AI Applied Radar: AI Applied To Asset Maintenance Management

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Executive Summary

This report provides a comprehensive assessment of AI-augmented use cases for asset maintenance management, helping asset maintenance leaders to assess each use case’s trustworthiness, business value and operational viability. The Verdantix AI Applied Radar evaluates a range of AI-driven solutions, using proprietary information obtained from expert interviews, global surveys and technical review. Verdantix categorized use cases based on levels of market adoption and tech availability, spanning mainstream and commercialized applications such as applying AI to predict asset failures, to emerging areas such as failure modes and effects analysis (FMEA) and failure modes, effects and criticality analysis (FMECA) library automation. The Radar provides actionable insights for asset maintenance leaders seeking scalable, high-impact AI adoption, as well as vendors providing AI capabilities.
Introducing the AI Applied Radar analysis
Overview of existing digital maturity and AI readiness for asset maintenance management
AI Applied Radar: asset maintenance management
Methodology overview
Identifying the three critical pillars of compelling AI use cases
Assessing the market adoption phase of AI use cases
Figure 1. AI Applied Radar for asset maintenance management
Figure 2. Description of AI use cases in asset maintenance management
Figure 3. Analysis of AI use cases for asset maintenance management
Figure 4. Agentic opportunity in asset maintenance management
Figure 5. AI use cases in scope

About the Authors

Jatinder Devgun

Jatinder Devgun

Senior Analyst

Jatinder is a Senior Analyst at Verdantix, providing insights on emerging technologies and market dynamics for technology buyers and software vendors. His expertise lies in as...

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Malavika Tohani

Malavika Tohani

VP Research

Malavika is a VP Research at Verdantix, guiding research that explores how digital technologies and services are reshaping industrial operations to become safer, more efficien...

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