A framework for Artificial Intelligence Organizational Readiness

Abstract

The applications of artificial intelligence (AI) are significant on a social and economic scale, and they can offer businesses great value and opportunities. However, because of AI's varied application areas, its inherent complexity, and the new organizational requirements that result from AI adoption, companies encounter pitfalls when deploying the technology. Potential implementation scenarios are not always clear. Understanding the AI readiness on an organizational level, which shows “the extent to which an organization has the ability to reap the benefits of AI” can improve the chances of effective AI deployment. This study aims to bridge the gap between academia and practice as most attention to artificial intelligence was paid to modeling steps in academia. Research on applying AI models to real-life problems and realizing business value has received insufficient attention. The current frameworks do not incorporate context-specific considerations for organizational readiness. This research investigates the organizational readiness of AI in the semiconductor industry. It is a critical step in avoiding costly failures, considering the capital-intensive characteristic of the semiconductor industry. A literature study has been conducted at first to review the existing AI organizational readiness framework and typical readiness factors. Then eight industry expert interviews are conducted to give a more holistic view of AI use cases across the semiconductor value chain as identifying the opportunity is the first step to establish AI readiness. Three challenges of AI deployment are summarized and the potential AI organizational readiness factors are listed to guide the case study interviews. The case study is carried out in ASML, one of the leading producers of chip-making equipment in the world. Through fourteen case study interviews, this research proposes an AI organizational readiness framework with six dimensions and conceptualizes twenty readiness factors. In the strategic alignment dimension, there are needs and added-value assessment, bottom-up proposal/innovation lab, top management support, business model innovation. In the resource dimension, there are talents, financial budget, IT infrastructure, competence center. In the process dimension, there are multidisciplinary team/collaboration, agile way of working, employee training, business process standardization. Regarding data dimension, there are data availability, data governance, data platform. In the AI model cluster, there are explainable AI with domain experts, context-aware modeling, model operation. In the external business environment, there are peers/competitors/software vendors and customer demand. Furthermore, twenty propositions on AI readiness in semiconductor organizations are given indicating their positive or negative influence on AI organizational readiness. This study contributes to the emerging literature on AI organizational readiness with the developed multi-dimension framework. It identifies AI-specific readiness factors under the dimensions of "Data" and "AI model". Moreover, among 20 readiness factors, 10 readiness factors are newly identified such as the agile way of working, competence center, context-aware modeling. Companies that seek to implement AI can use the proposed framework with readiness factors as a tool for assessment to help decision-makers, managers, and project teams to develop and deploy AI faster and more effectively.