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市場調查報告書
商品編碼
1736661

全球智慧型應用市場規模（按提供者、行業垂直、類型、區域覆蓋範圍和預測）

Global Intelligent Apps Market Size By Provider, By Vertical, By Type, By Geographic Scope And Forecast
出版日期: | 出版商: Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

智慧應用市場規模與預測

2024 年智慧應用市場規模價值為 351.7 億美元，預計到 2032 年將達到 3,381 億美元，預測期內（2026-2032 年）的複合年成長率為 36.07%。

全球智慧應用市場促進因素

智慧應用市場的促進因素可能受到多種因素的影響。這些因素包括：

人工智慧和機器學習的應用日益廣泛：隨著人工智慧和機器學習技術的日益融合，應用程式的功能越來越強大，效率也越來越高。這推動了對能夠提高業務效率並提供客製化體驗的智慧應用程式的需求。

對數據主導決策的需求日益成長：企業擴大利用數據分析來做出更明智的決策。能夠即時分析大量數據的智慧應用程式可以幫助企業獲得有用的洞察，並提高業務效率。

智慧型裝置的普及：隨著智慧型手機、平板電腦等智慧型裝置的普及，智慧型應用市場正在蓬勃發展。由於感測器、網路等智慧型裝置的先進功能，這些應用提供了尖端且引人注目的功能。

為智慧應用程式提供支援：隨著雲端平台提供創建和實施智慧應用所需的服務和基礎設施，雲端運算正在不斷發展，雲端運算的可擴展性、靈活性和可負擔性正在鼓勵企業採用智慧應用。

釋放客戶幸福感：智慧型應用程式使用人工智慧來了解用戶偏好和行為，以提供更好的用戶體驗，讓客戶更快樂、更投入，從而幫助推動市場擴張。

更重視消費者參與：企業致力於透過客製化互動來提高消費者參與度。智慧型應用程式使企業能夠為客戶提供量身定做的資訊、提案和服務，從而提高客戶保留率和忠誠度。

數位轉型計劃：為了保持競爭力，各行各業的公司都在進行數位轉型。透過流程自動化、效率提升和數據主導的洞察，智慧應用對於這項轉型至關重要。

自然語言處理 (NLP) 技術的進步：NLP 技術的進步使智慧應用程式能夠更有效地理解和回應人類語言，從而提高聊天機器人、虛擬助理和其他會話式 AI 系統的功能和採用率。

賦能企業：智慧應用程式使企業能夠透過進階分析和自動監控實現增強的資料安全性和法規遵循性，這正在推動企業採用智慧應用程式，尤其是在合規標準嚴格的行業。

加大對人工智慧Start-Ups和創新的投資：人工智慧及相關技術的投資增加將推動新型智慧應用的開發和創新。這將創造一個競爭性的市場環境，促進其進一步發展和普及。

限制全球智慧應用市場的因素

智慧應用市場面臨許多阻礙與挑戰，其中包括：

資料隱私和安全問題：智慧型應用需要收集和分析大量敏感個人資料。保護這些資料的隱私和安全是一個重大問題，尤其是在監管審查日益嚴格、資料外洩頻繁的當今世界。

部署成本高：創建和部署智慧應用需要投入巨額成本，用於基礎設施建設、專業人才培養和最尖端科技。希望利用智慧應用解決方案的小型企業可能會發現，這些高昂的前期成本令人望而卻步。

