AI integration creates competitive divide in global tech and communication sectors

Artificial intelligence is becoming a decisive force in how financial markets value technology and communication firms, with new evidence showing that AI strengthens the link between intangible assets and equity market value, particularly in advanced economies. The findings of a paper published in Humanities and Social Sciences Communications offer an early global signal that companies capable of integrating AI into their intangible-asset portfolios are likely to widen their valuation advantage over competitors that cannot. The results are presented in a paper titled “The mediating role of Artificial Intelligence on the relationship between intangible assets and equity market value: evidence from global context,” examines 940 firm-year observations across emerging and developed markets, using a dataset that includes firms from Brazil, India, Egypt, Vietnam, Mexico, the United States, the United Kingdom, Germany, Japan, and Australia. Intangible assets strongly influence market value, but developed markets capture more gains The study assesses how intangible assets such as research and development, software, and trademarks influence equity market value across global markets. The authors use Tobin’s Q to measure firm valuations and apply a five-year panel dataset covering 2020–2024. By comparing developed and emerging markets, the research uncovers significant asymmetries in how intangible assets affect firm value. The analysis finds that intangible assets have a strong positive effect on equity valuations across all markets, but the size of the impact differs sharply. In developed markets, intangible assets are far more tightly linked to market value, a finding the authors attribute to stronger intellectual property systems, deeper capital markets, and more mature digital ecosystems. Firms in these economies experience larger market rewards for investments in software, research, branding, and proprietary technologies. The authors argue that this difference reflects a structural valuation gap between market groups. Developed-market firms operate in environments where intangible assets are easier to protect, easier to monetize, and more visible to investors. Stronger disclosure systems, advanced financial analytics, and robust legal frameworks increase the credibility and transparency of intangible-heavy business models. As a result, market valuations in these economies incorporate intangible assets more efficiently and more aggressively. On the other hand, many emerging-market firms struggle to turn intangible assets into competitive value because of weaker institutions, underdeveloped capital markets, and higher levels of information asymmetry. These constraints can reduce investor confidence and limit the valuation benefits associated with intangible investment. According to the study, this institutional friction dampens the return that emerging-market firms can generate from their intangible portfolios. The findings show that emerging-market firms are not lacking in intangible investment; rather, they operate in environments where intangible-asset advantages are harder to translate into market performance. The authors conclude that intangible-asset expansion in these countries must be paired with institutional strengthening to unlock full value. AI adoption deepens the valuation divide between emerging and developed markets The study analyses how AI mediates the relationship between intangible assets and equity market value. The authors use TF-IDF text mining to identify firms that disclose AI integration in annual reports and regulatory filings. This method allows the researchers to measure AI adoption in a standardized, quantitative way across global firms. The results show that AI adoption is strongly driven by intangible assets in both market types, but the effect is much stronger in developed markets. Firms with rich intangible portfolios are more likely to adopt AI, and this adoption further amplifies the market value of those portfolios. The study finds that AI acts as a multiplier, converting intangible assets into higher equity valuations by increasing efficiency, scalability, and competitive differentiation. Notably, the mediating role of AI is far more powerful in developed markets. Institutions in these markets provide a stronger foundation for AI implementation, including digital infrastructure, data governance frameworks, high-skilled talent, and regulatory stability. These factors enhance the ability of AI to convert intangible-asset investments into market gains. In emerging markets, AI adoption also improves equity valuations, but the effect is diluted by institutional gaps. Weak data governance, limited digital infrastructure, uncertain regulatory environments, and shortages of specialized AI talent all restrict the impact of AI on firm value. As a result, even firms with sizable intangible investments capture a smaller valuation premium from AI than peers in advanced economies. The authors describe this pattern as an “IA–AI valuation gap,” meaning that the combined value of intangible assets and AI integration is significantly higher in advanced markets than in emerging ones. The study’s robustness tests, including t-tests and Generalized Method of Moments models, confirm these findings and show consistent asymmetry across all model specifications. This gap suggests that competitive advantages tied to AI adoption will grow unevenly across global markets. Firms in developed economies may expand leadership in software, data assets, and algorithmic capabilities, while companies in emerging markets risk falling behind despite increasing investment in intangibles. Closing the valuation gap requires institutional reform and strategic sequencing Firms in emerging markets should not attempt to replicate the AI strategies of advanced-economy companies without first strengthening their intangible-asset foundations, the authors warn. According to the study, intangible assets must develop before AI can function as a meaningful value-creation tool. Companies lacking strong R&D capacity, software portfolios, or proprietary knowledge bases risk implementing AI in ways that generate limited or no market benefits. For these firms, strategic sequencing is critical. The authors recommend that emerging-market companies prioritize building digital capabilities, improving organizational knowledge, investing in research, and developing trademark and branding assets. Only after these foundations are established should firms pursue deeper AI integration. This sequencing allows AI to amplify existing intangible capital, rather than acting as a standalone tool unanchored to firm-specific resources. Policymakers in emerging economies have a major role to play. Stronger intellectual property protection is needed to give firms confidence that intangible investments will generate returns. Improved digital infrastructure can support AI deployment at scale, while regulatory clarity can reduce uncertainty and increase investor trust. Financial markets must also develop mechanisms that recognize intangible assets more accurately to prevent underpricing in valuation metrics. For developed-market firms, the study argues that the main priority is maximizing the synergy between their existing intangible assets and emerging AI capabilities. These firms already possess strong institutional support and can leverage AI to extract deeper value from intellectual property, software ecosystems, and research pipelines. The authors suggest that firms in advanced economies will expand their competitive advantage if they align AI investments with long-standing intangible-asset portfolios.