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1859730

全球人工智慧驅動的預測性維護平台市場：未來預測（至2032年）—按組件、部署模式、技術、應用、最終用戶和地區進行分析

AI-Powered Predictive Maintenance Platforms Market Forecasts to 2032 - Global Analysis By Component (Hardware, Software and Services), Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球人工智慧驅動的預測性維護平台市場預計到 2025 年將達到 8.7418 億美元，到 2032 年將達到 20.4391 億美元，預測期內複合年成長率為 12.9%。

人工智慧驅動的預測性維護平台利用先進的人工智慧、機器學習和物聯網整合技術，在設備故障發生前進行預測。這些系統處理海量感測器產生的數據，以檢測機器異常、性能偏差和磨損模式。透過預測分析，企業可以更有效地規劃維護，減少非計畫故障，並延長資產壽命。製造業、能源、物流和醫療保健等行業正日益依賴這些平台來提高生產力並降低營運成本。透過支援數據主導策略，這些人工智慧工具將維護從被動或預防性流程轉變為預測性流程，從而提高可靠性、安全性和整體效率。

根據歐盟委員會的《人工智慧在製造業應用概況介紹》，預測性維護是歐洲工業界人工智慧應用的三大主要案例之一，截至 2023 年，超過 50% 的大型製造商正在試行或部署基於人工智慧的維護系統。

工業IoT和智慧感測器應用日益普及

工業IoT和智慧感測技術的擴展正顯著推動人工智慧驅動的預測性維護平台的發展。這些設備能夠擷取即時機器數據，人工智慧模型可以解讀這些數據，從而主動識別潛在的故障和維護需求。持續的數據監控提高了磨損和性能下降檢測的準確性，使企業能夠預防代價高昂的設備故障。隨著工業4.0的日益普及，工業界正迅速向數據主導的維護實踐轉型。智慧感測器助力企業最佳化流程、最大限度地延長運作並實現卓越營運，從而推動預測性維護平台在製造業、公共產業、交通運輸和工業自動化等行業的廣泛應用。

高昂的實施和整合成本

部署和整合人工智慧驅動的預測性維護平台的高昂成本是限制市場發展的主要因素。這些系統需要對先進的人工智慧工具、感測器網路、資料管理基礎設施和技術專長進行大量投資。小規模的企業很難承擔如此前期投資。此外，與過時的舊有系統整合通常涉及複雜的客製化和較長的部署時間。持續的系統升級和維護也會推高整體成本。雖然預測性維護能夠帶來長期的效率提升和營運成本節約，但最初的財務和技術障礙阻礙了許多公司，尤其是那些對成本敏感的部門，大規模採用此類先進的維護技術。

雲端運算和邊緣運算的日益普及

雲端運算和邊緣運算技術的日益普及，為人工智慧驅動的預測性維護平台開闢了新的成長途徑。雲端運算使企業能夠處理和儲存海量資料集，並隨時隨地即時存取人工智慧主導的洞察。邊緣運算則透過實現靠近裝置的快速本地化資料分析，進一步增強了雲端運算的功能，從而降低了延遲並加快了響應速度。這種混合架構提高了預測維修系統的運作靈活性、可靠性和擴充性。隨著企業採用分散式運算環境，雲端運算和邊緣框架與人工智慧的整合預計將加速發展，從而在各行各業實現更高的靈活性、成本節約和效能最佳化。

高度依賴數據品質和可用性

人工智慧驅動的預測性維護平台對資料品質和可存取性的高度依賴對其效能構成重大威脅。如果輸入資料不準確、不完整或不一致，預測演算法將產生不可靠的結果，導致代價高昂的維護錯誤。許多行業難以從感測器和舊有系統等各種來源收集統一的數據。部署階段資料量的不足也會限制模型的訓練和準確性。此外，資料集中的雜訊和不一致性會損害系統的可靠性和決策能力。這種對高品質數據的嚴重依賴持續挑戰工業環境中預測性維護解決方案的準確性和可靠性。

