Industrial Artificial Intelligence and Data Strategy
In the realm of industrial operations, the integration of Artificial Intelligence (AI) is revolutionizing the way businesses function. One such company leading this transformation is Cosmo Oil, which has successfully implemented contextualized dashboards to enable early failure detection using vibration thresholds [1].
The importance of contextualization in forming a rich digital twin that AI agents can navigate was highlighted by Joyce Shi, Senior Software Engineer at Cognite. This approach allows AI systems to understand complex industrial workflows deeply, acting as trusted advisors rather than mere copilots [1].
The agents drawn from these digital twins rely on structured knowledge graphs and real-time plant data, ensuring accuracy and trustworthiness. Mami Kubota from Cosmo Oil discussed the transition from fragmented maintenance records to predictive, collaborative maintenance, resulting in reduced downtime and increased reliability [1].
Lisa Nilsson from Moelven shared how energy and production data was standardized and contextualized across distributed sawmills, leading to improved sustainability reporting and energy efficiency [1]. The adoption of Cognite Data Fusion by Cosmo Oil led to a significant drop in field data collection time, from 1 to 2 hours to 10 minutes [1].
A strategy for industrial AI should be rooted in contextualization, low-code, domain-specific agents, and customer co-development and operational integration. Cameron Greenburg, Senior Product Manager at Atlas AI and Cognite, demonstrated how industrial agents can be built and customized using low-code tooling, without requiring traditional programming [1].
Key strategies and technologies to overcome common challenges in deploying industrial AI include building a unified, contextualized data foundation, developing tailored AI agents, integrating with control systems for real-time context, execution feedback and continuous learning, and the use of industrial AI copilots and conversational agents [2][3].
Examples of organizations successfully implementing these strategies include Aker BP, Cosmo Oil Company, and Moelven Timber, which improved data accessibility, accelerated decision-making, and scaled AI initiatives by adopting contextualization approaches [1].
In conclusion, contextualization transforms chaotic, disparate industrial data into actionable knowledge for AI agents, enabling automation that respects the physical realities of industrial processes, enhances flexibility and reliability, and delivers measurable business impact. Overcoming challenges involves adopting unified data strategies, advanced multi-agent architectures, real-time system integration, and robust feedback mechanisms [2][3]. The message is clear: AI won't transform operations unless data is organized, understood, and trusted.
Digital transformation in industrial operations is heavily reliant on data-and-cloud-computing and technology, as highlighted by the effective use of AI systems in contextualized digital twins. These AI agents, built and customized using low-code tooling, understand complex industrial workflows and act as trusted advisors, playing a crucial role in predictive, collaborative maintenance and improved sustainability reporting.
