AI adoption in Egyptian logistics correlates with higher competitiveness—lower costs, better productivity and customer experience—but smaller firms struggle with high implementation costs, poor data and limited expertise, limiting early-stage gains.

Main Finding

AI adoption in Egyptian logistics firms is positively associated with firm competitiveness—through cost reductions, improved service quality, and better customer experience—but early-stage adoption challenges (especially in small and medium enterprises) can reduce operational efficiency. Demographic factors (age, experience, company size) shape AI awareness and perceptions. Competitiveness mediates the relationship between AI adoption and business growth.

Key Points

• Primary result: Significant positive relationships between AI adoption and (i) cost-effectiveness, (ii) quality, (iii) customer experience, and (iv) overall competitiveness.
• Exception: AI showed a negative, non‑significant association with measured operational efficiency in this sample (authors attribute this to early-stage/immature adoption effects).
• Barriers for SMEs: high implementation costs, weak strategic alignment, poor data quality, limited in-house expertise, and employee resistance—these can temporarily reduce efficiency and delay competitive gains.
• Heterogeneity in perceptions: age, years of experience, and company size significantly influence AI awareness and competitiveness perceptions; gender, place of residence, and job role did not.
• Mediating role: Competitiveness acts as a mediator between AI adoption and business growth in the logistics sector.
• Contextual note: AI is framed as a strategic resource (not merely a cost-saving tool) that supports agility and customer-facing improvements in a rapidly digitalizing logistics market (e‑commerce growth emphasized).

Data & Methods

• Design: Post‑positivist quantitative study with exploratory qualitative interviews.
• Qualitative: Structured interviews with 41 logistics professionals (explored adoption status, costs, demographics, operational challenges, competitiveness outcomes).
• Quantitative: Questionnaire survey of 384 employees (senior management, managers, supervisors, staff) across logistics organizations.
  • Instrument: Three sections—AI (14 items), competitiveness (18 items across operational efficiency, cost effectiveness, quality, customer experience), and demographics (gender, age, residence, years of experience, decision‑maker position, company size). 5‑point Likert scale.
• Analysis: SPSS; descriptive stats, Pearson correlation matrix, Cronbach’s alpha for reliability, arithmetic means and SDs, simple linear regression to estimate AI→competitiveness impact, t-tests and Kruskal–Wallis for demographic comparisons.
• Key quantitative finding from correlation matrix: AI correlated positively and significantly with cost (.222), quality (.245), customer experience (.143), and competitiveness (.258); AI had a small negative, non‑significant correlation with operational efficiency (−.056, p>.05). Several internal inter-correlations among competitiveness subdimensions were strong (e.g., quality–competitiveness .798; cost–competitiveness .695).

Implications for AI Economics

• Productivity and diffusion dynamics:
  • AI can be a productivity-enhancing general-purpose technology in logistics but gains depend on implementation maturity, data quality, and complementary investments (training, process redesign). Early adoption may temporarily depress measured operational efficiency, especially in smaller firms.
  • Heterogeneous adoption and capability gaps imply uneven productivity diffusion across firms and potential increases in within‑sector inequality.
• Investment and policy:
  • Public policy and finance instruments (subsidies, low‑interest loans, tax incentives) should target SME adoption bottlenecks—particularly for data infrastructure, workforce upskilling, and pilot projects that demonstrate scalable ROI.
  • Public–private partnerships for shared data platforms and common AI tools (e.g., demand forecasting modules, routing optimizers) could reduce fixed costs and accelerate beneficial spillovers.
• Labor and complementarities:
  • Findings support the view that human–AI complementarities matter: firms must balance automation with human expertise to sustain service quality and customer experience.
  • Workforce policies should emphasize reskilling and role redesign to capture productivity gains without large negative distributive effects.
• Measurement and research:
  • Economists should be cautious using cross‑sectional firm surveys to infer productivity gains; short-term measures may understate eventual efficiency improvements if adoption is immature.
  • Future empirical work should use longitudinal and firm‑level administrative data (output, costs, employment, deliveries, on‑time metrics) to estimate causal effects and dynamic adoption trajectories.
• Competitive dynamics and market structure:
  • As large adopters realize cost and quality advantages, market concentration risks may rise unless SMEs receive targeted support—regulatory monitoring of anti‑competitive outcomes in logistics markets could be warranted.
• Practical recommendations for firms:
  • Prioritize data governance and incremental, modular AI deployments with clear KPIs; invest in change management to overcome resistance.
  • Focus initial AI use cases on customer‑facing transparency and logistics cost drivers (routing, inventory forecasting) with measurable payoffs.

Limitations noted by the authors (relevant for interpretation): Egypt‑specific context, cross‑sectional survey relying on self‑reported perceptions, and potential early‑adoption measurement issues—suggesting caution in generalizing to other countries or inferring long‑run causal productivity effects. Future research should exploit panel data, randomized pilots, and objective performance metrics.