Why AI Readiness Is an Organizational Learning Problem, Not a Technology Purchase
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913050493190144 |
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| author | McClure, Jeanne Gerdau, Gregg |
| author_facet | McClure, Jeanne Gerdau, Gregg |
| contents | Global corporate AI investment reached $252.3 billion in 2024, yet only 6% of firms report significant earnings impact. This article argues that AI project failure is fundamentally an organizational learning problem rather than a technology deficit. Drawing on a systematic synthesis of 19 large-scale industry and academic sources, including surveys of nearly 10,000 organizational leaders, we identify two categories of failure: organizational (culture, leadership alignment, governance, and human-AI learning deficits) and technical (semantic bottlenecks and output management challenges). We introduce the Siloed-Integrated-Orchestrated (SIO) progression model, which maps enterprise AI capability across five pillars -- Culture & Leadership, Human Capital & Operations, Data Architecture, Systems Infrastructure, and Governance & Regulatory Compliance -- and provides prescriptive guidance for advancing between stages. The implications challenge organizations to reframe AI investment as capability development rather than technology procurement. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_16369 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Why AI Readiness Is an Organizational Learning Problem, Not a Technology Purchase McClure, Jeanne Gerdau, Gregg Computers and Society Artificial Intelligence Computation and Language K.6.1; H.1.1; I.2.1 Global corporate AI investment reached $252.3 billion in 2024, yet only 6% of firms report significant earnings impact. This article argues that AI project failure is fundamentally an organizational learning problem rather than a technology deficit. Drawing on a systematic synthesis of 19 large-scale industry and academic sources, including surveys of nearly 10,000 organizational leaders, we identify two categories of failure: organizational (culture, leadership alignment, governance, and human-AI learning deficits) and technical (semantic bottlenecks and output management challenges). We introduce the Siloed-Integrated-Orchestrated (SIO) progression model, which maps enterprise AI capability across five pillars -- Culture & Leadership, Human Capital & Operations, Data Architecture, Systems Infrastructure, and Governance & Regulatory Compliance -- and provides prescriptive guidance for advancing between stages. The implications challenge organizations to reframe AI investment as capability development rather than technology procurement. |
| title | Why AI Readiness Is an Organizational Learning Problem, Not a Technology Purchase |
| topic | Computers and Society Artificial Intelligence Computation and Language K.6.1; H.1.1; I.2.1 |
| url | https://arxiv.org/abs/2604.16369 |