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Executive Summary

This report delivers a comprehensive assessment of AI-augmented use cases for industrial operations, enabling technology leaders to gauge trustworthiness at scale, business value and operational viability. The Verdantix AI Applied Radar analysis evaluates a spectrum of AI-driven solutions – spanning natural language search, code generation, knowledge enrichment and workflow automation – using a methodology grounded in expert interviews, global surveys and technical review. Use cases are mapped across mainstream, pilot and emerging phases of market adoption. Mainstream deployments encompass AI-powered search, analytics-oriented code generation, set point optimization and predictive maintenance. Pilots target document extraction, data quality and voice-enabled guidance. Emerging concepts, such as multi-step task completion and automated knowledge graph hydration, remain developer-focused. The Radar provides actionable insights for industrial buyers and vendors seeking scalable, high-impact AI adoption.

Table of contents

Introducing the AI Applied Radar analysis
Key questions answered by the AI Applied Radar analysis
AI Applied Radar analysis aligns with industrial technology buyers’ demands for practical AI system deployment
AI Applied Radar for industrial operations
Defining the market for emerging, pilot-phase and mainstream AI technologies for industrial operations
Methodology overview
Identifying the three critical pillars of compelling AI use cases
Assessing the market adoption phase of AI use cases
Determining the tech availability for AI use cases
AI Applied Radar: industrial operations
AI-augmented use cases ready for mainstream deployment
AI-augmented use cases worthy of piloting
AI-augmented use cases for developers to continue refining

Table of figures

Figure 1. Industrial AI comprises four foundational technologies
Figure 2. AI Applied Radar for industrial operations
Figure 3. AI Applied Radar use case groupings

About the authors

Joe Lamming

Senior Analyst
Joe is a Senior Analyst in the Verdantix Industrial Transformation practice. His current research agenda covers industrial DataOps, AI/ML analytics and applications of generative AI for industry and enterprise. Prior to joining Verdantix, Joe worked in the consumer electronics industry, where he gained experience in overseas manufacturing, product design and data science. Joe holds an MEng in Mechanical Engineering and Sustainable Energy Systems from the University of Southampton.

Malavika Tohani

Research Director, Industrial Transformation
Malavika leads the Verdantix Industrial Transformation practice. The team’s current research focusses on digital technologies and services that enable industrial operations become safer, efficient and sustainable such as asset maintenance and performance, industrial AI and data management, connected workforce and field service management, process safety and industrial engineering, design and construction. Malavika has over 20 years’ experience in research and strategy consulting. Malavika previously worked at Frost & Sullivan, managing and delivering advisory projects for clients involving expansion, acquisition, benchmarking and product development strategies. Malavika holds a MSc in Economics from Madras School of Economics.

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