In a previous blog, What is Industrial AI? (And Why Most Companies Get It Wrong), we defined Industrial AI as the comprehensive and purpose-built application of artificial intelligence technologies to address the unique challenges and opportunities of industrial operations.
Importantly, Industrial AI encompasses the entire AI value chain, from establishing a contextualized data foundation to deploying advanced AI agents. This is because deploying effective artificial intelligence solutions within complex industrial environments presents unique challenges. It requires the seamless integration of diverse data sources (OT, IT, ET), the application of specialized Industrial AI techniques, and robust deployment across various operational landscapes.
We previously highlighted how Cognite's purpose-built platform directly addresses these complexities by providing the most comprehensive suite of Industrial AI capabilities:
- Advanced Analytics & Machine Learning: For predictive maintenance, anomaly detection, and process optimization.
- Physics-Based Hybrid AI: Combining data-driven AI with physics-based models for enhanced accuracy and explainability.
- Generative AI: Enabling natural language interaction with industrial data, document intelligence, and synthetic data generation.
- Agentic AI: Building intelligent Industrial AI agents to automate tasks and optimize complex workflows.
Learn more about how Cognite infuses AI at every layer, from data to decision.
In this blog, we take a closer look at Advanced Analytics & Machine Learning.
Advanced analytics & machine learning
Cognite Data Fusion provides a robust foundation of proven AI/ML techniques, including regression, classification, clustering, and time series analysis. Our unique advantage lies in AI-powered industrial contextualization, which automatically and continuously enriches your data with relevant, industry-specific information and relationships related to your assets and use cases.
The primary benefits of a robust and continuous AI-powered contextualization engine are:
- Unify Industrial Data Silos: Break down traditional barriers between Operational Technology (OT), Engineering Technology (ET), and Information Technology (IT) data. Create a single, unified source of truth by seamlessly integrating data from diverse systems, including time series data, Piping and Instrumentation Diagrams (P&IDs), 3D models, and enterprise resource planning (ERP) systems.
- Intelligently Contextualize Industrial Data: Automatically map and understand the intricate relationships between industrial data points within a dynamic Industrial Knowledge Graph. This ensures both human experts and Industrial AI solutions can easily access and interpret accurate, relevant data within their operational context.
- Activate Data for Industrial AI Applications: Build a strong, contextualized data foundation that enables the rapid development and deployment of user-friendly industrial tools and advanced AI solutions. These applications directly transform operations by increasing production uptime, proactively mitigating risks, and significantly reducing operational costs.
- Gain Deeper, Predictive Industrial Insights: Extract valuable intelligence from historical and real-time data to drive informed decision-making. This includes accurately predicting equipment failures, proactively detecting operational anomalies, and continuously optimizing industrial processes for peak performance.
See it in practice in our in-depth product tour: Industrial AI in the Era of Contextualization
Summary
Cognite's approach to Industrial AI, particularly its emphasis on AI-powered contextualization within Advanced Analytics & Machine Learning, provides a robust framework for unlocking the full potential of industrial data. By unifying, intelligently contextualizing, and activating this data, companies can achieve significant operational improvements, from increased uptime and risk mitigation to substantial cost reductions.
Learn more about Cognite's approach to industrial AI: