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市場調查報告書
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2024098

低程式碼人工智慧開發平台市場預測至2034年—按組件、技術、平台類型、部署模式、應用、最終用戶和地區分類的全球分析

Low-Code AI Development Platforms Market Forecasts to 2034 - Global Analysis By Component (Platforms and Services), Technology, Platform Type, Deployment Mode, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球低程式碼 AI 開發平台市場預計將在 2026 年達到 68 億美元，到 2034 年達到 604 億美元，在預測期內複合年成長率為 31.5%。

低程式碼人工智慧開發平台是一種軟體環境，使用戶能夠以最少的手動編碼來設計、建置和部署人工智慧應用程式。這些平台提供視覺化介面、拖放工具、預先建置的機器學習模型和自動化工作流程，從而簡化了開發流程。這使得開發人員、業務分析師，甚至不具備技術專長的使用者都能快速創建人工智慧解決方案，例如預測分析、聊天機器人和自動化系統。透過降低傳統人工智慧開發的複雜性，低程式碼人工智慧平台能夠加速創新、降低開發成本，並幫助企業更有效率地在各種業務職能中部署人工智慧功能。

對快速應用開發的需求日益成長

在IT資源有限的情況下，企業面臨著不斷加快數位化解決方案交付速度的壓力。低程式碼AI平台透過視覺化建模和現成組件取代傳統的手動編碼，顯著縮短了開發週期。這使得企業能夠快速回應市場變化、客戶期望以及內部流程效率低下等問題。透過整合預測分析和自然語言處理等AI功能，無需高級專業知識即可進一步縮短價值實現時間。隨著企業將敏捷性和創新性置於優先地位，金融、醫療保健和零售等行業的企業對這些平台的採用率持續成長。

管治和安全問題

低程式碼環境的便利開發特性可能導致「影子IT」的出現，即在官方監管之外創建未經授權的應用程式。這引發了人們對資料隱私、GDPR和HIPAA等法規合規性以及網路威脅脆弱性的嚴重擔憂。許多平台缺乏大型企業所需的強大版本控制、審計追蹤和存取控制功能。此外，如果未經過適當檢驗，嵌入應用程式中的AI模型可能存在偏差或產生不可預測的結果。組織必須實施嚴格的管治結構並定期進行安全評估，才能有效降低這些風險。

與生成式人工智慧技術的整合

生成式人工智慧的快速發展為低程式碼平台帶來了改變的潛力。透過整合大規模語言模型和圖像生成功能，這些平台使用戶能夠以最少的努力建立複雜的聊天機器人、內容生成工具和程式碼助手。企業無需具備高階人工智慧專業知識即可實現客戶服務、文件處理和創新工作流程的自動化。供應商正擴大提供預先建置的生成式人工智慧連接器和模板，從而降低了部署的複雜性。隨著生成式人工智慧的成熟和普及，低程式碼平台將成為理想的交付方式，促進其在各個業務職能部門的更廣泛應用。

激烈的市場競爭與分散化

低程式碼人工智慧平台市場競爭異常激烈，供應商眾多，既有老牌科技巨頭，也有專注於特定領域的Start-Ups。這種分散化導致買家難以區分功能、定價模式和可擴展性，造成選擇困難。價格競爭和激進的行銷策略會擠壓供應商的利潤空間。此外，提供免費基礎低程式碼功能的開放原始碼方案也正在迅速發展。如果小規模供應商無法持續創新，則面臨淘汰的風險。客戶也面臨供應商鎖定問題，可能難以在不同平台之間切換。維持差異化需要大量的研發投入和生態系統建設。

新冠疫情的影響

疫情大大推動了低程式碼人工智慧的普及，各組織加速了營運數位轉型，以支援遠距辦公和非接觸式服務。封鎖措施擾亂了傳統的軟體開發模式，迫使團隊探索更快速的部署方式。醫療機構利用低程式碼平台在數週內建構了病患分流應用和疫苗追蹤系統。然而，在某些情況下，預算限制暫時延緩了全公司範圍的部署。這次危機凸顯了「公民開發」的價值，即業務用戶創建用於供應鏈管理和員工健康監測的應用程式。即使在疫情結束後，混合辦公模式仍然持續推動著對人工智慧驅動的快速應用開發的需求。

