AIコンテンツパーソナライゼーション市場シェア分析、業界動向と統計、成長予測 2026-2031年

AIコンテンツパーソナライゼーションの市場規模は2025年に14億9,000万ドルとなり、2026年には18億3,000万ドルが見込まれています。予測期間に年平均成長率（CAGR）20.98%で成長し、2031年には47億5,000万ドルに達するとMordor Intelligenceでは予測しています。

同市場の動向は、新規加入者の獲得よりも、既存顧客の維持（リテンション）や収益性の高いエンゲージメントを重視するようになったストリーミングサービスによって形作られています。パーソナライズされたマッチングツールは、プラットフォームが視聴者に適切な番組を提案し、ユーザーの離脱リスクを低減するのに役立ちます。こうした機能はストリーミング分野にとどまらず、小売業者、金融サービス事業者、通信会社などが、より個々の顧客に最適化されたデジタル体験を構築する際にも活用され始めています。市場競争においては、膨大なデータリソースを持つ大手テクノロジー・プラットフォームと、導入が容易なレコメンデーション・システムを提供する専門ベンダーとの間で棲み分けが進んでいます。これにより、ベンダーにとっては、顧客に多大な導入負担を強いることなく、複雑なカスタマージャーニーを支援する新たなビジネスチャンスが生まれています。

Mordor Intelligence（モードーインテリジェンス）「AIコンテンツパーソナライゼーション市場シェア分析、業界動向と統計、成長予測 2026-2031年 – AI Content Personalization – Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 – 2031)」はAIコンテンツパーソナライゼーションの世界市場を調査し、主要セグメント別に分析・予測を行っています。

調査対象セグメント

• 用途
  • コンテンツのレコメンデーション
  • マーケティング＆広告のパーソナライゼーション
  • 顧客体験のパーソナライゼーション
  • オーディエンス分析
  • その他の用途
• エンドユーザー
  • ストリーミングプラットフォーム
  • 放送局＆ケーブルテレビ局
  • 通信事業者
  • デジタルプラットフォーム
  • その他のエンドユーザー
• 地域
  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 南米
    • ブラジル
    • アルゼンチン
    • チリ
    • その他の南米
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他の欧州
  • アジア太平洋地域
    • 中国
    • 日本
    • インド
    • 韓国
    • オーストラリア
    • その他のアジア太平洋地域
  • 中東
    • サウジアラビア
    • アラブ首長国連邦（UAE）
    • カタール
    • その他の中東
  • アフリカ
    • エジプト
    • ナイジェリア
    • その他のアフリカ

レポートの主なポイント

• 用途別では、2025年のAIコンテンツパーソナライゼーション市場において「コンテンツレコメンデーション」が34.04%のシェアを占めました。一方、「顧客体験のパーソナライゼーション」は、2031年まで年平均成長率（CAGR）21.08%で拡大すると予測されています。
• エンドユーザー別では、2025年の同市場において「ストリーミングプラットフォーム」が37.42%のシェアを占めました。一方、「デジタルプラットフォーム」は、2031年までCAGR 21.89%で拡大すると予測されています。
• 地域別では、2025年のAIコンテンツパーソナライゼーション市場の収益において北米が42.76%を占めました。一方、アジア太平洋地域は、2031年までCAGR 21.73%で拡大すると予測されています。

エンドユーザー別：ストリーミング・プラットフォームが市場を主導し、デジタル・プラットフォームも拡大

2025年、AIコンテンツパーソナライゼーション市場において、ストリーミング・プラットフォームが37.42%のシェアを占めました。その優位性は、ファーストパーティの視聴データへの直接的なアクセス、サブスクリプション契約に基づく関係性、そしてコンテンツ・カタログの管理権限という3つの要素に支えられています。これらにより、運営側はパーソナライゼーションによる成果をより明確に把握できるようになります。一方、デジタル・プラットフォームは2026年から2031年にかけて年平均成長率（CAGR）21.89%で成長すると予測されています。この拡大は、ソーシャルコマース、ショート動画、インタラクティブ・メディアといった領域での利用が進んでいることを反映しており、これらの領域では頻繁なセッションを通じて密度の高い行動シグナルが生成されます。デロイトの調査によると、Z世代およびミレニアル世代の回答者の70%が、より有益でパーソナライズされた体験を得るためであれば、自身の閲覧、購入、アプリ利用に関するデータを共有してもよいと回答しています。これにより、デジタル・プラットフォームは、サブスクリプション契約を結んでいない場合であっても、ユーザーの同意に基づいた形でパーソナライゼーション・モデルを学習させるための基盤を確保できることになります。

