According to www.dcvelocity.com, 65% of pharma supply chain leaders have limited confidence in AI’s ability to predict or mitigate disruption. This finding comes from the WBR Insights LogiPharma Playbook: 2026 Supply Chain & Logistics Insights report, released ahead of the LogiPharma conference in Europe. The report is based on a Q1 2026 survey of 100 heads of supply chain across Europe, representing pharmaceutical manufacturers, hospitals, and logistics partners.
From Ambition to Isolated Deployment
The research confirms that the industry has moved beyond theoretical AI interest into practical, albeit fragmented, use. 36% of respondents are using AI in isolation or experimental cases, while 47% are actively planning or exploring AI integration as part of their formal supply chain roadmap. As the report states:
“This highlights that respondents are finding early success when it comes to AI, but the next stage is transforming this into an enterprise-wide impact.”
Where AI Is Being Applied — and Where It Falls Short
AI adoption is most advanced in three functional areas:
- Demand planning (59%)
- Inventory optimization (57%)
- Logistics orchestration (49%)
The report notes that digitally connected supply chains — those integrating data across systems and partners — are achieving measurable gains: 15% lower logistics costs, 35% lower inventory, and 65% higher service levels.
Structural Barriers to Scaling AI
Despite progress, systemic challenges persist. According to Loop — an AI vendor cited in the report — supply chains remain one of the hardest environments for AI deployment because underlying data is inconsistent, inaccessible, and spread across disconnected enterprise systems. Many organizations lack a single source of truth across operations, particularly across ERP, TMS, WMS, and order-management platforms. Loop CEO and Co-founder Matt McKinney observed:
“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.”
Practitioner Implications
For global supply chain professionals, these findings signal that AI readiness hinges less on algorithmic sophistication and more on foundational data hygiene and cross-functional alignment. Early wins in demand planning or inventory tools must be deliberately scaled — not by adding more point solutions, but by unifying data flows and decision rights across procurement, manufacturing, logistics, and regulatory compliance functions. The report underscores that “the core challenge ahead is achieving alignment across the supply chain”, especially amid rising regulatory scrutiny and volatility in sourcing and transportation.
Source: DC Velocity
Compiled from international media by the SCI.AI editorial team.