在預測期內，低程式碼人工智慧應用開發平台細分市場預計將成為最大的細分市場。

低程式碼人工智慧應用開發平台預計將在預測期內佔據最大的市場佔有率，因為它與企業的數位轉型重點高度契合。這些平台使用戶無需說明複雜的程式碼即可建立功能齊全、整合人工智慧的Web、行動和企業級應用程式。預置模板、拖放式介面和可重複使用元件顯著縮短了開發時間。企業正在利用這些平台建立客戶入口網站、內部儀表板和業務工具。基於使用者回饋快速迭代改進的能力進一步提升了這些平台的受歡迎程度。

在預測期內，公民開發人員產業預計將呈現最高的複合年成長率。

在預測期內，公民開發人員群體預計將呈現最高的成長率，這主要得益於非技術業務用戶中軟體創建的普及化。行銷、財務、人力資源和營運部門的員工擴大自行建立應用程式，以解決部門特定問題，而無需等待IT部門的回應。低程式碼人工智慧平台提供無需程式設計知識的直覺式介面，從而能夠快速原型製作和部署。這一趨勢減少了IT部門的營運延遲，並促進了營運層面的創新。各組織正在建立卓越中心，透過管治和培訓來支持公民開發人員。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於早期技術應用和領先平台供應商的存在。美國在企業人工智慧支出方面處於主導地位，這得益於醫療保健、金融和科技產業的強勁需求。強大的雲端基礎設施和技能嫻熟的開發團體正在加速平台的普及。旨在促進數位化的政府措施也為進一步成長提供了支持。加拿大也憑藉著充滿活力的Start-Ups生態系統做出了貢獻。區域內企業之間的策略性收購和合作正在推動市場滲透。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的數位化進程以及中小企業對數位化應用的日益普及。中國、印度和日本等國家對具成本效益的應用開發解決方案的需求激增。大量公民開發者和蓬勃發展的IT外包產業正在推動平台應用。政府主導的智慧城市計畫和製造自動化舉措也創造了更多機會。智慧型手機普及率的提高和行動優先策略的推行，也增加了對快速應用部署的需求。隨著企業從疫情衝擊中復甦，亞太地區正成為高成長的前線。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 主要參與者（最多3家公司）的SWOT分析
  • 區域細分
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球低程式碼人工智慧開發平台市場：按組件分類

  • 平台
    • 視覺化開發平台
    • 模型驅動開發平台
    • 人工智慧輔助開發平台
    • API 和整合平台
  • 服務
    • 諮詢服務
    • 實施與整合
    • 培訓和支持
    • 託管服務

第6章：全球低程式碼人工智慧開發平台市場：依技術分類

  • 機器學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 預測分析
  • 人工智慧世代
  • 強化學習

第7章：全球低程式碼人工智慧開發平台市場：依平台類型分類

  • 低程式碼人工智慧應用開發平台
  • 低程式碼資料科學平台
  • 低程式碼 MLOps 平台
  • 低程式碼工作流程自動化平台
  • 低程式互動式人工智慧平台

第8章：全球低程式碼人工智慧開發平台市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合實現

第9章：全球低程式碼人工智慧開發平台市場：按應用分類

  • Web應用開發
  • 行動應用開發
  • 桌面和企業應用開發
  • 人工智慧模型的開發與實施
  • 流程自動化與工作流程管理
  • 聊天機器人和虛擬助手

第10章：全球低程式碼人工智慧開發平台市場：按最終用戶分類

  • IT與軟體開發團隊
  • 業務部門/公民開發人員
  • 資料科學家和人工智慧工程師
  • 獨立軟體供應商 (ISV)
  • 政府/公共部門組織
  • 其他最終用戶

第11章：全球低程式碼人工智慧開發平台市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • Microsoft
  • Google
  • Amazon Web Services
  • IBM
  • Salesforce
  • ServiceNow
  • Appian
  • Pegasystems
  • Mendix
  • OutSystems
  • Zoho
  • Kissflow
  • Retool
  • Appsmith
  • Jitterbit
Product Code: SMRC35309

According to Stratistics MRC, the Global Low-Code AI Development Platforms Market is accounted for $6.8 billion in 2026 and is expected to reach $60.4 billion by 2034, growing at a CAGR of 31.5% during the forecast period. Low-Code AI Development Platforms are software environments that enable users to design, build, and deploy artificial intelligence applications with minimal manual coding. These platforms provide visual interfaces, drag-and-drop tools, prebuilt machine learning models, and automated workflows that simplify the development process. They allow developers, business analysts, and non-technical users to quickly create AI solutions such as predictive analytics, chatbots, and automation systems. By reducing the complexity of traditional AI development, low-code AI platforms accelerate innovation, lower development costs, and help organizations implement AI capabilities more efficiently across various business functions.

