AI-augmented dynamic capabilities for business sustainability: Enactment through humanistic and instrumental approaches
Ping-Jen Kao
Abstract
Firms are increasingly adopting predictive artificial intelligence (AI) to address economic, social and environmental sustainability challenges. However, there remains limited understanding of how predictive AI contributes to the development of AI capabilities that underpin sensing, seizing and reconfiguring dynamic capabilities for business sustainability. Drawing on a multi-case study, this research identifies six AI capabilities: pattern recognition and emotional intelligence (linked to sensing), agile decision-making and interdisciplinary integration (linked to seizing), and resource orchestration and transformation mastery (linked to reconfiguring). These AI capabilities augment the potential of firms’ dynamic capabilities by enhancing their ability to sense opportunities, seize them strategically and reconfigure resources in response to sustainability imperatives. This study further reveals two distinct yet interrelated approaches through which this augmented potential is realised: a humanistic, stakeholder-focused approach and an instrumental, firm-focused approach. These approaches shape how AI capabilities are embedded in dynamic capability processes and lead to different sustainability outcomes. The humanistic approach prioritises social and environmental objectives, with economic benefits as secondary, whereas the instrumental approach foregrounds economic performance, with social and environmental sustainability as secondary. By clarifying how AI capabilities and organisational approaches interact to influence dynamic capability realisation, this study advances theoretical understanding of AI-enabled strategic change and its role in achieving business sustainability. The findings contribute to academic discourse and inform managerial and policy decisions.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.