AI Applied Radar: AI Applied To Industrial Operations
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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 analysisKey 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 technologiesFigure 2. AI Applied Radar for industrial operations
Figure 3. AI Applied Radar use case groupings
Organisations mentioned
ABB, Alibaba, Anthropic, APERIO, AspenTech, Augmentir, Augury, AVEVA, Braincube, C3 AI, Cognex, Cognite, COMET, ComplianceQuest, Dartmouth College, Deepgram, DeepSeek, EHS Insight, ehsAI, ElevenLabs, Hexagon, HighByte, IBM, Intelex, Jupyter, KEYENCE, LandingAI, libryo, Litmus Automation, Matplotlib, Nanoprecise, Oden Technologies, OpenAI, Origami Risk, Overview, Palantir Technologies, Pandas, Protex AI, Python, Retrocausal, scikit-learn, Seeq, ServiceNow, Siemens, Streamlit, SymphonyAI, Timeseer.ai, Trendminer, US Bureau of Labor Statistics (BLS), Wolters Kluwer EnablonAbout the authors
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