# Industrial AI in Action: The Role of AI in Preventive Maintenance > Source: > Published: 2026-06-25 10:06:55+00:00 With the emergence of AI and AI-enabled tools and technologies, [industries such as manufacturing and production are being transformed through automation and ML. ](https://techstrong.ai/videos/ai-leadership-insights-the-impact-of-ai-in-manufacturing/) Whether you’re maintaining your industrial machines, keeping your cloud infrastructure running smoothly or maintaining distributed service environments, you should be able to prevent failure before it occurs and take preventative action as soon as possible. The usage of AI helps both preventive and predictive maintenance by enabling more accurate detection of equipment failures and better maintenance scheduling. This article discusses how AI can help in preventive and predictive maintenance and the challenges involved. ### What is Preventive Maintenance? Preventive maintenance is a proactive approach that emphasizes performing routine, pre-planned activities relating to inspections, maintenance and repairs of equipment, to help prevent unanticipated failures and increase the life of the equipment and maximize its performance. Preventive maintenance is also be referred to as proactive maintenance, scheduled maintenance or planned maintenance. Preventive maintenance helps businesses minimize costs by preventing breakdowns and repairing equipment that needs to be repaired. Preventive maintenance typically includes cleaning and lubricating equipment, repairing essential pieces of equipment or even replacing the equipment with an entirely new one. The following are the series of steps you should follow to create a preventive maintenance plan for your organization: - Identify the critical assets - Determine whether they need maintenance - Define the intervals at which maintenance should be performed - Educate your maintenance personnel - Monitor the performance - Review and update regularly ### Types of Preventive Maintenance There are five distinct ways of maintaining equipment. - Usage-Based Maintenance: As the name suggests, this type of maintenance is based on the usage of the equipment. The frequency at which the maintenance tasks should be performed is based on usage metrics of the equipment. - Time-Based Maintenance: In this strategy, maintenance is performed during a specific time frame, such as once a week or several times in a month. Essentially, this approach is typically used to ensure that the equipment is always operating at maximum production output with reduced downtime. - Condition-Based Maintenance: A condition-based maintenance approach uses monitoring tools to gather data from equipment and determine when preventive maintenance should be performed based on that equipment’s performance metrics. To determine whether preventative maintenance should be performed, the monitoring tools collect data using algorithms, ML and AI to identify trends and anomalies. - Predictive Maintenance: Predictive maintenance delivers measurable improvements in equipment reliability, asset life and system-wide efficiency in a complex industrial environment, where the complexity of machines makes maintenance management challenging. - Prescriptive Maintenance: Prescriptive maintenance goes one step further than predictive maintenance. While predictive maintenance indicates potential failure of a piece of equipment in the future, it does not provide corrective actions to minimize or prevent that failure. Prescriptive maintenance provides specific corrective actions to help prevent future failures. ### Why Should We Use AI in Preventive Maintenance? AI usage has transformed how we maintain modern machines with the implementation of connected devices using IoT sensors, ML and real-time data analytics. This enables you to respond to potential issues before they occur — thus reducing your equipment downtime, extending your machinery life and ultimately reducing costs by identifying potential equipment failures (i.e., excessive vibration or heat deviations) prior to them having a significant impact on the overall equipment performance. ### Benefits of AI in Preventive Maintenance Here are the key benefits of using AI: - Reduced Downtime: The use of AI can help you detect failures much before they cause any significant damage. - Cost Efficiency: By using AI, you can replace parts only when they are actually at the end of their working life, not in anticipation of a failure. - Reliability: The use of AI will help you in forecasting equipment degradation and identifying potential failures before they actually occur, thereby making your systems more predictable and reliable. - Operational Efficiency: The implementation of AI enhances operational efficiency by enabling preventive actions to be taken using a proactive approach instead of a reactive approach. ### Use Cases: AI in Preventive Maintenance The following are the key use cases of using AI in preventive maintenance: - Detecting anomalies at real-time - Predicting failures - Replacement of parts of the equipment - Calibration and adjustments - Optimized maintenance scheduling - Routine inspections to determine wear and tear ### What is Predictive Maintenance? Predictive maintenance is a maintenance strategy that leverages data analytics to predict when an asset will fail, enabling the most efficient way to keep your equipment up and running. Today, predictive maintenance utilizes the power of AI, combined with operational data and ML, to predict the durability of the equipment and provide an accurate indicator of the likelihood of failure. Predictive maintenance is becoming critical for industry as the demand to reduce downtime, maximize productivity and monitor the wear and tear of machine parts continues to increase. ### The Choice Between Preventive Maintenance and Predictive Maintenance Preventive maintenance and predictive maintenance are two approaches typically used to maintain equipment proactively and help organizations maximize the usefulness of their equipment. Choosing the best strategy between these two depends on how critical the equipment is to the organization. Preventive maintenance is a forward-thinking way or a proactive approach used to conduct regular maintenance at predefined intervals to prevent equipment failures. A typical preventative maintenance plan would involve checking your equipment regularly, no matter whether the equipment requires any repair or not. Predictive maintenance, on the other hand, looks at the real-time data emitted by sensors, such as your IoT gadgets and other monitoring tools, to help forecast equipment failures. Instead of sticking to a fixed schedule like you do with preventive maintenance, you only do maintenance when it’s really needed — based on how your equipment is doing. ### The Future With the increase in the adoption of AI and the tools and technologies it powers, predictive maintenance is expected to become far more autonomous and intuitive than it is today. While existing AI-based predictive maintenance tools do a great job of predicting failure, the next wave of such tools will continue to emerge in the coming years. As AI evolves, the sensors used with AI-based predictive maintenance are expected to become simpler, more effective and smarter day by day. The combination of AI with other cutting-edge technologies, such as the IoT and edge computing, will help boost real-time analysis and improve the accuracy and effectiveness of predictive analysis. ### Key Takeaways AI has completely changed how we approach equipment maintenance by converting maintenance from a reactive approach to one that is proactive, data-driven and a continuously learning system. AI-powered predictive maintenance has been transforming several industries, such as energy, health care, agriculture and manufacturing. Predictive maintenance isn’t just an afterthought or an option anymore; it’s now a necessity of any system that can’t afford to go offline. An organization can use a combination of predictive maintenance techniques with standard preventive or reactive maintenance practices to leverage the benefits of each of them.