In today’s Agile enterprises, Quality Engineering is hampered by a fragmented tool ecosystem—Jira, multiple test management platforms, diverse test frameworks, ServiceNow and more—while the effort required to build and maintain a scalable, cloud-based reporting infrastructure in-house can divert precious engineering resources and delay time-to-value. The strategic approach overcomes both obstacles by deploying lightweight, ground-level connectors that extract raw events and metrics from every tool, streaming them into a centralized, cloud-native data store where they are normalized and persisted at scale. A composite metrics engine then transforms this ground-level data into standardized Engineering KPIs, aligns them with Business OKRs, and computes unified “quality scores” across all Agile pods. Finally, an AI-driven continuous monitoring and optimization platform detects gaps, anomalies and risk patterns in real time, automates root-cause analysis by correlating failures with code changes and environment shifts, and recommends targeted process or resource adjustments. The result is a turnkey, data-driven blueprint that bypasses homegrown toil, enforces metric consistency, continuously optimizes performance through AI, and delivers boardroom-ready insights that elevate quality to a C-Suite priority. This approach helps bring quality to the C-Suite table in real time, satisfying executives’ demand for immediate, data-driven visibility.