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2080148

全球低程式碼人工智慧平台市場：按組件、人工智慧功能、部署、應用、企業規模和最終用戶產業分類－市場規模、產業動態、機會分析和預測（2026-2035 年）

Global Low Code AI Platform Market: By Component, AI Capability, Deployment, Application, Enterprise Size, End-Use Industry - Market Size, Industry Dynamics, Opportunity Analysis And Forecast For 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 220 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

低程式碼人工智慧平台市場正經歷快速且顯著的擴張，反映了全球加速數位轉型和智慧自動化的趨勢。2025年，該市場規模估計約為63.4億美元，預計到2035年將大幅成長至約573.2億美元。這意味著在2026年至2035年的預測期內，該市場將以24.63%的年複合成長率成長，凸顯了全球各行各業對低程式碼和人工智慧驅動的開發解決方案的持續成長需求。

這種爆炸性成長主要源自於各行各業（無論規模大小）對業務自動化日益成長的迫切需求。為了在瞬息萬變的全球市場中保持競爭力，企業不斷面臨簡化營運、減少人工工作量和提高整體效率的壓力。低程式碼人工智慧平台提供切實可行的解決方案，使企業能夠自動化複雜的流程、快速建置應用程式並部署數位化解決方案，而無需過度依賴傳統的程式設計資源。

顯著的市場趨勢

低程式碼人工智慧平台市場目前由少數幾家主要廠商主導，它們各自發揮獨特優勢，在企業數位轉型（DX）舉措中佔據穩固地位。Microsoft憑藉著強大的生態系統整合能力，保持著市場主導地位。 OutSystems 則在開發專為複雜企業架構設計的高度客製化應用方面，開闢了一片穩固的利基市場。

Mendix 因其對混合開發環境的強大支援以及對協作式軟體開發的重視而脫穎而出。 Appian 著重流程協作和企業工作流程自動化，致力於可靠地管理大量複雜的業務交易。 Salesforce 透過其低程式碼功能，能夠快速開發以客戶為中心的應用程式，引領客戶關係管理（CRM）領域的市場發展。

主要成長促進因素

受各產業加速數位轉型的推動，全球企業對低程式碼人工智慧平台的需求正經歷前所未有的成長。企業越來越重視能夠實現快速應用開發、無縫自動化和高效部署人工智慧解決方案的技術。在全球市場競爭日益激烈的背景下，企業面臨加速創新、最佳化營運和提升數位化體驗的持續壓力，而這些因素都推動了低程式碼人工智慧平台的快速普及。

新機會的趨勢

生成式AI的整合正成為塑造低程式碼人工智慧平台市場未來發展的關鍵機會。曾經被視為主要用於內容創建和支援的獨立功能，如今正迅速演變為應用開發環境的核心組件。生成式AI正日益直接地整合到低程式碼平台中，從根本上改變了各行業應用程式的設計、建置和部署方式。這種轉變透過使開發更加直覺、智慧且易於被更廣泛的用戶群所接受，擴展了低程式碼生態系統的價值提案。

最佳化障礙

低程式碼人工智慧平台市場成長面臨的主要挑戰之一是難以滿足人工智慧極其複雜的需求。儘管低程式碼平台目的是透過視覺化介面和現成元件簡化應用開發並加速人工智慧的普及應用，但它們並非總能滿足需要大量客製化、複雜建模技術和專業技術知識的高階人工智慧專案的需求。隨著企業開展更複雜的人工智慧舉措，低程式碼環境的限制可能會成為人工智慧更廣泛應用的一大障礙。

目錄

第1章 執行摘要：全球低程式碼人工智慧平台市場

第2章 調查方法與研究框架

  • 研究目標
  • 產品概述
  • 市場區隔
  • 定性研究
    • 一手和二手資訊
  • 量化研究
    • 一手和二手資訊
  • 主要調查受訪者組成：依地區分類
  • 本研究的前提
  • 市場規模估算
  • 資料三角測量

