HiveMQ Glossary
Explore HiveMQ's glossary of data streaming, data intelligence, agentic AI, Unified Namespace, and industrial operations terms, all in one place.
HiveMQ UNS Maturity Assessment
Take the assessment to gauge your UNS maturity, benchmark against peers, and get a roadmap for success.
AI Maturity Assessment
Take a short self-assessment to gauge your organization's readiness for AI-driven industrial operations.
What SmartWorX Told Us About the Next 10 Years of Industrial Data
Most industrial AI pilots fail before they scale. The reason is context. Here's what Europe's manufacturing leaders said about fixing the data foundation.
Securing Agent Access to the UNS Guardrails for Autonomous Actions
AI agents that write to operational data need more than authentication. Learn the four-pillar security model for governing autonomous agent actions on a MQTT-based UNS.
Achieving Real-Time Accurate Billing and Compliance for AI-Era Data Centers
AI workloads break traditional billing. Real-time telemetry streaming via Unified Namespace delivers accurate, auditable billing and compliance for AI-era data centers.
Closing The Industrial AI Value Gap
Real-time data streaming, governed operational data, and the right architecture. Learn the industrial AI playbook that turns pilots into platforms.
Building Trustworthy Industrial AI Systems: The Essential Foundation
Learn about the essential foundation for trustworthy industrial AI solutions, how to protect against structural blind spots, and more, here in this guide.
Measuring Industrial AI ROI: How to Know If Your Investment Is Working
Most industrial AI pilots succeed technically but fail to scale—because measurement breaks down across sites. Learn how to standardize OEE, downtime, and energy baselines so your AI ROI is defensible, comparable, and fundable enterprise-wide.
OT/IT Convergence for AI: Winning With Cross-Functional Ownership
Industrial AI fails when OT, IT, and AI teams operate in silos. Learn how cross-functional ownership and a shared data layer align priorities and unlock scale.
Edge AI in Manufacturing: Why Is It Harder Than Building the Model
Edge AI deployment challenges go beyond the model: compute constraints, connectivity gaps, lifecycle management, and security exposure at the production line.
Legacy OT Integration: The Hidden Tax on Every Industrial AI Use Case
Every industrial AI use case requires a custom data pipeline. Learn why legacy OT integration creates a hidden tax that compounds at scale and how to break the cycle.
Building Ontology Driven Intelligence for Industrial AI Agents
Learn how ontology-driven AI agents use semantic models, knowledge graphs, and structured data to enable reliable, scalable agentic automation in industrial operations.
Industrial AI Pilot: Why 68% of Manufacturers Can’t Scale Past the POC
Why 68% of industrial AI pilots fail to scale: non-replicable data pipelines, missing ROI baselines, and unclear ownership kill production deployment despite technical success.
The Blueprint for Agentic AI in Industrial Operations
A practical blueprint for operationalizing agentic AI in industrial operations using real-time data, contextual intelligence, and trusted governance.A practical blueprint for operationalizing agentic AI in industrial operations using real-time data, contextual intelligence, and trusted governance.
Building Ontology-Driven Intelligence for Industrial AI Agents
Learn how ontology-driven AI agents use semantic models, knowledge graphs, and structured data to enable reliable, scalable agentic automation in industrial operations.
Beyond Code: The Human Mind as the Change Architecture for AI Systems
Explore how human biases shape AI system architecture and why objectivity, intent design, and cultural transformation are essential for safe, effective agentic AI in manufacturing.
The Architecture of Alignment: Transforming into an Agentic AI Company from the Outside In
How to transform into an agentic AI company: encoding expertise, balancing innovation with reliability, and empowering teams through AI skills in the OT/IT convergence.
Everybody Can Be an Engineer and How AI Creates Expertise Flywheel
AI doesn’t replace experts; it codifies their knowledge into tools that empower everyone. Learn how AI enables a recursive self-improvement loop, turning domain expertise into an expertise flywheel.
Why Most Agentic AI Strategies in Manufacturing Fail
In this guide, you'll see why industrial Agentic AI manufacturing strategies stall and what you can do to avoid those problems.
Why Your Manufacturing Strategy Needs Agentic AI
Learn how Agentic AI transforms manufacturing with autonomous systems, smarter supply chains, and scalable innovation.
Accelerating Industrial AI in 2026: The Report
Industrial AI is accelerating, but most teams can’t scale past pilots due to data and integration gaps. Download the 2026 AI readiness survey report.
AI Adoption in Engineering: Lessons from a Year at HiveMQ
AI didn’t replace engineering at HiveMQ; it removed friction. Here are lessons from AI adoption and how to keep trust, quality, & ownership intact.
AI in Operational Technology: Unlocking Value Through Industrial Data
Learn how to unlock AI value in manufacturing OT with AI-ready data, Unified Namespace architecture, secure governance, and metrics to scale from pilots to autonomy.