Artificial Intelligence (AI)-Driven Predictive Maintenance Market Report 2024

This Artificial Intelligence (AI)-Driven Predictive Maintenance market report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.

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%. The growth during the historic period can be attributed to the increasing need for large enterprises, heightened concerns about asset maintenance, rising 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 during the forecast period can be attributed to rising preferences for predictive maintenance solutions, improved efficiency of customer-oriented processes, aging infrastructure, and increasing complexities across various industries. Key trends expected in this period include enhanced human-AI collaboration, integration with 5G networks, AI-enhanced predictive maintenance within supply chains, and alignment with circular economy practices.

The increasing adoption of cloud-based solutions is expected to drive the growth of the AI-driven predictive maintenance market in the future. Cloud-based solutions are cost-effective software or services hosted on the cloud that provide businesses with efficient, scalable, and accessible tools without significant upfront infrastructure investments. Their appeal is due to the reduced upfront costs through a subscription model and the ability to operate remotely, allowing businesses to scale and function efficiently from any location. These solutions are particularly beneficial for AI-driven predictive maintenance as they offer scalable computing power and storage for processing large volumes of sensor data in real time, facilitating accurate predictions of equipment failures. For example, in December 2023, Eurostat, the Luxembourg-based official website of the European Union, reported a 4.2% increase in cloud-based solution adoption throughout 2023, with 45.2% of businesses utilizing cloud computing services, a notable rise from 2021. Consequently, the growing demand for cost-effective cloud-based solutions is fueling the expansion of the AI-driven predictive maintenance market.

Major companies in the AI-driven predictive maintenance market are concentrating on developing technologically advanced solutions, such as cost-effective AI-driven predictive maintenance tools, to boost operational efficiency and reduce maintenance costs. These cost-effective solutions leverage artificial intelligence to predict equipment failures and optimize maintenance schedules while being affordable and efficient, thus lowering overall operational expenses. For example, in July 2024, Guidewheel, a US-based software company, introduced Scout, an AI-powered FactoryOps platform designed to enhance manufacturing operations through the integration of artificial intelligence technologies. This innovative tool works with any machine connected to the Guidewheel platform, is cost-effective, and does not require additional hardware. Scout integrates seamlessly with existing systems, using advanced AI models to monitor machine performance data for early anomaly detection. Its continuous learning capability enables it to log events and improve predictive accuracy over time.

In March 2023, AB SKF, a Sweden-based manufacturer of bearings and seals, acquired Presenso Ltd. for an undisclosed amount. This acquisition is intended to boost AB SKF's predictive maintenance capabilities through advanced AI technology, thereby enhancing operational efficiency and reducing equipment downtime for its clients. Presenso Ltd. is an Israel-based company specializing in AI-driven predictive maintenance software.

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.

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.

The countries covered in the artificial intelligence (AI)-driven predictive maintenance market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

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.

The main types of solutions in AI-driven predictive maintenance are integrated solutions and standalone solutions. An integrated solution refers to a comprehensive and cohesive system that combines multiple components, technologies, or services to work together seamlessly, addressing a specific need or problem. It can be deployed on both the cloud and on-premise and serves multiple industries, including automotive and transportation, aerospace and defense, manufacturing, healthcare, telecommunications, and others.

The AI-driven predictive maintenance market research report is one of a series of new reports that provides AI-driven predictive maintenance market statistics, including AI-driven predictive maintenance industry global market size, regional shares, competitors with an AI-driven predictive maintenance market share, detailed AI-driven predictive maintenance market segments, market trends and opportunities, and any further data you may need to thrive in the AI-driven predictive maintenance industry. This AI-driven predictive maintenance market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.

The artificial intelligence (AI)-driven predictive maintenance market includes revenues earned by entities by providing services such as system implementation and integration, digital twin development, customized predictive maintenance strategies, maintenance optimization, and consulting and advisory services. the market value includes the value of related goods sold by the service provider or included within the service offering. The artificial intelligence (AI)-driven predictive maintenance market also consists of sales of products including predictive analytics platforms, condition monitoring systems, asset management software, digital twins, maintenance scheduling tools, failure detection algorithms, and energy management systems. Values in this market are ‘factory gate’ values, that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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