AI Content Personalization Market Analysis by Mordor Intelligence

The AI content personalization market size is projected to expand from USD 1.49 billion in 2025 and USD 1.83 billion in 2026 to USD 4.75 billion by 2031, registering a CAGR of 20.98% between 2026 and 2031. The AI content personalization market is being shaped by streaming services that now place more weight on retention and profitable engagement than subscriber additions. Personalized matching tools can help platforms connect viewers with relevant programming and reduce the risk of user churn. The same capabilities are moving beyond streaming as retailers, financial services providers, and telecom companies assemble more tailored digital experiences. Competition is separating large technology platforms with broad data resources from specialist suppliers that offer ready-to-deploy recommendation systems. This creates opportunities for providers to support complex customer journeys without imposing heavy implementation demands.

Key Report Takeaways

• By application, content recommendation held 34.04% of the AI content personalization market share in 2025, while customer experience personalization is projected to expand at a CAGR of 21.08% through 2031.
• By end user, streaming platforms held 37.42% of the AI content personalization market share in 2025, while digital platforms are projected to expand at a CAGR of 21.89% through 2031.
• By geography, North America accounted for 42.76% of revenue in 2025 for the artificial intelligence (AI) content personalization market, while Asia-Pacific is projected to expand at a CAGR of 21.73% through 2031.

Note: Market size and forecast figures in this report are generated using Mordor Intelligence’s proprietary estimation framework, updated with the latest available data and insights as of January 2026.

Global AI Content Personalization Market Trends and Insights

Drivers Impact Analysis*

Rising Demand for Hyper-Personalized Viewer Journeys

Catalog growth is making generic content carousels less useful for many viewers, especially when several services compete for the same attention. Deloitte’s 2026 survey found that 22% of fans would use streaming video services more if they offered generative AI-based recommendations. Nearly 30% of respondents also wanted a personalized digest that combined streaming, social, and intellectual-property news. The underlying business case is strongest when recommendations help viewers complete more of the content they begin. Localized models can also give regional services a stronger basis for competing when language, viewing habits, and catalog preferences differ from those in Western training data. This makes the AI content personalization market important to services seeking to protect engagement as their libraries become broader and more varied.

GenAI-Assisted Creative and Row Generation

Generative AI is moving from an experimental feature toward a practical way to build streaming homepages. Netflix’s GenPage uses viewing history and request context to generate rows, entities, and page layouts into a single, structured output.[1] Netflix reported a 20% reduction in end-to-end serving latency and a statistically significant improvement in its core engagement metric during online A/B testing. Its offline work also found that prompt enrichment reduced the weighted binary classification loss by 6.9%, outperforming simply scaling model capacity. These approaches allow services to tailor row names, artwork, and page structure without depending on a separate human editorial process for every variation. The artificial intelligence (AI) content personalization market therefore benefits as operators seek more detailed personalization without slower page load times.

Expansion of Real-Time Behavioral Decisioning

Real-time behavioral decisioning transforms personalization from a fixed recommendation list into an interface that responds in real time during a live session. Platforms can use signals such as scroll depth, hover time, pause behavior, and device switches to adjust layout, messages, and offer timing. A unified first-party data layer is needed to connect customer data platforms, customer relationship systems, and content management tools. Adobe introduced CX Enterprise at Adobe Summit 2026 with Brand Intelligence and Engagement Intelligence capabilities that support this approach. Salesforce’s proposed acquisition of Contentful is intended to connect customer data with content assembly across channels. The AI content personalization market benefits from this convergence as customer data systems and media-focused personalization systems become more closely connected.

Rapid Adoption of Hybrid Recommendation Architectures

Hybrid systems combine collaborative filtering, content-based models, and large language model embeddings in one serving environment. They address a long-standing weakness of single-method systems, which often have less useful outputs for new users or newly released content. Research on cold-start recommendation identified large language model integration as a useful route for sparse-data settings, where semantic content information can supplement limited behavioral signals. The AI content personalization market is relevant to smaller streaming operators and telecom IPTV providers that have less proprietary behavioral data than the largest platforms. Research on LLMTreeRec also reported performance competitive with that of conventional deep recommendation models on the Huawei system. As these systems become easier to deploy, the AI content personalization market can extend beyond the largest streaming providers.

Restraints Impact Analysis*

Data Residency and Consent Complexity

Data residency and consent requirements can limit the behavioral information available to personalization systems. The European Data Protection Board’s 2025 guidance on the Digital Services Act and GDPR states that tracking-based profiling requires explicit consent and that recommender systems must provide non-profiling options.[2] Operators that rely only on consented first-party signals can have less data for training than systems built on broader behavioral graphs. A study of GDPR-compliant recommender systems found that, on average, more than 30 user interactions were needed before compelling predictions could be generated. The AI content personalization market can face a longer cold-start period for new users and create a greater burden for operators entering additional jurisdictions. The AI content personalization market must therefore support useful experiences while allowing customers to make meaningful privacy choices.

