A recent survey of North American transportation, logistics, and supply chain executives reveals a disconnect between what those leaders see as the promise of advanced artificial intelligence (AI) solutions, such as Agentic AI, and their readiness to implement them.
Conducted by global technology firm Ortec, which provides optimization software and analytics solutions to a range of industries, the survey examined the effects of adopting AI and machine learning (ML) in logistics. While nearly all of the survey’s 400 respondents said they recognize the potential of Agentic AI to modernize planning and execution, 42% said they are not yet exploring the technology and instead remain focused solely on traditional AI and machine learning (ML) approaches.
“The survey … found that only a small minority had active Agentic AI pilots or deployments at the end of 2025, even as 23% say they plan to pilot Agentic AI within the next 12 months—putting 2026 squarely in focus as a test-and-learn year for autonomous decision-making in logistics,” according to the report.There are key differences in traditional and advanced AI: Traditional AI solutions perform tasks based on predefined rules and algorithms—a common example is the virtual assistant Siri. Agentic AI solutions can make decisions without human intervention—examples include autonomous vehicles that can navigate traffic.
Despite a lack of industry testing and deployment of Agentic AI, respondents said they have high expectations for its use in supply chain operations, citing drastic cost savings through fuel and mileage optimization (30%), increased operational resilience (22%), and improved data quality (20%) as their top anticipated benefits.
That optimism is balanced by concerns about getting Agentic AI production-ready in 2026, according to the report. Respondents point to high integration costs with existing systems as their number one frustration (32%). They also cite a “lack of model explainability” (26%)—which refers to situations in which AI systems make planning or execution decisions, but logistics teams can’t clearly understand why a specific recommendation or action was taken. Poor data quality is another key concern (22%).Respondents said they are also concerned about a lack of in-house expertise and unclear ROI (return-on-investment) when it comes to implementing AI in general.
Despite the obstacles, executives say they have a clear view of where Agentic AI should be applied first in supply chains: First- and final-mile route scheduling is seen as the top target for AI-driven reinvention (35%), followed by global supply chain network design (20%).
When asked what would most accelerate adoption, respondents prioritized clear ROI measurement frameworks (30%), peer case studies from similar organizations (25%), and seamless integration with existing planning systems (24%).“Executives are entering 2026 with a clear mandate: make Agentic AI real, measurable, and safe for operations,” Daphne de Poot, Ortec’s senior vice president of operations for the Americas, said in a statement announcing the survey’s findings. “Our research shows they believe Agentic AI can fundamentally improve cost, service, and resilience, but they need transparent decisioning, reliable data, and a phased approach that keeps planners in control while AI gradually takes on more of the repetitive and complex decision-making work.
“These survey findings provide a detailed view into how leaders are thinking about the next wave of AI—beyond predictive analytics and into autonomous, decision-making systems that can continuously optimize complex logistics networks.”
