A recent survey of transportation, logistics, and supply chain executives in North America revealed a disconnect between what these leaders see as the promise of advanced artificial intelligence (AI) solutions, such as agenttic AI, and their readiness to implement them.
The study was conducted by Ortec, a global technology company that provides optimization software and analytics solutions to a variety of industries, and investigated the effects of implementing AI and machine learning (ML) in logistics. While nearly all of the survey’s 400 respondents said they recognized the potential of Agentic AI to modernize planning and execution, 42% said they had not yet considered the technology and remained focused solely on traditional AI and machine learning (ML) approaches.
“The survey… found that only a minority had active pilots or deployments of Agentic AI at the end of 2025, while 23% said they were planning to pilot Agentic AI in the next 12 months. 2026 is squarely in focus as a year of testing and learning for autonomous decision-making in logistics,” the report states. There is a big difference between traditional AI and advanced AI. Traditional AI solutions perform tasks based on predefined rules and algorithms. Siri, the virtual assistant. Agent-based AI solutions can make decisions without human intervention. Examples include self-driving cars that can navigate traffic.
Despite the lack of industry testing and deployment of Agentic AI, respondents said they have high expectations for its use in supply chain operations, citing significant cost savings through fuel and mileage optimization (30%), increased operational resiliency (22%), and improved data quality (20%) as the top expected benefits.
That optimism is balanced by concerns that Agentic AI could be operationally ready in 2026, according to the report. Respondents cited high integration costs with existing systems as their top complaint (32%). They also mentioned “lack of model explainability” (26%). This refers to situations where an AI system makes planning and execution decisions, but the logistics team does not have a clear understanding of why a particular recommendation or action was taken. Poor data quality is also a major concern (22%). Respondents also said they were concerned about a lack of in-house expertise and unclear ROI (return on investment) when it comes to AI implementation in general.
Despite the obstacles, executives say they have a clear vision of where to apply Agentic AI first in the supply chain. First-mile 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 best drive adoption, respondents prioritized a clear ROI measurement framework (30%), peer case studies from similar organizations (25%), and seamless integration with existing planning systems (24%). “Leaders enter 2026 with a clear mission to make Agentic AI a reality, measurable, and operationally secure,” Daphne de Poot, Ortec’s senior vice president of Americas operations, said in a statement announcing the survey results. “Our research shows that they believe agenttic AI can fundamentally improve costs, services, and resiliency, but requires transparent decision-making, reliable data, and a step-by-step approach that allows planners to maintain control while AI gradually takes over many of the repetitive and complex decision-making tasks.
“These findings provide detailed insight into how leaders are thinking about the next wave of AI, which goes beyond predictive analytics and includes autonomous decision-making systems that can continuously optimize complex logistics networks.”
