More than half of supply chain leaders are facing significant hurdles in scaling artificial intelligence, with legacy systems and talent shortages emerging as the biggest barriers, according to research by Gartner.
The survey found that 56% of chief supply chain officers (CSCOs) struggle to integrate AI into existing systems and processes, while 50% say they lack the internal expertise needed to deploy and manage AI effectively. The findings are based on responses from 140 senior leaders at organizations with annual revenues exceeding $250 million.
According to Snigdha Dewal, Director Analyst at Gartner, the core issue is not the technology itself but the outdated environments in which it is being implemented. She noted that many organizations, under pressure to deliver quick results, are using AI to make incremental improvements to legacy workflows rather than fundamentally rethinking how their supply chains operate.
Gartner describes an “AI-native supply chain” as one designed from the ground up to leverage AI capabilities, rather than layering them onto traditional systems. The firm’s research shows that organizations leading in AI adoption are taking a more transformative approach, redesigning their operating models to align with AI-driven workflows and decision-making.
These leading organizations are also reshaping their internal structures, moving away from traditional roles toward more flexible, AI-centric job models that enhance human productivity rather than simply automating existing tasks. At the same time, they are evolving their technology stacks by building unified data and agentic layers on top of legacy systems, allowing for more adaptable and scalable architectures.
Gartner emphasized that governance will play a critical role in this transition, with organizations needing to embed safeguards around data usage, security, and compliance as AI adoption expands.
The findings suggest that while interest in AI across supply chains is growing, meaningful impact will depend on how deeply organizations are willing to transform their systems, talent strategies, and operating models to support an AI-first future.
The survey found that 56% of chief supply chain officers (CSCOs) struggle to integrate AI into existing systems and processes, while 50% say they lack the internal expertise needed to deploy and manage AI effectively. The findings are based on responses from 140 senior leaders at organizations with annual revenues exceeding $250 million.
According to Snigdha Dewal, Director Analyst at Gartner, the core issue is not the technology itself but the outdated environments in which it is being implemented. She noted that many organizations, under pressure to deliver quick results, are using AI to make incremental improvements to legacy workflows rather than fundamentally rethinking how their supply chains operate.
Gartner describes an “AI-native supply chain” as one designed from the ground up to leverage AI capabilities, rather than layering them onto traditional systems. The firm’s research shows that organizations leading in AI adoption are taking a more transformative approach, redesigning their operating models to align with AI-driven workflows and decision-making.
These leading organizations are also reshaping their internal structures, moving away from traditional roles toward more flexible, AI-centric job models that enhance human productivity rather than simply automating existing tasks. At the same time, they are evolving their technology stacks by building unified data and agentic layers on top of legacy systems, allowing for more adaptable and scalable architectures.
Gartner emphasized that governance will play a critical role in this transition, with organizations needing to embed safeguards around data usage, security, and compliance as AI adoption expands.
The findings suggest that while interest in AI across supply chains is growing, meaningful impact will depend on how deeply organizations are willing to transform their systems, talent strategies, and operating models to support an AI-first future.
