The global predictive maintenance market size USD 8.06 Billion in 2025. The market is expected to grow at a CAGR of 29.10% during the forecast period of 2026-2035 to reach a value of USD 103.66 Billion by 2035. The integration of Artificial Intelligence (AI), Internet of Things (IoT), and Machine Learning (ML) is transforming traditional maintenance practices into proactive strategies, leading to significant cost savings and operational efficiency.
Growing adoption of advanced technologies such as AI, IoT, and ML are helping organizations shift toward smart, data driven maintenance and away from traditional reactive maintenance approaches, leading to improved operational efficiency and reduced downtime. According to the predictive maintenance market analysis, companies implementing predictive maintenance have observed a 25-30% reduction in maintenance costs and a 70% decrease in unexpected breakdowns.
Government initiatives are also playing a crucial role in accelerating the adoption of predictive maintenance solutions. In the United States, the Department of Defense has been actively promoting advanced predictive maintenance technologies to ensure operational readiness and reduce unplanned maintenance costs, boosting the predictive maintenance market expansion. Similarly, the European Union's Industry 4.0 initiative encourages the integration of smart manufacturing practices, including predictive maintenance, to enhance industrial competitiveness and sustainability.
Base Year
Historical Period
Forecast Period
Compound Annual Growth Rate
29.1%
Value in USD Billion
2026-2035
*this image is indicative*
The use of AI and ML in predictive maintenance offerings is transforming asset management through the ability to analyze data in real time and forecast impending failures. Through this combination, higher accuracy predictions are made, reducing unplanned downtime and increasing equipment life, raising future demand in the predictive maintenance market. For example, Siemens’ MindSphere platform and GE Digital’s Predix systems are using AI to enhance predictive maintenance capabilities, which translates into higher operational efficiency as well as cost-saving benefits.
Government policies and regulations play a major role in the adoption of predictive maintenance solutions. In the United States, the Department of Defense's focus on predictive maintenance solutions has accelerated spending on AI-based solutions to guarantee equipment reliability and minimize the cost of maintenance. According to the predictive maintenance market analysis, federal agencies can save USD 100 million or more by using predictive models to make investment decisions on deferred maintenance and repair work. In Europe, Industry 4.0 encourages smart manufacturing and predictive maintenance strategies, which enhance industrial competitiveness. Similarly, China’s Made in China 2025 program encourages digitalization in factories, driving demand for IoT-enabled predictive solutions across multiple sectors.
The proliferation of IoT devices and advanced sensor technologies is providing organizations with real-time data on equipment health, facilitating proactive maintenance strategies. Industry reports suggest that the number of Internet of Things (IoT) devices globally is forecasted to double, from 19.8 billion in 2025 to more than 40.6 billion IoT devices by 2034. This development allows businesses to continuously track equipment conditions, determine any unusual conditions, and conduct maintenance preemptively before failures occur, and decrease operational disruptions and thus catalyze growth in the predictive maintenance market.
The continuing digital transformation across industries is increasing the application of Industry 4.0 practices such as adopting predictive maintenance solutions. According to the NASSCOM report on India Industry 4.0 Adoption, it is projected that digital technologies will account for 40% of total manufacturing expenditure by 2025 as compared to 20% of expenditure in 2021. The shift toward automation, sharing of data, and further smart manufacturing is motivating organizations to invest in predictive maintenance technologies, so as to develop more operational efficiencies with reduced costs. The rising focus on digital transformation which supports industry 4.0 applications like predictive maintenance increases the potential predictive maintenance market opportunities.
Predictive maintenance is also being utilized to support sustainability and energy efficiency objectives. Companies can track the performance of equipment in real-time so that they can reduce energy consumption, carbon footprint, and waste. For example, Siemens and Schneider Electric have combined predictive analytics with energy management systems to minimize energy consumption within an industrial plant. Reports suggest that predictive maintenance in manufacturing and utilities sectors, can reduce energy consumption by as much as 10% and associated carbon emissions by up to 40%, reinforcing sustainability initiatives while also reducing operational costs and supporting regulatory compliance.
