It is considered that the degree of adoption of artificial intelligence (AI) technologies by manufacturing enterprises in emerging economies depends mainly on their internal capabilities and culture, while the surrounding ecosystem is considered only as a background, and not as a measurable factor. The authors’ research addresses this gap by developing and empirically testing a conceptual structural model based on nine hypotheses and eight constructs that links ecosystem networking, institutional and government support, digital infrastructure readiness, technological readiness, innovation culture, and dynamic opportunities with the introduction of AI and through it – with industrial innovation and competitiveness. The model combines the dynamic opportunity perspective, the institutional theory perspective, and the human-centered Industry 5.0 paradigm into a single ecosystem-level framework. At the same time, it is assumed that conditions at the ecosystem and infrastructure level form the internal readiness of the enterprise, which, in turn, encourages the introduction of AI and subsequent productivity. Primary survey data was collected from 182 managers from five manufacturing subsectors in Pakistan. Four of the nine hypotheses were confirmed: ecosystem connections were associated with technological readiness, digital infrastructure was associated with dynamic capabilities, institutional support was associated with an innovative culture, and the introduction of AI was closely related to indicators of competitiveness. Technological readiness and innovation culture did not demonstrate a reliable unique influence on decision-making, hypothetical deterrence from institutional support was not detected, and the two constructs – ecosystem connections and dynamic capabilities – were too correlated to be tested as independent predictors. The study provides one of the first tests of ecosystem and institutional prerequisites for assessing the implementation of AI using the example of South Asian industry at the enterprise level, reports on the limitations of measurement and allows formulating recommendations for managers in the field of industrial policy based on AI. Future research will focus on forming yet untested hypotheses using refined, abbreviated data to establish the direction of causal relationships between ecosystem conditions, AI adoption, and enterprise performance.