Choosing the Right Condition-Based Maintenance Software: Five Key Aspects to Consider in 2025

In 2025, the shift from planned and reactive maintenance to Condition-Based Maintenance (CBM) is accelerating as organizations seek to enhance asset reliability, reduce operational costs, and minimize downtime situations. 

With the rise of AI, IoT, and automation, condition-based maintenance is no longer just about monitoring asset conditions—it’s about proactively responding to deviations and fixing them before they escalate into major issues. Condition-based maintenance software solutions must evolve with new AI features that can analyze vast amounts of data and generate prescriptive recommendations, allowing maintenance teams to prioritize tasks, reduce manual work, and extend asset lifespan without unnecessary interventions.

However, not all CBM solutions are created equal. To fully harness the benefits of AI-driven maintenance, organizations must carefully evaluate software based on their needs and how they want to set themselves up for the future. This guide outlines the five critical factors that maintenance, engineering, and operations leaders should consider when selecting the right software for running condition-based maintenance in 2025 and beyond.

Five Key Aspects to Consider When Evaluating Condition-Based Maintenance Software in 2025

With so many CBM solutions on the market, it’s easy to get caught up in feature lists and flashy dashboards. But what really matters is how well the software fits into your existing operations, improves efficiency, and delivers measurable results. The right CBM platform should seamlessly integrate with your systems, ensure data quality, scale with your operations, automate workflows, and drive business impact. Here’s a breakdown of the five key aspects to focus on when making your decision:

1. Seamless Integration With Existing Systems

Why It Matters: Many maintenance teams struggle with disconnected systems, making it hard to get a unified view of asset health. If your CBM software can’t integrate with your CMMS, BMS, SCADA, or IoT devices, you’ll face data silos, inefficiencies, and limited visibility—defeating the purpose of CBM.

What to Look For: A future-proof CBM solution should support API-driven interoperability, allowing real-time data exchange across all your maintenance and asset management systems. It should also be designed to work with your existing infrastructure without disrupting IT operations. The goal is to centralize asset data for better decision-making while avoiding costly and complex system overhauls.

2. Data Quality: The Foundation for Leveraging AI in CBM

Why it Matters: AI is only as good as the data it learns from—and most maintenance teams deal with incomplete, inconsistent, or unstructured data from CMMS, CAFM, and IoT sources. Poor data quality can lead to inaccurate AI predictions, missed failure warnings, and unreliable maintenance recommendations.

What to Look For: The best CBM software should automate data structuring, validation, and anomaly detection, ensuring that raw data is cleansed and contextualized before AI-driven analysis. High-quality data enables AI Agents to provide accurate insights, helping engineers make faster, more informed decisions. Without this foundation, even the most advanced AI-powered CBM solution will fail to deliver meaningful results.

3. Scalability, Customization & Remote Operations Compatibility

Why It Matters: Maintenance teams today manage assets across multiple locations, often with distributed teams responsible for different sites. Your CBM software must be scalable and flexible, allowing you to monitor and manage diverse asset portfolios without limitations.

What to Look For: Look for a solution with robust remote monitoring capabilities, ensuring that engineers can access real-time asset health data, receive alerts, and take action from anywhere. Customization is equally critical—your CBM platform should allow you to configure workflows, alerts, and automation to fit your industry-specific challenges, whether you’re in facility management, energy services, or industrial operations.

4. Advanced Workflow Automation & Decision Support

Why It Matters: CBM isn’t just about monitoring assets—it’s about reducing manual work and making smarter decisions. Many maintenance teams spend too much time manually reviewing data, prioritizing alerts, and deciding on the next steps. Automation can eliminate these inefficiencies.

What to Look For: An effective CBM platform should provide engineer-first workflows that streamline maintenance processes. It should offer AI-powered decision support, helping teams prioritize issues, receive actionable recommendations, and follow standardized maintenance frameworks. This ensures consistency across operations while allowing teams to focus on solving critical issues instead of sorting through endless alerts.

5. Business Impact: Focus on Outcomes, Not Just Features

Why It Matters: The industry is shifting away from buying technology for its features to investing in solutions that deliver measurable outcomes. Your CBM software should demonstrate a real impact on cost savings, uptime, and reliability—not just provide a list of capabilities.

What to Look For:
Proven impact metrics: Does the software reduce downtime, optimize asset reliability, and deliver energy savings?
Beyond dashboards & reporting: Does it offer AI-powered recommendations that drive real maintenance improvements?
Alignment with business goals: Can it track maintenance KPIs related to asset performance, energy savings, cost reduction, productivity improvements, etc.?

Choosing the right CBM software means thinking beyond the technology itself—it’s about selecting a solution that improves how your team operates and delivers business value.

How to Evaluate CBM Vendors & Make the Right Choice

Making the right choice means finding a solution with the best —it’s about selecting a vendor that aligns with your operational needs, scales with your business, and delivers real impact. You need to assess CBM tech providers effectively and avoid common pitfalls.

