October 7, 2025 | 6 min read How AI, IIoT, and CMMS Are Powering Predictive Maintenance By: Eric Wallace, C.E.T.Reviewed by: Ivana LivancicFact-checked by: Liudmila Domakhina, PhD Back to blog Predictive maintenance is transforming how manufacturers minimize unplanned downtime, improve asset reliability, and use automated workflows to turn real-time data into actionable maintenance strategies. The following interview with PhD Data Scientist and GenAI expert Luda Domakhina explores the technologies, challenges, and trends shaping manufacturing maintenance strategies, from AI-driven predictions, IIoT sensors to CMMS integrations. A portion of this interview first appeared in Automation World’s article “Striking the Balance: Tailoring Maintenance Strategies for Optimal Efficiency,” published on May 15, 2025. Read the full conversation below. 1. Definition and importance of Predictive maintenance How would you define predictive maintenance, and how does it differ from reactive and preventive maintenance? Why is predictive maintenance important in industrial settings? Predictive maintenance combines data, sensors, and AI to forecast equipment failures before they occur, enabling timely interventions. Unlike reactive maintenance (opens in new tab), which waits for failures, or preventive maintenance (opens in new tab), which follows fixed schedules, predictive maintenance optimizes efficiency by ensuring maintenance only intervenes when needed. In industrial settings, minimizing unplanned downtime is critical: Failures can lead to safety risks, production losses, and supply chain disruptions. In response, predictive maintenance reduces failures, extends asset lifespan, and optimizes maintenance resources. The end result is more efficient and sustainable operations. 2. Technology stack for PdM What key automation technologies are needed for predictive maintenance, and why are they important? Predictive maintenance relies on IIoT sensors, AI, CMMS integrations, and automated workflows to turn real-time data into actionable maintenance strategies. IIoT Sensors & Edge Computing – Collects vibration, temperature, pressure, and acoustic data, enabling real-time anomaly detection at the machine level. AI & Machine Learning – Powers predictive insights through: Anomaly Detection – Identifies deviations from normal operating conditions. Generative AI (GenAI) – Synthesizes insights from logs and sensor data to recommend maintenance actions. Computer Vision – Detects wear, corrosion, and misalignment through AI-driven image and video analysis. CMMS (Computerized Maintenance Management System) Integrations – Transforms AI insights into condition-based and prescriptive maintenance actions: Automated Work Orders – AI-detected risks trigger maintenance tasks with recommended maintenance steps. Leveraged Maintenance History – AI analyzes past maintenance activities to generate prescriptions for future interventions. Optimized Maintenance Strategies – AI refines schedules and interventions based on historical asset performance. Digital Twins – Simulates failures and maintenance strategies to optimize real-world interventions. Automated Workflows, Mobile Alerts & Remote Monitoring – Ensures predictive insights trigger immediate actions, reducing downtime and improving asset reliability. Mobile Access & Real-Time Alerts – Enables technicians to receive instant notifications on critical risks and recommended actions. Remote Monitoring & Dashboarding – Provides a centralized view of asset health with AI-driven risk scores, maintenance trends, and performance analytics. Workflow Automation – AI-generated insights seamlessly integrate with CMMS and operational systems to trigger automated maintenance actions. By integrating AI-driven prediction technology with a mobile-connected CMMS and real-time monitoring, manufacturers can transition from reactive fixes to proactive, strategic maintenance, ultimately improving efficiency, reducing downtime, and extending asset life. 3. The transformative role for Artificial Intelligence How important is artificial intelligence to predictive maintenance? Is AI truly making a difference? Artificial Intelligence (AI) is transforming predictive maintenance by enabling a shift from reactive, rule-based approaches to intelligent, adaptable, data-driven decision-making. Traditional maintenance strategies often rely on static thresholds, leading to: False positives, where unnecessary maintenance increases costs. False negatives, where critical failures go undetected. AI overcomes these limitations by continuously learning from sensor data, historical failures, and operational conditions, identifying early warning signs with greater accuracy than human-defined rules. One way to think about this transformation is as an evolution from simple “automation” to more sophisticated “autonomy.” At Fiix by Rockwell Automation, I led the development of Fiix Asset Risk Predictor (ARP), Fiix Prescriptive Maintenance, and Fiix Maintenance Copilot, for which AI plays a crucial role in: Early Failure Detection – AI detects anomalies and degradation patterns in equipment before issues escalate, minimizing unplanned downtime. Prescriptive Analytics – AI doesn’t just predict failures—it provides root cause analysis and precise maintenance recommendations based on past interventions. Automated Workflows – Insights are seamlessly integrated into your CMMS, triggering work orders, predicting spare parts requests, and alerting technicians. An AI-powered Conversational Chatbot – Designed to empower technicians, the in-app Fiix Maintenance Copilot: Provides quick access to troubleshooting guides and historical maintenance data. Assists with AI-driven diagnostics and repair suggestions. Democratizes knowledge, helping new team members quickly gain expertise. The bottom line: AI is making predictive maintenance more accessible, actionable, and scalable across industrial environments. 