新冠疫情的影響：

新冠疫情為人工智慧驅動的預測性維護平台市場帶來了挑戰和機會。初期，工業停工、供應鏈問題和勞動力減少阻礙了系統部署和新投資。然而，疫情最終加速了數位轉型，企業紛紛採用基於人工智慧和物聯網的預測性維護技術來遠端監控設備並減少人工干預。事實證明，這些技術對於在疫情衝擊下維持生產效率和營運可靠性至關重要。疫情過後，許多企業持續整合人工智慧驅動的維護系統，以增強韌性、提高成本效益，並在一個更互聯、技術依賴的產業環境中支援主導營運。

預計在預測期內，軟體板塊將成為最大的板塊。

預計在預測期內，軟體領域將佔據最大的市場佔有率，因為它為智慧分析、機器學習和預測洞察提供了堅實的基礎。這些軟體工具能夠分析大量的機器和感測器數據，從而識別異常情況、預測潛在故障並制定及時的維護措施。基於雲端和主導的軟體平台增強了擴充性和連接性，使企業能夠有效率地即時管理資產。與現有企業系統的整合可實現流暢的資料流和明智的維護決策。隨著自動化和數位化最佳化的日益普及，各行業正在增加對預測性維護軟體的投資，從而鞏固其市場主導地位。

預計能源與公共產業產業在預測期內將實現最高的複合年成長率。

預計能源與公共產業產業在預測期內將呈現最高的成長率。該行業正日益廣泛地採用人工智慧技術來監控和維護渦輪機、變壓器和電網等關鍵資產。預測性維護支援持續監控、及早發現問題並提升設備效能，從而降低營運風險和停機時間。隨著產業擁抱數位化並向可再生能源和智慧電網系統轉型，基於人工智慧的預測工具已成為最佳化能源分配和可靠性的關鍵。物聯網、數據分析和人工智慧的結合提高了資產效率，推動了這些技術在能源與公共產業的快速應用。

比最大的地區

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於技術的快速發展以及人工智慧和物聯網在工業領域的廣泛應用。該地區成熟的基礎設施以及在製造業、能源和航太等領域的巨額投資，正推動預測維修系統的顯著普及。總部位於該地區的領先技術供應商和解決方案開發商正致力於技術創新和大規模部署。政府鼓勵廣泛自動化、數據主導營運和數位轉型的措施也推動了市場成長。憑藉其強大的先進產業生態系統和尖端的分析能力，北美透過以效率為導向的工業現代化，繼續在全球預測性維護領域佔據主導地位。

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，這主要得益於工業發展的加速和對自動化的高度重視。包括中國、日本、韓國和印度在內的多個國家正積極投資人工智慧主導的維護系統，以提高效率並減少計劃外設備故障。能源、汽車和電子等產業的智慧製造項目和數位轉型舉措的擴展是關鍵的成長驅動力。政府鼓勵採用工業4.0的支持性政策進一步提升了該地區的市場潛力。憑藉其不斷擴展的工業生態系統以及物聯網和分析技術的日益融合，亞太地區有望引領未來的成長。

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    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 原始研究資料
    • 次級研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的影響

第4章 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
  • 競爭對手之間的競爭

5. 全球人工智慧驅動型預測性維護平台市場（按組件分類）

  • 硬體
  • 軟體
  • 服務

6. 全球人工智慧驅動的預測性維護平台市場（按部署模式分類）

  • 本地部署
  • 雲端基礎的
  • 混合

7. 全球人工智慧驅動的預測性維護平台市場（依技術分類）

  • 機器學習
  • 自然語言處理
  • 電腦視覺
  • 邊緣人工智慧

8. 全球人工智慧驅動的預測性維護平台市場（按應用分類）

  • 狀態監測
  • 故障檢測與診斷
  • 剩餘使用壽命（RUL）估算
  • 最佳化能源效率
  • 自動化維護計劃
  • 停機風險預測

9. 全球人工智慧驅動的預測性維護平台市場（按最終用戶分類）

  • 離散製造
  • 工藝製造
  • 運輸/物流
  • 航太與國防
  • 醫療機構
  • 能源與公共產業
  • 石油和天然氣
  • 通訊
  • 採礦和重型設備
  • 智慧農業

第10章：全球人工智慧驅動的預測性維護平台市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與併購
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第12章 企業概況