第3章 全球低程式碼人工智慧平台市場概述

  • 產業價值鏈分析
  • 產業展望
    • 全球低程式碼/無程式碼人工智慧開發產業概覽
    • 在軟體人才持續短缺的背景下，公民開發人員群體崛起。
    • 企業級部署的管治、安全與影子IT管理
  • PESTLE分析
  • 波特五力分析
  • 市場成長及前景
    • 2020-2035年市場收入估算與預測
    • 價格趨勢分析：依組件

第4章 全球低程式碼人工智慧平台市場分析

  • 競爭儀錶板
    • 市場集中度
    • 企業市場占有率分析，2025年
    • 競爭對手分析與基準測試

第5章 全球低程式碼人工智慧平台市場分析

  • 市場動態和趨勢
    • 成長促進因素
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測，2020-2035年
    • 依組件
    • 依人工智慧功能
    • 依部署
    • 依用途
    • 依公司規模
    • 依最終用途行業分類
    • 依地區

第6章 北美市場分析

第7章 歐洲市場分析

第8章 亞太市場分析

第9章 中東和非洲市場分析

第10章 南美市場分析

第11章 公司簡介

  • TrackVia Inc.
  • ServiceNow Inc.
  • Salesforce Inc.
  • RunMyProcess
  • RETOOL
  • Quickbase Inc.
  • Pegasystems Inc.
  • OutSystems Software em Rede SA
  • Nintex Global Ltd.
  • Microsoft Corp.
  • Mendix Technology BV
  • Kissflow Inc.
  • Huawei Cloud Computing Technologies Co. Ltd.
  • Caspio Inc.
  • Betty Blocks BV
  • Autonom8 Inc.
  • Appian Corp.
  • AgilePoint Inc.
  • Zoho Corp. Pvt. Ltd.
  • Oracle Corp.
  • 其他主要公司

第12章 附錄

簡介目錄
Product Code: AA06261817

The low-code AI platforms market is experiencing rapid and significant expansion, reflecting a broader global shift toward accelerated digital transformation and intelligent automation. In 2025, the market is valued at approximately USD 6.34 billion, and it is projected to grow dramatically to around USD 57.32 billion by 2035. This represents a strong compound annual growth rate (CAGR) of 24.63% over the forecast period from 2026 to 2035, highlighting sustained and accelerating demand for low-code and AI-driven development solutions across industries worldwide.

This explosive growth is largely being fueled by the increasing urgency for business automation across enterprises of all sizes. Organizations are under continuous pressure to streamline operations, reduce manual workloads, and improve overall efficiency in order to remain competitive in fast-changing global markets. Low-code AI platforms provide a practical solution by enabling companies to automate complex workflows, build applications rapidly, and deploy digital solutions without requiring extensive traditional programming resources.

Noteworthy Market Developments

The low-code AI platform market is currently shaped by a small group of dominant players, each leveraging distinct strengths to secure strong positions across enterprise digital transformation initiatives. Microsoft maintains a dominant position in the market largely due to its extensive ecosystem integration capabilities. OutSystems has carved out a strong niche in the development of highly customized applications designed for complex enterprise architectures.

Mendix stands out for its robust support of hybrid development environments and its strong emphasis on collaborative software creation. Appian specializes in process orchestration and enterprise workflow automation, focusing on managing large volumes of complex business transactions with high reliability. Salesforce leads the market in customer relationship management (CRM) expansion through its low-code capabilities that enable rapid development of customer-centric applications.

Core Growth Drivers

The low-code AI platform market is currently experiencing an unprecedented surge in global corporate technology demand, driven by the accelerating pace of digital transformation across industries. Enterprises are increasingly prioritizing technologies that enable rapid application development, seamless automation, and efficient deployment of AI-powered solutions. As competition intensifies across global markets, organizations are under constant pressure to innovate faster, optimize operations, and deliver enhanced digital experiences, all of which are fueling strong adoption of low-code AI platforms.

