The convergence between artificial intelligence, sustainability measurement and knowledge management is reshaping how organisations diagnose vulnerability, create value and build resilience. This paper examines the Noverim case as the basis for a study hypothesis: AI-enabled sustainability ratings may help identify organisational disequilibrium before conventional accounting data reveal it. The article does not present original empirical evidence. It develops a conceptual and case-based framework centred on an intelligent predictive system for assessing sustainability and business equilibrium. The reference to 500 Italian knowledge-intensive firms is used solely as a prospective field for future validation; it is not an observed sample, a tested dataset or evidence supporting the hypothesis. The study asks how heterogeneous knowledge assets, sustainability ratings and predictive analytics can be integrated to support early diagnosis, resilience-building and knowledge-driven innovation. The proposed framework interprets sustainability as an endogenous component of equilibrium, since ESG and organisational conditions affect risk exposure, adaptive capacity, stakeholder trust and the durability of value creation. The Noverim architecture combines supervised machine-learning techniques with unsupervised behavioural clustering to identify latent risk profiles and possible trajectories of imbalance. On this basis, the paper formulates three propositions: first, such systems may anticipate disequilibrium over a 12-to-18-month horizon; second, weak signals such as reduced intangible investment, turnover of key personnel, deteriorating internal sustainability indicators and strategy-conduct misalignment may reveal early fragility; third, intelligent ratings may help distinguish reversible from structural crises. These propositions require future empirical testing. The paper contributes to the literature on knowledge-based organisations by conceptualising predictive intelligence as a knowledge infrastructure for organisational learning, resilience mapping and accountable decision-making.