세계의 AI 활용 예지보전 시스템 시장은 예측 기간 중 2025-2032년 CAGR이 10.14%에 달하며, 2024년 8억 6,613만 달러에서 2032년에는 18억 7,561만 달러로 성장할 것으로 예측됩니다. AI 활용 예지보전 시스템 세계 시장은 다운타임을 완화하고 보다 스마트한 계획적 보전을 가능하게 하는 실시간 모니터링에 대한 수요로 인해 크게 성장하고 있습니다. 예지보전 시스템은 인공지능을 활용하여 잠재적인 고장을 조기에 발견하고, 효율성을 높이고 비용을 절감하며, 산업계를 능동적인 자산관리로 전환시킵니다.
산업계가 장비의 고장을 미연에 방지하고 상품을 저렴하게 생산하기 위해 노력하는 가운데, 인공지능을 활용한 예지보전시스템 세계 시장은 흥미로운 성장세를 보이고 있습니다. 예지보전 시스템은 지정된 기간 내(일반적으로 이력) 신규 설비와 기존 설비 모두에서 데이터를 분석할 수 있는 혁신적인 기술을 활용하고 있습니다. 이러한 시스템은 문제나 고장을 미리 예측하고 고장이 발생하기 직전에 유지보수를 완료할 수 있도록 함으로써 비용이 많이 드는 고장을 방지하고 효율적인 유지보수를 가능하게 하는 것을 목표로 합니다. 제조업, 물류, 제약, 부동산, 병원 등 다양한 산업에서 예지보전 시스템을 도입하여 무음 운영을 촉진하고 조직내 안전성을 향상시키고 있습니다. 또한 지속적인 디지털 전환의 추진은 운영의 지속가능성과 신뢰성을 보장하기 위해 일련의 정교한 장비에 의존하고 있다는 신호가 점점 더 커지고 있습니다. 예지보전 시스템의 발전은 미래의 기술 주도형 운영 준비 태세를 선도하는 중요한 프로세스와 기능으로 계속 형성되고 있습니다.
예를 들어 2024년 9월 지멘스 AG는 Merck KGaA와 제휴하여 스마트 제조를 더욱 발전시켰습니다. 이는 AI를 활용한 신기술을 통해 실현되었습니다. 그들의 업무의 주요 목적은 예지보전을 촉진하고 산업 환경에서 운영의 디지털 최적화를 구현하는 것입니다.
모든 부문은 대상이 되는 모든 지역과 국가에 대해 제공됩니다.
상기 기업은 시장 점유율에 따른 순위를 보유하지 않으며, 조사 작업 중 입수 가능한 정보에 따라 변경될 수 있습니다.
Global AI-powered predictive maintenance systems market is projected to witness a CAGR of 10.14% during the forecast period 2025-2032, growing from USD 866.13 million in 2024 to USD 1875.61 million in 2032. The global AI-powered predictive maintenance systems market is growing significantly due to the demand for real-time monitoring that mitigates downtime and enables smarter, planned maintenance. Predictive Maintenance Systems utilize artificial intelligence to early-detect potential failures, enhance efficiency, reduce costs, and transition industries to proactive asset management.
As industries strive to prevent equipment failure before it causes a rupture, allowing them to produce goods affordably, the global market for predictive maintenance systems powered by artificial intelligence is experiencing tantalizing growth. Predictive maintenance systems utilize innovative technologies that can analyze data from both new and existing equipment within a specified period (typically historical). These systems aim to predict concerns and faults in advance, allowing for maintenance to be completed just before they occur, thereby avoiding costly breakdowns and enabling efficient maintenance. Industries, across the spectrum such as manufacturing, logistics, pharmaceuticals, real estate and hospitals have embraced predictive maintenance systems to facilitate talk-free operations and improve safety within their organizations. Furthermore, the push for continued digital transformation adds fuel to the growing smoke signal that operational sustainability and reliability assurance rely on a set of sophisticated equipment. The advancement of predictive maintenance systems continues to mold itself into a crucial process and function that will spearhead the readiness of future technology-driven operations.
For instance, in September 2024, Siemens AG partnered with Merck KGaA to further develop smart manufacturing. This was achieved through their new AI-powered technologies. The primary objectives of their work are to drive predictive maintenance and implement digital optimization of operations in industrial environments.
Growing Demand for Smart Asset Monitoring Propels Market Growth
Many industries are embracing predictive maintenance tools because they need to improve their real-time monitoring of equipment. These AI systems provide predictive failure alerts early, analyzing sensor data and machine behavior to give companies visibility into potential problems before they become too disruptive. Organizations can prevent sudden breakdowns or other disruptions that impact factory productivity. They can also better budget for service and maintenance activities. This implies lower costs, with less disturbance to production, and a greater life expectancy of their equipment. Accurate projections of asset health will continue to have growing demand and greater adoption, especially for organizations with complex processes. As adoption increases, real-time asset monitoring is becoming a primary enabler of predictive maintenance technology adoption.