Emerging Opportunity Trends

The integration of Generative AI is emerging as a significant opportunity shaping the future growth of the low-code AI platform market. What was once primarily viewed as a standalone capability for content creation and assistance is now rapidly evolving into a core component of application development environments. Generative AI is increasingly being embedded directly into low-code platforms, fundamentally changing how applications are designed, built, and deployed across industries. This shift is expanding the value proposition of low-code ecosystems by making development even more intuitive, intelligent, and accessible to a broader range of users.

Barriers to Optimization

One of the major challenges that may hinder the growth of the low-code AI platform market is the difficulty associated with handling highly complex artificial intelligence requirements. While low-code platforms are designed to simplify application development and accelerate AI adoption through visual interfaces and pre-built components, they are not always capable of meeting the demands of advanced AI projects that require extensive customization, sophisticated modeling techniques, and specialized technical expertise. As organizations increasingly pursue more complex AI initiatives, the limitations of low-code environments can become a significant barrier to broader adoption.

Detailed Market Segmentation

By Component, the Platform Software segment is expected to account for approximately 70% of the low-code AI platform market in 2025. This substantial market share reflects the growing reliance of organizations on comprehensive low-code development environments that provide the essential infrastructure required to design, build, deploy, and manage AI-powered applications. As enterprises continue to accelerate their digital transformation initiatives, platform software has become the foundation upon which modern low-code AI ecosystems are built, enabling organizations to streamline application development while reducing technical complexity.

By AI Capability, Predictive AI continues to hold the largest share within the low-code AI platform market, accounting for approximately 30% of the total market revenue. This dominant position reflects the growing importance of data-driven decision-making across industries and the increasing demand for technologies that can anticipate future outcomes with a high degree of accuracy. Organizations are increasingly leveraging predictive AI capabilities integrated within low-code platforms to transform large volumes of historical and real-time data into actionable insights that support strategic planning, operational optimization, and risk management.

By Application, IT and Business Process Automation has emerged as the leading application segment in the low-code AI platform market, accounting for approximately 28% of the overall market share. This strong market position reflects the growing emphasis organizations place on improving operational efficiency, reducing costs, and accelerating digital transformation initiatives. As businesses face increasing pressure to remain competitive in rapidly evolving markets, they are turning to low-code AI platforms to automate complex workflows, streamline operations, and enhance productivity across various departments.

By End User, the Banking, Financial Services, and Insurance (BFSI) sector is projected to account for more than 22% of the global low-code AI platform market share. Financial institutions are increasingly adopting low-code AI platforms to address the growing demand for rapid digital transformation, enhanced customer experiences, and operational efficiency. In a highly competitive environment where speed, agility, and innovation are essential, low-code platforms provide BFSI organizations with the ability to develop, test, and deploy applications much faster than traditional software development approaches.

Segment Breakdown

By Component

  • Platform Software
  • Services-Consulting
  • Integration & Deployment
  • Training & Support
  • Managed Services

By AI Capability

  • Predictive AI, Generative AI
  • Conversational AI
  • Computer Vision AI
  • Intelligent Process Automation AI

By Deployment

  • Cloud-Based
  • On-Premise
  • Hybrid

By Application

  • Customer Experience & Service
  • Sales & Marketing
  • Operations Management
  • Finance & Accounting
  • Human Resources
  • Supply Chain & Logistics
  • IT & Business Process Automation

By Enterprise Size

  • Large Enterprises
  • SMEs

By End-Use Industry

  • BFSI
  • Healthcare & Life Sciences
  • Retail & E-commerce
  • Manufacturing
  • Government
  • IT & Telecom
  • Education
  • Others

By Region

  • North America
  • The U.S.
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia & New Zealand
  • South Korea
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • Saudi Arabia
  • South Africa
  • UAE
  • Rest of MEA
  • South America
  • Argentina
  • Brazil
  • Rest of South America

