Maturity Metrics for Engineering Teams in AI-First Application-Development Projects

Abstract

This study is conducted to define a practical maturity-metrics model for AI-First engineering that links delivery and ML lifecycle to business outcomes, enabling reproducible, responsible, scalable operations across process, platform, team, and product. Comparative analysis of maturity models and delivery indicators (DORA Four Keys) combined with ML-specific metrics (retrain time, drift alerts, explainability coverage). Systematic review of MLOps practices and enterprise cases. SMART-based operationalization, automated collection via unified observability and event telemetry; explicit addition of a product plane to Microsoft’s MLOps model. Triangulation against McKinsey and DORA survey data. The model formalizes maturity across four planes—process, platform, team/culture, and product—mapped to five levels from no MLOps to fully automated operations. It integrates delivery indicators (lead time, deployment frequency, change failure rate, MTTR) with ML metrics (retrain time, drift alerts, explainability coverage) and requires SMART, automated, actionable collection. It further extends the metric set to cover generative AI and LLM-specific behavioural indicators such as factuality, instruction-following, retrieval effectiveness, safety violations and cost per useful token, and introduces repeatable proxy measures for people and culture—including knowledge diffusion, psychological safety and role-ratio tracking—so that human and organisational factors are observable and auditable. Evidence from DORA and McKinsey indicates that teams instrumenting both delivery and ML metrics achieve higher productivity, steadier SLA adherence, and reduced burnout; meanwhile, rapid AI tool uptake coexists with low trust in generated code (only 24% fully trust), underscoring an adoption–resilience gap the model addresses. Clustering metrics by change delivery, ML lifecycle, user/business value, reliability and quality, governance/ethics, and people/culture focuses investment. Operational enablers—event-based telemetry, unified observability, infrastructure as code, and SLO-prioritized alerting—support uninterrupted releases and faster recovery. The framework functions as an audit checklist and portfolio-planning instrument, translating measurements into managed, value-linked actions. Unifies DORA delivery metrics with ML lifecycle indicators across four planes, adding a product layer and operational enablers (event telemetry, IaC, SLO-prioritized alerting) to make metrics actionable for investment.