마케팅용 AI 시장 : 솔루션 유형별, 업계별, 도입 형태별, 조직 규모별 - 세계 예측(2026-2032년)

The AI in Marketing Market was valued at USD 25.72 billion in 2025 and is projected to grow to USD 27.79 billion in 2026, with a CAGR of 8.52%, reaching USD 45.60 billion by 2032.

How modern marketing organizations are integrating machine intelligence into customer engagement and operational workflows to drive measurable competitive differentiation

Artificial intelligence is transforming marketing from a set of point solutions into an integrated capability that reshapes customer experience, operational efficiency, and strategic decision-making. Leaders across sectors are moving beyond proofs of concept to operationalize AI across the customer lifecycle, embedding analytics into campaign orchestration, personalization, and recommendation processes. This shift is driven by improved model maturity, richer customer signals, and increasingly accessible compute, which together enable more precise targeting and real-time adaptation.

As a result, marketing organizations are recalibrating workflows, skill sets, and vendor relationships. Data engineering, model governance, and measurement practices are growing in importance, and teams that can combine technical proficiency with commercial insight gain a meaningful edge. Meanwhile, privacy and regulatory expectations are prompting new approaches to consent management and explainability, which inform both product choices and vendor selection.

In this context, executives must view AI in marketing as both a capability and a program: a capability that augments creative and operational roles, and a program that requires governance, investment sequencing, and cross-functional alignment. The following sections summarize pivotal landscape shifts, tariff impacts, segmentation intelligence, regional dynamics, vendor behaviors, recommended actions for senior leaders, and the methodology used to derive these findings.

Emerging structural shifts in marketing technology stacks and customer engagement models that are reshaping vendor ecosystems and buyer priorities

Over the past 24 months the marketing technology landscape has experienced several transformative shifts that are redefining vendor strategies and buyer expectations. First, the consolidation of data sources and the rise of platform approaches have reduced friction for enterprises seeking end-to-end campaign orchestration, turning once-discrete tools into interconnected stacks that emphasize interoperability and unified measurement.

Second, model-driven personalization has evolved from rule-based targeting to continuous, algorithmic optimization. Marketers increasingly favor adaptive personalization engines that learn from real-time signals rather than static segmentation, enabling dynamically tailored journeys that respond to context. Third, the proliferation of multimodal conversational interfaces - incorporating text, visuals, and voice - is changing where and how brands engage customers, expanding the remit of chatbots beyond simple FAQ resolution to sales assistance and complex service interactions.

Finally, heightened regulatory scrutiny and consumer expectations for transparency have elevated the importance of privacy-aware design and explainable AI. Together, these shifts demand a new operating model where technology selection, data governance, and creative strategy are tightly coordinated to deliver consistent, compliant, and scalable outcomes.

Analyzing the complex cumulative effects of new 2025 United States tariff policies on AI infrastructure procurement, deployment strategies, and vendor supply chains

The implementation of new United States tariff measures in 2025 has exerted a multifaceted influence on the marketing AI ecosystem, with cumulative effects that extend across hardware procurement, cloud economics, and supply chain design. Tariffs on imported semiconductors, specialized accelerators, and certain networking equipment raised acquisition costs for compute-intensive infrastructure, prompting organizations to reassess on-premises investments and to accelerate negotiations with hyperscalers for capacity or managed services.

Consequently, decision-makers faced trade-offs between capital expenditure on localized infrastructure versus variable operating expenditure for cloud-based processing. In many cases, procurement teams pushed for longer supplier contracts and introduced clauses to mitigate future tariff volatility, which slowed replacement cycles and favored vendors that could demonstrate transparent total cost of ownership. The tariffs also influenced vendor roadmaps: hardware vendors prioritized supply resilience and localized manufacturing partnerships, while software vendors highlighted optimizations that reduce dependency on specialized chips.