Geography Breakdown

  • North America emerged as the dominant region in the global low-code AI platform market, accounting for the largest share during recent market assessments. This strong market position was primarily driven by the United States and Canada, both of which have established themselves as leaders in technological innovation and digital transformation. The region's advanced IT infrastructure, high levels of technology investment, and strong presence of major software and cloud service providers have created a favorable environment for the widespread adoption of low-code AI solutions.
  • In the United States, organizations across sectors such as finance, healthcare, retail, manufacturing, and government possess substantial financial resources that enable them to invest heavily in emerging technologies. These enterprises have rapidly integrated advanced low-code and visual development platforms into their operations to accelerate application development, streamline business processes, and reduce dependence on traditional coding methods.

Leading Market Participants

  • TrackVia Inc.
  • ServiceNow Inc.
  • Salesforce Inc.
  • RunMyProcess
  • RETOOL
  • Quickbase Inc.
  • Pegasystems Inc.
  • OutSystems Software em Rede SA
  • Nintex Global Ltd.
  • Microsoft Corp.
  • Mendix Technology BV
  • Kissflow Inc.
  • Huawei Cloud Computing Technologies Co., Ltd.
  • Caspio Inc.
  • Betty Blocks BV
  • Autonom8 Inc.
  • Appian Corp.
  • AgilePoint Inc.
  • Zoho Corp. Pvt. Ltd.
  • Oracle Corp.
  • Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global Low Code AI Platform Market

Chapter 2. Research Methodology & Research Framework

  • 2.1. Research Objective
  • 2.2. Product Overview
  • 2.3. Market Segmentation
  • 2.4. Qualitative Research
    • 2.4.1. Primary & Secondary Sources
  • 2.5. Quantitative Research
    • 2.5.1. Primary & Secondary Sources
  • 2.6. Breakdown of Primary Research Respondents, By Region
  • 2.7. Assumption for Study
  • 2.8. Market Size Estimation
  • 2.9. Data Triangulation

Chapter 3. Global Low Code AI Platform Market Overview

  • 3.1. Industry Value Chain Analysis
    • 3.1.1. Cloud Infrastructure & Compute Providers
    • 3.1.2. Foundation Model & AI/ML Framework Developers
    • 3.1.3. Low-Code AI Platform & Visual Development Vendors
    • 3.1.4. System Integrators & Implementation Partners
    • 3.1.5. Enterprise & Citizen Developers (BFSI, Healthcare, Retail, Manufacturing)
  • 3.2. Industry Outlook
    • 3.2.1. Overview of the Global Low-Code / No-Code AI Development Industry
    • 3.2.2. Citizen-Developer Democratization Amid Persistent Software Talent Shortages
    • 3.2.3. Governance, Security & Shadow-IT Management for Enterprise-Scale Adoption
  • 3.3. PESTLE Analysis
  • 3.4. Porter's Five Forces Analysis
    • 3.4.1. Bargaining Power of Suppliers
    • 3.4.2. Bargaining Power of Buyers
    • 3.4.3. Threat of Substitutes
    • 3.4.4. Threat of New Entrants
    • 3.4.5. Degree of Competition
  • 3.5. Market Growth and Outlook
    • 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
    • 3.5.2. Price Trend Analysis, By Component

Chapter 4. Global Low Code AI Platform Market Analysis

  • 4.1. Competition Dashboard
    • 4.1.1. Market Concentration Rate
    • 4.1.2. Company Market Share Analysis (Value %), 2025
    • 4.1.3. Competitor Mapping & Benchmarking

