The research question centers on how AI impacts a traditional firm's scalability, as measured by its EBITDA, a proxy for economic and financial margins. The study's context is the rapidly evolving landscape of AI's integration into businesses. AI's capacity for self-learning and data analysis offers the potential for significant improvements in efficiency and revenue generation. The increasing adoption of AI across various sectors highlights the need to understand its impact on firm valuation and sustainability. This study aims to partially fill the gap in understanding AI's value-generating mechanisms and adoption in organizations, focusing on its scalability features enhanced by network theory properties (power laws). The target value chain includes digital applications such as social networks, the metaverse, eCommerce, and Industry 4.0. The core argument is that AI increases EBITDA by reducing operational expenses (OPEX) and boosting revenues, improving the overall valuation and sustainability of the firm. This builds upon existing literature highlighting AI's profit-generating potential through improved efficiency and market expansion. The study explores how AI transforms business models, providing a framework for understanding its unique value creation compared to traditional approaches. This contrasts AI business models with traditional ones, particularly focusing on how AI enhances scalability and business sustainability, aligning with current ESG targets. The study employs two distinct yet complementary methodologies: sensitivity analysis within a business plan and network theory analysis.

The paper draws on a substantial body of literature surrounding AI's impact on business. Studies show AI's potential for cost reduction through automation, enhanced decision-making through data analysis, and improved customer experience leading to revenue growth. The literature also emphasizes AI's role in predictive analytics, improved productivity and innovation, and increased scalability and flexibility. The concept of synergistic intangibles, such as big data, cloud storage, and blockchain, are discussed as further enhancing scalability. Existing research on AI for decision-making, business planning, and value creation through cost/benefit analysis and EBITDA generation provides relevant context. The study also references research that examines the taxonomy of AI business models, practical examples of AI implementation, and the creation of value and competitive advantage through AI adoption, particularly through a comparison of "with" and "without" AI scenarios. The paper notes the existing literature on AI's impact on business planning, predictive analytics, and cost/benefit analysis, emphasizing the use of advanced forecasting methods like ARIMA and artificial neural networks. However, the authors highlight a gap in research concerning the link between AI-sensitive business planning and the dynamic network theory forecasting, showing how AI influences not just sales predictions but future revenues and OPEX. The study also notes the scarcity of research into the impact of AI on company valuation and sustainability, emphasizing the controversial nature of this issue in the literature.

The study uses a two-pronged approach to analyze the impact of AI on firm valuation and sustainability. First, a sensitivity analysis is conducted using a business plan template. This analysis models a "traditional" (AI-free) business plan and compares it to a modified plan incorporating AI-driven cost savings. The sensitivity analysis focuses on the impact of AI-driven cost reductions and revenue increases on EBITDA, net operating efficiency, net profitability, return on marketing investment, and overall sustainability. The analysis assesses the impact of different levels of revenue increase and cost reduction on key financial indicators, including EBITDA, net income, net financial position, and equity and enterprise value (using both discounted cash flow (DCF) and market multiplier methods). This methodology utilizes a "with-or-without" approach common in intangible asset valuation. Second, the study incorporates network theory to provide a complementary perspective. The network analysis compares two ecosystems: one without AI and another integrated with AI, examining how AI adds nodes and strengthens existing connections within the network. This is shown through adjacency matrices, illustrating how AI expands the network's size and strengthens links. This part uses a dynamic network approach where AI introduces new nodes and links, strengthening existing ones. Metcalfe's Law is applied to assess the network value increase. The authors explore evolving network models, showing how AI influences the probabilities of payment and introduces new clients and suppliers. This is illustrated through directed graphs. Finally, the study combines the quantitative findings from the sensitivity analysis with the qualitative insights from the network analysis to provide a comprehensive assessment of AI's impact on firms.

The sensitivity analysis shows a substantial positive impact of AI adoption on key financial metrics. A 10% increase in revenue and 10% decrease in OPEX results in a significant increase in EBITDA, net income, net financial position, and both equity and enterprise value (as calculated using DCF and market multiplier approaches). The network theory analysis complements these findings by demonstrating how AI expands and strengthens the firm's network ecosystem. The introduction of AI leads to an increase in nodes (new clients and suppliers) and links within the network. This expanded network, visualized through adjacency matrices, reflects increased transaction volumes and improved network connectivity. Metcalfe's Law illustrates the significant increase in network value. The "with or without AI" comparisons demonstrate the additive value that AI brings to a traditional business. The analysis shows that AI enhances probabilities of payment between clients and suppliers, contributing to financial sustainability. Dynamic network models illustrate how AI's self-learning capabilities can fuel a virtuous cycle of growth, expanding the network and improving payment probabilities. The study observes that AI improves EBITDA through both revenue increases and cost reductions, aligning with industry findings where at least 5% of EBIT/EBITDA is attributable to AI. This impact might be underestimated, as the study doesn't fully account for AI's long-term, self-fulfilling effects. The study also uses a case study to exemplify the sensitivity analysis and illustrate the positive effect of AI adoption on a company's economic and financial metrics and valuation.

The findings strongly support the hypothesis that AI adoption positively impacts the financial performance and sustainability of traditional firms. The sensitivity analysis demonstrates the quantitative impact of AI-driven cost savings and revenue growth on key financial indicators, while the network theory analysis offers a complementary perspective on AI's role in expanding and strengthening network ecosystems. The combined approach provides a holistic understanding of how AI creates value for firms. The "with-or-without" approach, borrowed from intangible asset valuation, provides a strong analytical framework for assessing AI's contribution. The study's findings highlight the potential for AI to create a virtuous cycle of value creation, fostering both financial gains and enhanced network resilience. However, the limitations of the static network model and the potential underestimation of AI's long-term effects suggest avenues for future research. The results support the notion that AI investments yield substantial returns, reflected in the superior performance of technology stocks compared to the broader market, especially from 1994 to 2022. This suggests that AI-driven innovation contributes to improved operating performance and long-term competitive advantage.

This study demonstrates a clear positive relationship between AI adoption and improvements in traditional firms' financial health and sustainability. By combining sensitivity analysis with a network theory perspective, the paper presents a comprehensive methodology for evaluating the impact of AI. Future research should explore the impact of other intangible assets, the dynamic aspects of AI's self-learning capabilities, and the reduction in cash flow volatility resulting from AI adoption. Addressing these aspects will provide a more nuanced understanding of AI's total contribution to firm value. Further research into the specifics of AI implementation across various industries and organizational contexts would enhance practical applications of this innovative approach.

The study uses a hypothetical business plan for the sensitivity analysis, limiting the generalizability of findings to real-world scenarios. The network theory model, while insightful, is a simplified representation of complex real-world ecosystems. The study acknowledges limitations in the inability to comprehensively quantify the impact of AI's self-fulfilling properties and the long-term impact on the overall market value due to a potential reduction in the discount factor of discounted cash flow estimations. Furthermore, the study's focus on EBITDA as the primary metric overlooks other potential impacts of AI. Lastly, the lack of empirical data backing the revenue and cost variations used in the sensitivity analysis is acknowledged. Despite these limitations, the combined methodological approach used provides a significant contribution to the understanding of AI's impact.