Operationally, the tariffs encouraged greater adoption of hybrid deployment patterns, enabling critical workloads to remain on lower-cost, locally sourced infrastructure while variable or experimental workloads moved to public cloud platforms. Marketing organizations responded by refining model inference strategies to minimize high-cost compute at scale, adopting lighter-weight models for personalization tasks, and shifting batch processing windows to optimize cloud pricing. Overall, the net effect has been a reorientation toward supply chain resilience, cost-effective architecture, and tighter collaboration between procurement, IT, and marketing teams to preserve innovation momentum under new trade constraints.

A comprehensive segmentation perspective showing how solution types, deployment choices, organizational scale, and vertical priorities determine adoption, risk, and value realization

A granular segmentation lens reveals how solution distinctions, deployment preferences, organization size, and vertical focus shape adoption patterns and operational priorities. Based on solution type, analytics platforms, campaign management tools, chatbots, personalization engines, and recommendation engines each follow distinct value arcs: analytics platforms concentrate on descriptive, predictive, and prescriptive capabilities to convert data into strategic signals; campaign management tools orchestrate omnichannel execution and measurement; chatbots automate customer interaction across textual, visual, and voice modalities; personalization engines tailor experiences through rule-based and algorithmic approaches; and recommendation engines drive conversion and retention through relevance models. Within analytics platforms, the descriptive layer emphasizes reporting and dashboards, the predictive layer relies on machine learning analytics and statistical modeling to forecast behavior, and the prescriptive layer recommends optimal actions to maximize objectives. The predictive segment itself bifurcates into machine learning analytics, which favors large-scale pattern recognition and feature engineering, and statistical modeling, which emphasizes interpretability and hypothesis-driven insights. Meanwhile, chatbots differentiate by modality: text-based interfaces handle high-volume inquiries efficiently, visual chatbots enable image-driven discovery or assistance, and voice-based chatbots support hands-free, contextual engagement.

Based on deployment mode, organizations evaluate cloud, hybrid, and on-premises alternatives through the lenses of agility, control, and compliance. Cloud options split into private and public cloud variations that address different risk and performance profiles, while on-premises choices break down into licensed software and owned software models that afford varying degrees of customization and capital commitment. This deployment taxonomy influences speed-to-market, data residency, and integration complexity.

Based on organization size, adoption trajectories diverge between large enterprises and small and medium enterprises. Large enterprises comprise multinational corporations and regional enterprises that prioritize scale, governance, and cross-market consistency; they typically invest in robust data architectures and centralized model governance. Small and medium enterprises span medium, micro, and small enterprises and often emphasize rapid time-to-value, hosted solutions, and pragmatic automation that reduces manual workload.

Based on industry vertical, adoption drivers and success metrics vary significantly across BFSI, healthcare, IT telecom, and retail. BFSI prioritizes compliance, fraud detection, and lifetime value optimization; healthcare focuses on privacy, clinical collaboration, and patient engagement; IT telecom emphasizes network-aware personalization and churn reduction; and retail concentrates on conversion, inventory-aware recommendations, and immersive shopping experiences. These vertical lenses shape feature roadmaps, partnership models, and the metrics used to evaluate vendor fit.

Regional market dynamics and regulatory differences that shape infrastructure choices, talent strategies, and localized customer experience priorities across global markets

Regional dynamics materially influence the trajectory of AI-enabled marketing initiatives, with unique regulatory, talent, and infrastructure characteristics shaping how organizations invest and scale. In the Americas, mature cloud ecosystems and strong venture capital flows drive rapid experimentation and commercial partnerships, but regional privacy laws and consumer expectations also require tight governance and transparent data practices. Many organizations in this region leverage dense talent clusters and large-scale data assets to operationalize personalization at scale.

In Europe, Middle East & Africa, a diverse regulatory landscape and heightened emphasis on privacy-by-design steer enterprises toward on-premises or private-cloud architectures and toward vendors that can demonstrate rigorous compliance capabilities. Market trajectories in this region often prioritize cross-border data transfer safeguards and explainability, which affects deployment speed and vendor selection. Meanwhile, localized innovation hubs and government-led digital initiatives create differentiated opportunities across regional markets.