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The EMR’s report titled “Global Predictive Maintenance Market Report and Forecast 2026-2035” offers a detailed analysis of the market based on the following segments:
Market Breakup by Component
Key Insight: The predictive maintenance market is dominated by a wide variety of components such as software and services. The category of software components has the highest market share due to the requirement for sophisticated analytics and AI functionalities, enabling real-time insights and improved decision-making capabilities. The services category is growing at the fastest pace due to the need for expert skills to deploy and manage predictive maintenance solutions, alongside customized consulting, training, and ongoing technical support.
Market Breakup by Deployment Mode
Key Insight: The deployment modes considered in the predictive maintenance market report include cloud and on-premises solutions, both of which serve different needs of organizations. Cloud deployment is characterized by scalability and cost savings and is the prevailing option for most businesses. On-premises deployment, however, is increasing popularity in industries with high data security and compliance requirements, making it the market's fastest-growing mode.
Market Breakup by Organization Size
Key Insight: The predictive maintenance industry experiences different patterns of adoption by organization size. SMEs are witnessing fast growth fueled by cost-effective, cloud-enabled solutions that ensure optimal operational performance and lower costs. Large enterprises occupy a significant share of the market, aided by their AI, IoT, and machine learning investments to facilitate predictive analytics in multiple facilities. Both categories enjoy government incentives to adopt technology and optimize operations.
Market Breakup by Application
Key Insight: Manufacturing dominates predictive maintenance market revenue through Industry 4.0 and smart factory adoption, while energy and utilities increase most rapidly, driven by aged infrastructure and critical service reliability needs. Transportation, healthcare, and logistics industries also benefit from predictive monitoring, minimizing unplanned downtime and optimizing resource utilization. Government initiatives and industrial modernization projects across geographies boost adoption, providing incentives and technical assistance.
Market Breakup by Region
Key Insight: Regional trends in the predictive maintenance market vary significantly. North America holds the largest share due to advanced industrial infrastructure, widespread technology integration, and government support for smart manufacturing. Asia Pacific emerges as the fastest-growing region, propelled by industrial expansion, digital transformation initiatives, and supportive government programs. Europe, Latin America, and the Middle East and Africa are also witnessing increased adoption due to smart factory programs, energy modernization, and infrastructure optimization efforts.
Software components dominate the market with increased demand for advanced analytics and AI-driven solutions
Software components are at the forefront of the global market as organizations rapidly adopt higher levels of analytics and AI-driven solutions which support enhanced predictive capabilities. The use of AI and ML in software platforms enables corporations to sift through amounts of data, recognize unusual patterns, and predict the probability of impending equipment failure with increasing precision. Preventing unplanned downtime is one of the main driving factors of predictive maintenance solutions' adoption across a number of industries who have assets.
Services are growing faster in the predictive maintenance market because there are growing requirements for consulting services, deployment, and ongoing maintenance services that are typically a combination of services. As organizations adopt predictive maintenance solutions, they increasingly seek specialized expertise and structured processes to maximize system effectiveness. In response, a growing number of service providers now offer end-to-end solutions, including training, system implementation, and ongoing program monitoring.
Cloud deployment mode holds the leading market position owing to its scalability, flexibility, and cost-effectiveness
Cloud-based deployment continues to dominate the overall predictive maintenance market value, particularly due to their scalability, flexibility, and cost-effectiveness advantages. When utilizing a cloud platform, organizations have remote storage and methods for analyzing large amounts of data, enabling real-time monitoring and predictive analytics. Cloud-based deployment is also relevant for organizations seeking to limit or eliminate on-premises infrastructure costs or lessen barriers to accessing data involved in predictive maintenance systems.
The predictive maintenance market report indicates notable growth in on-premises deployment, especially in sectors with strict security and compliance requirements such as healthcare and finance. These organizations prefer on-premises models to maintain full control over sensitive data and meet regulatory obligations. Growing concerns around data privacy and cybersecurity are further accelerating the adoption of on-premises deployment in the predictive maintenance market.
