When Metrics Govern: Pre-Algorithmic Optimisation and the Ethics of Artificial Intelligence

Contemporary debates in AI ethics often associate algorithmic governance with advanced computational systems, automated decision-making, and machine learning. This focus, however, risks overlooking how similar governance effects emerge through non-AI optimisation infrastructures that precede and normalise algorithmic control. This paper introduces the concept of algorithmic governance without algorithms to examine how metrics, dashboards, and performance indicators embedded within Lean–Green manufacturing systems reshape authority, agency, and ethical responsibility in organisational life. Drawing on mixed-method empirical material from Nigerian manufacturing organisations—including surveys, semi-structured interviews, and observational data—the study analyses how optimisation regimes function as socio-technical systems of governance rather than neutral managerial tools. The findings reveal three interrelated dynamics: the dominance of quantified metrics in decision-making, the reconfiguration of worker agency through conditional participation, and the transformation of sustainability from a collective ethical commitment into a measurable performance output. These dynamics demonstrate that ethical concerns commonly attributed to artificial intelligence—such as displacement of human judgement, responsibility gaps, and moral deskilling—are already present within everyday organisational technologies. By extending ethical analysis beyond narrowly defined AI systems, this paper contributes to AI ethics scholarship by highlighting the need to address governance risks embedded in pre-algorithmic optimisation infrastructures that shape how institutions value efficiency, responsibility, and human judgement.