Balancing flexibility and regulatory risk: AI-assisted strategies for non-GAAP disclosures in competitive markets

Purpose This paper develops an optimal control model to analyze how firms optimize their non-generally accepted accounting principles (GAAP) disclosure strategies in competitive markets while managing regulatory risks, and examines how artificial intelligence (AI) capabilities in regulatory navigation and operational efficiency influence these optimal disclosure decisions. Design/methodology/approach The study employs an open-loop optimal control framework with a differential-game payoff structure, using a Hamiltonian formulation and Pontryagin's maximum principle to derive closed-form optimal disclosure strategies. Findings The research reveals that optimal disclosure strategies emerge from balancing the valuation benefits of disclosure flexibility against regulatory risk. AI capabilities affect firm strategy through two channels – (1) reduced regulatory sensitivity enables firms to sustain higher equilibrium flexibility levels, and (2) reduced adjustment costs accelerate convergence to target equilibrium flexibility. Comparative statics analysis identifies how competitive intensity, AI capabilities and firm characteristics influence equilibrium disclosure choices. Research limitations/implications The model employs an open-loop formulation and is limited to a two-firm setting. It assumes constant model parameters over the planning horizon and therefore does not capture time-varying market conditions or strategic feedback between firms. Practical implications The framework provides corporate managers with analytical insights into how investments in AI-enhanced regulatory navigation and operational efficiency influence optimal disclosure strategies. It also provides regulators with a theoretical basis for understanding how AI capabilities moderate the marginal relationship between disclosure flexibility and regulatory intervention. Originality/value This paper presents the first analytical model of a non-GAAP disclosure strategy that explicitly distinguishes between AI-enhanced regulatory navigation and operational efficiency, yielding closed-form solutions that facilitate rigorous comparative statics and generate testable empirical predictions.