By organization size, large enterprises register largest share in the market due to advanced technology integration
Large enterprises substantially drive the predictive maintenance market value, leveraging their capacity to invest in AI, IoT, and machine learning technologies. These organizations implement enterprise-wide predictive maintenance strategies across multiple facilities, integrating real-time analytics to forecast equipment failures accurately. The adoption of these advanced solutions reduces downtime, improves operational efficiency, and lowers maintenance costs to a significant extent. In addition, government initiatives, particularly in sectors like defense and manufacturing, encourage large organizations to implement advanced predictive maintenance frameworks, further consolidating their market dominance.
Small and medium-sized enterprises (SMEs) are increasingly adopting predictive maintenance solutions to improve operational efficiency and reduce unplanned downtime. Cloud-based and scalable platforms allow SMEs to monitor equipment health, predict failures, and schedule maintenance without large upfront investments, accelerating further predictive maintenance demand growth. The affordability of these solutions makes it easier for smaller businesses to compete with larger enterprises, optimize asset utilization, and extend machinery lifespan.
By application, manufacturing accounts for the largest share in the market due to Industry 4.0 technology integration
The manufacturing sector is the dominant application for predictive maintenance, fueled by Industry 4.0 adoption. Companies are embedding IoT sensors, AI algorithms, and predictive analytics into production lines to monitor equipment performance in real-time. This approach allows early detection of potential failures, reducing unplanned downtime and maintenance expenses. Governments are supporting smart manufacturing initiatives through funding and technical assistance, encouraging manufacturers to adopt predictive maintenance solutions.
Energy and utilities companies are rapidly increasing their predictive maintenance adoption to manage aging infrastructure and critical operations efficiently. Predictive analytics enables monitoring of turbines, transformers, and pipelines to forecast failures before they occur, allowing maintenance during non-peak hours. Governments are actively supporting smart grid and infrastructure modernization programs, providing incentives for predictive maintenance deployment.
North America accounts for largest share due to advanced industrial infrastructure
North America dominates the predictive maintenance demand forecast due to widespread adoption of AI, IoT, and machine learning technologies. The region benefits from established industrial infrastructure across manufacturing, aerospace, and automotive sectors. Large-scale deployment of predictive maintenance solutions is further supported by government programs promoting smart factories, digitalization, and operational efficiency. The presence of leading solution providers and strong R&D capabilities ensures continuous innovation, while companies focus on minimizing downtime and reducing maintenance costs through enterprise-level predictive maintenance strategies, maintaining North America’s market leadership.
The predictive maintenance industry in the Asia Pacific region is projected to witness the fastest growth in terms of adoption, driven by rapid industrialization and government-led initiatives such as “Make in India” and “Made in China 2025.” Expansion of manufacturing, energy, and logistics facilities creates a strong demand for predictive maintenance solutions. Companies are deploying IoT-enabled monitoring and analytics to improve operational efficiency, optimize asset utilization, and reduce unplanned downtime.
The industry is undergoing rapid evolution, with key predictive maintenance market players working towards AI-driven solutions, IoT solution integration, and cloud-based platforms that improve operational efficiency and minimize downtime. Companies are also investing in advanced analytics, machine learning algorithms, and real-time monitoring systems to predict equipment failures before they occur. In addition, notable strategic partnerships such as Microsoft's relationship with Symphony Industrial AI are also accelerating the adoption of predictive maintenance across a range of different industries.
Furthermore, government initiatives promoting digital transformation and Industry 4.0 are expected to result in further market growth. With ongoing digital transformation, many industries are adopting predictive maintenance to optimize asset performance and reduce operational disruptions. This shift is intensifying competition among leading predictive maintenance companies, pushing them to innovate and deliver increasingly advanced solutions.
