Unraveling the unintended consequences of AI in agriculture: A netnographic analysis and tri-phasic framework for enhanced uncertainty management

The agricultural sector has been a slow adopter of AI technologies, primarily due to concerns over the unpredictability of AI and the inherent uncertainties within the industry itself. This hesitation stems from the sector's reliance on complex, variable conditions that challenge the stability of AI solutions. The convergence of AI's unpredictability and agriculture's inherent uncertainty calls for a closer examination of the unintended consequences of AI decision-making in this domain. This research addresses such a dilemma by employing a netnography design to analyze 15 podcasts. The analysis identified three critical themes: predictive dissonance, techno-indecisiveness, and readiness deficit. This research makes three valuable contributions by pioneering an empirical investigation into the unintended consequences of AI in agriculture at the decision-making level, developing an AI concentric-nested ecosystem at the deployment level, and introducing a quantifiable scale graph that acts as a tangible assessment tool for AI's unintended consequences.