmentation
  • 8.5.2. Demographic Segmentation
  • 8.5.3. Psychographic Segmentation
• 8.6. Lead Generation
  • 8.6.1. Automated Outreach
  • 8.6.2. Predictive Lead Scoring

9. Artificial Intelligence in Marketing Market, by Industry Vertical

• 9.1. BFSI
• 9.2. Healthcare
• 9.3. IT And Telecom
• 9.4. Manufacturing
  • 9.4.1. Automotive
  • 9.4.2. Consumer Electronics
  • 9.4.3. Industrial Manufacturing
• 9.5. Media And Entertainment
  • 9.5.1. Gaming
  • 9.5.2. Publishing
  • 9.5.3. Streaming Services
• 9.6. Retail

10. Artificial Intelligence in Marketing Market, by Deployment

• 10.1. Cloud
• 10.2. On-Premise

11. Artificial Intelligence in Marketing Market, by Organization Size

• 11.1. Large Enterprises
• 11.2. Small & Medium Enterprises

12. Artificial Intelligence in Marketing Market, by Region

• 12.1. Asia-Pacific
• 12.2. North America
• 12.3. Latin America
• 12.4. Europe
• 12.5. Middle East
• 12.6. Africa

13. Artificial Intelligence in Marketing Market, by Group

• 13.1. ASEAN
• 13.2. GCC
• 13.3. European Union
• 13.4. BRICS
• 13.5. G7
• 13.6. NATO

14. Artificial Intelligence in Marketing Market, by Country

• 14.1. United States
• 14.2. Canada
• 14.3. Mexico
• 14.4. Brazil
• 14.5. United Kingdom
• 14.6. Germany
• 14.7. France
• 14.8. Russia
• 14.9. Italy
• 14.10. Spain
• 14.11. China
• 14.12. India
• 14.13. Japan
• 14.14. Australia
• 14.15. South Korea

15. Competitive Landscape

• 15.1. Market Concentration Analysis, 2025
  • 15.1.1. Concentration Ratio (CR)
  • 15.1.2. Herfindahl Hirschman Index (HHI)
• 15.2. Recent Developments & Impact Analysis, 2025
• 15.3. Product Portfolio Analysis, 2025
• 15.4. Benchmarking Analysis, 2025

16. Company Profiles

• 16.1. Adobe Inc.
• 16.2. Alphabet Inc.
• 16.3. Amazon.com, Inc.
• 16.4. Anthropic PBC
• 16.5. Appier Group Inc.
• 16.6. C3.ai, Inc.
• 16.7. Canva Pty Ltd
• 16.8. Cohere Inc.
• 16.9. Copy.ai, Inc.
• 16.10. Databricks, Inc.
• 16.11. DataRobot, Inc.
• 16.12. DeepL SE
• 16.13. Drift.com, Inc.
• 16.14. HubSpot, Inc.
• 16.15. Hugging Face, Inc.
• 16.16. IBM Corporation
• 16.17. Jasper AI, Inc.
• 16.18. Meta Platforms, Inc.
• 16.19. Microsoft Corporation
• 16.20. NVIDIA Corporation
• 16.21. OpenAI, Inc.
• 16.22. Oracle Corporation
• 16.23. Palantir Technologies Inc.
• 16.24. Salesforce, Inc.
• 16.25. Scale AI, Inc.
• 16.26. Snowflake Inc.
• 16.27. Surfer sp. z o.o.
• 16.28. Synthesia Limited
• 16.29. Uniphore Technologies Inc.
• 16.30. Yellow.ai Inc.