Who Are the AI Scalers?

How Artificial Intelligence (AI) supports the performance of traditional firms remains uneven, despite firms having similar access to AI. Using a sample of global publicly quoted companies, we confirm early studies that heterogeneity in firms' ability to scale AI is related to the early orchestration of four AI-specific assets/resources (data, technology infrastructure, AI/data talent, and organisational design). Expanding on early academic work, we uncover four distinct resource-based segments, one of which operates with the full bundle of AI resources and which we call “AI Leaders”. This segment is comprised of the so- called “AI scalers”, i.e. firms for which more than 30% of their costs and revenues are already influenced by AI technologies, as they are able to capitalise on the stock of AI resources invested a few years earlier. We conclude that AI technologies are promising, but the translation of AI investments into significant macroeconomic effects will take time, given that only a small fringe of companies are AI leaders so far and that AI scaling depends on a compounding stock effect. We offer a check list to turbocharge AI scaling based on all the findings in the research.

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Publication Details

Journal
IEEE Engineering Management Review
Published
2025-05-15
DOI
https://doi.org/10.1109/emr.2025.3570622
Citations
1
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
2.48
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article

Who Are the AI Scalers?

Jacques Bughin
1 citations
IEEE Engineering Management Review
Explainable Artificial Intelligence (XAI)
2.48
article

Who Are the AI Scalers?

Jacques Bughin
article en
1 citations

Abstract

How Artificial Intelligence (AI) supports the performance of traditional firms remains uneven, despite firms having similar access to AI. Using a sample of global publicly quoted companies, we confirm early studies that heterogeneity in firms' ability to scale AI is related to the early orchestration of four AI-specific assets/resources (data, technology infrastructure, AI/data talent, and organisational design). Expanding on early academic work, we uncover four distinct resource-based segments, one of which operates with the full bundle of AI resources and which we call “AI Leaders”. This segment is comprised of the so- called “AI scalers”, i.e. firms for which more than 30% of their costs and revenues are already influenced by AI technologies, as they are able to capitalise on the stock of AI resources invested a few years earlier. We conclude that AI technologies are promising, but the translation of AI investments into significant macroeconomic effects will take time, given that only a small fringe of companies are AI leaders so far and that AI scaling depends on a compounding stock effect. We offer a check list to turbocharge AI scaling based on all the findings in the research.

IEEE Engineering Management ReviewVol. 54(1)
Université Libre de Bruxelles (BE), International Solvay Institutes (BE)
Openalex Percentile: Top 11%
Explainable Artificial Intelligence (XAI)
2.48
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