Market Dynamics:

Driver:

Growing demand for rapid application development

Organizations are under constant pressure to deliver digital solutions faster while managing limited IT resources. Low-code AI platforms significantly reduce development cycles by replacing traditional hand-coding with visual modeling and pre-built components. This allows enterprises to respond swiftly to market changes, customer expectations, and internal process inefficiencies. The ability to integrate AI capabilities like predictive analytics and natural language processing without deep expertise further accelerates time-to-value. As businesses prioritize agility and innovation, adoption of these platforms continues rising across sectors such as finance, healthcare, and retail.

Restraint:

Concerns over governance and security

The ease of development in low-code environments can lead to shadow IT, where unauthorized applications are created outside official oversight. This raises significant concerns regarding data privacy, compliance with regulations such as GDPR and HIPAA, and vulnerability to cyber threats. Many platforms lack robust version control, audit trails, and access management features required by large enterprises. Additionally, AI models embedded within applications may introduce bias or produce unpredictable outcomes without proper validation. Organizations must enforce strict governance frameworks and conduct regular security assessments to mitigate these risks effectively.

Opportunity:

Integration with generative AI technologies

The rapid evolution of generative AI is opening transformative possibilities for low-code platforms. By incorporating large language models and image generation capabilities, these platforms enable users to build sophisticated chatbots, content generators, and code assistants with minimal effort. Enterprises can automate customer service, document processing, and creative workflows without extensive AI expertise. Vendors are increasingly offering pre-built generative AI connectors and templates, reducing implementation complexity. As generative AI matures and becomes more accessible, low-code platforms will serve as ideal delivery mechanisms, driving broader adoption across business functions.

Threat:

Intense market competition and fragmentation

The low-code AI platform market is becoming highly crowded with numerous vendors ranging from established tech giants to niche startups. This fragmentation creates confusion for buyers struggling to differentiate features, pricing models, and scalability. Price wars and aggressive marketing tactics can erode profit margins for providers. Furthermore, open-source alternatives are gaining traction, offering basic low-code capabilities at no cost. Smaller vendors risk obsolescence if unable to continuously innovate. Customers may also face vendor lock-in concerns, making migration between platforms difficult. Sustaining differentiation requires substantial R&D investment and ecosystem development.

Covid-19 Impact

The pandemic acted as a powerful catalyst for low-code AI adoption as organizations urgently digitized operations to support remote work and contactless services. Lockdowns disrupted traditional software development, forcing teams to seek faster deployment methods. Healthcare providers used low-code platforms to build patient triage apps and vaccine tracking systems within weeks. However, budget constraints temporarily delayed some enterprise-wide implementations. The crisis highlighted the value of citizen development, with business users creating applications to manage supply chains and employee health monitoring. Post-pandemic, hybrid work models continue driving demand for rapid, AI-enabled application development.

The low-code AI application development platforms segment is expected to be the largest during the forecast period

The low-code AI application development platforms segment is expected to account for the largest market share during the forecast period, due to its direct alignment with enterprise digital transformation priorities. These platforms enable users to build full-featured web, mobile, and enterprise applications with integrated AI capabilities without writing complex code. Pre-built templates, drag-and-drop interfaces, and reusable components dramatically reduce development effort. Organizations use them for customer portals, internal dashboards, and operational tools. The ability to iterate quickly based on user feedback further drives preference.

The citizen developers segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the citizen developers segment is predicted to witness the highest growth rate, driven by the democratization of software creation across non-technical business users. Employees in marketing, finance, HR, and operations are increasingly building their own applications to solve department-specific problems without waiting for IT. Low-code AI platforms provide intuitive interfaces that require no programming knowledge, enabling rapid prototyping and deployment. This trend reduces IT backlogs and fosters innovation at the grassroots level. Organizations are establishing centers of excellence to support citizen developers with governance and training.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share fuelled by early technology adoption and presence of major platform vendors. The United States leads in enterprise AI spending, with strong demand from healthcare, finance, and technology sectors. Robust cloud infrastructure and skilled developer communities accelerate platform utilization. Government initiatives promoting digital modernization further support growth. Canada also contributes through its thriving startup ecosystem. Strategic acquisitions and partnerships among regional players enhance market penetration.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by rapid digitalization and expanding SME adoption. Countries like China, India, and Japan are witnessing surging demand for cost-effective application development solutions. Large populations of citizen developers and growing IT outsourcing industries fuel platform usage. Government-backed smart city projects and manufacturing automation initiatives create additional opportunities. Rising smartphone penetration and mobile-first strategies drive need for rapid app deployment. As businesses recover from pandemic disruptions, Asia Pacific becomes a high-growth frontier.

