Using Artificial Intelligence (AI) to predict organizational agility

Abstract

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.

eXplainable Artificial Intelligence (XAI) for improving organisational regility

“…As a second editorial, this study is a sequel to our preceding work [ 12 ]. In the referring paper, the prediction process employed seven classification models, including SVM, KNN, DT, RF, GBM, NB, and LR.…”

“…RF algorithm had better performance in comparison with other algorithms such as SVM, GBM, DT, LR, and NB to predict the organisational agility [ 12 ]. Meanwhile, with the test accuracy of 97.67%, the RF algorithm is ranked higher than other algorithms for agility prediction.…”

“…This study serves as an inherent continuation of our preceding work titled “Using AI to predict organisational agility” [ 12 ]. In this referring paper, we detailed the prediction process comprising seven machine learning models tailored for predicting organisational agility.…”

“…Regility is closely linked to the execution of an organisation’s agility strategy. Benefits of organisational agility include improved engagement and retention of talent, acceleration of organisational learning, and increased responsiveness when making business decisions [ 12 – 15 ]. Organisational resilience includes having a strong culture of learning and adaptation, a clear sense of purpose, strong leadership, and adequate resources to withstand disruptions [ 16 ].…”

“…However, the prediction of organisational agility which is a real-world application of AI in this field was still unknown. [ 12 ] predicted the organisational agility for data gathered from 44 respondents including private and public organisations in the industry sector. Different machine learning methods have been employed as predictors and the Random forest method had the best performance with a 97.67% accuracy.…”