Key Questions to Ask Before Committing

Before selecting a CBM solution, ask yourself: Can it seamlessly integrate with our existing tools? If the platform doesn’t work well with your CMMS, BMS, SCADA, or IoT systems, it will create more inefficiencies instead of solving them. How does it handle data accuracy and security? Effective condition-based maintenance insights rely on high-quality, structured data—ensure the software includes data quality checks to prevent misleading insights. Finally, evaluate its decision-support capabilities—does it simply present data and leave it for interpretation, or does it help engineers take the right action at the right time?

Criteria for Future-Proofing Your Investment

Your CBM software should grow with you, not become obsolete as your operations expand. Look for a scalable architecture that supports multi-site operations, asset variety, and increasing data volumes. The software should also be configurable—allowing you to adjust workflows, alerts, and automation as your maintenance strategy evolves. AI and automation are rapidly advancing, so choose a solution that adapts to new technologies instead of locking you into outdated methods.

Common Pitfalls to Avoid

Many teams regret their CBM software choice due to hidden costs, vendor lock-in, and poor post-implementation support. Some vendors charge extra for integrations, API access, or additional users, making scaling expensive. Others restrict data access, making it difficult to switch providers later. Always clarify pricing, data ownership policies, and support terms upfront. Lastly, don’t overlook ongoing support and training—ensure the vendor provides hands-on onboarding and continuous assistance to maximize long-term value.

Why Invest in AI Agents Like Xempla for Condition-Based Maintenance

Current condition-based maintenance solutions are still manual-heavy, suffer from data fragmentation, and don’t optimize for the operations and maintenance process as a whole. AI-driven agentic solutions like Xempla go beyond alerts and reports—they help maintenance teams automate workflows, make data-driven decisions, and drive real business impact. Here’s how Xempla transforms CBM into a proactive, intelligent, and scalable operation.

1. System Integrations & Data Consolidation: Xempla seamlessly integrates with CMMS, BMS, SCADA, IoT, and work order management systems, eliminating data silos. Its API-driven architecture ensures smooth data flow, enabling centralized insights across multiple sources without disrupting existing IT infrastructure. This unified data foundation is essential for fast and accurate analysis and automation.

Learn more about data requirements, integrations, onboarding, and more in this article.

2. Remote Monitoring & Management Capabilities: With distributed teams and multi-site operations, real-time remote visibility into asset performance is critical. Xempla’s Remote Engineering Ops Agent provides AI-driven monitoring, anomaly detection, and actionable insights, allowing teams to track asset health, respond to issues faster, and optimize maintenance strategies from anywhere—minimizing on-site interventions.

3. Automated Triaging & Intelligent Prioritization of Alerts: Not all alerts require immediate action. Xempla filters out noise and prioritizes critical issues using AI-based triaging. It analyzes sensor data, failure patterns, and historical insights to determine which alerts need attention first—helping teams focus on high-impact problems instead of losing time to false alarms.

4. Expert Decision Support & Dynamic Recommendations: Xempla acts as a virtual engineering assistant, offering real-time decision support based on historical performance, sensor trends, and operational context. Instead of just flagging anomalies, it provides clear, actionable recommendations—helping engineers resolve issues efficiently without manually analyzing complex data sets.

5. Enhanced Collaboration for Faster & Better Resolutions: Maintenance is a team effort, and Xempla ensures seamless knowledge sharing across teams. It connects field technicians, engineers, and managers with context-rich insights, suggested actions, and past resolutions—helping teams collaborate efficiently to solve problems faster and improve service quality.

6. Standardized Workflows for Consistent Service Quality: Inconsistent maintenance approaches lead to inefficiencies. Xempla provides structured workflows, pre-built maintenance frameworks, and guided processes to ensure every team member follows best practices. This reduces errors, standardizes decision-making, and improves overall operational reliability.

7. Effective Knowledge Capture & Sharing Mechanism: Retaining institutional knowledge is crucial as teams evolve. Xempla automatically captures engineering insights, resolutions, and asset behavior trends, creating a centralized knowledge base. This enables new team members to learn from past experiences, reducing dependency on individual expertise.

8. Near-Autonomous Operations With Minimal Supervision: AI-driven CBM isn’t just about monitoring—it’s about automating routine decision-making. Xempla learns from past and ongoing interactions, recommends pre-validated solutions, and minimizes human intervention for repetitive tasks. This allows teams to focus on strategic improvements while AI handles routine maintenance actions in the background.

Conclusion: Unlocking the Full Value of Condition-Based Maintenance With Xempla

Condition-based maintenance is essential for FM / Engineering service companies looking to improve reliability, reduce costs, and scale operations efficiently. However, not all CBM solutions are created equal. To unlock its full potential, you need software that excels in five key areas: seamless system integration, high-quality data management, scalability for multi-site operations, AI-powered automation, and a clear focus on business impact.

Xempla goes beyond traditional CBM software, empowering maintenance and engineering teams with AI-driven automation, expert decision support, and intelligent workflows. By consolidating data across systems, prioritizing alerts, and enabling near-autonomous operations, Xempla ensures that your maintenance strategy is not just predictive—but truly proactive and optimized for long-term efficiency.

If you’re ready to elevate your CBM strategy and see how AI-powered automation can transform your maintenance operations, book a demo with our product expert today.