Artificial intelligence is deeply transforming the business world by improving operational efficiency, decision-making, and innovation. However, the oldest companies face major challenges in attracting and integrating professionals specialized in this field. A one-standard-deviation increase in a company\u2019s age reduces the share of employees dedicated to artificial intelligence within it by 5.2%. This phenomenon is explained by outdated systems, deeply ingrained habits, and resistance to change, which hinder the adoption of new technologies.<\/p>\n
Mature companies, however, have significant assets, such as abundant historical data, established networks, and financial stability. These resources can facilitate the integration of artificial intelligence, provided structural obstacles are overcome. Investments in research and development, as well as in modern infrastructure, play a key role in mitigating these challenges. They enable the modernization of operations and the attraction of qualified talent, while strengthening innovation capacity.<\/p>\n
The composition of boards of directors also influences this dynamic. In older companies, a strong presence of women or members from minority groups on boards can slow down the adoption of artificial intelligence. This is due to more cautious decision-making and a slower consensus-building process when facing complex and risky technologies. While diversity on boards generally improves governance, it can also introduce additional challenges in a context of technological transformation.<\/p>\n
Companies that succeed in integrating artificial intelligence talent reap considerable benefits. Their market value and operational efficiency improve significantly, as evidenced by indicators such as market capitalization, profitability, and return on assets. These results highlight the strategic importance of artificial intelligence in maintaining a competitive edge.<\/p>\n
For mature companies, modernizing existing infrastructure and systems must be an absolute priority. Investments in capital expenditures and research help offset the disadvantages related to age. Continuous employee training programs are also essential to prepare teams to work with these new technologies. Finally, boards of directors must be supported to better understand the stakes of artificial intelligence and make informed decisions. These measures turn challenges into opportunities and ensure a successful transition to an increasingly digital economy.<\/p>\n
DOI:<\/strong> https:\/\/doi.org\/10.1007\/s11573-026-01271-0<\/a><\/p>\n Title:<\/strong> The AI workforce and firm maturity: old firms, new tech<\/p>\n Journal:<\/strong> Journal of Business Economics<\/p>\n Publisher:<\/strong> Springer Science and Business Media LLC<\/p>\n Authors:<\/strong> Pattanaporn Chatjuthamard; Pornsit Jiraporn; Pandej Chintrakarn; Woraphon Wattanatorn<\/p>\n","protected":false},"excerpt":{"rendered":" Older Companies Struggle to Integrate Artificial Intelligence Talent Artificial intelligence is deeply transforming the business world by improving operational efficiency, decision-making, and innovation. However, the oldest companies face major challenges in attracting and integrating professionals specialized in this field. A one-standard-deviation increase in a company\u2019s age reduces the share of employees dedicated to artificial intelligence… Continue reading Older Companies Struggle to Integrate Artificial Intelligence Talent<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-33","post","type-post","status-publish","format-standard","hentry","category-international","entry"],"_links":{"self":[{"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/posts\/33","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/comments?post=33"}],"version-history":[{"count":1,"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/posts\/33\/revisions"}],"predecessor-version":[{"id":34,"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/posts\/33\/revisions\/34"}],"wp:attachment":[{"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/media?parent=33"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/categories?post=33"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/economicsfoundation.com\/en\/wp-json\/wp\/v2\/tags?post=33"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}