Autonomous Workflow and Infrastructure Intelligence
DOI:
https://doi.org/10.5281/xzc96a44Keywords:
Adaptive Enterprise Intelligence,Autonomous Workflow Coordination,Predictive Infrastructure Optimization,Enterprise Service Management,Intelligent Workflow Automation,AI-Driven Decision Support,Predictive Analytics,Infrastructure Monitoring and Optimization,Digital Service Orchestration,Cloud-Native Enterprise SystemsAbstract
Adaptive Enterprise Service Intelligence (AESI) improves how enterprises respond to internal and external change by making Service Intelligence (SI) procedures themselves more adaptive. It targets two capabilities essential to enterprise survival: coordinating workflows without centralized control, and provisioning infrastructure in a context-aware manner.
AESI advances SI research on enterprise process coordination by enabling tasks to self-organize at runtime. This supports the task orchestration and task choreography patterns from workflow management theory, integrating information from a distributed control loop. We demonstrate this by identifying enterprise automation tasks that involve equipment the enterprise cannot directly control, and by proposing a method for decomposing such tasks into smaller units that can be allocated ad hoc to sub-operational agents during execution. This decomposition supports dynamic process reallocation and real-time fault tolerance.
A second contribution of AESI is context-aware infrastructure provisioning for automation tasks that depend on service infrastructure from other enterprises without full contractual arrangements. Enterprise architecture theory holds that no enterprise can operate without adequate service infrastructure, yet prior SI control loops have addressed only the processes an enterprise directly operates. To address this gap, we introduce a provision-control loop—analogous to the supply-control loop in the Theory of Constraints—that accounts for the adaptive behavior of computing resources among an enterprise’s cloud-service competitors. This produces a data-fabric view of both the enterprise’s own context and the context of any enterprise it supports through cloud computing, implemented via an enterprise data fabric combined with cross-enterprise AWS PrivateLink connections. This data-fabric view enables the sensing of contextual information that improves Enterprise Service Intelligence decisions across connected enterprises, and directly supports the provisioning of services for SI tasks that lack full contractual arrangements.
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