High Integration Effort Across CMS, CRM, CDP, and Ad Tech Stacks

Large deployments often require personalization software to exchange data with content management systems, customer relationship platforms, customer data platforms, and advertising tools. These systems are frequently built separately and may not have been designed for real-time data interchange. Adobe launched the Adobe Experience Platform Agent Orchestrator in March 2025, with 10 purpose-built AI agents and integration partnerships, to reduce this friction. IBM and Salesforce have also described zero-copy data activation for IBM Z environments, which is intended to enrich customer profiles without moving data. Mid-sized media operators can face a larger challenge because they often lack specialist engineering teams. Inconsistent consent signals across advertising technology systems can further weaken personalization quality and slow deployments in the AI content personalization market.

*Our forecasts treat driver/restraint impacts as directional, not additive. The impact forecasts reflect baseline growth, mix effects, and variable interactions.

Segment Analysis

By Application: Recommendation Infrastructure Supports Current Revenue

Content recommendation accounted for 34.04% of the AI content personalization market size in 2025. Its position reflects the value of accumulated interaction data, because each additional session can strengthen collaborative filtering signals. That data advantage can make it more difficult for later entrants to match established recommendation quality. Customer experience personalization is projected to grow at a CAGR of 21.08% from 2026 to 2031. This shift reflects a move from standalone content rows to session-wide adjustments across homepages, navigation, and search. Netflix’s GenPage illustrates page-level generation, while Adobe’s CX Enterprise represents a broader approach to customer experience orchestration. The AI content personalization market is moving toward a broader definition of personalization that encompasses the surrounding experience rather than just the title selected for a viewer.

Marketing and advertising personalization also benefits from the growing use of consent-based first-party signals as third-party cookie pools become less central. Taboola’s Realize+ uses a decision engine for cross-campaign budget decisions and an element generator for creative and targeting optimization. Audience analytics supports these applications by gathering signals and feeding them back into the optimization process. It is consequently an enabling layer rather than a separate experience for many buyers. Other applications include voice assistant personalization and interactive content adaptation, which remain less developed at a commercial scale. Connected television settings may provide rich behavioral signals beyond traditional screen sessions. The AI content personalization industry can also serve enterprise learning, healthcare content portals, and financial services, where streaming-derived methods may support higher-value engagements. These adjacent uses broaden the commercial role of core personalization capabilities.

By End User: Streaming Platforms Lead While Digital Platforms Expand

Streaming platforms accounted for 37.42% of the AI content personalization market in 2025. Their position is supported by direct access to first-party viewing data, subscription relationships, and control over the content catalog. These three elements make the return from personalization more visible to operators. Digital platforms are projected to grow at a CAGR of 21.89% from 2026 to 2031. Their expansion reflects use across social commerce, short-form video, and interactive media, where frequent sessions produce dense behavioral signals. Deloitte found that 70% of Gen Z and millennial respondents were willing to share their browsing, purchase, and app usage data for more useful, personalized experiences. This provides digital platforms with a consent-based basis for training personalization models even without a subscription relationship.

AnyMind Group introduced AnyAI Video in June 2026 to combine AI-generated social commerce video with live-streaming recommendation signals across brand channels in Asia. Broadcaster and cable networks are also moving from broad audience segmentation toward streaming-style personalization to protect viewing time and advertising yield. ThinkAnalytics launched ThinkMediaAI in February 2025 as a unified platform for content monetization, contextual advertising, and recommendations. Telecom operators have household-level IPTV and bundled OTT data that can improve discovery across multiple screens. Other users include enterprise learning services and patient engagement portals that adapt media-centered techniques to their own content. This demand gives the AI content personalization market a broader group of buyers than streaming alone. The artificial intelligence (AI) content personalization market has a common requirement for matching useful content across an increasing number of digital touchpoints.

Complete Report Scope:

• By Application
  • Content Recommendation
  • Marketing and Advertising Personalization
  • Customer Experience Personalization
  • Audience Analytics
  • Other Applications
• By End User
  • Streaming Platforms
  • Broadcaster and Cable Networks
  • Telecom Operators
  • Digital Platforms
  • Other End Users
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Chile
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Rest of Asia-Pacific
  • Middle East
    • Saudi Arabia
    • United Arab Emirates
    • Qatar
    • Rest of Middle East
  • Africa
    • South Africa
    • Egypt
    • Nigeria
    • Rest of Africa

Geography Analysis

North America held 42.76% share of the regional total in 2025. The region benefits from concentrated technology research and development spending and large subscription video revenue pools. Deloitte reported that nearly 90% of US households subscribed to an average of 4 subscription video services. In this mature AI content personalization market, engagement quality and retention are central competitive issues. Programmatic advertising systems also support closer links between personalized discovery and advertising yield.

