Developing an Adaptive Water–Energy–AI Organisational Emergence Model: The Case of LADS Africa in Zimbabwe

The paper examines the growing challenges of water scarcity, deteriorating infrastructure, and governance inefficiencies in developing economies, with a specific focus on Zimbabwe’s volatile, uncertain, complex, and ambiguous (VUCA) environment. These pressures create an urgent need for innovative and sustainable digital solutions in water governance. The paper is motivated by the need to understand how emerging organisations can respond to ecological stress and institutional fragility while leveraging technological advancements. It focuses on LADS Africa, a digital enterprise that integrates artificial intelligence (AI) and smart metering technologies to enhance efficiency, transparency, and sustainability in municipal water systems. The paper adopts a conceptual and theoretical synthesis approach, integrating Innovation Management Theory, Chaos Theory, Strategic Decision-Making Theory, Entrepreneurial Orientation Theory, and the Viable System Model (VSM). Through this multi-theoretical framework, the paper develops the Adaptive Water Energy–AI Organisational Emergence Model (AWEA-OEM), which explains how sustainability pressures, AI capability, institutional complexity, and resource constraints interact to shape organisational emergence and adaptation. This approach enables a comprehensive understanding of how organisations function as complex adaptive systems within turbulent environments. The findings indicate that environmental sustainability pressures, particularly water scarcity and infrastructure failure, act as key drivers of innovation. AI-enabled systems enhance predictive governance, improve operational efficiency, and increase transparency through real-time data analytics. However, the paper also finds that AI introduces additional complexity and increases system sensitivity to environmental and institutional disruptions. As a result, organisational success depends on adaptive strategic leadership, entrepreneurial proactiveness, and effective cybernetic feedback mechanisms that stabilise operations in non-linear and uncertain conditions. The paper concludes that emerging organisations such as LADS Africa are best understood as complex adaptive systems whose viability depends on continuous adaptation rather than linear growth. The AWEA-OEM model contributes to theory by positioning sustainability pressure as a key driver of digital transformation and by highlighting the importance of integrating AI with adaptive governance mechanisms in fragile institutional contexts. The paper recommends that organisations institutionalise adaptive governance structures, implement responsible AI frameworks, integrate sustainability metrics into performance systems, and strengthen monitoring and feedback mechanisms. It also emphasises the importance of multi-stakeholder collaboration, phased scalability, and continuous organisational learning to enhance resilience. For future research, the paper suggests empirical validation of the AWEA-OEM model across different sectors and regions, as well as longitudinal and comparative studies to better understand how adaptive governance and AI-enabled systems evolve over time in complex environments