Using Artificial Intelligence (AI) to predict organizational agility

被引:24
|
作者
Shafiabady, Niusha [1 ,2 ]
Hadjinicolaou, Nick [3 ]
Din, Fareed Ud [4 ]
Bhandari, Binayak [5 ]
Wu, Robert M. X. [6 ]
Vakilian, James [2 ]
机构
[1] Charles Darwin Univ, Fac Sci & Technol, Haymarket, NSW, Australia
[2] Torrens Univ Australia, Design & Creat Technol, Sydney, NSW, Australia
[3] Torrens Univ Australia, Global Project Management, Adelaide, SA, Australia
[4] Univ New England, Fac Sci Agr Business & Law, Sch Sci & Technol, Armidale, NSW, Australia
[5] Univ New South Wales, ARC Training Ctr Automated Manufacture Adv Composi, Sydney, NSW, Australia
[6] Cent Queensland Univ, Sch Engn & Technol, Rockhampton, Qld, Australia
来源
PLOS ONE | 2023年 / 18卷 / 05期
关键词
PROJECT PORTFOLIO MANAGEMENT; SUPPORT VECTOR MACHINE; PEM FUEL-CELL; INDUSTRY; STORAGE;
D O I
10.1371/journal.pone.0283066
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Since the pandemic organizations have been required to build agility to manage risks, stakeholder engagement, improve capabilities and maturity levels to deliver on strategy. Not only is there a requirement to improve performance, a focus on employee engagement and increased use of technology have surfaced as important factors to remain competitive in the new world. Consideration of the strategic horizon, strategic foresight and support structures is required to manage critical factors for the formulation, execution and transformation of strategy. Strategic foresight and Artificial Intelligence modelling are ways to predict an organizations future agility and potential through modelling of attributes, characteristics, practices, support structures, maturity levels and other aspects of future change. The application of this can support the development of required new competencies, skills and capabilities, use of tools and develop a culture of adaptation to improve engagement and performance to successfully deliver on strategy. In this paper we apply an Artificial Intelligence model to predict an organizations level of future agility that can be used to proactively make changes to support improving the level of agility. We also explore the barriers and benefits of improved organizational agility. The research data was collected from 44 respondents in public and private Australian industry sectors. These research findings together with findings from previous studies identify practices and characteristics that contribute to organizational agility for success. This paper contributes to the ongoing discourse of these principles, practices, attributes and characteristics that will help overcome some of the barriers for organizations with limited resources to build a framework and culture of agility to deliver on strategy in a changing world.
引用
收藏
页数:37
相关论文
共 50 条
  • [21] Regulation of artificial intelligence (AI)
    Sury, Ursula
    Informatik-Spektrum, 2022, 45 (05) : 325 - 327
  • [22] Artificial intelligence (AI) for quantum and quantum for AI
    Zhu, Yingzhao
    Yu, Kefeng
    OPTICAL AND QUANTUM ELECTRONICS, 2023, 55 (08)
  • [23] AI (anthropological inquiry) on AI (artificial intelligence)
    Bekerman, Zvi
    RED-REVISTA DE EDUCACION A DISTANCIA, 2024, 24 (78):
  • [24] Artificial Intelligence (AI) in Healthcare
    Besch, Christian
    ZEITSCHRIFT FUR PALLIATIVMEDIZIN, 2020, 21 (04): : 146 - 147
  • [25] AI IN BARIATRICS Artificial intelligence
    Puranik, A.
    OBESITY SURGERY, 2022, 32 (SUPPL 2) : 43 - 43
  • [26] AAC and Artificial Intelligence (AI)
    Sennott, Samuel C.
    Akagi, Linda
    Lee, Mary
    Rhodes, Anthony
    TOPICS IN LANGUAGE DISORDERS, 2019, 39 (04) : 389 - 403
  • [27] Artificial Intelligence (AI) in Sports
    Ratiu, Ioan Gheorghe
    Badau, Dana
    Carstea, Claudia Georgeta
    Badau, Adela
    Paraschiv, Florin
    PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES, 2010, : 93 - 97
  • [28] Artificial intelligence and organizational management
    Turnes, Luiza
    Melchioretto, Albio Fabian
    NAVUS-REVISTA DE GESTAO E TECNOLOGIA, 2024, 15 : 1 - 2
  • [29] Using Artificial Intelligence to Better Predict and Develop Biomarkers
    Michelhaugh, Sam A.
    Januzzi Jr, James L.
    CLINICS IN LABORATORY MEDICINE, 2023, 43 (01) : 99 - 114
  • [30] Using artificial intelligence to predict spontaneous preterm delivery
    Nodelman, Elizabeth
    Molitoris, John
    Holbert, Michael
    AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY, 2020, 222 (01) : S350 - S350