AI Applied Radar: AI Applied To Industrial Operations
02 Jun, 2025
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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.
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
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
Figure 1. Industrial AI comprises four foundational technologies
Figure 2. AI Applied Radar for industrial operations
Figure 3. AI Applied Radar use case groupings
Figure 2. AI Applied Radar for industrial operations
Figure 3. AI Applied Radar use case groupings
Seeq, HighByte, ElevenLabs, ABB, Protex AI, Litmus Automation, ComplianceQuest, Cognite, Retrocausal, DeepSeek, KEYENCE, Hexagon, Streamlit, libryo, Trendminer, Dartmouth College, Cognex, EHS Insight, OpenAI, Overview, Anthropic, Origami Risk, scikit-learn, ServiceNow, SymphonyAI, Wolters Kluwer Enablon, APERIO, Palantir Technologies, AspenTech, Augury, AVEVA, Augmentir, LandingAI, IBM, C3 AI, Jupyter, Siemens, Deepgram, Python, Timeseer.ai, Nanoprecise, Pandas, ehsAI, US Bureau of Labor Statistics (BLS), Braincube, Oden Technologies, Intelex, Alibaba, COMET, Matplotlib
About the Authors
Joe Lamming
Senior Analyst
Joe is a Senior Analyst and Technical Lead at Verdantix, specializing in AI-first platforms and applications and data management. Combining hands-on development of software an…
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Malavika Tohani
Research Director
Malavika is a Research Director at Verdantix, guiding research that explores how digital technologies and services are reshaping industrial operations to become safer, more ef…
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