Asia-Pacific is projected to be the fastest-growing region, with a CAGR of 21.73% from 2026 to 2031. Growth is supported by expanding OTT services in India, South Korea, Japan, and Australia, as well as by stronger AI engineering capabilities across the region. Media Partners Asia projected that India will surpass China as the largest subscription video market by 2030, with 358 million individual subscriptions. India presents a demanding localization setting because regional-language viewing accounts for a major share of OTT consumption. AI systems are being used for subtitles, electronic program guide descriptions, and caption translation across Indian languages. China’s domestic video platforms are also developing generative recommendation and live-streaming personalization systems that could compete in South and Southeast Asia.

Europe and South America were the third- and fourth-largest regional markets, respectively. European adoption is moderated by privacy and transparency requirements, although Germany and the United Kingdom remain important centers for privacy-compliant personalization. A 2025 BVDW study found that 41% of German internet users were willing to share their usage data to improve content recommendations. South America, led by Brazil, is building personalization capacity alongside OTT expansion and local-language programming. The Middle East and Africa remain early-stage markets, though media digitalization in Saudi Arabia and broader OTT adoption in South Africa support longer-term demand. These regional differences require the artificial intelligence (AI) content personalization market to combine localization, data governance, and operational simplicity.

Competitive Landscape

The AI content personalization market has a moderately concentrated vendor supply structure. Large technology platforms operate alongside specialist suppliers focused on media and digital experiences. Adobe, Salesforce, and Oracle are building wider platforms that combine data management, content assembly, and campaign execution. This can reduce the number of separate systems an enterprise must connect. Adobe introduced CX Enterprise Coworker in April 2026 with open standards, including Model Context Protocol and Agent2Agent.[4] Its NVIDIA partnership also addresses governed deployment in regulated settings.

Proprietary interaction data remains an important competitive asset as access to foundation models becomes less distinctive. Bloomreach launched Loomi Marketing Agent for general availability in June 2026, using commerce-oriented interaction data to turn one prompt into a campaign workflow. Specialist vendors such as ThinkAnalytics, ContentWise, Viaccess-Orca, and Kaltura compete through media-specific functions and lower implementation requirements. ThinkMediaAI expanded ThinkAnalytics’ focus from recommendations to content monetization and contextual advertising. Kaltura added integrations with Adobe Experience Manager and WordPress in April 2026, along with conversational avatars and video intelligence capabilities. These moves show that vendors are seeking a larger role in daily content operations.

Taboola launched Realize+ in April 2026 for publisher-side advertising decisioning and creative optimization. Its database of 9,000 publisher integrations gives it behavioral signal depth that individual operators may not have. Emerging suppliers are also exploring on-device inference to limit data residency concerns. The AI content personalization market is still early in this space, but it could become more relevant in environments with strict privacy requirements. The artificial intelligence (AI) content personalization market will continue to reward suppliers that combine strong domain data with clear integration paths. Buyers in the AI content personalization market will assess whether a broad enterprise platform or a media-focused provider better fits their data resources and operating model.

Recent Industry Developments

• July 2026: Netflix published GenPage on its technology blog, disclosing an end-to-end generative AI homepage construction system that autoregressively generates rows, entities, and page layout in a single model inference pass. GenPage delivered a statistically significant lift on Netflix’s core user engagement metric and a 20% reduction in end-to-end serving latency relative to its prior multi-stage production recommender in online A/B testing.
• July 2026: Oracle introduced an AI-native builder experience for Oracle AI Agent Studio for Fusion Applications, enabling customers and partners to create and run agentic applications that reason, coordinate, and decide within Oracle Fusion Cloud, directly expanding the enterprise AI personalization stack for CRM-connected experience deployment.
• June 2026: Bloomreach announced the general availability of its Loomi Marketing Agent, enabling marketers to convert a single prompt into a fully built campaign workflow spanning content creation, audience segmentation, journey orchestration, and AI-optimized message timing. The agent is trained on more than 12 years of interaction data from global e-commerce brands.
• June 2026: Salesforce signed a definitive agreement to acquire Fin, formerly Intercom, an AI-native customer agent platform, for USD 3.6 billion, to expand Agentforce’s autonomous agent capabilities across customer service operations and add fast-to-value deployment options for small and medium-sized business customers.