與舊有系統整合：許多企業仍在使用難以與現代智慧應用技術整合的過時系統。由於與現有系統整合既困難又昂貴，這些應用的廣泛普及受到了阻礙。

知識有限：潛在消費者通常缺乏有關智慧應用程式的優勢和功能的知識，這使得他們不願意採用此類技術，尤其是在技術不太精通的行業。

對優質數據的依賴：智慧型應用幾乎總是需要高品質的數據才能正常運作。不準確、不完整或偏差的數據會降低應用效能，導致決策不理想，從而限制智慧應用的有效性和普及度。

科技日新月異：人工智慧和機器學習日新月異，智慧應用領域也正在快速發展。對於資源匱乏的公司來說，要跟上改變的步伐，就需要不斷投入與適應。

技能短缺：數據分析、機器學習和人工智慧是需要專業知識來開發和維護智慧應用程式的專業領域，由於缺乏具備這些技能的專業人員，企業難以找到並留住合適的人才。

目錄

第1章：全球智慧應用市場簡介

  • 市場概覽
  • 研究範圍
  • 先決條件

第2章執行摘要

第3章：已驗證的市場研究調查方法

  • 資料探勘
  • 驗證
  • 第一手資料
  • 資料來源列表

第4章 全球智慧應用市場預測

  • 概述
  • 市場動態
    • 驅動程式
    • 限制因素
    • 機會
  • 波特五力模型
  • 價值鏈分析

第5章 全球智慧應用市場類型

  • 概述
  • 消費者應用程式
  • 企業應用程式

第6章：全球智慧應用市場（依供應商分類）

  • 概述
  • 基礎設施
  • 資料收集和準備
  • 機器智慧

第7章 全球智慧應用市場（依產業垂直分類）

  • 概述
  • BFSI
  • 通訊
  • 零售與電子商務
  • 醫療保健和生命科學
  • 教育
  • 其他

第 8 章：全球智慧應用市場（按地區）

  • 概述
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 其他亞太地區
  • 世界其他地區
    • 拉丁美洲
    • 中東和非洲

第9章 全球智慧應用市場競爭格局

  • 概述
  • 各公司市場排名
  • 主要發展策略

第10章 公司簡介

  • IBM Corporation
  • Google LLC
  • AWS
  • Microsoft Corporation
  • Salesforce
  • Oracle Corporation
  • Apple, Inc.
  • Baidu
  • SAP SE
  • ServiceNow

第11章 附錄

  • 相關調查
簡介目錄
Product Code: 38454

Intelligent Apps Market Size And Forecast

Intelligent Apps Market size was valued at USD 35.17 Billion in 2024 and is projected to reach USD 338.1 Billion by 2032, growing at a CAGR of 36.07 % during the forecast period 2026-2032.

Global Intelligent Apps Market Drivers

The market drivers for the Intelligent Apps Market can be influenced by various factors. These may include:

Increasing Use of AI and Machine Learning: Applications are becoming more and more capable and efficient as a result of the increasing integration of AI and ML technologies. The need for intelligent apps that can enhance operational efficiency and offer customised experiences is fueled by this.

Growing Need for Data-Driven Decision Making: Companies are using data analytics to make better decisions more and more. By real-time analysis of huge amounts of data, intelligent apps enable businesses to obtain useful insights and streamline their operations.

Proliferation of Smart Devices: The market for intelligent apps is increased by the extensive usage of smartphones, tablets, and other smart devices. With the sophisticated capabilities of smart devices, such sensors and networking, these apps provide cutting-edge and engaging features.

Empowering Intelligent Applications: Cloud computing is growing since cloud platforms offer the services and infrastructure required to facilitate the creation and implementation of intelligent applications. Intelligent applications are encouraged to be adopted by companies by cloud computing's scalability, flexibility, and affordability.

Unlocking Customer Happiness: Intelligent apps use AI to comprehend user preferences and behaviour, so providing better user experiences. Increased customer happiness and engagement follow, which propels the market expansion.

Growing Attention to consumer Engagement: Companies are emphasising on enhancing consumer involvement by means of customised interactions. Companies may provide tailored information, suggestions, and services thanks to intelligent apps, which increases client retention and loyalty.

Projects for Digital Transformation: To remain competitive, companies in a variety of sectors are going through digital transformation. Through automation of procedures, increased efficiency, and data-driven insights, intelligent apps are essential to this revolution.

Natural Language Processing (NLP) Technology Advancements: More efficient comprehension and response of human language by intelligent apps is made possible by advances in NLP. This improves chatbots', virtual assistants', and other conversational AI systems' capabilities and encourages their use.

Empowering Enterprises: Enhanced data security and regulatory compliance can be achieved by enterprises using intelligent apps by means of sophisticated analytics and automated monitoring. Their acceptance is driven by this, especially in sectors with strict compliance standards.

