Artificial intelligence (AI) improves organizational performance, yet the financial mechanisms through which these gains become financeable cash flows remain insufficiently specified. While research emphasizes productivity and innovation, lenders focus on profitability, cash generation, debt-service capacity, leverage, liquidity, and covenant compliance. In this paper, we developed a finance-oriented framework linking AI-enabled organizational coordination to lender-oriented financial indicators. Using a transparent explanatory simulation anchored to a reference business plan, we modeled how AI capability, organizational readiness, coordination, implementation timing, and financial absorptive capacity translated into revenues, operating costs, EBITDA, operating cash flow, cash available for debt service, debt-service coverage, and leverage. The framework separated AI operating effects from ESG considerations and provided an auditable basis for scenario analysis, lender communication, and future empirical calibration. Its central implication was that AI-related operating gains create financing value only when they are sufficiently credible, persistent, and timely to survive the implementation trough and preserve debt-service capacity.
Citation: Roberto Moro-Visconti, Salvador Cruz Rambaud, Joaquín López Pascual. Artificial intelligence's networking scalability: ESG value creation and bankability issues[J]. Quantitative Finance and Economics, 2026, 10(4): 697-727. doi: 10.3934/QFE.2026027
Artificial intelligence (AI) improves organizational performance, yet the financial mechanisms through which these gains become financeable cash flows remain insufficiently specified. While research emphasizes productivity and innovation, lenders focus on profitability, cash generation, debt-service capacity, leverage, liquidity, and covenant compliance. In this paper, we developed a finance-oriented framework linking AI-enabled organizational coordination to lender-oriented financial indicators. Using a transparent explanatory simulation anchored to a reference business plan, we modeled how AI capability, organizational readiness, coordination, implementation timing, and financial absorptive capacity translated into revenues, operating costs, EBITDA, operating cash flow, cash available for debt service, debt-service coverage, and leverage. The framework separated AI operating effects from ESG considerations and provided an auditable basis for scenario analysis, lender communication, and future empirical calibration. Its central implication was that AI-related operating gains create financing value only when they are sufficiently credible, persistent, and timely to survive the implementation trough and preserve debt-service capacity.
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Figures(2) / Tables(9)
Roberto Moro-Visconti, Salvador Cruz Rambaud, Joaquín López Pascual. Artificial intelligence's networking scalability: ESG value creation and bankability issues[J]. Quantitative Finance and Economics, 2026, 10(4): 697-727. doi: 10.3934/QFE.2026027
Organizational-to-finance transmission architecture (Source: Authors' conceptualization and prompt; graphical rendering produced using OpenAI)
Bankability waterfall: from technological potential to lender capacity (Source: Authors' conceptualization and prompt; graphical rendering produced using OpenAI)
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