AI-Driven Strategic Management for Technology Diffusion: A Case Study of UAV Adoption in Indian Agriculture

Emerging technologies face adoption barriers that require not only technical optimization but also strategic alignment with local ecosystems. This study investigates UAV adoption in Indian agriculture through a hybrid strategic management model integrating forecasting, structural equation modeling (SEM), and stakeholder engagement. Data were collected from 180 farmers, service providers, and policymakers across Uttar Pradesh and Madhya Pradesh, combined with AI-based diffusion forecasting models. Results reveal that adoption intention was significantly influenced by perceived cost savings (p<0.01), regulatory clarity (p<0.05), and trust in service providers (p<0.01). AI-based forecasting aligned closely with SEM outputs, validating the role of hybrid approaches for strategic planning. Furthermore, scenario analyses demonstrated that early policy interventions such as subsidies or community spraying collectives can accelerate adoption by up to 35% in certain regions. By integrating computational forecasting with qualitative ecosystem mapping, this framework provides a more holistic lens to design diffusion strategies for disruptive technologies. The research also emphasizes feedback loops between field-level outcomes (yield gain, input reduction) and strategic adoption models, showing how empirical evidence can refine management decisions. The study demonstrates that strategic management of emerging technologies benefits from combining quantitative adoption modeling with ecosystem-level strategies. For agritech entrepreneurs and administrators, this research offers a replicable framework to accelerate diffusion of disruptive technologies while mitigating socio-regulatory resistance. Beyond UAVs, the approach is transferable to other emerging domains such as healthtech and fintech, where innovation often outpaces regulation.