Title: Aligning AI: a new paradigm for decision-making in resource-limited agritechs

Authors: Hussein Lakkis; Helmi Issa

Addresses: Antonine University, Baabda, Lebanon ' ESSCA School of Management, Angers, France

Abstract: Artificial intelligence (AI) is transforming businesses by driving automation, predictive analytics, and data-driven decisions across industries. However, the intersection of AI's unpredictability with resource-limited and risk-sensitive sectors like agriculture create uncertainties and challenges that demand cautious management. This research empirically examines the impact of three diverse AI characteristics (i.e., autonomy, ambidexterity, and alignment) on decision-making with resource allocation as a moderator in the context of agritechs. Data was collected from multiple sources that mainly focused on agritech (agriculture technology) start-ups in France (n = 151). The findings revealed significant linear relationships for autonomy and ambidexterity characteristics and a nonlinear relationship for the alignment characteristic. This research introduces 'alignment' as a new AI characteristic for optimal decision-making and proposes 'Amber AI' as a transformative paradigm beyond red and green AI. It also develops practical simulation-based tools for detecting AI misalignment and optimising resource allocation in agricultural management.

Keywords: AI; agriculture; resource allocation; decision-making.

DOI: 10.1504/IJTIP.2026.154531

International Journal of Technology Intelligence and Planning, 2026 Vol.14 No.2, pp.153 - 181

Received: 29 Dec 2025
Accepted: 27 Jan 2026
Published online: 02 Jul 2026 *

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