Organizations are adopting artificial intelligence faster than they are building the institutions required to govern it. Existing maturity models usually privilege data, infrastructure, and model deployment, while treating fairness, explanation, human oversight, cybersecurity, sustainability, and recourse as adjacent concerns. This study develops and provides exploratory validation of the Responsible AI Maturity Framework (RAIMF), a six-level organizational maturity model and a composite Responsible AI Maturity Index (RAIMI) for healthcare, finance, energy, and public supply chains. The empirical design combines structured content analysis of 40 governance instruments available by December 31, 2024 with a secondary reanalysis of industry maturity distributions from a 2024 survey of 1,000 organizations. Ten dimensions were coded on a five-point evidence scale. The instrument showed acceptable internal consistency (Cronbach alpha = 0.750), marginal-to-adequate factorability (KMO = 0.608; Bartlett chi-square = 234.66, p < .001), and a three-component structure explaining 76.2 percent of variance. Governance assurance and human-centered transparency achieved composite reliability above 0.85, average variance extracted above 0.60, and HTMT of 0.360. Across 19 industries, organizational maturity did not predict operational maturity (beta = 0.085, p = .740), revealing a substantial planning-execution gap. Sector RAIMI scores ranged from 59.3 to 61.7 after an execution penalty, placing all four critical sectors at the Governed AI level rather than Responsible or Institutionally Accountable AI. Public supply chains showed the strongest conversion of organizational intent into operational controls, while sustainability remained the least developed dimension across the standards corpus. RAIMF contributes a measurable account of Responsible AI as an institutional capability, not merely a technical property, and provides a reproducible benchmarking method for managers, regulators, auditors, and public administrators.