Asia-Pacific exhibits a broad spectrum of adoption patterns: some markets lead in mobile-first experiences and conversational commerce, while others prioritize infrastructure investments and rapid scaling. The region's combination of high consumer engagement rates and increasing local cloud capacity stimulates ambitious personalization and recommendation initiatives, but organizations must still navigate complex regulatory regimes and fragmented language and cultural contexts that influence model design and content strategies. Across all regions, successful adopters align technical choices with regulatory realities and localized consumer preferences to maximize relevance and minimize compliance risk.

How vendors, hyperscalers, startups, and systems integrators are aligning product roadmaps and partnerships to solve integration, governance, and go-to-market challenges

Vendors and partners in the AI marketing ecosystem are pursuing differentiated strategies to capture value while addressing buyer concerns about integration, governance, and cost. Hyperscalers continue to compete on scalable compute, managed AI services, and embedded analytics that reduce time-to-value for enterprise buyers. Enterprise software vendors focus on pre-built connectors, enterprise-grade security, and packaged vertical workflows to lower integration risk for complex organizations. At the same time, pure-play AI vendors and specialized startups are innovating rapidly in areas such as recommendation quality, lightweight inference, and conversational intelligence, frequently partnering with larger vendors to accelerate distribution.

System integrators and consultancies are playing a growing role in implementation and change management, offering services that bridge technical implementation with creative execution. Channel and reseller strategies favor flexible licensing models and outcome-based commercial structures that reduce upfront barriers for buyers. Across the vendor landscape, open-source components and model sharing have become central to product roadmaps, enabling faster innovation but also increasing the importance of governance layers that manage model provenance and bias. Strategic partnerships, selective acquisitions, and co-development arrangements are the primary mechanisms through which vendors scale offerings while addressing client-specific needs.

Practical strategic steps for executives to align architecture, procurement, talent, governance, and partnerships to scale AI-enabled marketing responsibly and efficiently

Industry leaders must act decisively to translate technological potential into sustained business outcomes by aligning strategy, capability, and governance. First, executives should prioritize a modular architecture that balances centralized data governance with federated execution, enabling rapid experimentation without sacrificing control. Integrating measurement frameworks that link model outputs to revenue and retention metrics will make investment decisions more defensible and reveal the true ROI of AI-driven campaigns.

Second, procurement and IT should collaborate to create flexible commercial terms that mitigate hardware and tariff risk while preserving innovation budgets. This includes negotiating trial credits with cloud providers, staged license commitments, and options for managed services. Third, talent strategies should focus on cross-functional teams that combine data engineering, product management, creative strategy, and legal oversight; upskilling existing marketing staff in model literacy will accelerate adoption and reduce dependence on external consultants.

Fourth, embed privacy-by-design and explainability into solution selection and deployment to maintain consumer trust and regulatory compliance. Lastly, cultivate a partner ecosystem that blends hyperscaler capacity, specialized vendor capabilities, and integrator delivery to optimize speed and resilience. By operationalizing these priorities, leaders can reduce time-to-value, limit vendor lock-in, and scale AI initiatives in a risk-aware manner.

A robust mixed-methods research approach combining primary executive interviews, targeted surveys, technical documentation analysis, and validation briefings to ensure reliable insights

The findings summarized here derive from a mixed-methods research approach designed to triangulate vendor positioning, buyer behavior, and technology trends. Primary research included structured interviews with senior marketing, IT, and procurement leaders across multiple industries, supplemented by expert roundtables with solution architects and data scientists who operationalize models in production. Secondary research incorporated analysis of publicly available technical documentation, regulatory guidance, product release notes, and company disclosures to validate capabilities and roadmaps.

