How much is the artificial intelligence (ai)-driven predictive maintenance market worth, and how is it expected to expand?
The artificial intelligence (AI)-driven predictive maintenance market size has grown rapidly in recent years. It will grow from $0.88 $ billion in 2024 to $1.02 $ billion in 2025 at a compound annual growth rate (CAGR) of 15.7%. The growth in the historic period can be attributed to the growing need for large enterprises, growing concerns over asset maintenance, growing technological awareness, and growing preferences among SMEs.
The artificial intelligence (AI)-driven predictive maintenance market size is expected to see rapid growth in the next few years. It will grow to $1.8 $ billion in 2029 at a compound annual growth rate (CAGR) of 15.4%. The growth in the forecast period can be attributed to growing preferences for predictive maintenance solutions, increasing efficiency of customer-oriented processes, aging infrastructure, and increasing complexities in various industries. Major trends in the forecast period include enhanced human-AI collaboration, integration with 5G networks, AI-enhanced predictive maintenance in supply chains, and integration with circular economy practices.
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Which industry factors have accelerated the artificial intelligence (ai)-driven predictive maintenance market’s expansion?
The growing adoption of cloud-based solutions is expected to propel the growth of the artificial intelligence (AI)-driven predictive maintenance market going forward. Cloud-based solutions refer to affordable software or services hosted on the cloud that provide businesses with efficient, scalable, and accessible tools without significant upfront infrastructure investments. The adoption of cloud-based solutions is driven by their ability to reduce upfront costs through a subscription model and provide remote accessibility, enabling businesses to scale and operate efficiently from anywhere. Cloud-based solutions are beneficial for AI-driven predictive maintenance by offering scalable computing power and storage to process vast amounts of sensor data in real time, enabling accurate predictions of equipment failures. For instance, in December 2023, according to Eurostat, a Luxembourg-based official website of the European Union, cloud-based solutions experienced a 4.2% increase in adoption throughout 2023, with 45.2% of businesses using cloud computing services, a significant rise from 2021. Therefore, there is a growing demand for cost-effective cloud-based solutions, driving the growth of the artificial intelligence (AI)-driven predictive maintenance market.
What are the primary 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 firms are leading 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
Which market trends are set to define the future of the artificial intelligence (ai)-driven predictive maintenance market?
Major companies operating in the artificial intelligence (AI)-driven predictive maintenance market are focusing on developing technologically advanced solutions, such as cost-effective AI-driven predictive maintenance solutions, to enhance operational efficiency and minimize maintenance costs. Cost-effective AI-driven predictive maintenance solutions refer to advanced systems that use artificial intelligence to forecast equipment failures and optimize maintenance schedules while remaining affordable and efficient, thus reducing overall operational costs. For instance, in July 2024, Guidewheel, a US-based software company, launched Scout, an AI-powered FactoryOps platform designed to enhance manufacturing operations by integrating artificial intelligence technologies. This innovative tool operates on any machine connected to the Guidewheel platform, is cost-effective, and requires no additional hardware. Scout seamlessly integrates with existing systems, utilizing advanced AI models to monitor machine performance data for early detection of anomalies. Its continuous learning capability allows it to log events and improve its predictive accuracy over time.
Which geographic trends are shaping the artificial intelligence (ai)-driven predictive maintenance market, and which region has the highest market share?
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 Does The Artificial Intelligence (AI)-Driven Predictive Maintenance Market Report 2025 Offer?
The artificial intelligence (ai)-driven predictive maintenance market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
Artificial intelligence (AI)-driven predictive maintenance refers to the use of artificial intelligence technologies to anticipate when equipment or machinery is likely to fail or require maintenance. This approach leverages various AI techniques, such as machine learning, data analysis, and pattern recognition, to analyze data from sensors, historical records, and other sources. The goal of AI-driven predictive maintenance is to predict potential failures before they occur, allowing for timely maintenance that can prevent unplanned downtime and extend the lifespan of equipment.
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