  • IBM
  • GE Digital
  • Siemens
  • C3.ai
  • Hitachi Vantara
  • ABB
  • SAP
  • Uptake
  • PTC
  • OpenText
  • Dassault Systemes
  • Rapid Innovation
  • Schneider Electric
  • Microsoft
  • Honeywell
Product Code: SMRC31954

According to Stratistics MRC, the Global AI-Powered Predictive Maintenance Platforms Market is accounted for $874.18 million in 2025 and is expected to reach $2043.91 million by 2032 growing at a CAGR of 12.9% during the forecast period. AI-powered predictive maintenance platforms utilize advanced artificial intelligence, machine learning, and IoT integration to anticipate equipment malfunctions before they happen. These systems process vast amounts of sensor-generated data to detect irregularities, performance drifts, and wear patterns in machines. Through predictive analytics, organizations can schedule maintenance more effectively, reduce unexpected breakdowns, and extend the service life of assets. Sectors like manufacturing, energy, logistics, and healthcare increasingly rely on such platforms for improved productivity and reduced operational costs. By enabling data-driven strategies, these AI tools transform maintenance from a reactive or preventive process into a predictive one, enhancing reliability, safety, and overall efficiency.

According to the European Commission's Fact Sheet on AI in Manufacturing, predictive maintenance is one of the top three AI use cases in European industry, with over 50% of large manufacturers piloting or deploying AI-based maintenance systems as of 2023.

Market Dynamics:

Driver:

Growing adoption of industrial IoT and smart sensors

The expansion of Industrial IoT and smart sensing technologies is significantly fueling the growth of AI-powered predictive maintenance platforms. These devices capture real-time machine data, which AI models interpret to identify potential faults and maintenance needs in advance. Continuous data monitoring enhances precision in detecting wear or performance decline, allowing companies to prevent costly equipment failures. With Industry 4.0 adoption increasing, industries are rapidly transitioning toward data-driven maintenance practices. Smart sensors empower organizations to optimize processes, maximize uptime, and achieve operational excellence, driving the widespread implementation of predictive maintenance platforms across sectors such as manufacturing, utilities, transportation, and industrial automation.

Restraint:

High implementation and integration costs

The substantial expenses associated with implementing and integrating AI-powered predictive maintenance platforms act as a key market restraint. These systems require heavy investment in advanced AI tools, sensor networks, data management infrastructure, and technical expertise. Smaller organizations find it challenging to justify these upfront expenditures. Moreover, integration with outdated legacy systems often involves complex customization and extended deployment periods. Continuous system upgrades and maintenance also add to total costs. Although predictive maintenance provides long-term efficiency and operational savings, the initial financial and technical barriers discourage many enterprises-particularly in cost-sensitive sectors-from adopting these advanced maintenance technologies at scale.

Opportunity:

Rising adoption of cloud and edge computing

The growing deployment of cloud and edge computing technologies is opening new growth avenues for AI-powered predictive maintenance platforms. Cloud computing allows enterprises to process and store massive datasets while accessing AI-driven insights from anywhere in real time. Edge computing complements this by enabling rapid, localized data analysis close to the equipment, ensuring low latency and faster responses. This hybrid architecture enhances operational agility, reliability, and scalability for predictive maintenance systems. As organizations embrace distributed computing environments, the integration of AI with cloud and edge frameworks is expected to accelerate, supporting greater flexibility, cost reduction, and performance optimization across industries.

Threat:

High dependency on data quality and availability

AI-powered predictive maintenance platforms are highly reliant on data quality and accessibility, posing a significant threat to their performance. When input data is inaccurate, incomplete, or inconsistent, the predictive algorithms generate unreliable results, leading to costly maintenance errors. Many industries struggle to gather uniform data from varied sources such as sensors and legacy systems. Limited data during deployment phases also restricts model training and precision. Moreover, noise or discrepancies in datasets can compromise system reliability and decision-making. This strong dependence on high-quality data continues to challenge the accuracy and credibility of predictive maintenance solutions across industrial environments.