Increasing Investment in AI Startups and Innovations: New intelligent app development and innovation are encouraged by the increase in investments in AI and associated technologies. The competitive market environment this produces encourages more developments and acceptance.

Global Intelligent Apps Market Restraints

Several factors can act as restraints or challenges for the Intelligent Apps Market. These may include:

Concerns about data privacy and security: Using intelligent apps frequently necessitates gathering and analysing large volumes of sensitive and personal data. Protection of this data's privacy and security is a big problem, especially in light of growing regulatory scrutiny and data breach frequency.

High Implementation Costs: Creating and deploying intelligent apps calls for significant expenditures in infrastructure, knowledgeable staff, and cutting-edge technology. Small and medium-sized organisations (SMEs) hoping to use intelligent app solutions may find these high starting expenses prohibitive.

Integration with Legacy Systems: A lot of companies continue to use antiquated systems that are difficult to integrate with contemporary intelligent app technology. Widespread use of these apps is hampered by their sometimes difficult and expensive integration with current systems.

Limited Knowledge: Prospective consumers frequently lack knowledge of the advantages and features of intelligent apps. Adopting these technologies may become reluctant as a result, particularly in less tech-savvy sectors.

Dependency on Good Data: To work well, intelligent apps mostly depend on having good data available. The efficacy and uptake of intelligent apps can be limited by inaccurate, incomplete, or biassed data that results in poor app performance and less than ideal decision-making.

Rapid Technical Changes: Artificial intelligence and machine learning are always improving, and the field of intelligent apps is developing quickly as well. For companies with little resources, keeping up with these changes calls for constant investment and adaptation.

Skill Shortages: Data analytics, machine learning, and artificial intelligence are among the specialised fields in which intelligent app development and maintenance call for expertise. Because there aren't enough experts with these abilities, businesses struggle to find and keep the right people.

Global Intelligent Apps Market Segmentation Analysis

The Global Intelligent Apps Market is Segmented on the basis of Provider, Vertical, Type, And Geography.

Intelligent Apps Market, By Provider

  • Infrastructure
  • Data Collection and Preparation
  • Machine Intelligence

Based on Provider, the market is bifurcated into Infrastructure, Data Collection & Preparation, and Machine Intelligence. The machine intelligence segment is estimated to witness the highest CAGR during the forecast period. The factors that can be attributed as it helps developers make their job simple by offering application-specific pre-built models are driving the demand for this segment.

Intelligent Apps Market, By Vertical

  • BFSI
  • Telecom
  • Retail and eCommerce
  • Healthcare and Lifer Sciences
  • Education
  • Others

Based on Vertical, the market is bifurcated into BFSI, Telecom, Retail and E-Commerce, Healthcare and Lifer Sciences, Education, and Others. The media and entertainment vertical holds the largest market share during the forecast period. The intelligent apps help them understand user profiles and thereby assist in delivering personalized web pages to users.

Intelligent Apps Market, By Type

  • Consumer Apps
  • Enterprise Apps

Based on Type, the market is bifurcated into Consumer Apps and Enterprise Apps. The enterprise apps segment is estimated to witness the highest CAGR during the forecast period. Enterprises have commenced employing intelligent apps in various use cases. The consumer apps segment holds the largest market share.

Intelligent Apps Market, By Geography

  • North America
  • Europe
  • Asia Pacific
  • Rest of the World
  • On the basis of regional analysis, the Global Intelligent Apps Market is classified into North America, Europe, Asia Pacific, and the Rest of the world. North America holds the largest market share. The growing demand for intelligent apps by various industries to analyze large volumes of data, increasing adoption of advanced technologies, and ongoing projects will boost the market in the North American region.