Quantitative inputs were collected through targeted surveys that probed deployment preferences, decision timelines, and operational challenges, and were analyzed alongside qualitative case studies highlighting successful implementations and common failure modes. Additional methods included patent and funding trend analysis to identify innovation trajectories, and a review of job postings and talent flows to assess skills demand. All findings were cross-validated through iterative vendor briefings and buyer feedback loops to ensure relevance and accuracy. The methodology emphasizes transparency, repeatability, and a bias toward practical, deployable insight.

Concluding synthesis that emphasizes the necessity of coordinated strategy, modular infrastructure, and governance to capture the full potential of AI-enabled marketing

In summary, AI in marketing has reached an inflection point where strategic integration, operational rigor, and prudent governance determine who captures the greatest value. The landscape is simultaneously more opportunity-rich and more complex: advances in personalization, recommendation, and conversational AI open new revenue and engagement pathways while tariffs, regulatory dynamics, and deployment choices introduce operational constraints that require deliberate response.

Successful organizations will treat AI as a cross-functional program that combines modular technical architectures, outcome-based measurement, and adaptive procurement practices. They will favor partnerships that accelerate delivery without compromising control, invest in talent pathways that blend technical and creative skill sets, and institutionalize privacy and explainability as non-negotiable components of product design. By following these principles, leaders can convert technological momentum into differentiated customer experiences and scalable commercial returns.

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. Porter's Five Forces Analysis
• 4.4. PESTLE Analysis
• 4.5. Market Outlook
  • 4.5.1. Near-Term Market Outlook (0-2 Years)
  • 4.5.2. Medium-Term Market Outlook (3-5 Years)
  • 4.5.3. Long-Term Market Outlook (5-10 Years)
• 4.6. 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 United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. AI in Marketing Market, by Solution Type

• 8.1. Analytics Platforms
  • 8.1.1. Descriptive Analytics
  • 8.1.2. Predictive Analytics
    • 8.1.2.1. Machine Learning Analytics
    • 8.1.2.2. Statistical Modeling
  • 8.1.3. Prescriptive Analytics
• 8.2. Campaign Management Tools
• 8.3. Chatbots
  • 8.3.1. Text Based Chatbots
  • 8.3.2. Visual Chatbots
  • 8.3.3. Voice Based Chatbots
• 8.4. Personalization Engines
• 8.5. Recommendation Engines

9. AI in Marketing Market, by Industry Vertical

• 9.1. BFSI
• 9.2. Healthcare
• 9.3. IT Telecom
• 9.4. Retail

10. AI in Marketing Market, by Deployment Mode

• 10.1. Cloud
  • 10.1.1. Private Cloud
  • 10.1.2. Public Cloud
• 10.2. Hybrid
• 10.3. On Premises

11. AI in Marketing Market, by Organization Size

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

12. AI in Marketing Market, by Region

• 12.1. Americas
  • 12.1.1. North America
  • 12.1.2. Latin America
• 12.2. Europe, Middle East & Africa
  • 12.2.1. Europe
  • 12.2.2. Middle East
  • 12.2.3. Africa
• 12.3. Asia-Pacific

13. AI 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. AI 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. United States AI in Marketing Market

16. China AI in Marketing Market

17. Competitive Landscape

• 17.1. Market Concentration Analysis, 2025
  • 17.1.1. Concentration Ratio (CR)
  • 17.1.2. Herfindahl Hirschman Index (HHI)
• 17.2. Recent Developments & Impact Analysis, 2025
• 17.3. Product Portfolio Analysis, 2025
• 17.4. Benchmarking Analysis, 2025
• 17.5. Adobe Inc.
• 17.6. Google LLC
• 17.7. HubSpot, Inc.
• 17.8. International Business Machines Corporation
• 17.9. Microsoft Corporation
• 17.10. Nvidia Corporation
• 17.11. Oracle Corporation
• 17.12. Pegasystems Inc.
• 17.13. Salesforce, Inc.
• 17.14. SAP SE
• 17.15. SAS Institute Inc.
• 17.16. Siemens AG