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市场调查报告书
商品编码
1736661

全球智慧型应用市场规模（按提供者、行业垂直、类型、区域覆盖范围和预测）

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个工作天内

价格
简介目录

智慧应用市场规模与预测

2024 年智慧应用市场规模价值为 351.7 亿美元，预计到 2032 年将达到 3,381 亿美元，预测期内（2026-2032 年）的复合年增长率为 36.07%。

全球智慧应用市场驱动因素

智慧应用市场的驱动因素可能受到多种因素的影响。这些因素包括：

人工智慧和机器学习的应用日益广泛：随着人工智慧和机器学习技术的日益融合，应用程式的功能越来越强大，效率也越来越高。这推动了对能够提高业务效率并提供客製化体验的智慧应用程式的需求。

对数据主导决策的需求日益增长：企业越来越多地利用数据分析来做出更明智的决策。能够即时分析大量数据的智慧应用程式可以帮助企业获得有用的洞察，并提高业务效率。

智慧型装置的普及：随着智慧型手机、平板电脑等智慧型装置的普及，智慧型应用市场正在蓬勃发展。由于感测器、网路等智慧型装置的先进功能，这些应用提供了尖端且引人注目的功能。

为智慧应用程式提供支援：随着云端平台提供创建和实施智慧应用所需的服务和基础设施，云端运算正在不断发展，云端运算的可扩展性、灵活性和可负担性正在鼓励企业采用智慧应用。

释放客户幸福感：智慧型应用程式使用人工智慧来了解用户偏好和行为，以提供更好的用户体验，让客户更快乐、更投入，从而帮助推动市场扩张。

更重视消费者参与：企业致力于透过客製化互动来提高消费者参与度。智慧型应用程式使企业能够为客户提供量身定制的资讯、提案和服务，从而提高客户保留率和忠诚度。

数位转型计划：为了保持竞争力，各行各业的公司都在进行数位转型。透过流程自动化、效率提升和数据主导的洞察，智慧应用对于这项转型至关重要。

自然语言处理 (NLP) 技术的进步：NLP 技术的进步使智慧应用程式能够更有效地理解和回应人类语言，从而提高聊天机器人、虚拟助理和其他会话式 AI 系统的功能和采用率。

赋能企业：智慧应用程式使企业能够透过进阶分析和自动监控实现增强的资料安全性和法规遵循性，这正在推动企业采用智慧应用程序，尤其是在合规标准严格的行业。

加大对人工智慧Start-Ups和创新的投资：人工智慧及相关技术的投资增加将推动新型智慧应用的开发和创新。这将创造一个竞争性的市场环境，促进其进一步发展和普及。

限制全球智慧应用市场的因素

智慧应用市场面临许多製约与挑战，其中包括：

资料隐私和安全问题：智慧型应用需要收集和分析大量敏感个人资料。保护这些资料的隐私和安全是一个重大问题，尤其是在监管审查日益严格、资料外洩频繁的当今世界。

部署成本高：创建和部署智慧应用需要投入巨额成本，用于基础设施建设、专业人才培养和最尖端科技。希望利用智慧应用解决方案的小型企业可能会发现，这些高昂的前期成本令人望而却步。

与旧有系统整合：许多企业仍在使用难以与现代智慧应用技术整合的过时系统。由于与现有系统整合既困难又昂贵，这些应用的广泛普及受到了阻碍。

知识有限：潜在消费者通常缺乏有关智慧应用程式的优势和功能的知识，这使得他们不愿意采用此类技术，尤其是在技术不太精通的行业。

对优质数据的依赖：智慧型应用几乎总是需要高品质的数据才能正常运作。不准确、不完整或偏差的数据会降低应用效能，导致决策不理想，从而限制智慧应用的有效性和普及度。

科技日新月异：人工智慧和机器学习日新月异，智慧应用领域也正在快速发展。对于资源匮乏的公司来说，要跟上改变的步伐，就需要不断投入与适应。

技能短缺：数据分析、机器学习和人工智慧是需要专业知识来开发和维护智慧应用程式的专业领域，由于缺乏具备这些技能的专业人员，企业难以找到并留住合适的人才。

目录

第一章：全球智慧应用市场简介

  • 市场概览
  • 研究范围
  • 先决条件

第二章执行摘要

第三章：已验证的市场研究调查方法

  • 资料探勘
  • 验证
  • 第一手资料
  • 资料来源列表

第四章 全球智慧应用市场预测

  • 概述
  • 市场动态
    • 驱动程式
    • 限制因素
    • 机会
  • 波特五力模型
  • 价值链分析

第五章 全球智慧应用市场类型

  • 概述
  • 消费者应用程式
  • 企业应用程式

第六章：全球智慧应用市场（依供应商划分）

  • 概述
  • 基础设施
  • 资料收集和准备
  • 机器智能

第七章 全球智慧应用市场（依产业垂直划分）

  • 概述
  • BFSI
  • 通讯
  • 零售与电子商务
  • 医疗保健和生命科学
  • 教育
  • 其他的

第 8 章：全球智慧应用市场（按地区）

  • 概述
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 其他亚太地区
  • 世界其他地区
    • 拉丁美洲
    • 中东和非洲

第九章 全球智慧应用市场竞争格局

  • 概述
  • 各公司市场排名
  • 主要发展策略

第十章 公司简介

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

第十一章 附录

  • 相关调查
简介目录
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