Transformed industrial vision involving AI and data management techniques
In the world of industrial AI, raw data is no longer enough. To unlock the full potential of AI in operational improvements, organizations need to build a unified, contextualized data foundation. This approach, as demonstrated by companies like Cognite, Atlas AI, and Moelven, is key to turning disconnected data into smart, actionable intelligence.
Joyce Shi, Senior Software Engineer at Cognite, explained that contextualization is more than just document tagging. It involves understanding symbol relationships, piping connectivity, and asset hierarchies. By adopting Cognite Data Fusion, organizations can centralize and contextualize information into a dynamic data twin of refinery assets, resulting in a drop in field data collection time, early failure detection, and a transition from reactive to predictive maintenance.
Cameron Greenburg, Senior Product Manager at Atlas AI and Cognite, demonstrated how low-code tooling enables teams to build and customize industrial agents without traditional programming. These agents are deeply embedded in the operational context, drawing from structured knowledge graphs and real-time plant data to ensure accuracy and trustworthiness. The industrial agents created by Atlas AI can retrieve spec sheets, analyze temperature deviations, and make recommendations using CDF data.
Moelven, a leading company in the forestry industry, standardized and contextualized energy and production data across distributed sawmills in Sweden and Norway. With Cognite Data Fusion, they created a unified dashboard that displays order-specific energy usage and production metrics, boosting sustainability reporting and energy efficiency.
The key takeaway from these presentations is that a successful industrial AI strategy should be rooted in contextualization, low-code, domain-specific agents, and customer co-development and operational integration.
Mami Kubota, from Cosmo Oil's Maintenance Strategy Group, presented Cosmo Oil's transition from fragmented maintenance records to predictive, collaborative maintenance. By leveraging industrial data strategies on cloud platforms like AWS, organizations can accelerate the industrial AI transformation and enable generative AI applications.
In conclusion, to overcome the challenge of "data without context" in industrial AI, organizations need to integrate diverse industrial data sources into a cohesive framework that organizes, understands, and trusts the data. This foundation enables AI systems to leverage contextualized data—linking equipment, process, and operational relationships—to power tailored AI agents that support complex workflows like root cause analysis and equipment troubleshooting. By following these approaches, industrial organizations can transform raw data into smart, actionable intelligence for industrial AI initiatives.
Digital transformation in the industrial sector requires more than just data-and-cloud-computing and technology; it necessitates a focus on contextualization. For instance, companies like Cognite and Atlas AI employ contextualization methods to centralize and understand data, leading to efficient data collection, early failure detection, and predictive maintenance.
The successful integration of diverse industrial data sources into a cohesive framework, as demonstrated by companies such as Cognite and Moelven, plays a crucial role in overcoming the challenge of "data without context" in industrial AI. This approach enables AI systems to effectively use contextualized data, thereby powering tailored AI agents that can perform complex workflows like root cause analysis and equipment troubleshooting.