Chapter 5. Global Low Code AI Platform Market Analysis

  • 5.1. Market Dynamics and Trends
    • 5.1.1. Growth Drivers
    • 5.1.2. Restraints
    • 5.1.3. Opportunity
    • 5.1.4. Key Trends
  • 5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 5.2.1. By Component
      • 5.2.1.1. Key Insights
        • 5.2.1.1.1. Platform Software
        • 5.2.1.1.2. Services
          • 5.2.1.1.2.1. Consulting
          • 5.2.1.1.2.2. Integration & Deployment
          • 5.2.1.1.2.3. Training & Support
          • 5.2.1.1.2.4. Managed Services
    • 5.2.2. By AI Capability
      • 5.2.2.1. Key Insights
        • 5.2.2.1.1. Predictive AI
        • 5.2.2.1.2. Generative AI
        • 5.2.2.1.3. Conversational AI
        • 5.2.2.1.4. Computer Vision AI
        • 5.2.2.1.5. Intelligent Process Automation AI
    • 5.2.3. By Deployment
      • 5.2.3.1. Key Insights
        • 5.2.3.1.1. Cloud-Based
        • 5.2.3.1.2. On-Premise
        • 5.2.3.1.3. Hybrid
    • 5.2.4. By Application
      • 5.2.4.1. Key Insights
        • 5.2.4.1.1. Customer Experience & Service
        • 5.2.4.1.2. Sales & Marketing
        • 5.2.4.1.3. Operations Management
        • 5.2.4.1.4. Finance & Accounting
        • 5.2.4.1.5. Human Resources
        • 5.2.4.1.6. Supply Chain & Logistics
        • 5.2.4.1.7. IT & Business Process Automation
    • 5.2.5. By Enterprise Size
      • 5.2.5.1. Key Insights
        • 5.2.5.1.1. Large Enterprises
        • 5.2.5.1.2. SMEs
    • 5.2.6. By End-Use Industry
      • 5.2.6.1. Key Insights
        • 5.2.6.1.1. BFSI
        • 5.2.6.1.2. Healthcare & Life Sciences
        • 5.2.6.1.3. Retail & E-commerce
        • 5.2.6.1.4. Manufacturing
        • 5.2.6.1.5. Government
        • 5.2.6.1.6. IT & Telecom
        • 5.2.6.1.7. Education
        • 5.2.6.1.8. Others
    • 5.2.7. By Region
      • 5.2.7.1. Key Insights
        • 5.2.7.1.1. North America
          • 5.2.7.1.1.1. The U.S.
          • 5.2.7.1.1.2. Canada
          • 5.2.7.1.1.3. Mexico
        • 5.2.7.1.2. Europe
          • 5.2.7.1.2.1. Western Europe
            • 5.2.7.1.2.1.1. The UK
            • 5.2.7.1.2.1.2. Germany
            • 5.2.7.1.2.1.3. France
            • 5.2.7.1.2.1.4. Italy
            • 5.2.7.1.2.1.5. Spain
            • 5.2.7.1.2.1.6. Rest of Western Europe
          • 5.2.7.1.2.2. Eastern Europe
            • 5.2.7.1.2.2.1. Poland
            • 5.2.7.1.2.2.2. Russia
            • 5.2.7.1.2.2.3. Rest of Eastern Europe
        • 5.2.7.1.3. Asia Pacific
          • 5.2.7.1.3.1. China
          • 5.2.7.1.3.2. India
          • 5.2.7.1.3.3. Japan
          • 5.2.7.1.3.4. Australia & New Zealand
          • 5.2.7.1.3.5. South Korea
          • 5.2.7.1.3.6. ASEAN
          • 5.2.7.1.3.7. Rest of Asia Pacific
        • 5.2.7.1.4. Middle East & Africa (MEA)
          • 5.2.7.1.4.1. Saudi Arabia
          • 5.2.7.1.4.2. South Africa
          • 5.2.7.1.4.3. UAE
          • 5.2.7.1.4.4. Rest of MEA
        • 5.2.7.1.5. South America
          • 5.2.7.1.5.1. Argentina
          • 5.2.7.1.5.2. Brazil
          • 5.2.7.1.5.3. Rest of South America

