Industrial Artificial Intelligence and Data Strategies Outlook
In a groundbreaking presentation, Cognite, a leading AI company, outlined their innovative strategy for industrial AI, highlighting the importance of contextualization, low-code, domain-specific agents, and customer co-development and operational integration.
Joyce Shi, Senior Software Engineer at Cognite, explained that contextualization goes beyond document tagging, encompassing the understanding of symbol relationships, piping connectivity, and asset hierarchies. This approach, demonstrated by the successful implementation at Cosmo Oil, enabled early failure detection and transformed teams from reactive to predictive maintenance, a significant leap forward in the industry.
Lisa Nilsson, Process Engineer at Moelven, shared how Moelven standardized and contextualized energy and production data across distributed sawmills in Sweden and Norway using Cognite Data Fusion. This resulted in a unified dashboard that displays order-specific energy usage and production metrics, freeing up staff time for process improvement.
Cameron Greenburg, Senior Product Manager at Atlas AI and Cognite, demonstrated how to build and customize industrial agents using low-code tooling without traditional programming. Greenburg emphasized that with the right data foundation, these tailored AI agents can scale across teams and workflows with minimal effort. Agents created by Atlas AI are deeply embedded in operational context, drawing from structured knowledge graphs and real-time plant data.
Cognite's approach leverages contextualized data and Industrial AI to streamline complex industrial workflows. By transforming fragmented raw data into a connected, contextualized knowledge graph, AI agents can understand operations holistically and autonomously act within workflows. This conversion of data from isolated silos into reusable, scalable insights drives measurable business impact such as cost savings, improved efficiency, and reduced operational risk.
Key elements of Cognite's methodology include contextualization as a foundation, industrial AI tailored for operations, interactive workflows and collaboration, measurable business impact, and extensible AI and reusable data products. Cognite Data Fusion automates the ingestion and transformation of raw industrial data, creating a unified contextual model around assets, processes, and workflows. This enables consistent and reliable data use across the organization, reducing fragmentation and siloed data pools.
The integration of domain-specific context and operational knowledge enables AI-powered agents to understand workflows deeply, detect anomalies, diagnose causes, and take automated actions like scheduling maintenance or ordering materials. The Canvas platform allows human workers to interact with AI agents via natural language, enabling seamless troubleshooting, work order creation, and data summarization within a collaborative workspace.
The combination of contextualized data and AI-driven automation delivers tangible gains including reduced downtime, accelerated decision-making, and cost efficiency in sectors such as energy, manufacturing, and renewables. Cognite emphasizes scaling beyond isolated AI pilots to enterprise-wide systems that learn and improve continuously, providing ongoing operational value.
Field data collection time dropped significantly for Cosmo Oil engineers after adopting Cognite Data Fusion, and the company transitioned from fragmented maintenance records to predictive, collaborative maintenance. Moelven, another successful user, created a unified dashboard that displays order-specific energy usage and production metrics, freeing up staff time for process improvement.
In summary, Cognite's approach demonstrates that embedding contextualized data into Industrial AI workflows unlocks operational insight at scale, enabling automation, trusted AI collaboration, and measurable efficiency improvements across complex industrial environments. This innovative strategy promises to revolutionize the way industries operate, driving efficiency, reducing risk, and fostering a more predictive, proactive approach to maintenance and operations.
[1] Cognite. (n.d.). Product Tour: Industrial AI in the Era of Contextualization. Retrieved from https://www.cognite.com/product-tour/ [3] Cognite. (n.d.). Blog: The Power of Contextualized Data in Industrial AI. Retrieved from https://www.cognite.com/blog/the-power-of-contextualized-data-in-industrial-ai/ [4] Cognite. (n.d.). Case Study: Cosmo Oil. Retrieved from https://www.cognite.com/case-studies/cosmo-oil-case-study/
- The digital transformation of industries, facilitated by Cognite's strategy, encompasses data-and-cloud-computing and technology, as demonstrated by their innovative approach using contextualized data and Industrial AI to streamline complex workflows.
- Leveraging contextualized data and AI, Cognite's methodology seeks to revolutionize industries by delivering automation, trusted collaboration, and efficiency improvements, merging data-and-cloud-computing and technology to drive industrial operations towards a proactive, predictive maintenance model.
