Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms

The paper investigates how AI affects the scalability and valuation of traditional firms by focusing on EBITDA as a proxy for economic/financial margins. It frames AI as self-learning systems that analyze data to improve decision-making and operational efficiency. Addressing a literature-identified gap in understanding the value-generating mechanisms of AI adoption, the research question asks how AI-driven scalability influences EBITDA and thereby market value and sustainability. The study targets digital applications and Industry 4.0 contexts where bundling of intangibles and network effects are salient. The conceptual framework proposes that AI ignites networked scalability that increases revenues and/or reduces OPEX, thereby improving EBITDA, valuation (via DCF and market multiples), and sustainability (including ESG alignment).

The paper integrates literature throughout, highlighting several streams: (1) Value creation and business model innovation from AI: Berawi (2020) argues AI creates profits by enhancing efficiency and expanding digital markets; Lee et al. (2019) link proactive AI use to business model innovation; Winter (2021) gives practical cases of AI-enabled offerings; Wodecki (2019) ties AI to value creation and competitive advantage. (2) Market valuation effects: Huang and Lee (2023) find positive abnormal returns from AI implementation announcements; Lui et al. (2022) discuss AI investment’s impact on firm value. (3) Taxonomy of AI business models: Widayanti and Meria (2023) classify AI startups’ models. (4) AI for decision-making and forecasting: Dear (2019), Duan et al. (2019), Gupta et al. (2022), Zellner et al. (2021), Fang et al. (2022), Abiodun et al. (2018) cover decision support and forecasting methods; these works typically focus on prediction rather than explicit effects on revenues/OPEX. (5) Scalability and enabling technologies: Gupta et al. (2017) on IoT scalability; Belgaum et al. (2019, 2021), Saadia (2021), Soni and Kumar (2022) discuss integrating AI with big data, cloud, and SDN; Popkova and Sergi (2020) address Industry 4.0. (6) AI, ESG, and sustainability: Škapa et al. (2023) on AI-driven ESG investing; Kar et al. (2022) review AI’s sustainability impacts. (7) Network theory foundations for evolving ecosystems: Barabási (2016), Erdős and Rényi (1959), Barabási and Albert (1999), Bianconi and Barabási (2001), Bianconi (2018) provide network models applicable to AI-enhanced ecosystems. The authors note a gap linking AI-sensitive business planning to dynamic network-theoretic impacts on revenues and OPEX.

The study employs two complementary approaches using a with-or-without framework anchored in International Valuation Standard 210 for intangibles: (a) Sensitivity analysis embedded in a traditional business plan (AI-free) versus an AI-adopting plan. AI effects are injected as revenue uplifts and OPEX reductions in two prudential scenarios: +5% revenues/−5% OPEX and +10% revenues/−10% OPEX. Resulting impacts flow through EBITDA to operating cash flows (FCFF), net results, net financial position, equity, and valuation via DCF and market multiples. The DCF uses unlevered cash flows discounted at WACC for enterprise value and levered cash flows discounted at cost of equity for equity value. The EBITDA multiple method estimates enterprise value, then adjusts by the net financial position to derive equity value. (b) Network theory interpretation comparing a baseline traditional network (3 nodes) with an AI-enhanced network (6 nodes) via adjacency matrices. AI adds nodes (new clients/suppliers) and strengthens edges (interactions), increasing connectivity and potential transactions (and thus EBITDA). Metcalfe’s law is invoked for illustrative network value scaling (n^2), with the example moving from 3 nodes (value 9) to 6 nodes (value 36) under equal weight assumptions. The model is extended to evolving multilayer networks capturing AI’s dynamic, self-learning, and real-options-like expansion. A directed graph payment model introduces probabilities of payment from clients to firm (p) and firm to suppliers (q), with AI increasing these probabilities (p', q') and adding new client/supplier sets (TC/NC, TS/NS). The framework computes effective payment probabilities under circular, bilateral relationships, showing AI improves net payment reliability and transactional flows. Core cash flow formulation: AI-driven Sales minus AI-driven OPEX determine AI-driven EBITDA; adjusted for changes in operating working capital and CAPEX to derive operating cash flows; these are discounted to obtain enterprise value, while equity value follows after financing effects. Risk adjustments in WACC are discussed conceptually; empirical risk reduction due to AI is noted as uncertain at present.

