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In-Depth Analysis of the Artificial Intelligence (AI)-Driven Predictive Maintenance Market: Growth Opportunities, Key Trends, and Forecast 2025-2034
Artificial Intelligence (AI)-Driven Predictive Maintenance Market Size
The size of the predictive maintenance market, powered by artificial intelligence (AI), has seen significant growth in the past few years. This market is projected to expand from $0.88 billion in 2024 to $1.02 billion in 2025 with a compound annual growth rate (CAGR) of 15.7%. The growth during the historical period is credited to the increasing demand from large corporations, rising worries regarding asset maintenance, enhanced technological consciousness, and a growing preference among small and medium enterprises (SMEs).
Propelled by artificial intelligence (AI), the predictive maintenance market is anticipated to register significant growth in the upcoming years, reaching a worth of $1.8 billion by 2029 with a compound annual growth rate (CAGR) of 15.4%. This surge in the forecasted period can be ascribed to factors such as the increasing preference for predictive maintenance solutions, higher efficiency in customer-focused processes, the ageing infrastructure, and amplified complexities across various sectors. Noteworthy trends projected for this span encompass improved collaboration between humans and AI, adoption of 5G network integration, AI-enhanced predictive maintenance within supply chains, and incorporation of circular economy methods.
What Is Driving the Growth Trajectory of the Artificial Intelligence (AI)-Driven Predictive Maintenance Market?
The rise in the use of cloud-based solutions is projected to fuel the expansion of the market for artificial intelligence (AI)-based predictive maintenance. As affordable software or services hosted in the cloud, these solutions provide businesses with effective, scalable, and accessible tools without requiring substantial initial infrastructure investment. These solutions, popular for their ability to cut down on upfront costs due to their subscription model and provide remote access, enable businesses to scale and function effectively from any location. In terms of AI-based predictive maintenance, cloud-based solutions are advantageous due to their ability to provide scalable computing power and storage to process large quantities of sensor data in real time, thereby facilitating accurate predictions of equipment failures. For example, in December 2023, Eurostat, an official website of the European Union based in Luxembourg, reported a 4.2% increase in the use of cloud-based solutions throughout 2023, with 45.2% of businesses utilizing cloud computing services, marking a noteworthy increase from 2021. Consequently, the demand for cost-effective cloud-based solutions is increasing, contributing to the growth of the AI-based predictive maintenance market.
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Which Leading Companies Are Shaping the Growth of the Artificial Intelligence (AI)-Driven Predictive Maintenance Market?
Major companies operating in the artificial intelligence (AI)-driven predictive maintenance market are Microsoft Corporation, Hitachi Ltd., General Electric Company, International Business Machines Corporation, Schneider Electric SE, Honeywell International Inc., ABB Ltd., Emerson Electric Co., HCL Technologies, Rockwell Automation Inc., Flowserve Corporation, SAS Institute Inc., Fluke Corporation, Cloudera Inc., TIBCO Software Inc., RoviSys Company, Aspen Technology Inc., C3.ai Inc., SparkCognition Inc., Uptake Technologies Inc., Gastops Ltd., Senseye Ltd., MachineMetrics Inc., Presenso, MachineStalk Inc., LNS Research Inc., Pivotal Software Inc., Guidewheel
What Are the Major Trends Shaping the Artificial Intelligence (AI)-Driven Predictive Maintenance Market?
Leading firms in the AI-based predictive maintenance market are concentrating their efforts on creating highly advanced solutions, including cost-efficient AI-based predictive maintenance systems that can improve operations and cut down on maintenance expenses. These cost-effective solutions utilize artificial intelligence to predict equipment malfunctions and fine-tune maintenance schedules, providing an affordable and efficient way to reduce total operational costs. Guidewheel, a software corporation based in the US, for instance, launched an AI-enabled FactoryOps platform called Scout in July 2024. The platform, which functions on any machine connected to the Guidewheel platform, offers affordability and doesn't necessitate additional hardware. By seamlessly merging with existing infrastructure and using sophisticated AI algorithms to keep track of machine performance data for early problem identification, Scout provides a continual learning function, recording events to enhance its future predictive accuracy.
What Are the Key Segments of the Artificial Intelligence (AI)-Driven Predictive Maintenance Market?
The artificial intelligence (AI)-driven predictive maintenance market covered in this report is segmented -
1) By Solution: Integrated Solution, Standalone Solution
2) By Deployment: Cloud, On-Premise
3) By Industry: Automotive And Transportation, Aerospace And Defense, Manufacturing, Healthcare, Telecommunications, Other Industries
Subsegments:
1) By Integrated Solution: AI-Powered Asset Management Systems, Enterprise Resource Planning (ERP) Integration, IoT-Enabled Predictive Maintenance Platforms, Condition Monitoring Systems
2) By Standalone Solution: Predictive Analytics Software, Machine Learning Models For Maintenance, Diagnostic Tools And Sensors, Reporting And Visualization Tools
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Which Region Dominates the Artificial Intelligence (AI)-Driven Predictive Maintenance Market?
North America was the largest region in the artificial intelligence (AI)-driven predictive maintenance market in 2024. The regions covered in the artificial intelligence (AI)-driven predictive maintenance market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Is Covered In The Artificial Intelligence (AI)-Driven Predictive Maintenance Global Market Report?
- Market Size Analysis: Analyze the Artificial Intelligence (AI)-Driven Predictive Maintenance Market size by key regions, countries, product types, and applications.
- Market Segmentation Analysis: Identify various subsegments within the Artificial Intelligence (AI)-Driven Predictive Maintenance Market for effective categorization.
- Key Player Focus: Focus on key players to define their market value, share, and competitive landscape.
- Growth Trends Analysis: Examine individual growth trends and prospects in the Market.
- Market Contribution: Evaluate contributions of different segments to the overall Artificial Intelligence (AI)-Driven Predictive Maintenance Market growth.
- Growth Drivers: Detail key factors influencing market growth, including opportunities and drivers.
- Industry Challenges: Analyze challenges and risks affecting the Artificial Intelligence (AI)-Driven Predictive Maintenance Market.
- Competitive Developments: Analyze competitive developments, such as expansions, agreements, and new product launches in the market.
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