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Adaptive Cloud

Connects industrial systems so that data becomes usable, processes controllable, and automation effective.
Challenge

Distributed OT/plant data is difficult to integrate, hardly comparable, and rarely usable in real-time.

Industrial companies today face a structural integration problem: machines, systems, and data exist, but they don't work together. Information is generated in a distributed manner, processed with delays, and can only be used to a limited extent for operational decisions.

Adaptive Cloud addresses precisely this gap. The approach connects cloud, edge, and existing infrastructures so that data flow, system interaction, and decision logic function seamlessly. Technologies are integrated based on real operating conditions – not ideal target architectures.

As part of the Enterprise AI Reference Framework, Adaptive Cloud forms the basis for data-driven processes, automation, and AI to be effectively implemented in an industrial context.

Relevance

Why this matters now

  • When production and IoT data exist but cannot be made operationally usable
  • When systems exist side-by-side but do not enable continuous data flows
  • When cloud initiatives fail due to edge, machine, or real-time requirements
  • When decisions are based on delayed or aggregated data
  • When automation is limited by system boundaries or lack of integration
  • When existing infrastructures are not to be replaced, but integrated and further developed
  • When AI use cases are to be deployed directly in the operational process
Approach

System & Structure of the Approach

Adaptive Cloud creates a seamless integration and processing layer across:

  • Machines, sensors, and IoT systems
  • existing IT and application landscapes
  • Cloud, edge, and hybrid infrastructures

Core Principles:

  • Seamless Data Flows
    Data is systematically collected, processed, and made available where it is needed
  • Distributed Architecture
    Processing is performed as needed in the cloud, at the edge, or directly within the system
  • Context-based Automation
    Systems respond to real states and events instead of static logic
  • Integration instead of Replacement
    Existing systems are integrated and further developed, not replaced
  • Distributed Decision Logic
    Data, models, and control are directly intertwined within the operational process
  • Scalable Target Architecture
    Solutions are designed to grow with requirements, locations, and use cases
Outcome

Structural added value for companies

  • Production and process data become usable in real time
  • Decisions can be made directly in the operational context
  • Systems operate in an integrated manner instead of in isolation
  • Automation functions across system boundaries
  • Existing infrastructure is specifically leveraged instead of being replaced
  • Processes become controllable, measurable, and continuously optimizable
  • New use cases can be built on existing data and systems
  • Companies create the foundation for scalable industrial AI

Implementation is carried out by specialized teams

IoT & Adaptive Cloud

Cloud, Edge, and AI solutions for real-world industrial processes. We connect systems, machines, and data – for smart factories and adaptive automation.