- Sensitivity analysis results (Table 3, averages over t1–t3): • Average Sales: Base € 121,36,667; +5/−5 € 133,11,250; +10/−10 € 145,60,000. • Average OPEX: Base € 97,09,333; +5/−5 € 88,27,000; +10/−10 € 80,00,000. • Average EBITDA: Base € 24,27,333; +5/−5 € 44,84,250; +10/−10 € 65,60,000. • Average Net Result: Base € 13,18,203; +5/−5 € 27,70,397; +10/−10 € 42,35,000. • Average Net Financial Position: Base € 1,99,549; +5/−5 € 22,04,533; +10/−10 € 42,20,977. • Average Equity: Base € 54,48,563; +5/−5 € 77,91,670; +10/−10 € 1,01,49,333. • Equity value (DCF): Base € 1,85,99,227; +5/−5 € 3,64,11,256; +10/−10 € 5,43,76,197. • Equity value (multiples): Base € 1,78,06,273; +5/−5 € 3,45,90,713; +10/−10 € 5,15,28,833. • Enterprise value (DCF): Base € 2,05,99,994; +5/−5 € 3,84,12,023; +10/−10 € 5,63,76,964. • Enterprise value (multiples): Base € 1,98,07,040; +5/−5 € 3,65,91,480; +10/−10 € 5,35,29,600. - Across both DCF and multiples approaches, AI-induced +5/−5 and +10/−10 scenarios substantially increase EBITDA, net results, liquidity/solvency metrics (NFP), and both enterprise and equity values. - Network interpretation: Moving from a 3×3 to a 6×6 adjacency matrix increases nodes and links; under Metcalfe’s law, illustrative network value rises from 9 to 36, and AI strengthens edge intensities, implying higher transaction volumes and data flow supporting revenue growth and OPEX reductions. - Operational implications: AI adoption improves debt service capacity, reduces delinquency risk, and supports sustainability/ESG alignment through enhanced efficiency and margins. - External benchmarks cited indicate AI commonly contributes at least ~5% to EBIT in practice, supporting the plausibility of modeled improvements.

Findings demonstrate that AI-driven increases in revenues and decreases in OPEX translate into higher EBITDA, which propagates through cash flow–based valuation (DCF) and market multiple approaches to significantly higher enterprise and equity values. The results address the research question by quantifying how AI-enabled scalability improves core financial margins and, consequently, valuation and sustainability. The network theory lens clarifies the mechanism: AI expands and densifies the ecosystem by adding nodes (new customers/suppliers) and strengthening edges (interactions), increasing transaction reliability and intensity. This structural evolution supports sustained revenue growth, cost efficiencies, and better bankability (improved debt service capacity). Stakeholder value co-creation emerges as the AI-enabled platform incentivizes and facilitates collaboration across the ecosystem, consistent with circular value creation dynamics. While immediate risk parameter (WACC) reductions are not empirically established, the enhanced robustness and resilience of AI-augmented networks suggest potential future impacts on discount rates, which would further amplify value creation.

AI positively affects firms’ economic and financial margins by raising revenues and reducing OPEX, enabling scalable operations, data-driven decision-making, and new growth opportunities. Sensitivity simulations indicate substantial improvements in EBITDA and valuation under prudent +5/−5 and +10/−10 scenarios. The network perspective shows AI increases nodes and link strengths, enhancing connectivity, reliability, and transaction flows that underpin financial performance. Overall, AI is a key enabler of sustainable, accelerated growth and stakeholder value co-creation. Future research should examine complementary intangibles (e.g., blockchain-enabled validation, scalable platforms), refine theory on AI within scalable ecosystems, and deepen industry-specific analyses of AI-enabled business model innovation and its valuation implications.

- Empirical evidence directly quantifying AI’s precise impact on revenue uplift and OPEX reductions across industries remains limited; hence reliance on sensitivity analysis. - The study does not quantify changes in cash flow volatility or cost of capital (WACC/Ke) due to AI; the extent to which AI reduces risk is left for future research. - Dynamic network evolution under AI (multilayer, weighted, time-varying graphs) is simplified; modeling these effects rigorously is challenging. - Generalizability is constrained by AI’s domain specificity and heterogeneous industry contexts; sectoral segmentation and matched AI-use analyses are needed. - CAPEX and tax effects are treated as minor or exogenous to AI in valuation; more granular treatment could refine estimates.