Global AI-driven Predictive Maintenance Market Size was valued at USD 837.1 Mn in 2024 and is predicted to reach USD 2556.4 Mn by 2034 at a 12.0% CAGR during the forecast period for 2025-2034.
AI-based predictive maintenance solutions can simplify obtaining useful knowledge from data on device functioning and health, which can improve overall manufacturing operations for reliability and maintenance professionals. The rising need for AI-driven predictive maintenance proves that proactive maintenance techniques, which may anticipate equipment problems and optimize maintenance schedules, are gaining popularity. The need for predictive maintenance solutions powered by artificial intelligence is growing rapidly as industries strive to improve asset reliability, boost productivity, and reduce operational expenses.
Furthermore, the rise of innovative AI algorithms tailored to specific industry needs, increased public understanding of predictive maintenance's benefits, and other technological advancements are all factors propelling this expansion.
However, the market growth is hampered by the lack of awareness criteria for the safety and health of AI-driven Predictive Maintenance Market and the product's inability to prevent fog in environments with dramatic temperature fluctuations or high low code technology in insurance, because applying AI-driven predictive maintenance requires trained personnel with understanding of maintenance domains as well as data analytics and machine learning, which is currently in short supply. This shortage is a major barrier to the widespread use of predictive maintenance systems. Due to the COVID-19 pandemic, which has affected the worldwide market and forced the closure of numerous factories in an effort to protect their personnel from contracting the virus, the expansion of the industry may be hindered.
The AI-driven predictive maintenance market is segmented based on solution and industry. Based on solution, the market is segmented into integrated solution and standalone solution. By industry, the market is segmented into automotive & transportation, aerospace & defense, manufacturing, healthcare, telecommunications, and others.
The integrated solution AI-driven predictive maintenance market is expected to hold a major global market share in 2022. Through system integration, increased automation, easier processes, and team member agency are made possible. In real-time, business executives have access to data and can make decisions on validated metrics. Using this method, businesses can increase output while decreasing expenditure.
The manufacturing industry makes up the bulk of acrylic acid ester usage due to the increasing need for repairs to industrial robots, pumps, elevators, and other gear in order to decrease total downtimes. With the help of manufacturing, raw resources may be transformed into completed things on a big scale. This process helps create a profit because finished goods are more expensive than raw materials, especially in countries like the US, Germany, the UK, China, and India.
The North American AI-driven predictive maintenance market is expected to register the highest market share in revenue in the near future. It can be attributed to the increasing popularity of predictive maintenance solutions that utilize cutting-edge technologies such as the Internet of Things (IoT), data centers, neural networks, and artificial intelligence (AI). In addition, Asia Pacific is projected to grow rapidly in the global AI-driven predictive maintenance market due to the growing recognition and investment in predictive maintenance technology by organizations as a means to achieve a competitive edge.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 837.1 Mn |
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Revenue Forecast In 2034 |
USD 2556.4 Mn |
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Growth Rate CAGR |
CAGR of 12.0% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Mn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Solution, By Industry and By Region |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
DB E.C.O. Group, Radix Engineering and Software, Machinestalk, KCF Technologies, Inc., Infinite Uptime OCP Maintenance Solutions, Emprise Corporation, ONYX Insight, Gastops, and PROGNOST Systems GmbH. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI-driven Predictive Maintenance Market Snapshot
Chapter 4. Global AI-driven Predictive Maintenance Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Solution Estimates & Trend Analysis
5.1. by Solution & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Solution:
5.2.1. Integrated Solution
5.2.2. Standalone Solution
Chapter 6. Market Segmentation 2: by Industry Estimates & Trend Analysis
6.1. by Industry & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Industry:
6.2.1. Automotive & Transportation
6.2.2. Aerospace & Defense
6.2.3. Manufacturing
6.2.4. Healthcare
6.2.5. Telecommunications
6.2.6. Others
Chapter 7. AI-driven Predictive Maintenance Market Segmentation 3: Regional Estimates & Trend Analysis
7.1. North America
7.1.1. North America AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Solution, 2021-2034
7.1.2. North America AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Industry ,2021-2034
7.1.3. North America AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
7.2. Europe
7.2.1. Europe AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Solution, 2021-2034
7.2.2. Europe AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Industry ,2021-2034
7.2.3. Europe AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
7.3. Asia Pacific
7.3.1. Asia Pacific AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Solution, 2021-2034
7.3.2. Asia Pacific AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Industry2021-2034
7.3.3. Asia Pacific AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
7.4. Latin America
7.4.1. Latin America AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Solution, 2021-2034
7.4.2. Latin America AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Industry, 2021-2034
7.4.3. Latin America AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
7.5. Middle East & Africa
7.5.1. Middle East & Africa AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Solution, 2021-2034
7.5.2. Middle East & Africa AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by Industry, 2021-2034
7.5.3. Middle East & Africa AI-driven Predictive Maintenance Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 8. Competitive Landscape
8.1. Major Mergers and Acquisitions/Strategic Alliances
8.2. Company Profiles
8.2.1. DB E.C.O. Group
8.2.2. Radix Engineering and Software
8.2.3. machinestalk
8.2.4. KCF Technologies, Inc.
8.2.5. Infinite Uptime
8.2.6. OCP Maintenance Solutions
8.2.7. Emprise Corporation
8.2.8. ONYX Insight
8.2.9. Gastops
8.2.10. PROGNOST Systems GmbH
8.2.11. Other Prominent Players
AI-driven Predictive Maintenance Market- By Solution-
AI-driven Predictive Maintenance Market- By Industry-
AI-driven Predictive Maintenance Market- By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