Key Players

  • The major players in the Intelligent Apps Market are:
  • IBM Corporation
  • Google LLC
  • AWS
  • Microsoft Corporation
  • Salesforce
  • Oracle Corporation
  • Apple, Inc.
  • Baidu
  • SAP SE
  • ServiceNow

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL INTELLIGENT APPS MARKET

  • 1.1 Overview of the Market
  • 1.2 Scope of Report
  • 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

  • 3.1 Data Mining
  • 3.2 Validation
  • 3.3 Primary Interviews
  • 3.4 List of Data Sources

4 GLOBAL INTELLIGENT APPS MARKET OUTLOOK

  • 4.1 Overview
  • 4.2 Market Dynamics
    • 4.2.1 Drivers
    • 4.2.2 Restraints
    • 4.2.3 Opportunities
  • 4.3 Porters Five Force Model
  • 4.4 Value Chain Analysis

5 GLOBAL INTELLIGENT APPS MARKET, BY TYPE

  • 5.1 Overview
  • 5.2 Consumer Apps
  • 5.3 Enterprise Apps

6 GLOBAL INTELLIGENT APPS MARKET, BY PROVIDER

  • 6.1 Overview
  • 6.2 Infrastructure
  • 6.3 Data Collection and Preparation
  • 6.4 Machine Intelligence

7 GLOBAL INTELLIGENT APPS MARKET, BY VERTICAL

  • 7.1 Overview
  • 7.2 BFSI
  • 7.3 Telecom
  • 7.4 Retail and eCommerce
  • 7.5 Healthcare and Lifer Sciences
  • 7.6 Education
  • 7.7 Others

8 GLOBAL INTELLIGENT APPS MARKET, BY GEOGRAPHY

  • 8.1 Overview
  • 8.2 North America
    • 8.2.1 U.S.
    • 8.2.2 Canada
    • 8.2.3 Mexico
  • 8.3 Europe
    • 8.3.1 Germany
    • 8.3.2 U.K.
    • 8.3.3 France
    • 8.3.4 Rest of Europe
  • 8.4 Asia Pacific
    • 8.4.1 China
    • 8.4.2 Japan
    • 8.4.3 India
    • 8.4.4 Rest of Asia Pacific
  • 8.5 Rest of the World
    • 8.5.1 Latin America
    • 8.5.2 Middle East & Africa

9 GLOBAL INTELLIGENT APPS MARKET COMPETITIVE LANDSCAPE

  • 9.1 Overview
  • 9.2 Company Market Ranking
  • 9.3 Key Development Strategies

10 COMPANY PROFILES

  • 10.1 IBM Corporation
    • 10.1.1 Overview
    • 10.1.2 Financial Performance
    • 10.1.3 Product Outlook
    • 10.1.4 Key Developments
  • 10.2 Google LLC
    • 10.2.1 Overview
    • 10.2.2 Financial Performance
    • 10.2.3 Product Outlook
    • 10.2.4 Key Developments
  • 10.3 AWS
    • 10.3.1 Overview
    • 10.3.2 Financial Performance
    • 10.3.3 Product Outlook
    • 10.3.4 Key Developments
  • 10.4 Microsoft Corporation
    • 10.4.1 Overview
    • 10.4.2 Financial Performance
    • 10.4.3 Product Outlook
    • 10.4.4 Key Developments
  • 10.5 Salesforce
    • 10.5.1 Overview
    • 10.5.2 Financial Performance
    • 10.5.3 Product Outlook
    • 10.5.4 Key Developments
  • 10.6 Oracle Corporation
    • 10.6.1 Overview
    • 10.6.2 Financial Performance
    • 10.6.3 Product Outlook
    • 10.6.4 Key Developments
  • 10.7 Apple, Inc.
    • 10.7.1 Overview
    • 10.7.2 Financial Performance
    • 10.7.3 Product Outlook
    • 10.7.4 Key Developments
  • 10.8 Baidu
    • 10.8.1 Overview
    • 10.8.2 Financial Performance
    • 10.8.3 Product Outlook
    • 10.8.4 Key Developments
  • 10.9 SAP SE
    • 10.9.1 Overview
    • 10.9.2 Financial Performance
    • 10.9.3 Product Outlook
    • 10.9.4 Key Developments
  • 10.10 ServiceNow
    • 10.10.1 Overview
    • 10.10.2 Financial Performance
    • 10.10.3 Product Outlook
    • 10.10.4 Key Developments

11 Appendix

  • 11.1 Related Research