Improving farm productivity and profitability through AI diffusion: evidence from Punjab, Pakistan

Khalid, Aeman and Akhurst, Colin and Gilani, Sayed Abdul Majid and Armosh, Firas and Zanib, Syeda Aqsa and Boath, Jack and Peel, Richard Stanley and Jinadasa, Yohani (2026) Improving farm productivity and profitability through AI diffusion: evidence from Punjab, Pakistan. Frontiers in Artificial Intelligence, 9. ISSN 2624-8212

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Abstract

Introduction

Agriculture remains central to Pakistan's economy, with Punjab accounting for the largest share of national crop production. Despite the rapid global advancement of artificial intelligence (AI)-enabled agricultural technologies, their diffusion across farms in developing economies remains uneven and underexplored. This study examines the extent of AI diffusion among farmers in Punjab, Pakistan, and assesses its associations with perceived farm productivity and profitability.

Methods

Drawing on Diffusion of Innovations (DoI) theory, complemented by the Resource-Based View (RBV) and the Technology-Organisation-Environment (TOE) framework, the study adopts a quantitative, cross-sectional design using survey data from 101 farmers.

Results

The findings indicate that while awareness and perceived benefits of AI are relatively high, actual adoption remains moderate (AI Diffusion: M = 18.27, SD = 3.71), indicating a gap between perceived value and implementation. AI diffusion was positively correlated with perceived farm productivity ( r = 0.617, p < 0.001) and perceived farm profitability ( r = 0.607, p < 0.001). The findings therefore demonstrate statistically significant associations between greater AI diffusion and stronger perceived farm performance, while not establishing causal effects.

Discussion

The observed perception-adoption gap is consistent with structural barriers identified in previous research, including high investment costs, limited digital infrastructure and technical capability constraints. The study contributes context-specific evidence on digital agriculture in a developing economy by jointly examining AI diffusion, productivity and profitability. The findings highlight the importance of infrastructure, institutional support and capability development in facilitating wider and more effective adoption of AI-enabled agricultural technologies.

Item Type:	Article
Identification Number:	10.3389/frai.2026.1890862
Dates:	Date Event 30 September 2026 Accepted 21 October 2026 Published Online
Uncontrolled Keywords:	agriculture, artificial intelligence, farm productivity, innovation di�usion, Pakistan
Subjects:	CAH17 - business and management > CAH17-01 - business and management > CAH17-01-02 - business studies
Divisions:	Business School > Management, Business and Marketing
Depositing User:	Gemma Tonks
Date Deposited:	23 Oct 2026 10:46
Last Modified:	23 Oct 2026 10:46
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17282

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