List of Companies Covered in this Report:

1. Netflix, Inc.
2. Amazon.com, Inc.
3. Alphabet Inc.
4. Adobe Inc.
5. International Business Machines Corporation
6. Salesforce, Inc.
7. Oracle Corporation
8. Sitecore A/S
9. Bloomreach, Inc.
10. Taboola.com Ltd.
11. Outbrain Inc.
12. Revcontent, Inc.
13. ThinkAnalytics Ltd.
14. Kaltura, Inc.
15. Brightcove Inc.
16. Viaccess-Orca
17. Dynamic Yield (Mastercard)
18. Wurl, LLC
19. ContentWise S.r.l.
20. Personyze Ltd.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

Table of Contents

1 INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE
4.1 Market Overview
4.2 Market Drivers
4.2.1 Rising Demand for Hyper-Personalized Viewer Journeys
4.2.2 Expansion of Real-Time Behavioral Decisioning
4.2.3 Rapid Adoption of Hybrid Recommendation Architectures
4.2.4 Growing Need to Monetize Long-Tail Content Catalogs
4.2.5 Demand for Privacy-First Personalization Architectures
4.2.6 GenAI-Assisted Creative and Row Generation
4.3 Market Restraints
4.3.1 Cold-Start Accuracy Gaps for New Users and New Titles
4.3.2 Data Residency and Consent Complexity
4.3.3 High Integration Effort Across CMS, CRM, CDP and Ad Tech Stacks
4.3.4 Personalization Fatigue and Perceived Manipulation Risk
4.4 Industry Value Chain Analysis
4.5 Regulatory Landscape
4.6 Technological Outlook
4.7 Porter’s Five Forces Analysis
4.7.1 Bargaining Power of Buyers
4.7.2 Bargaining Power of Suppliers
4.7.3 Threat of New Entrants
4.7.4 Threat of Substitutes
4.7.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)
5.1 By Application
5.1.1 Content Recommendation
5.1.2 Marketing and Advertising Personalization
5.1.3 Customer Experience Personalization
5.1.4 Audience Analytics
5.1.5 Other Applications
5.2 By End User
5.2.1 Streaming Platforms
5.2.2 Broadcaster and Cable Networks
5.2.3 Telecom Operators
5.2.4 Digital Platforms
5.2.5 Other End Users
5.3 By Geography
5.3.1 North America
5.3.1.1 United States
5.3.1.2 Canada
5.3.1.3 Mexico
5.3.2 South America
5.3.2.1 Brazil
5.3.2.2 Argentina
5.3.2.3 Chile
5.3.2.4 Rest of South America
5.3.3 Europe
5.3.3.1 Germany
5.3.3.2 United Kingdom
5.3.3.3 France
5.3.3.4 Italy
5.3.3.5 Spain
5.3.3.6 Rest of Europe
5.3.4 Asia-Pacific
5.3.4.1 China
5.3.4.2 Japan
5.3.4.3 India
5.3.4.4 South Korea
5.3.4.5 Australia
5.3.4.6 Rest of Asia-Pacific
5.3.5 Middle East
5.3.5.1 Saudi Arabia
5.3.5.2 United Arab Emirates
5.3.5.3 Qatar
5.3.5.4 Rest of Middle East
5.3.6 Africa
5.3.6.1 South Africa
5.3.6.2 Egypt
5.3.6.3 Nigeria
5.3.6.4 Rest of Africa

6 COMPETITIVE LANDSCAPE
6.1 Market Concentration
6.2 Strategic Moves
6.3 Vendor Positioning Analysis
6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Products and Services, Recent Developments)
6.4.1 Netflix, Inc.
6.4.2 Amazon.com, Inc.
6.4.3 Alphabet Inc.
6.4.4 Adobe Inc.
6.4.5 International Business Machines Corporation
6.4.6 Salesforce, Inc.
6.4.7 Oracle Corporation
6.4.8 Sitecore A/S
6.4.9 Bloomreach, Inc.
6.4.10 Taboola.com Ltd.
6.4.11 Outbrain Inc.
6.4.12 Revcontent, Inc.
6.4.13 ThinkAnalytics Ltd.
6.4.14 Kaltura, Inc.
6.4.15 Brightcove Inc.
6.4.16 Viaccess-Orca
6.4.17 Dynamic Yield (Mastercard)
6.4.18 Wurl, LLC
6.4.19 ContentWise S.r.l.
6.4.20 Personyze Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK
7.1 White-Space and Unmet-Need Assessment

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