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Industrial AI: 3 Use Cases

If you’re wondering how AI can actually deliver value in your industrial environment, you’re not alone. In this article, we’ll walk through three real-world use cases with industrial AI, step by step, covering what you need to get started!

Let’s talk about:

  • Predictive Maintenance
  • Energy Optimisation
  • Agent-Assisted Process Insight via MCP

Predictive Maintenance

Predictive maintenance help you avoid unexpected equipment failures by identifying issues before they lead to downtime. By analyzing time-series data collected from sensors, typically stored in a historian like Proficy Historian, machine learning models can spot early warning signs, such as unusual trends or deviations from normal operating conditions.

Once trained, these models can run either at the edge or in a central system, depending on the setup. The results can be integrated into SCADA platforms like Proficy iFIX to trigger alarms, assign health scores, or support maintenance planning in real time.

To get started, you’ll need:

  • Time-series data from sensors on critical equipment
  • A historian system to collect and organize the data (like Proficy Historian)
  • A machine learning or analytics tool to build and deploy models (like Proficy CSense)
  • Integration with a SCADA or HMI system to visualize results and generate alerts (e.g., Proficy iFIX)

Energy Optimisation

Energy optimisation uses AI to reduce energy consumption without compromising how efficiently a plant runs. It works by collecting real-time sensor data from the plant and using that data to train a model that understands how the process behaves.

The AI model predicts the best control actions (like adjusting setpoints) based on current operating conditions and system constraints. These recommendations are sent to the SCADA system, which then communicates with the control hardware to apply the changes. The process runs continuously in a closed loop, similar to how model predictive control works – always adjusting to find the most efficient operating point.

Once trained, these models can run either at the edge or in a central system, depending on the setup. The results can be integrated into SCADA platforms like Proficy iFIX to trigger alarms, assign health scores, or support maintenance planning in real time.

What you’ll need:

  • Real-time data from energy meters and process sensors
  • A historian to store and organize the data (e.g., Proficy Historian)
  • An AI or optimization model to recommend control setpoints (like Proficy CSense)
  • A SCADA system to apply the setpoints and monitor performance (e.g., Proficy iFIX)
  • (Optional) On-prem or edge deployment for faster real-time response

Agent-Assisted Process Insight via MCP

This use case leverages a Model Context Protocol (MCP) server (e.g., HighByte Intelligence Hub) to expose rich, contextualized plant data to an LLM-based agent (e.g., built using LangChain or LangFlow).

The agent can subscribe to live process data, historian data, and modeled relationships, enabling it to reason about the current state of the plant, generate natural-language insights, and recommend control actions. These insights are either presented to human operators or passed to SCADA for further action.

The loop functions like a smart assistant: pulling live and historical data, applying structured reasoning, and feeding back actionable intelligence – creating a new layer of human-AI collaboration in industrial operations.

You’ll need:

  • Plant Data: Sensor, PLC, and control data
  • Historian System: like Proficy Historian for storing time series
  • MCP Server: e.g., HighByte Intelligence Hub to model and contextualize data
  • LLM/AI Agent: e.g., LangChain, local GPT model, or Azure OpenAI agent
  • SCADA System: e.g., Proficy iFIX to visualize or enforce recommendations
  • Optional UI: Operator console with chat interface

AI use cases based on your needs!

Want to take the first small step with AI? We’re just a message away! Let’s figure out together what’s realistic, valuable, and doable – starting from where you are.

Contact Our AI Consultants

The article is written by: Cameron Bolt
Product Specialist Process Optimization, Novotek Benelux

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