A recent survey of 1,800 global executives conducted by Prologis and The Harris Poll suggests a fundamental strategic shift in supply chain design, with a majority forecasting more localized networks by 2030 rather than continued globalization. The findings indicate that proximity and control may be outweighing traditional cost advantages as companies recalibrate priorities from cost optimization toward risk mitigation.
Key Takeaways
- Survey of 1,800 executives suggests 58% forecast localized supply chains by 2030 versus 31% expecting continued globalization, indicating strategic shift toward risk mitigation
- 75% of surveyed companies identify AI as top capital investment priority for 2026, surpassing supplier relationships, automation, energy efficiency, and talent development
- Quality control, inspection, and risk identification represent leading AI application areas, with 70% of executives reporting advanced implementation progress
- Survey leaders report observing 77% return on AI investments within 12 months, with 63% expecting AI-driven decision-making across major functions within five years
- Economic volatility (55%), tariff increases (48%), geographic instability (38%), and cybersecurity threats (38%) rank as primary supply chain risk concerns for 2026
Regional Network Development
According to research, 58% of executives forecast more localized supply chains by 2030, while only 31% expect continued globalization. More than three-quarters of surveyed companies have already implemented or are actively building regional networks, particularly around consumption centers. Energy reliability, cited by 40% of respondents, and labor costs, mentioned by 36%, are major drivers of operational location decisions.
The survey authors characterize this as marking the end of the globalization era in supply chain design, suggesting that strategic planning must now account for higher operational costs offset by reduced risk exposure and improved operational reliability. Such dispersal may help mitigate risks associated with fragile global networks, though risks will inevitably persist regardless of network configuration.
Primary Risk Concerns
Survey participants identified several top concerns for 2026 supply chain operations. Economic volatility ranked highest at 55%, followed by tariff increases and trade barriers at 48%. Geographic instability and cybersecurity threats each received 38% of responses as significant concerns.
These risk factors reflect the volatile environment that supply chains navigate, encompassing economic disruptions, weather events, energy shortages, trade policy changes, cyberattacks, and geopolitical conflicts. The persistent nature of these challenges suggests that even regionalized networks will require sophisticated management approaches to maintain reliability.
AI as Risk Mitigation Tool
Executives are reportedly turning to artificial intelligence to reduce operational risks while enabling more responsive, consistent operations across localized supply chains. According to the survey, 70% of executives indicate they are well along with applying AI to supply chain functions.
Quality control, inspection processes, and risk identification are among the leading application areas for AI deployment, according to survey responses. Notably, AI investment ranks as the top capital priority for 75% of companies surveyed for 2026, surpassing supplier relationships at 38%, automation and robotics at 37%, energy efficiency at 36%, and talent development at 31%.
Leaders identified in the survey report observed a 77% return on AI investments within 12 months. A majority of these leaders—up to 63%—expect AI to be making decisions across all major supply chain functions within the next five years, suggesting confidence in the technology's maturation trajectory.
Competitive Implications
The survey authors assert that the supply chain industry has crossed an AI adoption threshold, positioning it at the forefront of AI commercialization. Their analysis suggests that organizations still in early-stage AI implementation risk competitive obsolescence as AI-driven decision-making becomes the operational standard.
This interpretation implies that AI adoption has shifted from competitive advantage to competitive necessity—organizations that delay implementation may find themselves at an increasing disadvantage as peers leverage AI capabilities for faster decision-making, improved risk assessment, and enhanced operational efficiency.
Strategic Considerations
The survey findings present several implications for supply chain strategy development. First, the shift toward regionalization represents a fundamental recalibration of cost-benefit analysis, where risk mitigation justifies higher operational expenses that global networks previously avoided. Second, AI investment prioritization suggests a widespread belief that technology can address complexity challenges inherent in both international and regional supply chain configurations.
However, several questions remain about implementation effectiveness. While survey respondents report strong AI adoption intentions and positive early returns, actual deployment success rates, integration challenges, and long-term performance data will ultimately determine whether these investments deliver sustained competitive advantages.
The regionalization trend also creates potential trade-offs. While localized networks may reduce certain geopolitical and logistics risks, they could increase exposure to regional economic downturns, natural disasters, or regulatory changes affecting specific geographic areas. The benefits of diversification that global networks provide may diminish as companies concentrate their operations regionally.
Execution Challenges
Successfully implementing both regionalization strategies and AI capabilities requires addressing multiple operational challenges simultaneously. Companies must establish new regional facilities or partnerships while integrating AI systems across planning, execution, and monitoring functions. This dual transformation demands substantial capital investment, technical expertise, and change management capabilities.
Data quality and integration remain fundamental requirements for effective AI deployment. Systems require access to real-time information from multiple sources to generate accurate risk assessments and operational recommendations. Without comprehensive data foundations, AI implementations may deliver limited value regardless of technology sophistication.
Trax provides freight audit and data management solutions that normalize transportation information across complex carrier networks and regional operations. Our platform delivers the data-quality foundation that AI applications require while providing visibility across both global and regional supply chain configurations. Contact our team to discuss how comprehensive data management supports strategic network transformation.