From Fragmented Systems to Unified Industrial AI Workspaces
Industrial AI automation is moving from back-office experimentation to the plant floor. Cognite Flows illustrates this shift by giving factory and industrial teams a single-screen interface where real-time operational data, AI-driven recommendations, and task-specific applications coexist. Instead of operators hopping between disconnected systems for alarms, maintenance histories, and procedures, workflow optimization tools consolidate these elements into one contextual workspace. Built on an Industrial Knowledge Graph, Cognite Flows keeps sensor data, asset hierarchies, and process states linked to their live operating context, ensuring AI insights are never divorced from what is happening in the plant at that moment. This integration of real-time operational data with AI guidance is critical in environments where downtime, safety, and compliance pressures leave little margin for error. The result is a more intuitive, AI-first environment that allows frontline workers, not just central engineering teams, to benefit from advanced analytics and automation.
App Development Acceleration: From Months to Days
Cognite Flows is redefining app development acceleration for industrial users by pairing agentic AI coding tools with an AI-native architecture. Instead of traditional projects that demand large teams and multi-month timelines, organizations are reporting implementation cycles measured in days. In one case, a global pharmaceutical company used Flows to deliver automated AI workflows that would previously have required more than 20 specialists and several months just to produce a prototype; with Flows, the prototype phase took four days. The lead-up to user acceptance testing shrank from a typical six to nine months to about two. This capability stems from reusable data models, pre-built workflow components, and generative AI that helps assemble and validate code. For system integrators and partners, this reduction in friction means more rapid iteration, faster deployment of mission-critical solutions, and the ability to scale industrial AI automation projects across multiple sites with far less overhead.
Capturing Expertise and Operational Context in Real Time
Beyond speed, industrial AI automation is increasingly about capturing human expertise and binding it to live data. At Idemitsu, Cognite Flows is being used to transform decades of specialist plant knowledge into a digital legacy, turning tacit know-how into AI-accessible logic. By tying this knowledge to a real-time knowledge graph, the company expects its tools to evolve into proactive AI agents capable of managing complex operations rather than merely visualizing data. B. Braun, meanwhile, is leveraging Flows for clearer visibility into asset health across its sites. Within four weeks, it refined how operational data is presented, delivering near-instant UX updates based on user feedback. These examples show how integrating real-time operational data with domain expertise allows organizations to create smarter, context-aware workflows. Decisions can be made faster, and frontline workers receive tailored insights that reflect both current plant conditions and accumulated operational wisdom.
AI Workflow Automation Spreads to Media and Beyond
The same AI-native workflow patterns transforming industrial plants are now reshaping media production, as seen with tools like Metadata Flow in film and TV. While Cognite Flows focuses on industrial AI automation, both platforms leverage workflow optimization tools that orchestrate complex, multi-step processes with minimal manual intervention. In media, this can mean automating metadata tagging, content routing, or quality checks across large asset libraries, enabling creative teams to move from planning to delivery far more quickly. In industry, integrating AI with real-time operational data accelerates everything from maintenance scheduling to root-cause analysis. The broader trend is clear: AI-driven workflow engines are becoming sector-specific backbones that reduce development cycles, standardize best practices, and keep human decision-makers in the loop. As these tools mature, they are likely to blur the line between software development and operations, turning workflows themselves into continuously evolving, data-driven products.