Industrial AI + The Intelligence Hub | HighByte

Building Your Foundation for AI Use Cases

Industrial AI is transforming the way manufacturers and industrial organizations operate—unlocking new levels of efficiency, automation, and insight. But without the right data infrastructure, scaling AI initiatives can be slow, costly, and unsustainable.

Succeeding with AI in the industrial space requires new tools and practices that continue to drive IT and OT teams closer together and make it easier to engineer for the challenges they face. To succeed with AI, industrial companies must adopt Industrial DataOps as a foundational discipline for quick and impactful value.

While AI technologies constantly evolve, the team at HighByte is using its deep industrial data experience and partner network to prepare solutions that make industrial data AI-ready. Use this page as a guide to build your knowledge of the possibilities that exist when symbiotically using DataOps and AI.

Defining Industrial AI

AI can be generally defined under these three subcategories.

	Agentic AI	Generative AI	Traditional AI / ML
Primary Function	Goal-oriented action and decision-making	Content generation (e.g., text, code, images)	Automating repetitive tasks and identifying trends
Autonomy	High; operates with minimal human oversight	Variable; may require user prompts or guidance	Low; relies on specific algorithms and set rules
Learning	Reinforced learning; improves through experience	Data-driven learning; learns from existing data	Relies on predefined rules and human behavior
Use Cases	Asset maintenance Cell inventory Line performance Quality inspection	Auto map data tags from OT systems Ad-hoc information access	Cloud based predictive maintenance Defect detection

Industrial AI + DataOps: A Symbiotic Relationship

AI for DataOps

By applying AI, organizations can improve the efficiency and scalability of DataOps technology—automating the way data from various sources is gathered, contextualized, and prepared for analysis, driving faster and more accurate insights.

DataOps for AI

DataOps is essential for AI readiness in industrial environments. It serves as the underlying framework that curates, organizes, and prepares data from disparate sources, providing a reliable foundation for successful AI deployments.

Industrial Data is Not Created AI-Ready

AI models are only as strong as the data they are trained on. In industrial environments, where data comes from a wide range of sources and formats, ensuring that information is complete, accurate, and properly prepared is essential. This includes reducing data gaps and blind spots to unlock the full potential of industrial AI projects. To get AI-ready, organizations should consider the following:

Accessibility

Can you access your data? The first step to making data AI-ready is knowing if you can actually access it. Where does the data live—an MES, OPC server, MQTT broker, or data lake? Depending on the source, you may need to build new connections to pull data from the edge or other critical systems.

Completeness

How complete is the data you are working with? For optimal performance, AI models are properly trained using complete datasets that do not contain missing values. Ideally, your organization has a process in place and the tools required to ensure that industrial data coming from the edge is being validated for quality and scanned for anomalies.

Standardization

Can you align the data format to a single standard? Standardizing the structure of data can strongly influence its usability in AI projects. When obtaining data from the industrial edge, an effective way to standardize data is through modeling it. Data models may be designed manually but can be replicated and used as instances for specific use cases, like an AI application.

Contextualization

Can you add contextualization around the datasets provided to AI platforms? When captured at the Edge—close to the data’s source—this context can be very helpful for many downstream analysis projects, and especially to AI models training on edge data that can otherwise seem completely esoteric.

"AI agents are yet another application processing massive amounts of data—only this time, they promise real transformation. The catch? Without a data infrastructure that can support contextualized, high-quality data operations, moving high volumes of data securely to the cloud, these agents will struggle to deliver meaningful outcomes. Many industrial companies still have DataOps gaps that prevent them from fully leveraging Industry 4.0 applications."

John Harrington, Chief Product Officer at HighByte

Steps to Building an Industrial AI Strategy

Assess current AI-readiness by understanding culture and capabilities around people, process, and technology.

Assemble the right team, ensuring that technical resources are able to collaborate at both enterprise and site levels.

Identify specific use cases that can drive early value, potentially relating to current-day business problems or operational pain points.

Establish a data foundation that promotes the orchestration, observability, quality, and governance of industrial data.

Work with internal teams or trusted system integrators to source the right mix of AI tools that can be trusted for capability, specificity, and security.

Work with internal teams or trusted system integrators to source the right mix of AI tools that can be trusted for capability, specificity, and security.

Industrial AI + HighByte Intelligence Hub Product Demos

OPC Tag Mapping

Tag mapping has historically been a highly manual and repetitive task. Now, with generative AI mapping in the Intelligence Hub, users can define a data model that is then leveraged in the AI mapping feature to automate subsequent tag mapping operations.

Industrial MCP Server

The embedded Model Context Protocol (MCP) Server available now in the Intelligence Hub exposes Pipelines using the “API Trigger” as accessible tools for AI Agents. Curating data for Agentic AI platforms just got a lot easier.

Additional Resources

BLOG

Edge AI + Intelligence Hub: A Match in the Making

This blog explores how Edge AI is reshaping Industrial DataOps. As AI moves to the factory floor, the need for real-time, contextualized data at the edge is becoming urgent. Learn why the Intelligence Hub is a key enabler of this shift—and what it means for the future of OT and IT convergence.

WEBCAST

Build Industrial APIs for Edge AI

In this webcast led by HighByte product leaders, learn how to easily build APIs for your industrial data that can be used for a wide variety of use cases, like providing programmatic access to line-of-business applications or prototyping endpoints for Edge AI.

VIDEO

Industrial DataOps & AI Interview with John Harrington

While AI dominates conversations, a lot of the focus is on its challenges. For operational technology (OT), new advancements are available, but obstacles like data quality, governance, security, and observability remain. In this interview, John Harrington discusses how to overcome these obstacles with a DataOps approach.

VIDEO

Get Your Foundation Right: How VIVIX Architected for AI Success with HighByte & AWS

Learn how VIVIX, a major float glass manufacturer, transformed its maintenance operations using HighByte Intelligence Hub and AWS. Discover how they leverage Generative and Traditional AI to reduce downtime, extend asset life, and cut costs across their Brazilian plants.