Key players in the market

Some of the key players in Low-Code AI Development Platforms Market include Microsoft, Google, Amazon Web Services, IBM, Salesforce, ServiceNow, Appian, Pegasystems, Mendix, OutSystems, Zoho, Kissflow, Retool, Appsmith, and Jitterbit.

Key Developments:

In March 2026, IBM and ETH Zurich announced a 10-year collaboration to advance the next generation of algorithms at the intersection of AI and quantum computing. This initiative represents the latest milestone in the long-standing collaboration between the two institutions, further strengthening a scientific exchange that has helped create the future of information technology.

In September 2025, Mendix announced its continued commitment to collaborate with Snowflake, the AI Data Cloud company, to further enable the enterprise to drive value from data through modern software development.

Components Covered:

  • Platforms
  • Services

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Predictive Analytics
  • Generative AI
  • Reinforcement Learning

Platform Types Covered:

  • Low-Code AI Application Development Platforms
  • Low-Code Data Science Platforms
  • Low-Code MLOps Platforms
  • Low-Code Workflow Automation Platforms
  • Low-Code Conversational AI Platforms

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid Deployment

Applications Covered:

  • Web Application Development
  • Mobile Application Development
  • Desktop & Enterprise Application Development
  • AI Model Development & Deployment
  • Process Automation & Workflow Management
  • Chatbots & Virtual Assistants

End Users Covered:

  • IT & Software Development Teams
  • Business Units / Citizen Developers
  • Data Scientists & AI Engineers
  • Independent Software Vendors (ISVs)
  • Government & Public Sector Organizations
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Low-Code AI Development Platforms Market, By Component

  • 5.1 Platforms
    • 5.1.1 Visual Development Platforms
    • 5.1.2 Model-Driven Development Platforms
    • 5.1.3 AI-Assisted Development Platforms
    • 5.1.4 API & Integration Platforms
  • 5.2 Services
    • 5.2.1 Consulting Services
    • 5.2.2 Implementation & Integration
    • 5.2.3 Training & Support
    • 5.2.4 Managed Services

6 Global Low-Code AI Development Platforms Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Natural Language Processing (NLP)
  • 6.3 Computer Vision
  • 6.4 Predictive Analytics
  • 6.5 Generative AI
  • 6.6 Reinforcement Learning

7 Global Low-Code AI Development Platforms Market, By Platform Type

  • 7.1 Low-Code AI Application Development Platforms
  • 7.2 Low-Code Data Science Platforms
  • 7.3 Low-Code MLOps Platforms
  • 7.4 Low-Code Workflow Automation Platforms
  • 7.5 Low-Code Conversational AI Platforms

8 Global Low-Code AI Development Platforms Market, By Deployment Mode

  • 8.1 Cloud-Based
  • 8.2 On-Premises
  • 8.3 Hybrid Deployment

9 Global Low-Code AI Development Platforms Market, By Application

  • 9.1 Web Application Development
  • 9.2 Mobile Application Development
  • 9.3 Desktop & Enterprise Application Development
  • 9.4 AI Model Development & Deployment
  • 9.5 Process Automation & Workflow Management
  • 9.6 Chatbots & Virtual Assistants

10 Global Low-Code AI Development Platforms Market, By End User

  • 10.1 IT & Software Development Teams
  • 10.2 Business Units / Citizen Developers
  • 10.3 Data Scientists & AI Engineers
  • 10.4 Independent Software Vendors (ISVs)
  • 10.5 Government & Public Sector Organizations
  • 10.6 Other End Users

11 Global Low-Code AI Development Platforms Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Microsoft
  • 14.2 Google
  • 14.3 Amazon Web Services
  • 14.4 IBM
  • 14.5 Salesforce
  • 14.6 ServiceNow
  • 14.7 Appian
  • 14.8 Pegasystems
  • 14.9 Mendix
  • 14.10 OutSystems
  • 14.11 Zoho
  • 14.12 Kissflow
  • 14.13 Retool
  • 14.14 Appsmith
  • 14.15 Jitterbit

