Apr 2020· International Conference on Evaluation & Assessment in Software Engineering· pp. 1-10· 20 citations· ⚡ 1 influential· 40 references
Computer ScienceEngineering
TL;DR
This investigation revealed different types of AI systems and different AI development approaches, but it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development ofAI systems.
Abstract
There is a rapidly increasing amount of Artificial Intelligence (AI) systems developed in recent years, with much expectation on its capacity of innovation and business value generation. However, the promised value of AI systems in specific business contexts might not be understood, and further integrated into the development processes. We wanted to understand how software engineering processes and practices can be applied to develop AI systems in a fast-faced, business-driven manner. As the first step, we explored contextual factors of AI development and the connections between AI developments to business opportunities. We conducted 12 semi-structured interviews in seven companies in Brazil, Norway and Southeast Asia. Our investigation revealed different types of AI systems and different AI development approaches. However, it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development of AI systems. The findings have implications for future research on business-driven AI development and supporting tools and practices.
This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.
Giedrius Čyras, Vita Marytė Janušauskienė· International Scientific Con...· 1 citation
This study aims to analyze the role of Artificial Intelligence (AI) in driving digital business transformation through a systematic literature review approach. Digital business transformation has become increasingly essential for organizations seeking to maintain competitiveness in the era of Industry 4.0 and the emerging Industry 5.0 paradigm. As a core component of technological advancement, AI provides advanced capabilities in data processing, process automation, predictive analysis, and the generation of valuable business insights. Through an analysis of 25 selected scientific publications from reputable academic databases, this study identifies four main dimensions of AI’s role in digital business transformation: (1) intelligent business process automation, (2) data-driven personalization of customer experiences, (3) supply chain and operational optimization, and (4) AI-based strategic decision-making. The findings indicate that organizations successfully integrating AI into their core business strategies experience improvements in operational efficiency, enhanced customer satisfaction, and increased business performance. However, challenges such as digital talent shortages, implementation costs, data privacy concerns, ethical considerations, and resistance to organizational change remain significant barriers. This study contributes to a comprehensive understanding of how AI is reshaping the contemporary digital business landscape and provides practical insights for business leaders in developing effective digital transformation strategies.
Ilham Karunia Akbar, Rafly Verdyansyah Pratama, Ahmad Rifa’i et al.· Devotion : Journal of Resear...· 0 citations
It is concluded that value creation through artificial intelligence depends on the alignment of technological, organizational, and human capabilities, as well as on governance strategies that support responsible, ethical, and sustainable implementation within organizations.
Guadalupe Esmeralda, Rivera García, M. I. Hernández et al.· 0 citations
Artificial intelligence (AI) is becoming an important part of modern product development. However, most existing studies focus on software development or individual AI applications rather than the entire product lifecycle. This study explores how AI can support different stages of product development in a lean technology startup. A comparative case study was conducted using two similar product development projects completed by the same company. The projects had similar functionality, target market, technology stack, and development process, but differed in the level of AI adoption. The study combined quantitative analysis of labor effort with qualitative observations of how AI tools were used throughout the product lifecycle. The results show that AI supported activities from market research and hypothesis validation to software development, testing, release preparation, and post-release product improvement. The greatest benefits were observed in research, requirements preparation, documentation, and design, while software development and testing also became more efficient. Overall labor effort was reduced by 35.8% in the AI-assisted project. The findings suggest that AI can support the entire product development lifecycle, helping lean startup teams work more efficiently while leaving key decisions and expert judgment to people.
Against the backdrop of the deepening development of the digital economy and the ongoing integration of the digital and physical worlds, artificial intelligence has evolved from a single-function technical tool into a core strategic capability that supports organizations in building long-term competitive advantages. As a core construct that explains differences in the value transformation of AI technology, AI capability has become a hot topic of research in the fields of strategic management and information systems. However, existing research is scattered across diverse disciplinary perspectives and application scenarios, and has yet to form a unified research framework or theoretical system. Based on 33 core publications in the field of AI capabilities, this paper conducts a systematic review following the logical framework of “conceptual evolution-theoretical foundations-application scenarios-research outlook.” The study traces the evolutionary path of AI capabilities, from their origins in IT capability research to the development of a general three-dimensional construct, and further expansion into specialized technological forms and specific application scenarios; it synthesizes a theoretical framework centered on the resource-based view and dynamic capabilities theory, complemented by multiple theoretical perspectives; and summarizes the application progress and heterogeneity in value realization of AI capabilities across eight major scenarios, including green innovation, supply chain management, public governance, and business model innovation; Finally, it identifies limitations in existing research regarding research design, theoretical perspectives, scenario coverage, and risk governance, and proposes future research directions. This paper integrates and constructs a comprehensive research framework for AI capabilities, clarifies the field’s consensus and research gaps, and not only enriches the theoretical research landscape in the field of AI capabilities but also provides practical guidance for organizations of various types to systematically build AI capabilities and achieve the transformation of technological value.
Hao Jiang· International Journal of Edu...· 0 citations
Reuse-based development has become increasingly important in the creation of complex systems, offering significant opportunities to reduce costs, improve quality, and accelerate time-to-market. Product Line Engineering (PLE) provides a systematic approach to realizing this potential by enabling the efficient creation, management, and customization of product families by reusing shared assets and capabilities. PLE involves addressing numerous complex decisions, including feature selection, variability management, and configuration optimization, which are critical to the success of a product line. Despite its promise, the systematic integration of Artificial Intelligence (AI) into PLE processes has not yet been comprehensively explored. In this paper, we propose a methodological framework to support the systematic integration of AI into PLE and evaluate its effectiveness through a multi-case study conducted in an industrial context.
B. Tekinerdoğan· arXiv.org· 0 citations
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