The artificial intelligence (ai)-driven predictive maintenance global market report 2024 from The Business Research Company provides comprehensive market statistics, including global market size, regional shares, competitor market share, detailed segments, trends, and opportunities. This report offers an in-depth analysis of current and future industry scenarios, delivering a complete perspective for thriving in the industrial automation software market.
Artificial Intelligence (AI)-Driven Predictive Maintenance Market, 2024 report by The Business Research Company offers comprehensive insights into the current state of the market and highlights future growth opportunities.
Market Size –
The artificial intelligence (AI)-driven predictive maintenance market size has grown rapidly in recent years. It will grow from $0.76 billion in 2023 to $0.88 billion in 2024 at a compound annual growth rate (CAGR) of 15.0%. 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 $1.56 billion in 2028 at a compound annual growth rate (CAGR) of 15.5%. 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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Scope Of Artificial Intelligence (AI)-Driven Predictive Maintenance Market
The Business Research Company’s reports encompass a wide range of information, including:
1. Market Size (Historic and Forecast): Analysis of the market’s historical performance and projections for future growth.
2. Drivers: Examination of the key factors propelling market growth.
3. Trends: Identification of emerging trends and patterns shaping the market landscape.
4. Key Segments: Breakdown of the market into its primary segments and their respective performance.
5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.
6. Macro Economic Factors: Assessment of broader economic elements impacting the market.
Artificial Intelligence (AI)-Driven Predictive Maintenance Market Overview
Market Drivers –
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.
Market Trends –
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.
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
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Regional Insights –
North America was the largest region in the artificial intelligence (AI)-driven predictive maintenance market in 2023. 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.
Key Companies –
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
Table of Contents
1. Executive Summary
2. Artificial Intelligence (AI)-Driven Predictive Maintenance Market Report Structure
3. Artificial Intelligence (AI)-Driven Predictive Maintenance Market Trends And Strategies
4. Artificial Intelligence (AI)-Driven Predictive Maintenance Market – Macro Economic Scenario
5. Artificial Intelligence (AI)-Driven Predictive Maintenance Market Size And Growth
…..
27. Artificial Intelligence (AI)-Driven Predictive Maintenance Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix
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