For example, in June 2024, IBM Corporation announced version 9.0 of its Maximo Apply Suite, featuring newly enhanced AI-driven predictive maintenance, improved real-time IoT integrations, and a redesigned interface.
Assistance in digital transformation in Industry Proliferates the Global Market Growth
As industries evolve, predictive maintenance systems will be crucial in navigating this transformation. Predictive maintenance systems are ultimately not only about preventing equipment failure; they also assist in optimizing operational efficiency by reducing energy consumption and improving production techniques to promote sustainability. Artificial intelligence and machine learning enable companies to optimize operational efficiencies, as organizations can identify common patterns in how their equipment operates. With all industries needing to become more efficient to achieve stakeholder and environmental expectations, the promotion of smart factories and automated operations has positioned predictive maintenance as an enabler within industrial programs. As organizations become increasingly reliant on technology and enhancements in computerized functions, the demand for predictive maintenance is expected to continue growing within the market.
For example, in June 2024, C3.ai, Inc. delivered its AI Reliability solution to Holcim, a global supplier of building materials, furthering the company's digital transformation and sustainability initiatives, which assisted in digital transformation within the industry.
Service Segment Dominates Global AI-Powered Predictive Maintenance Systems Market Share
The services segment enjoys (or plays) a significant position in this market due to the increasing reliance of companies on advanced predictive maintenance by third-party suppliers. These services vary to include system integration, cloud analytics with training the AI model and ongoing support. Most firms prefer outsourcing versus building a solution in-house because it reduces their cost and complexity. Providers develop expertise in the installation and maintenance of AI-based solutions, enabling clients to hit the ground running. As companies transition from exploration to implementation, the services sector is expected to remain dominant due to its flexibility, reliability, and rapid deployment.
For example, in June 2025, Siemens AG deployed its Senseye Predictive Maintenance solution at Sachsenmilch Leppersdorf GmbH to monitor real-time equipment responses and integrate them with the SAP maintenance system.
North America Leads in Global AI-Powered Predictive Maintenance Systems Market
The North America region is a major player in the predictive maintenance market, primarily due to the region's industrial strength and early adoption of more advanced technologies. In the United States and Canada, businesses remain committed to investing in maintenance plans to enable the use of AI tools and make more informed, intelligent decisions more quickly. Automation, using data to drive decision-making, and digital upgrades are an emerging trend for industries across the region, as they push toward predictive solutions. Organizations can also take advantage of government programs supporting smart manufacturing and digital infrastructure. The region's IT ecosystem, which develops a wide variety of industrial IoT devices, provides a clear playbook (advantage) for market growth.
For example, in March 2024, General Electric Vernova provided its predictive analytics software to TASNEE to support early failure detection and to avoid downtime in industrial settings.
Impact of U.S. Tariffs on Global AI-Powered Predictive Maintenance Systems Market
U.S. tariffs on imported electronics and industrial components may slightly slow the market growth pace by increasing costs for hardware-based solutions. Predictive maintenance solutions rely heavily on sensors, processors and network devices, several of which can come from other parts of the world. This means adding costs that could slow down deployment, especially in small- and medium-sized enterprises. However, there is substantial demand domestically, there are local manufacturers, and there is a rising investment in AI software, which may help mitigate this over time. While recent trade actions have created disruptions in the market, it is ultimately expected to remain strong despite these trade policies and rising costs.
Key Players Landscape and Outlook
The market landscape continues to evolve, driven by rapid innovation and intense competition among technology companies developing proprietary AI-enhanced platforms for maintenance. In some cases, these platforms offer capabilities that encompass fault prediction, diagnostics, or even workflow automation, enabling customer organizations to move towards proactive rather than reactive strategies. The longer-term picture is mainly positive, as many leading vendors are concentrating on leveraging AI to increase the platform's functions, combine cloud functionality and use data across disparate systems. At the same time, the sector is transitioning from a standalone tooling phase to a multi-faceted maintenance ecosystem that enables data-driven and real-time decision-making, a capability that was previously lacking. This provides a strong foundation for further development, as industries are looking to scale their operations, improve uptime, and reduce costs, even as they transition to intelligent maintenance solutions.
For instance, in March 2025, Siemens AG expanded its Industrial Copilot portfolio with a generative AI maintenance tool that integrates with predictive platforms to support the entire maintenance cycle.
All segments will be provided for all regions and countries covered
Companies mentioned above DO NOT hold any order as per market share and can be changed as per information available during research work.