According to a Gartner survey cited by DC Velocity, 56% of chief supply chain officers struggle to integrate AI with legacy systems. The study of 140 senior leaders (from $250M+ revenue firms, Oct–Nov 2025) identifies legacy environments—not AI technology—as the top scaling barrier. Gartner defines an 'AI-native supply chain' as one built from the ground up for AI, not bolted onto analog-era workflows. Experts warn such retrofitting locks in inefficiencies. Vendor responses include Loop’s $95M funding round and GreyOrange’s AI simulation tools. Fragmented data across ERP, TMS, and WMS remains a core obstacle.
According to www.dcvelocity.com, more than half (56%) of chief supply chain officers (CSCOs) report significant difficulty integrating artificial intelligence (AI) with legacy systems and processes — a key barrier to scaling AI across supply chains.
Legacy Infrastructure Hinders AI Adoption
Gartner’s survey of 140 senior supply chain leaders from organizations with annual revenues of $250 million or more, conducted from October to November 2025, identified legacy environments—not AI technology itself—as the greatest friction point in AI scaling. The research defines an AI-native supply chain as one designed from the ground up to leverage AI, rather than layering AI functionality atop traditional workflows.
“Bolting AI onto an analog-era foundation only locks in existing inefficiencies and yields local optimizations.” — Snigdha Dewal, Director Analyst in Gartner’s Supply Chain practice
Dewal emphasized that pressure to deliver quick results often leads leaders to adopt AI for incremental improvements instead of systemic redesign. According to the report, leading CSCOs are reimagining not only technology but also operating models, team roles, and supporting infrastructure to build AI-native supply chains.
Real-World Deployment Challenges
The source states that supply chains remain among the hardest environments for AI deployment due to inconsistent, inaccessible, and siloed data spread across disconnected enterprise systems — including ERP, TMS, WMS, and order-management platforms. This fragmentation means many businesses lack a single source of truth across operations, undermining AI’s predictive and prescriptive capabilities.
This challenge is compounded by external volatility: tariffs, supplier diversification needs, and rising energy costs are intensifying pressure on organizations already constrained by trapped data and limited operational and financial visibility.
Emerging Vendor Responses
In response, AI-focused supply chain vendors are expanding platform capabilities. Loop, a San Francisco-based AI provider, recently raised $95 million in Series C funding — led by Valor Equity Partners and the Valor Atreides AI Fund, with participation from 8VC, Founders Fund, Index Ventures, J.P. Morgan Growth Equity Partners, and Tao Capital Partners. According to the source, Loop aims to broaden its platform across supplier, trade and compliance, warehouse, procurement, and inbound logistics data, while strengthening integrations across core systems.
“We see every day how much pressure companies are under to manage supply chains through constant disruption, and how often critical decisions are still being made on top of fragmented data and brittle systems.” — Matt McKinney, CEO and Co-founder, Loop
Meanwhile, GreyOrange launched GreyMatter Foundry — an AI simulator for warehouse automation design — at the Modex trade show, illustrating how vendors are targeting specific pain points in physical infrastructure planning.
Source: DC Velocity
Compiled from international media by the SCI.AI editorial team.