Founded in 1975 in New Mexico, Microsoft provides predictive maintenance solutions using its Azure platform. It incorporates AI, IoT, and machine learning to help businesses monitor equipment health, predict failures of equipment, and optimize maintenance schedules and efficiency.
Hitachi Limited was established in 1910 and is headquartered in Japan. Hitachi has predictive maintenance services that incorporate machine learning and remote monitoring of equipment. Hitachi is ultimately able to interprets equipment data and detect anomalies, potentially preventing failures and downtime in industries such as manufacturing and energy.
Founded in 1836, Schneider Electric is located in Rueil-Malmaison, France. Schneider Electric is able to provide predictive maintenance through their EcoStruxure platform. Schneider Electric utilizes real-time data analytics and integrating artificial intelligence to provide their customers with optimized asset performance, as well as increasing energy efficiency and minimizing down-time across different sectors.
Founded in 1892, GE is headquartered in New York, United States. GE provides predictive maintenance including through GE Vernova, utilizes digital twin technology and analytics to enable industry to anticipate equipment even before a failure occurs to improve maintenance strategies and operational reliability.
*Please note that this is only a partial list; the complete list of key players is available in the full report. Additionally, the list of key players can be customized to better suit your needs.*
Other key players in the market are General Electric Company, SAP SE, and International Business Machines Corporation, among others.
Explore the latest trends shaping the predictive maintenance market 2026-2035 with our in-depth report. Gain strategic insights, future forecasts, and key market developments that can help you stay competitive. Download a free sample report or contact our team for customized consultation on predictive maintenance market trends 2026.
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*While we strive to always give you current and accurate information, the numbers depicted on the website are indicative and may differ from the actual numbers in the main report. At Expert Market Research, we aim to bring you the latest insights and trends in the market. Using our analyses and forecasts, stakeholders can understand the market dynamics, navigate challenges, and capitalize on opportunities to make data-driven strategic decisions.*
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In 2025, the predictive maintenance market reached an approximate value of USD 8.06 Billion.
The market is projected to grow at a CAGR of 29.10% between 2026 and 2035.
The market is estimated to witness a healthy growth in the forecast period of 2026-2035 to reach about USD 103.66 Billion by 2035.
Stakeholders are investing in AI-driven predictive maintenance solutions, collaborating with technology partners, and aligning with government initiatives to enhance operational efficiency and reduce downtime.
The key trends guiding the market growth includes the increasing research and development activities and rising industrialisation.
The major regions in the market are North America, Latin America, the Middle East and Africa, Europe, and the Asia Pacific.
The major components of predictive maintenance in the market are solutions and services.
The significant deployment modes in the market are cloud and on-premises.
The leading organization sites of predictive maintenance in the market are small and medium-sized enterprises and large enterprises.
The major applications of predictive maintenance are government and defence, manufacturing, energy and utilities, transportation and logistics, and healthcare and life sciences, among others.
The key players in the market include Microsoft Corporation, Hitachi, Ltd., Schneider Electric SE, General Electric Company, SAP SE, and International Business Machines Corporation, among others.
Companies face challenges such as high implementation costs, data integration complexities, and the need for skilled personnel to manage advanced predictive maintenance systems.
Explore our key highlights of the report and gain a concise overview of key findings, trends, and actionable insights that will empower your strategic decisions.
| REPORT FEATURES | DETAILS |
| Base Year | 2025 |
| Historical Period | 2019-2025 |
| Forecast Period | 2026-2035 |
| Scope of the Report |
Historical and Forecast Trends, Industry Drivers and Constraints, Historical and Forecast Market Analysis by Segment:
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| Breakup by Component |
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| Breakup by Deployment Mode |
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| Breakup by Organization Size |
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| Breakup by Application |
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| Breakup by Region |
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| Market Dynamics |
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| Competitive Landscape |
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| Companies Covered |
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| Report Price and Purchase Option | Explore our purchase options that are best suited to your resources and industry needs. |
| Delivery Format | Delivered as an attached PDF and Excel through email, with an option of receiving an editable PPT, according to the purchase option. |
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