Covid-19 Impact:

The outbreak of COVID-19 created both challenges and opportunities for the AI-powered predictive maintenance platforms market. In the early stages, industrial shutdowns, supply chain issues, and reduced workforce capacity hindered system deployment and new investments. Yet, the pandemic ultimately accelerated digital transformation as companies adopted AI and IoT-based predictive maintenance for remote equipment monitoring and reduced manual intervention. These technologies proved essential for maintaining production efficiency and operational reliability amid disruptions. Following the pandemic, many organizations continued integrating AI-powered maintenance systems to strengthen resilience, improve cost efficiency, and support automation-driven operations in a more connected and technology-dependent industrial landscape.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, as it serves as the backbone for intelligent analytics, machine learning, and predictive insights. These software tools analyze extensive machine and sensor data to identify irregularities, predict potential breakdowns, and plan timely maintenance actions. Cloud-enabled and AI-driven software platforms offer enhanced scalability and connectivity, enabling organizations to manage assets efficiently in real time. Integration with existing enterprise systems allows for smooth data flow and informed maintenance decisions. With the growing emphasis on automation and digital optimization, industries are increasingly investing in predictive maintenance software, reinforcing its leading position within the market.

The energy & utilities segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the energy & utilities segment is predicted to witness the highest growth rate. This sector increasingly employs AI technologies to oversee and maintain essential assets like turbines, transformers, and power grids. Predictive maintenance supports continuous monitoring, early issue detection, and improved equipment performance, reducing operational risks and downtime. As the industry embraces digitalization and shifts toward renewable energy and smart grid systems, AI-based predictive tools are becoming essential for optimizing energy distribution and reliability. The combination of IoT, data analytics, and AI enhances asset efficiency, driving rapid adoption within the energy and utilities segment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by rapid technological advancements and strong industrial adoption of AI and IoT. The region's mature infrastructure and high investment in sectors such as manufacturing, energy, and aerospace have driven significant implementation of predictive maintenance systems. Major technology providers and solution developers headquartered in the region contribute to innovation and large-scale deployment. Widespread automation, data-driven operations, and government initiatives encouraging digital transformation also enhance market growth. With its strong ecosystem of advanced industries and cutting-edge analytics capabilities, North America continues to dominate the global predictive maintenance landscape through efficiency-focused industrial modernization.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to its accelerating industrial development and strong focus on automation. Nations including China, Japan, South Korea, and India are actively investing in AI-driven maintenance systems to improve efficiency and reduce unexpected equipment failures. Expanding smart manufacturing programs and digital transformation initiatives across industries such as energy, automotive, and electronics are key growth drivers. Supportive government policies encouraging Industry 4.0 adoption are further enhancing regional market potential. With its growing industrial ecosystem and increasing integration of IoT and analytics, Asia-Pacific is set to lead future growth.

Key players in the market

Some of the key players in AI-Powered Predictive Maintenance Platforms Market include IBM, GE Digital, Siemens, C3.ai, Hitachi Vantara, ABB, SAP, Uptake, PTC, OpenText, Dassault Systemes, Rapid Innovation, Schneider Electric, Microsoft and Honeywell.

Key Developments:

In October 2025, IBM announced that it has signed a definitive agreement to acquire Cognitus with industry-specific, AI-powered solutions. Cognitus will bring mission-critical SAP skills, including in RISE and GROW with SAP, as well as an extensive portfolio of software assets. This combination of services, software and industry expertise, aligns with IBM's asset-based approach to digital transformation, driving increased productivity and operational efficiency for clients around the world.

In October 2025, Hitachi Vantara and Supermicro have announced collaboration aimed at helping enterprises in Southeast Asia modernise their AI infrastructure by integrating their respective data storage and compute solutions. The partnership seeks to address the increasing challenges faced by organisations deploying AI and generative AI workloads, particularly those related to data fragmentation and infrastructure bottlenecks.

In May 2025, C3 AI announced a multi-year renewal and expansion of their joint venture agreement through June 2028. Under the terms of the agreement, C3 AI and Baker Hughes will continue to develop, deliver, and market Enterprise AI solutions to the oil and gas and chemical industries. C3 AI will also continue to deliver Enterprise AI solutions for internal use at Baker Hughes, who will expand deployments of C3 AI Sourcing Optimization, C3 AI Inventory Optimization, and the C3 AI Sustainability Suite.

