Technology readiness and the organizational journey towards AI adoption: An empirical study

Готовность технологий и организационный путь к внедрению ИИ: эмпирическое исследование
John Steven Edwards, Victoria Uren
2022-09-26

AI adoptionPeople, Processes, Technology, DataTechnology Readiness Levelscross-functional collaborationorganizational readiness
Artificial Intelligence (AI) is viewed as having potential for significant economic and social impact. However, its history of boom and bust cycles can make potential adopters wary. A cross-sectional, qualitative study was carried out, with a purposive sample of AI experts from research, development and business functions, to gain a deeper understanding of the adoption process. Technology Readiness Levels were used as a benchmark against which the experts could align their experiences. A model of AI adoption is proposed which embeds an extended version of the People, Processes, Technology lens, incorporating Data. The model suggests that people, process and data readiness are required in addition to technology readiness to achieve long term operational success with AI. The findings further indicate that innovative organizations should build bridges between technical and business functions.
1
A cross-sectional qualitative study used purposively sampled AI experts from research, development, and business functions to examine organizational AI adoption.
2
Innovative organizations should build bridges between technical and business functions to support AI adoption.
3
Long-term operational success with AI requires readiness in people, processes, and data in addition to technological readiness.
4
Technology Readiness Levels provided a benchmark for aligning experts’ experiences with the AI adoption process.
5
The proposed AI adoption model extends the People, Processes, Technology framework by incorporating Data as a distinct readiness dimension.

Organizational AI adoption process

the readiness of people, processes, data, and technology, and cross-functional integration required for long-term operational success with AI

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2022-09-26
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John Steven Edwards
Victoria Uren
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