Many digital transformation projects fail due to fragmented operational landscapes, inflexible ERP systems, and unmanaged complexity.
Industrial enterprises must scale digitization quickly while maintaining the stability and governance required for their complex, mission-critical workflows.
While AI coding tools dramatically increase developer productivity, they don't change the fundamental industrial need for deterministic execution and operational resilience.
The solution for industrial apps is combining AI's rapid development speed with a low-code backbone that provides industrial integration and a deterministic execution layer.
A robust platform for industrial apps includes reusable components, governed low-code plugins, a deterministic process engine, and Git-based version management.
A demonstration shows how a natural language prompt is used to generate 80% of a functional shop floor application, including UI and data sources.
A German manufacturer created a single, unified worker app to orchestrate production by integrating ERP, quality management, and machine data.
Future agentic systems require a platform with layers for system integration, execution logic combining agents with deterministic processes, and human collaboration.
AI agents are already being used safely and effectively in manufacturing for tasks like predictive maintenance, production planning, and automated document validation.
Industrial companies will build thousands of AI-generated apps, requiring orchestration platforms that provide a scalable framework for integration and governance.
Jobs that call for the skills explored in this talk.