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Organizational Architecture for Distributed Computing: The Next Frontier in Systems Design
Published in Margrethe H. Olson, Technological Support for Work Group Collaboration, 2020
I have argued that organizational architecture will serve as the next beachhead of innovative systems design. This emphasis will develop thanks to new challenges posed by the triumphs of the two preceding architectural domains of subsystems configuration and the user interface. Their legacy is networked computing with high-fidelity user interfaces. This new computational environment requires considering organizational architecture as an integral part of systems design. As a result, the design of systems and organizations will converge and begin to mutually constrain one another. Systems must be organizationally architected, whereas organizations, to be capable of assimilating this new technology, must be informationally architected to generate valid information and seize it.
The why and when of architectural change
Published in Eberhardt Rechtin, Systems Architecting of Organizations, 2017
The first step in being able to answer them is to assess present strengths. No organization is, or can be expected to be, uniformly strong. Every organizational architecture is the result of choices of features that determine how well, under given constraints, that architecture can perform now and in the future. Each feature, as described in Table 10.1, is strong in some ways and weak in others. Features presumably would be chosen that match the organization’s perceived needs.
Managing Service Delivery
Published in Stephen Holloway, Airlines: Managing to Make Money, 2017
Organizational architecture is a term now commonly used to describe the totality of an organization’s internal design or structure and its external linkages (vertical and horizontal). It can have a profound impact on the efficiency and effectiveness of service delivery. We will consider first some of the popular theory on the subject, and then some of the practical issues.
Explore success factors that impact artificial intelligence adoption on telecom industry in China
Published in Journal of Management Analytics, 2021
The rapid development of AI is profoundly changing the world. However, AI application in actual use cases is still far. According to CAICT and Gartner (2018), only 4% of the firms invest on AI and deploy AI technologies. Most firms are still considering AI and making plans for AI. The telecom industry is one of the fastest growing industries in the world. Since the introduction of the telephone in 1876, the telecom industry had gone through a serial of incremental innovation. The earliest AI application in the telecom industry was available in the 1980s and mainly focused on expert system (Qi et al., 2007). The AI application was applied to improve operations and maintenance of telecom networks and services. This application triggered the studies on AI application in the telecom industry. For example, Macleish (1988) demonstrates how the first-generation expert system can help diagnose complex equipment in the telecom industry in an off-line mode. Seshadri (1996) summarizes the technologies and applications of AI applied by telecom operators and indicates that AI technologies can help solve practical problems in the telecom industry. After 2000, the focus of the telecom industry switched from basic telephone and Internet services to high-tech and data-centric networks. The change caused the shift of service from voice calls to video and data. Telecom operators in the world have begun to explore the application of AI technology, and have achieved good results in some areas. For example, AT&T is investigating how to use AI algorithms to enable drones to check and repair base stations. SK Telecom in South Korea is using machine learning to analyze network traffic to detect anomalies and strengthen network operations. However, most AI application in telecom networks is still in the stage of academic research and exploration. AI adoption is not as simple as plug and play. The AI development strategy, from data collection to organizational architecture design, and how to prioritize AI projects, is as complex as the technology itself. Telecom operators face challenges involving governments, competition, organizational environment, and their expertize to scale AI adoption. Thus, it is still difficult for telecom operators to launch effective AI solutions so far.