AI-driven supply chains boost agility but not all fiirms adopt robotics

A new study finds that artificial intelligence (AI) is reshaping supply chains in emerging markets, but only certain operational capabilities translate digital investments into real automation gains. Researchers report that while AI-driven supply chains strengthen both adaptability and agility, only agility directly drives automation and robotics adoption when markets become volatile.

The findings are presented in The Role of AI-Driven Supply Chains in Shaping Agility, Adaptability, and Technology Adoption Under Market Turbulence, published in the journal Logistics, where the authors analyze survey data from 337 manufacturing managers to examine how AI capabilities interact with market uncertainty.

AI-driven supply chains strengthen core operational capabilities

According to the study, AI-driven supply chains are higher-order dynamic capabilities that enhance firms' ability to respond to change. Using dynamic capabilities theory as a foundation, the authors argue that artificial intelligence improves sensing, learning, and reconfiguration processes across supply networks.

The data confirm that AI-driven supply chains have a strong positive impact on both supply chain adaptability and supply chain agility. Adaptability refers to a firm's capacity to redesign sourcing strategies, restructure supplier relationships, and modify operational configurations over the long term. Agility, by contrast, captures the ability to respond quickly to short-term disruptions such as sudden demand shifts, logistics bottlenecks, or supply interruptions.

AI tools contribute to these capabilities by improving real-time visibility across the supply chain. Predictive analytics enable firms to anticipate disruptions before they escalate. Integrated dashboards consolidate fragmented data sources, enhancing coordination between procurement, production, and distribution units. Machine learning systems detect inefficiencies and recommend optimized routing or inventory adjustments.

In practical terms, firms equipped with AI-driven systems can detect risk signals earlier, simulate alternative sourcing scenarios, and adjust production schedules more efficiently. This digital responsiveness reduces operational blind spots and enhances situational awareness.

The research confirms that AI's contribution is not limited to incremental efficiency gains. Instead, AI-driven supply chains reshape the structural foundations of operational performance by enabling continuous adaptation and rapid execution. These capabilities are especially critical in emerging markets where external volatility is often more pronounced.

Agility, not adaptability, drives automation and robotics adoption

While AI strengthens both adaptability and agility, the study finds a decisive divergence when it comes to technology adoption outcomes. Only supply chain agility has a significant direct effect on the adoption of automation and robotics technologies.

Firms that demonstrate high agility are more likely to integrate robotics into production lines, deploy automated warehousing systems, and implement advanced material-handling technologies. Agility appears to function as an execution-oriented capability that translates digital readiness into tangible technological investment.

Adaptability, despite being positively influenced by AI systems, does not directly drive automation and robotics adoption. Long-term structural flexibility alone does not guarantee readiness for high-cost automation investments. Firms may be able to reconfigure supply relationships or adjust strategic sourcing plans without committing to capital-intensive robotics systems.

The authors suggest that this disconnect may reflect practical barriers such as financial constraints, skill shortages, and implementation complexity. In emerging economies, even adaptable firms may hesitate to adopt automation due to uncertainty surrounding return on investment or workforce readiness.

The moderated–mediation model tested in the study further clarifies these relationships. AI-driven supply chains enhance adaptability and agility, but only agility mediates the relationship between AI deployment and automation adoption. This finding highlights the importance of operational speed and real-time responsiveness in driving digital transformation.

In effect, agility acts as a conversion mechanism. It enables firms to move from digital capability building to concrete technological execution. Without agility, AI investments may improve structural flexibility but fail to catalyze robotics adoption.

Market turbulence shapes technology investment decisions

The study also integrates institutional theory to assess how external environmental pressures influence internal capability outcomes. Market turbulence, defined as rapid and unpredictable changes in customer preferences and competitive conditions, plays a critical moderating role.

The analysis reveals that market turbulence negatively moderates the relationship between adaptability and automation adoption. In highly volatile markets, even adaptable firms may delay automation investments. Heightened uncertainty can increase risk aversion, prompting managers to preserve liquidity rather than commit to capital-intensive robotics systems.

Conversely, market turbulence does not significantly moderate the relationship between agility and automation adoption. Agile firms continue to pursue technology investments even in unstable environments. This suggests that agility provides a buffering effect against uncertainty, allowing firms to sustain innovation momentum despite external shocks.

In turbulent markets, adaptability alone may not be sufficient to drive transformation. Firms require agility to maintain forward motion under pressure. Agility enhances confidence in decision-making and enables rapid recalibration when market signals shift.

The findings offer strategic lessons for managers navigating digital transformation. Building AI-enabled visibility and predictive capability is only the first step. Firms must cultivate agile processes that translate data insights into decisive operational action. Investment in workforce training, cross-functional coordination, and real-time decision systems becomes critical.

For policymakers in emerging economies, the study underscores the need to strengthen digital infrastructure and support capacity-building initiatives. Financial incentives, innovation grants, and skills development programs can help reduce the barriers that prevent adaptable firms from converting digital capability into automation adoption.