Components Covered:

  • Hardware
  • Software
  • Services

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based
  • Hybrid

Technologies Covered:

  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Edge AI

Applications Covered:

  • Condition Monitoring
  • Failure Detection & Diagnosis
  • Remaining Useful Life (RUL) Estimation
  • Energy Efficiency Optimization
  • Maintenance Scheduling Automation
  • Downtime Risk Forecasting

End Users Covered:

  • Discrete Manufacturing
  • Process Manufacturing
  • Transportation & Logistics
  • Aerospace & Defense
  • Healthcare Facilities
  • Energy & Utilities
  • Oil & Gas
  • Telecommunications
  • Mining & Heavy Equipment
  • Smart Agriculture

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
  • 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

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI-Powered Predictive Maintenance Platforms Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software
  • 5.4 Services

6 Global AI-Powered Predictive Maintenance Platforms Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud-Based
  • 6.4 Hybrid

7 Global AI-Powered Predictive Maintenance Platforms Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning
  • 7.3 Natural Language Processing
  • 7.4 Computer Vision
  • 7.5 Edge AI

8 Global AI-Powered Predictive Maintenance Platforms Market, By Application

  • 8.1 Introduction
  • 8.2 Condition Monitoring
  • 8.3 Failure Detection & Diagnosis
  • 8.4 Remaining Useful Life (RUL) Estimation
  • 8.5 Energy Efficiency Optimization
  • 8.6 Maintenance Scheduling Automation
  • 8.7 Downtime Risk Forecasting

9 Global AI-Powered Predictive Maintenance Platforms Market, By End User

  • 9.1 Introduction
  • 9.2 Discrete Manufacturing
  • 9.3 Process Manufacturing
  • 9.4 Transportation & Logistics
  • 9.5 Aerospace & Defense
  • 9.6 Healthcare Facilities
  • 9.7 Energy & Utilities
  • 9.8 Oil & Gas
  • 9.9 Telecommunications
  • 9.10 Mining & Heavy Equipment
  • 9.11 Smart Agriculture

10 Global AI-Powered Predictive Maintenance Platforms Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 IBM
  • 12.2 GE Digital
  • 12.3 Siemens
  • 12.4 C3.ai
  • 12.5 Hitachi Vantara
  • 12.6 ABB
  • 12.7 SAP
  • 12.8 Uptake
  • 12.9 PTC
  • 12.10 OpenText
  • 12.11 Dassault Systemes
  • 12.12 Rapid Innovation
  • 12.13 Schneider Electric
  • 12.14 Microsoft
  • 12.15 Honeywell

List of Tables

  • Table 1 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Software (2024-2032) ($MN)
  • Table 5 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Services (2024-2032) ($MN)
  • Table 6 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 7 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 8 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 9 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 10 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Technology (2024-2032) ($MN)
  • Table 11 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 12 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Natural Language Processing (2024-2032) ($MN)
  • Table 13 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 14 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Edge AI (2024-2032) ($MN)
  • Table 15 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Application (2024-2032) ($MN)
  • Table 16 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Condition Monitoring (2024-2032) ($MN)
  • Table 17 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Failure Detection & Diagnosis (2024-2032) ($MN)
  • Table 18 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Remaining Useful Life (RUL) Estimation (2024-2032) ($MN)
  • Table 19 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Energy Efficiency Optimization (2024-2032) ($MN)
  • Table 20 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Maintenance Scheduling Automation (2024-2032) ($MN)
  • Table 21 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Downtime Risk Forecasting (2024-2032) ($MN)
  • Table 22 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 23 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Discrete Manufacturing (2024-2032) ($MN)
  • Table 24 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Process Manufacturing (2024-2032) ($MN)
  • Table 25 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Transportation & Logistics (2024-2032) ($MN)
  • Table 26 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
  • Table 27 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Healthcare Facilities (2024-2032) ($MN)
  • Table 28 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 29 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Oil & Gas (2024-2032) ($MN)
  • Table 30 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Telecommunications (2024-2032) ($MN)
  • Table 31 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Mining & Heavy Equipment (2024-2032) ($MN)
  • Table 32 Global AI-Powered Predictive Maintenance Platforms Market Outlook, By Smart Agriculture (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.