From Fragmented Systems to Unified Industrial AI Platforms
Industrial operators have long wrestled with fragmented systems: SCADA for control, historians for production data and separate tools for analytics. This patchwork slows innovation because every new app demands complex integration and specialist coding. A new generation of industrial AI platforms is attacking this bottleneck by unifying data, context and applications in a single environment. Cognite Flows is a prominent example, built on an Industrial Knowledge Graph that keeps AI recommendations tied to live operating conditions. Instead of building custom pipelines for each use case, developers and plant teams work from an integrated workspace that understands assets, processes and relationships out of the box. This tighter coupling of operational data with AI-native architecture is key to app development acceleration, enabling teams to move from concept to deployment far faster than with traditional industrial software stacks while preserving the reliability expectations of plant environments.
Cognite Flows: AI-Native Architecture for Rapid App Development
Cognite Flows reshapes industrial application development by combining agentic AI coding tools with contextualized plant data in a single interface. Developers and engineers can design workflows, automate analysis and embed AI recommendations directly into frontline screens without building everything from scratch. Cognite reports that work which previously required more than 20 specialists and several months to reach a prototype has been completed in just four days using Flows. In other cases, implementation timelines that once stretched from six to nine months have been compressed to around two months before user acceptance testing. Industrial operators like Idemitsu and B. Braun are already applying the platform to capture expert knowledge, improve asset health visibility and refine user interfaces in weeks instead of quarters. By treating AI as a built-in capability rather than an add-on, Cognite Flows delivers industrial workflow automation at a pace aligned with fast-changing operational needs.
Low-Code SCADA Development With Advantech EdgeView
While Cognite Flows focuses on AI-powered workflows, Advantech’s EdgeView targets low-code SCADA development for visualization. Instead of replacing existing control systems, EdgeView sits above SCADA, production databases and IoT platforms as a dedicated visualization layer. It supports OPC UA, major databases and REST APIs, allowing integrators to connect diverse data sources while leaving control logic, tags and communication architecture intact. A low-code design environment with built-in industrial components and live data preview lets engineers build and test dashboards without disrupting devices or rewriting control programs. EdgeView deployments can go live in as little as two weeks, substantially reducing the effort traditionally required for custom HMI and monitoring projects. With support for Windows and Linux clients, web browsers and mobile devices, the platform enables faster rollout of modern dashboards and alarm views, aligning visualization projects with the accelerated cadence of contemporary industrial AI platforms.
Democratizing Industrial Software for Frontline Teams
A common thread across these platforms is the democratization of industrial software development. Cognite Flows was designed so operators and engineers no longer need to jump between disconnected tools to act on data. Instead, they receive AI-driven recommendations, live plant status and workflow actions within a single, role-tailored workspace. This reduces reliance on central IT or data science teams for every new use case. Similarly, EdgeView’s low-code approach enables system integrators and in-house engineers to deliver modern visualization and alarm interfaces without extensive custom coding. As industrial AI platforms evolve into proactive agents capable of managing complex operations, frontline staff gain access to advanced analytics wrapped in familiar, task-focused applications. The result is more inclusive industrial workflow automation, where domain experts can participate directly in app design, shortening feedback loops and helping organizations scale digital initiatives more reliably across plants and assets.