KPI2KVI: A Multi Agent Workflow for Calculating Key Value Indicators from Service Descriptions
arXiv:2605.22825v1 Announce Type: cross Abstract: Key Value Indicators (KVIs) provide a decision oriented view of a service by summarizing how operational performance translates into stakeholder value, risk, and outcomes. However, in many domains KVIs are difficult to compute in practice because they require selecting relevant KVI categories, defining measurable Key Performance Indicators (KPIs), collecting KPI values, and applying consistent calculation logic, all of which is typically performed manually and inconsistently from unstructured service documentation. This paper presents KPI2KVI,
The proliferation of complex digital services and the rise of autonomous agents necessitate more efficient and reliable methods for performance evaluation that translate directly into business value.
This development addresses a critical challenge in operational intelligence, enabling automatic, consistent, and decision-oriented measurement of service performance and value creation, which is vital for strategic management.
The manual and inconsistent process of defining and calculating Key Value Indicators (KVIs) may be automated and standardized, significantly enhancing efficiency and data-driven decision-making for service-based organizations.
- · AI-powered service management platforms
- · Large Enterprises with complex service portfolios
- · Consulting firms specializing in business process automation
- · Manual data analysts and KPI specialists
- · Traditional business intelligence tools
- · Organizations relying on unstructured documentation
Automated calculation of KVIs will lead to more transparent and actionable insights into service performance and its impact on stakeholder value.
This automation could drive a broader shift towards agentic workflow management, where AI autonomously identifies, measures, and optimizes operational processes.
The widespread adoption of such systems could create new regulatory requirements for auditability and explainability in AI-driven performance assessment, especially in critical sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI