This study investigates whether variation in firm-level AI maturity is associated with differences in financial performance among large public firms. Using panel data on 130 S&P 500 firms appearing in all three HG Insights AI 1000 releases between 2023 and 2025, the analysis constructs a rank-based AI maturity score and evaluates its relationship with Tobin's Q, return on assets, operating efficiency, and excess stock returns. Multiple AI maturity specifications are employed, including quartile-based, nonlinear, and a supplemental real-score validation. Financial data measured in the regression models are estimated using financial data from Compustat and CRSP. The results show that AI maturity is most strongly associated with firm valuation. Firms with low AI maturity tend to receive weaker valuation outcomes, while higher levels of AI maturity are associated with stronger valuation effects in nonlinear specifications. In contrast, the evidence for profitability, efficiency, and sustained excess returns is limited in the baseline models, though subgroup analyses reveal important heterogeneity across firm size and sector. The findings suggest that AI maturity is more clearly reflected in forward-looking market valuation than in near-term accounting or return outcomes, and that its financial relevance depends on firm-specific context rather than adoption alone.
