What happened
Researchers at Jouf University surveyed 280 decision‑makers from Saudi SMEs across manufacturing, logistics, technology services, retail and related sectors between November 2025 and May 2026. Using structural‑equation modelling, they linked four technology‑specific resources—Internet of Things (IoT), blockchain, artificial intelligence (AI), and big‑data analytics—to a higher‑order construct called Technological Capabilities, and then examined how these capabilities and three organizational capabilities (Digital, Innovativeness, Management) affect Eco‑Innovation and Sustainable Performance. The analysis found AI capabilities had the largest standardized path coefficient (0.609, large effect) to Technological Capabilities, while big‑data analytics contributed modestly. IoT and blockchain showed small, statistically nonsignificant relationships. Technological Capabilities, more than Eco‑Innovation, predicted Sustainable Performance (path coefficient 0.517, large effect). The study, published in *Discover Artificial Intelligence* (Vol 6, No 1, 2026), argues that AI‑driven forecasting, planning, risk assessment and decision support enable firms to integrate and reconfigure multiple technologies, delivering economic, operational, environmental, social and competitive benefits.
The research team, led by Wafa Naif Alwakid, collected survey responses from owners, managers and senior executives of Saudi SMEs identified through the national SME authority, Monsha'at. Respondents were predominantly senior managers with over five years of experience and reported moderate‑to‑high exposure to digital technologies.
Structural‑equation modelling revealed that AI capabilities—defined as the ability to apply machine‑learning for forecasting, planning, risk assessment and decision support—had a standardized path coefficient of 0.609 to Technological Capabilities, a large effect size. Big‑data analytics showed a smaller but significant contribution, while IoT and blockchain coefficients were negative and not statistically significant.
Technological Capabilities, representing the firm‑wide ability to deploy, configure and reconfigure multiple technologies, were the stronger predictor of Sustainable Performance (path coefficient 0.517) compared with Eco‑Innovation, which captured environmentally oriented product and process changes.
Source details: bioengineer.org ↗
Why it matters
The findings challenge the prevailing narrative that simply adopting the latest digital tools guarantees sustainability gains. By isolating AI and big‑data analytics as the only technologies that meaningfully build firm‑wide Technological Capabilities, the study provides empirical evidence for policymakers and business leaders that investment priorities should focus on AI‑enabled analytics and the organizational routines that support them. For Saudi Arabia’s Vision 2030 agenda, which heavily promotes AI and digital transformation, the research suggests that without accompanying capability‑building—training, data‑quality processes, integration frameworks—technology spend may not translate into the intended sustainability outcomes. The study also highlights the limited impact of IoT and blockchain when deployed in isolation, underscoring the importance of ecosystem readiness, cybersecurity, and partner participation for these tools to add value.
The study provides a data‑driven counterpoint to the “technology‑for‑its‑own‑sake” approach that has dominated many digital‑transformation initiatives, especially in policy‑driven contexts like Saudi Vision 2030.
By pinpointing AI and analytics as the primary levers for building integrative capabilities, the research informs where limited SME budgets can achieve the greatest sustainability returns.
The limited impact of IoT and blockchain in this sample suggests that without supporting infrastructure, skills and network participation, these technologies may remain isolated pilots rather than drivers of systemic performance improvement.
Interactive Mechanism: How It Actually Works
Explore the underlying technology behind this development interactively.
Which component of an AI application is the machine-learning model itself?
What to watch next
Future research should test the causal direction of the relationships with longitudinal data, explore sector‑specific variations, and assess whether similar patterns hold in other emerging economies. Practitioners will watch for follow‑up guidance on how to structure AI projects to maximize capability development, and for government programs that tie AI funding to demonstrable integration metrics rather than mere technology adoption.
Longitudinal studies that can establish whether AI capability development precedes sustainable performance gains.
Sector‑specific follow‑up analyses to determine if manufacturing firms, for example, experience different effects than service‑oriented SMEs.
Policy adjustments in Saudi Arabia and similar economies that tie AI funding to measurable capability‑building outcomes.
Guidance from industry bodies on best practices for integrating AI into existing SME processes, including training, data governance and change‑management frameworks.