Some would say that ChatGPT unleashed the iPhone moment for digital transformation. Arguably, it is generative AI that will unleash the transformation of industry. But while generative AI may be a bullet train that has left the station, to harness it properly, it must be safe, secure and hallucination-free.
The business value of generative AI is only in its application to the real-world needs of field engineers and others operating in asset-heavy industries. When used correctly, generative AI can enable better collaboration, task automation, field productivity, maintenance planning and robotic automation, but the technology is only as strong as its data foundation.
Drowning in Data, Starving for Context
Generative AI can generate new data, content or solutions based on existing data patterns. Unlike traditional AI models, generative AI can learn from context-enriched data without explicit guidance, making it a powerful tool for industries where data is abundant but complex.
Generative AI can ingest diverse datasets, including historical maintenance records, sensor data, work orders and even unstructured data such as maintenance reports. However, for it to be effective, context is vital. Without it, generative AI cannot provide deterministic solutions that will benefit you in your line of work.
Industrial facilities generate an overwhelming amount of data, and they are often siloed. Without context, it becomes an obstacle rather than an asset. To power generative AI, and empower subject matter experts and field engineers, data must be contextualized and easily accessible. This involves liberating data from siloed source systems and creating a strong contextualization engine.
The goal is to enable SMEs to extract the full value of industrial data. Operationalizing data and establishing a robust data foundation are key steps toward achieving this. It not only enhances work efficiency but also lays the groundwork for generative AI to accelerate various workflows.
LLMs + Knowledge Graph = Data You Can Trust
The key to making AI work for industry lies in the magic formula of: Large Language Models (LLMs) + Knowledge Graph. An industrial knowledge graph provides an additional metadata layer to navigate relationships for both humans and generative AI to make sense of industrial data. An industrial knowledge graph is the output of data contextualization and represents the connections between the many data types. This graph ensures that LLMs can understand and provide reliable and deterministic responses to even the most complex queries within your field. And, by applying this formula, you are able to have a complete and trustworthy digital representation of your industrial reality, free from AI hallucinations.
Industrial Canvas = Data You Can Use
Generative AI’s transformative potential extends across all areas in asset-heavy industries. From field productivity to maintenance planning and robotic automation, generative AI can make a significant impact by making operations more productive, safe and sustainable. However, for it to thrive, a platform must provide essential AI features that grant easy access to complex industrial data for engineers, data scientists and subject matter experts.
A promising example of an interactive user experience is an Industrial Canvas. Delivering simple access to all industrial data in a single workspace requires a unique way to leverage contextualized data. Field workers deserve a way to work with live sensor data, interactive engineering diagrams, images, 3D models and more within a visual workspace where they can explore data in context, perform root cause analysis and collaborate by tagging other users in an open, free-form environment.
From Digital Maturity to Industrial Transformation
Digital transformation might be seen as a priority in the industry, but achieving real ROI still remains a challenge. Many organizations invest in cloud data warehouses and data lakes, but data often ends up unused. The true value of data lies in trust and usability.
To master the industrial data problem, we need a shift towards industrial AI at scale. This approach focuses on delivering business-ready, trusted, actionable data to all users. It promises to improve time to value, quality, predictability and scalability in data analytics.
Generative AI is a catalyst propelling the manufacturing industry towards new heights of efficiency and innovation. By combining this powerful technology with a robust industrial knowledge graph and intuitive user interface, we can address the challenges of complexity, context and usability in industrial data. This fusion of technology not only transforms operations but also sets the stage for a new era of industrial excellence.
Moe Tanabian is chief product officer at Cognite, an industrial software provider of scalable industrial digital solutions, including a comprehensive suite of industrial generative AI capabilities.