Asset operators and facilities teams are facing increased pressure to reduce downtime, cut waste, and maximize performance amid supply chain and economic uncertainty. To meet this demand, operations leaders are looking to AI to improve efficiency, but many struggle with where to start.
Key takeaways:
- Digital tools are the foundation for using AI in maintenance management. AI relies on clean, structured, and centralized data, which is only possible through digitalization.
- CMMS and AI work better together. A modern CMMS connects asset data, automates workflows, and enables AI to deliver proactive, intelligent maintenance insights.
- Readiness is a process, not a switch. Teams must build AI readiness through step-by-step improvements, such as standardizing processes and naming conventions, documenting consistent data, and unifying systems.
For AI to deliver on the promise of transforming maintenance management, organizations must first lay the groundwork with digitalization. That means moving away from fragmented tools by connecting the systems you use to manage assets, which starts with capturing clean, usable data.
By leveraging digital technologies to standardize processes, enhance data collection, and improve asset monitoring, organizations can create a solid foundation for AI adoption and prepare for the future of predictive maintenance.
How digitalization prepares maintenance for AI
Digital maintenance tools create the foundation for smarter maintenance operations. They help you move away from disconnected spreadsheets, paper logs, and manual processes. With the right platform in place, you can capture consistent data, track asset history, automate tasks, and centralize everything in one place.
This foundation is critical for AI.
Artificial intelligence depends on structured, accessible, and complete data to function effectively. Yet according to Eptura’s 2025 Workplace Index, 50% of companies are still using an average of 17 separate tools, and only 4% have fully integrated systems. That gap shows how far infrastructure must evolve to support what companies expect from AI.
AI readiness in maintenance starts with digitalization
The best AI maintenance strategies start with strong digital habits. That includes:
- Clean asset records with full service histories
- Real-time data from IoT sensors and building systems
- Automated workflows and work order processing
- Standardized processes across locations
AI models rely on consistent inputs to recognize failure patterns, track anomalies, and recommend actions. The better your digital tools are at capturing that data, the better your AI outputs will be.
And with break/fix work orders taking nearly double the technician hours to resolve versus pre-scheduled tickets, there’s more pressure than ever to make maintenance proactive, not reactive.
Why CMMS and AI go hand in hand
A computerized maintenance management system (CMMS) is essential for enabling AI in maintenance management. It acts as a central hub that connects your assets, work orders, parts inventory, and technician workflows. And when combined with AI, it becomes even more powerful.
With a modern CMMS, teams can:
- Detect anomalies early with sensor integrations
- Automate task prioritization based on asset performance
- Trigger work orders when AI predicts a failure
- Benchmark across buildings and time periods
These are the exact capabilities that set the stage for predictive maintenance.
Intelligent maintenance in 2025
According to Eptura data, 70% of companies already use AI for dashboards or chatbots, but only 54% apply it to more advanced functions like maintenance analytics. That means many teams are still just beginning to tap into AI’s full potential.
Here’s what leaders are prioritizing next:
- 68% want to automate workflows like visitor check-ins and maintenance scheduling
- 50% plan to implement AI for facilities automation and asset management
- 37% report having 11+ employees just to consolidate and analyze operational data — a job AI could help streamline
AI will play a growing role in facilities and maintenance operations. However, 58% of operational leaders cited insufficient employee skill set and knowledge as the top challenge to implementing AI, followed by insufficient cross-platform integration/data consolidation (52%) and inconsistent data for AI to deliver meaningful values (48%).
Preparing maintenance tools and teams for AI
AI in maintenance management is already reshaping how teams work. But to see real results, your systems and technicians need to be ready. That starts with adopting a CMMS, digitizing your workflows, and AI training for operational teams.
From there, you can layer in intelligence with tools like IoT sensors for real-time asset condition monitoring. AI doesn’t just help fix things faster — it helps make better decisions, optimize maintenance scheduling, and extend asset life.
Frequently asked questions
By Jonathan Davis
As a content creator at Eptura, Jonathan Davis covers asset management, maintenance software, and SaaS solutions, delivering thought leadership with actionable insights across industries such as fleet, manufacturing, healthcare, and hospitality. Jonathan’s writing focuses on topics to help enterprises optimize their operations, including building lifecycle management, digital twins, BIM for facility management, and preventive and predictive maintenance strategies. With a master's degree in journalism and a diverse background that includes writing textbooks, editing video game dialogue, and teaching English as a foreign language, Jonathan brings a versatile perspective to his content creation.