4. Challenges and solutions for adoption What are the common challenges and pitfalls (e.g., data integration, cybersecurity, and initial investment costs) for adopting predictive maintenance technologies? What advice can you offer for surmounting those challenges and avoiding those pitfalls? Adopting predictive maintenance (PdM) comes with unique challenges, but they can be addressed strategically: 1. Data Integration & System Interoperability Challenge: Many manufacturers struggle with fragmented data across ERP, CMMS, SCADA, and IoT systems. Solution: Standardize data formats, use APIs for seamless integration, and leverage Agentic AI frameworks to unify predictive models across platforms and allow different predictive models to work together efficiently and adapt dynamically to new data sources. 2. Cybersecurity & Data Privacy Risks Challenge: Connecting sensors and cloud-based AI increases cybersecurity risks. Solution: Implement zero-trust security practices, encrypt data, and use AI-driven anomaly detection to monitor threats. Discover how Fiix safeguards your data SOC 2 Type 2, enterprise encryption, backups, and DRP Learn more (Opens in new tab) 3. High Initial Investment & ROI Uncertainty Challenge: The cost of sensors, data infrastructure, and AI implementation can be a major barrier, especially for organizations unsure of the return on investment. Solution: Instead of investing in AI just for the sake of it, start with identifying the core need for your customers and market—whether it’s reducing downtime, improving safety, or optimizing asset performance. Then: Leverage your strengths—if your company specializes in sensors, cloud solutions, or has a unique dataset, build on that advantage. Don’t reinvent the wheel—use secure, proven tools available on the market rather than building infrastructure from scratch. Adopt a product-first approach—experiment, iterate, and ensure AI solutions align with real operational needs. Consider Generative AI (GenAI) for faster ROI—unlike traditional AI/ML, which requires extensive data training, GenAI can quickly address multiple use cases, such as automated report generation, troubleshooting assistance, and predictive recommendations, accelerating your time-to-value. 4. Workforce Adoption & Change Management Challenge: AI adoption fails without technician trust. Solution: Ensure AI insights are explainable. Tools like Fiix Maintenance Copilot provide conversational AI insights, reducing resistance. Offer training and validation processes to build trust and confidence. 5. Lack of Actionable Insights Challenge: Even with the best data and AI models, predictive maintenance struggles if insights don’t lead to timely maintenance actions. Solution: Ensure AI-driven insights are directly integrated into CMMS workflows to trigger automated work orders, alerts, and prescriptive maintenance recommendations. For example, Fiix Asset Risk Predictor automatically generates actionable work orders in your CMMS with specific maintenance tasks, ensuring technicians receive clear, AI-driven instructions that translate into the real-world. General Advice Start small, focus on real customer needs, use proven tools, and adopt a product-first approach. When implemented correctly, GenAI delivers fast ROI and helps companies realize the benefits of automated troubleshooting, reporting, and AI-driven recommendations. 5. Emerging trends and the future of PdM What trends are emerging in predictive maintenance for manufacturing? What should manufacturing management be watching out for? Predictive maintenance is rapidly evolving, driven by advances in AI and automation on the one hand, and tempered by ethical or security considerations on the other. Key trends include: 1. AI-Powered Maintenance Copilots AI assistants like Fiix Maintenance Copilot provide real-time insights and recommendations from across all information sources, helping: Technicians access troubleshooting guides instantly, reducing downtime; New team members learn faster, addressing workforce shortages; and Providing seamless access to asset history, status, and documentation. 2. Self-Healing Systems AI is advancing towards “self-healing” systems, in which: Systems automatically adjust parameters to prevent failures; AI learns from deviations, optimizing performance; and Maintenance shifts from reactive fixes to AI-driven optimizations. 3. The Need for Transparent & Ethical AI As AI adoption grows, explainability, trust, and ethics are critical. Future AI regulations will likely enforce: Explainability – Teams need clear AI predictions, including access to source data for faster validation and troubleshooting and risk visibility to support informed decision-making. Actionability – AI must seamlessly integrate into workflows to drive measurable impact. Ethical AI – AI should augment, not replace, human expertise. Fiix Predictive Maintenance solutions address these concerns by: Providing dashboards for AI insights, historical trends, and predictions; Maintaining full historical records of AI-driven recommendations; and Using Generative AI to break down complex predictions into human-readable insights. By embedding explainability and visibility within our products, we help ensure AI is a trusted partner that helps teams work efficiently, retain knowledge, and train new talent. 4. Sustainability & Circular Economy Sustainability is a growing priority, with predictive maintenance helping companies reduce energy use, extend equipment life, and minimize waste through optimized asset management. 5. Scalable AI & Cloud-Native Solutions Manufacturers are shifting to scalable, cloud-based AI to: Enable faster deployment with minimal infrastructure; Support multi-site and remote monitoring for global operations; and Automate enterprise-wide maintenance workflows for efficiency. Final Thoughts Industry leaders should focus on scalable, explainable AI that integrates with operations. Trust, ethics, and human augmentation will define the future of PdM, ensuring AI remains a responsible and high-impact tool in manufacturing. Please tell me about how (or send me a case study that shows how) you helped a manufacturer to move to predictive maintenance? What were the main considerations, and how did you help the manufacturer act upon them? PCI Case Study: Achieving Predictive Maintenance with Fiix ARP (opens in new tab) Various images may be found on our Fiix ARP page here. In particular, the “How Fiix Asset Risk Predictor Works” infographic. Discover how AI “smart manufacturing” is reshaping maintenance in 2025: (opens in new tab) (opens in new tab) (opens in new tab)