List of Tables

  • Table 1 Global Low-Code AI Development Platforms Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Low-Code AI Development Platforms Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Low-Code AI Development Platforms Market Outlook, By Platforms (2023-2034) ($MN)
  • Table 4 Global Low-Code AI Development Platforms Market Outlook, By Visual Development Platforms (2023-2034) ($MN)
  • Table 5 Global Low-Code AI Development Platforms Market Outlook, By Model-Driven Development Platforms (2023-2034) ($MN)
  • Table 6 Global Low-Code AI Development Platforms Market Outlook, By AI-Assisted Development Platforms (2023-2034) ($MN)
  • Table 7 Global Low-Code AI Development Platforms Market Outlook, By API & Integration Platforms (2023-2034) ($MN)
  • Table 8 Global Low-Code AI Development Platforms Market Outlook, By Services (2023-2034) ($MN)
  • Table 9 Global Low-Code AI Development Platforms Market Outlook, By Consulting Services (2023-2034) ($MN)
  • Table 10 Global Low-Code AI Development Platforms Market Outlook, By Implementation & Integration (2023-2034) ($MN)
  • Table 11 Global Low-Code AI Development Platforms Market Outlook, By Training & Support (2023-2034) ($MN)
  • Table 12 Global Low-Code AI Development Platforms Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 13 Global Low-Code AI Development Platforms Market Outlook, By Technology (2023-2034) ($MN)
  • Table 14 Global Low-Code AI Development Platforms Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 15 Global Low-Code AI Development Platforms Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 16 Global Low-Code AI Development Platforms Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 17 Global Low-Code AI Development Platforms Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 18 Global Low-Code AI Development Platforms Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 19 Global Low-Code AI Development Platforms Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 20 Global Low-Code AI Development Platforms Market Outlook, By Platform Type (2023-2034) ($MN)
  • Table 21 Global Low-Code AI Development Platforms Market Outlook, By Low-Code AI Application Development Platforms (2023-2034) ($MN)
  • Table 22 Global Low-Code AI Development Platforms Market Outlook, By Low-Code Data Science Platforms (2023-2034) ($MN)
  • Table 23 Global Low-Code AI Development Platforms Market Outlook, By Low-Code MLOps Platforms (2023-2034) ($MN)
  • Table 24 Global Low-Code AI Development Platforms Market Outlook, By Low-Code Workflow Automation Platforms (2023-2034) ($MN)
  • Table 25 Global Low-Code AI Development Platforms Market Outlook, By Low-Code Conversational AI Platforms (2023-2034) ($MN)
  • Table 26 Global Low-Code AI Development Platforms Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 27 Global Low-Code AI Development Platforms Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 28 Global Low-Code AI Development Platforms Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 29 Global Low-Code AI Development Platforms Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 30 Global Low-Code AI Development Platforms Market Outlook, By Application (2023-2034) ($MN)
  • Table 31 Global Low-Code AI Development Platforms Market Outlook, By Web Application Development (2023-2034) ($MN)
  • Table 32 Global Low-Code AI Development Platforms Market Outlook, By Mobile Application Development (2023-2034) ($MN)
  • Table 33 Global Low-Code AI Development Platforms Market Outlook, By Desktop & Enterprise Application Development (2023-2034) ($MN)
  • Table 34 Global Low-Code AI Development Platforms Market Outlook, By AI Model Development & Deployment (2023-2034) ($MN)
  • Table 35 Global Low-Code AI Development Platforms Market Outlook, By Process Automation & Workflow Management (2023-2034) ($MN)
  • Table 36 Global Low-Code AI Development Platforms Market Outlook, By Chatbots & Virtual Assistants (2023-2034) ($MN)
  • Table 37 Global Low-Code AI Development Platforms Market Outlook, By End User (2023-2034) ($MN)
  • Table 38 Global Low-Code AI Development Platforms Market Outlook, By IT & Software Development Teams (2023-2034) ($MN)
  • Table 39 Global Low-Code AI Development Platforms Market Outlook, By Business Units / Citizen Developers (2023-2034) ($MN)
  • Table 40 Global Low-Code AI Development Platforms Market Outlook, By Data Scientists & AI Engineers (2023-2034) ($MN)
  • Table 41 Global Low-Code AI Development Platforms Market Outlook, By Independent Software Vendors (ISVs) (2023-2034) ($MN)
  • Table 42 Global Low-Code AI Development Platforms Market Outlook, By Government & Public Sector Organizations (2023-2034) ($MN)
  • Table 43 Global Low-Code AI Development Platforms Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.