The term "Industrial AI" is gaining traction, but its true meaning is often misunderstood. Many solutions on the market today fall short, narrowly focusing on specific applications while failing to address the complex data landscape of industrial operations. Industrial AI is more than just a tool for document summarization or a single-point solution; it's a comprehensive suite of AI capabilities that spans the entire spectrum from the data foundation to decision-making, purpose-built to handle the intricacies of industrial operations.
Industrial AI is the comprehensive and purpose-built application of artificial intelligence technologies to address the unique challenges and opportunities of industrial operations. It encompasses the entire AI value chain, from establishing a contextualized data foundation to deploying advanced AI agents that drive informed decision-making, optimize complex processes, and automate tasks across the industrial sector.
The Pitfalls of a Piecemeal Approach
Many companies adopt a fragmented approach to Industrial AI. They might offer a tool for predictive maintenance or a platform for data visualization, but these point solutions fail to address the fundamental, interconnected challenges of industrial data. A primary challenge is the prevalence of data silos. Industrial data is frequently scattered across disparate systems, including Operational Technology (OT), Information Technology (IT), and Engineering Technology (ET). This siloing effect creates barriers that hinder the effective implementation of AI. AI models require a holistic view of operations to function correctly. However, when data is locked away in isolated systems, these models can only access a limited and fragmented view, leading to incomplete or inaccurate insights.
Another significant issue is the lack of context. Raw industrial data, on its own, is essentially meaningless to AI. To be valuable, AI needs to understand the intricate relationships between individual data points, the assets they represent, and the complex industrial processes that generate these data points. Without this context, AI struggles to provide actionable insights.
These challenges become particularly apparent when applying generic, large language models (LLMs) to industrial use cases. While powerful in other domains, these models are often trained on broad datasets that lack specific knowledge of industrial operations, assets, and processes. Consequently, they lack access to the real-time and historical data necessary to provide accurate and relevant insights in an industrial context. This can result in outputs that are essentially educated guesses, providing general knowledge without any grounding in a company's specific assets, processes, or real-time conditions.
Some companies attempt to address this by training an LLM on their industrial data. The idea is to feed all available industrial data into an AI model so it can learn everything about the operations. However, this strategy presents substantial obstacles. Industrial data is characterized by its sheer volume, scattered nature, inconsistency, and variety of formats. It is often stored across numerous systems with inconsistent naming conventions, and a significant portion is unstructured and tied to legacy systems. The effort required to train an LLM to effectively process and understand this complex data landscape is extremely expensive and time-consuming. Even with extensive training, the resulting LLM can still struggle with accuracy and reliability.
For example, imagine an engineer using an LLM-based agent to inquire about the status of a valve before performing maintenance. If the LLM was trained on the company's entire dataset, it might provide an answer based on statistical probabilities rather than actual real-time data. If most valves were open when the LLM was trained, it might predict that the valve in question is open, even if it is currently closed. Furthermore, the LLM might not be able to provide the source of its answer, making it difficult to verify the information and establish trust in its accuracy. This is because traditional LLMs are probabilistic and do not inherently retrieve real, structured data to formulate their responses.
Finally, many solutions overlook the importance of physics. Fundamental physical laws govern industrial processes, providing critical context for understanding and predicting behavior. AI solutions that fail to incorporate physics-based modeling may miss essential relationships and produce results that are difficult to explain or validate.
It's common to see solutions that address only a single aspect of the challenge. Some emphasize data warehousing and analytics, providing a place to store and visualize data, but they lack the AI tools to extract deeper insights and automate actions. Others might focus on specific AI tools, such as generative AI for document summarization, while neglecting to establish the robust and contextualized data foundation that Gen AI needs to be effective in an industrial setting. Still others offer general-purpose AI platforms that lack the specialized tools, contextualization capabilities, and domain expertise required to address the unique challenges of industrial use cases.
The Cognite Difference: Comprehensive Industrial AI
Cognite takes a fundamentally different approach. We provide comprehensive Industrial AI capabilities that address the entire AI value chain, from data to decisions.
- A Unified Data Foundation: Cognite Data Fusion breaks down data silos by integrating data from diverse sources into a single, unified Industrial Knowledge Graph. This graph contextualizes data, providing the rich context that AI models need to deliver accurate and reliable insights.
- Purpose-Built Industrial AI Capabilities: Cognite offers a suite of AI capabilities tailored to industrial needs, including:
- 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.
- Focus on Trust and Reliability: Cognite's platform is designed to ensure the trustworthiness of Industrial AI solutions. The Industrial Knowledge Graph minimizes hallucinations and provides a traceable lineage for AI-driven insights.
- Open and Extensible Architecture: Cognite's platform integrates with existing industrial systems and tools, ensuring flexibility and future-proofing AI investments.
Industrial AI is a holistic approach that acknowledges the unique challenges and opportunities within the industrial sector. Cognite's comprehensive approach, encompassing advanced analytics, physics-based models, and cutting-edge generative and Agentic AI, is the only solution that truly delivers on the promise of Industrial AI. It empowers companies to transform their operations and achieve unprecedented levels of efficiency, sustainability, and growth.
Learn more about Cognite’s approach.