Chapter 6. North America Market Analysis

  • 6.1. Market Dynamics and Trends
    • 6.1.1. Growth Drivers
    • 6.1.2. Restraints
    • 6.1.3. Opportunity
    • 6.1.4. Key Trends
  • 6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 6.2.1. Key Insights
      • 6.2.1.1. By Component
      • 6.2.1.2. By AI Capability
      • 6.2.1.3. By Deployment
      • 6.2.1.4. By Application
      • 6.2.1.5. By Enterprise Size
      • 6.2.1.6. By End-Use Industry
      • 6.2.1.7. By Country

Chapter 7. Europe Market Analysis

  • 7.1. Market Dynamics and Trends
    • 7.1.1. Growth Drivers
    • 7.1.2. Restraints
    • 7.1.3. Opportunity
    • 7.1.4. Key Trends
  • 7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 7.2.1. Key Insights
      • 7.2.1.1. By Component
      • 7.2.1.2. By AI Capability
      • 7.2.1.3. By Deployment
      • 7.2.1.4. By Application
      • 7.2.1.5. By Enterprise Size
      • 7.2.1.6. By End-Use Industry
      • 7.2.1.7. By Country

Chapter 8. Asia Pacific Market Analysis

  • 8.1. Market Dynamics and Trends
    • 8.1.1. Growth Drivers
    • 8.1.2. Restraints
    • 8.1.3. Opportunity
    • 8.1.4. Key Trends
  • 8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 8.2.1. Key Insights
      • 8.2.1.1. By Component
      • 8.2.1.2. By AI Capability
      • 8.2.1.3. By Deployment
      • 8.2.1.4. By Application
      • 8.2.1.5. By Enterprise Size
      • 8.2.1.6. By End-Use Industry
      • 8.2.1.7. By Country

Chapter 9. Middle East & Africa Market Analysis

  • 9.1. Market Dynamics and Trends
    • 9.1.1. Growth Drivers
    • 9.1.2. Restraints
    • 9.1.3. Opportunity
    • 9.1.4. Key Trends
  • 9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 9.2.1. Key Insights
      • 9.2.1.1. By Component
      • 9.2.1.2. By AI Capability
      • 9.2.1.3. By Deployment
      • 9.2.1.4. By Application
      • 9.2.1.5. By Enterprise Size
      • 9.2.1.6. By End-Use Industry
      • 9.2.1.7. By Country

Chapter 10. South America Market Analysis

  • 10.1. Market Dynamics and Trends
    • 10.1.1. Growth Drivers
    • 10.1.2. Restraints
    • 10.1.3. Opportunity
    • 10.1.4. Key Trends
  • 10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 10.2.1. Key Insights
      • 10.2.1.1. By Component
      • 10.2.1.2. By AI Capability
      • 10.2.1.3. By Deployment
      • 10.2.1.4. By Application
      • 10.2.1.5. By Enterprise Size
      • 10.2.1.6. By End-Use Industry
      • 10.2.1.7. By Country

Chapter 11. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

  • 11.1. TrackVia Inc.
  • 11.2. ServiceNow Inc.
  • 11.3. Salesforce Inc.
  • 11.4. RunMyProcess
  • 11.5. RETOOL
  • 11.6. Quickbase Inc.
  • 11.7. Pegasystems Inc.
  • 11.8. OutSystems Software em Rede SA
  • 11.9. Nintex Global Ltd.
  • 11.10. Microsoft Corp.
  • 11.11. Mendix Technology BV
  • 11.12. Kissflow Inc.
  • 11.13. Huawei Cloud Computing Technologies Co. Ltd.
  • 11.14. Caspio Inc.
  • 11.15. Betty Blocks BV
  • 11.16. Autonom8 Inc.
  • 11.17. Appian Corp.
  • 11.18. AgilePoint Inc.
  • 11.19. Zoho Corp. Pvt. Ltd.
  • 11.20. Oracle Corp.
  • 11.21. Other Prominent Players

Chapter 12. Annexure

  • 12.1. List of Secondary Sources
  • 12.2. Key Country Markets- Macro Economic Outlook/Indicators