Agentic AI Will Not Transform Logistics Until Logistics Is Ready for It

This guest post by Parth Dave, CCLP, CCLMP discusses the state of readiness of the logistics industry for agentic AI

Readiness Is Uneven Across Logistics Organizations

Agentic AI is often described as the next step beyond predictive analytics in logistics. Yet adoption data suggests that most organizations remain far from operational deployment.
A recent ORTEC survey of more than 2,000 transportation, logistics, and supply chain executives across North America shows that 42% of organizations are not exploring Agentic AI at all, continuing to rely on traditional AI and machine learning tools. By the end of 2025, only a small subset reported active pilots or live deployments.

Looking ahead, 23% of respondents plan to pilot Agentic AI within the next 12 months, positioning 2026 as a year of experimentation rather than transformation.
This distinction matters. Pilots are useful for learning, but they do not indicate readiness to embed autonomous decision-making into day-to-day logistics execution. They typically operate in controlled environments with limited scope, simplified data flows, and explicit guardrails. Scaling beyond that requires confidence in how decisions are made, who owns them, and how exceptions are handled when automation encounters real-world variability. The survey suggests that while interest in Agentic AI is growing, far fewer organizations believe their current operating models can support it at scale. The gap between curiosity and readiness remains wide.

Clarity Becomes the Real Constraint

Agentic AI rarely fails because it makes poor recommendations. It struggles when organizations cannot clearly explain how decisions are made in the first place.
The survey reflects this tension. Twenty-six percent of respondents cited concerns about understanding how AI models make decisions as a major barrier to adoption. While often framed as a transparency issue, this concern usually points to a deeper challenge.

In many logistics organizations, decision logic is distributed across planning systems, spreadsheets, informal workarounds, and individual experience. Planners navigate this complexity effectively, but the logic itself is rarely documented or standardized. When an AI system is asked to replicate or improve those decisions, gaps in clarity become immediately visible.

Agentic AI forces organizations to confront questions they have been able to postpone. Which decisions can be automated without unacceptable risk? Which decisions require human judgment, and under what conditions? How should trade-offs be evaluated when cost, service, and resilience move in different directions?

These questions remain unresolved for many teams. Forty-two percent of respondents indicated that business processes would need to be redesigned to support autonomous decision-making. Process redesign is not a technical exercise. It is an exercise in making decision ownership, escalation paths, and exception handling explicit.

Without that clarity, autonomy becomes uncomfortable. Organizations hesitate to act on AI-generated recommendations when the rationale cannot be clearly explained or defended. Adoption slows, not because the technology is unproven, but because accountability is unclear. In this sense, Agentic AI acts less as a replacement for human decision-making and more as a mirror. It reflects the maturity of an organization’s decision framework. Where clarity exists, AI accelerates execution. Where it does not, AI amplifies uncertainty.

Integration Cost Is a Signal, Not the Problem

Integration cost is the most frequently cited barrier to Agentic AI adoption. Thirty-two percent of respondents ranked high integration costs with existing systems as their top frustration. At first glance, this appears to be a budget issue.

In practice, integration cost is rarely about the AI itself. It is a signal of accumulated complexity within the logistics technology stack.

Most logistics organizations operate with a layered mix of transportation management systems, planning tools, execution platforms, spreadsheets, and manual workarounds that have evolved over time. Few were designed with shared data models, consistent decision logic, or real-time interoperability in mind. Agentic AI exposes this fragmentation quickly. Autonomous systems require clean inputs, stable interfaces, and well-defined decision boundaries. When those prerequisites are missing, integration effort increases sharply.

The survey reinforces this dynamic. Twenty-two percent of respondents cited poor or inconsistent data, while another 22% pointed to the need for reliable, real-time data feeds. These are not new challenges. They are long-standing issues that Agentic AI makes difficult to ignore.

Integration cost, then, should be interpreted carefully. It does not indicate that Agentic AI is overly complex. It indicates that many logistics environments were not built to support continuous decision-making across systems.

Where Leaders Are Starting, and Why That Matters

When asked where Agentic AI could deliver the most value, respondents did not point to fully autonomous, end-to-end planning. Instead, they identified specific, bounded problem areas.

First- and final-mile route scheduling ranked as the top use case, cited by 35% of respondents, followed by global supply chain network design at 20%.

These choices are telling. First- and final-mile routing involves frequent decisions, well-understood constraints, and fast feedback through metrics such as mileage, fuel consumption, and service reliability. This makes it a practical entry point for Agentic AI, with limited scope and manageable risk.

Network design represents a different starting point. While decisions are less frequent, they are structurally important and typically governed by clear parameters. In this context, AI supports scenario evaluation and trade-off analysis without immediately assuming operational control.

Notably absent from the top use cases is full cross-functional autonomy. Leaders are sequencing adoption around areas where decision logic is already relatively mature, rather than rushing toward end-to-end automation.

Agentic AI as a Readiness Test, Not a Technology Bet

The survey data points to a clear conclusion. Agentic AI is not being held back by a lack of interest or technical capability. It is being constrained by the readiness of the systems and decision structures it is meant to support.
Organizations that struggle to adopt Agentic AI are not falling behind innovation. They are confronting long-standing issues around data consistency, decision ownership, and process clarity that autonomous systems surface quickly. In that sense, Agentic AI does not introduce new complexity. It exposes existing complexity.

This explains why 2026 is shaping up to be a year of pilots rather than transformation. Pilots allow organizations to test not only the technology, but also their ability to define decisions, manage exceptions, and trust outcomes at speed.
For logistics leaders, the implication is straightforward. Preparing for Agentic AI does not start with software selection. It starts with clarifying how decisions are made, which decisions can tolerate autonomy, and where human judgment must remain firmly in the loop.

Agentic AI will eventually reshape logistics execution and service reliability. When it does, the differentiator will not be access to technology, but the discipline of the organizations deploying it.

The survey does not suggest hesitation. It suggests realism. And in logistics, realism remains the most reliable foundation for progress.

Parth Davé, CCLP
Founder
NexaFlux

Parth Davé is the founder and principal consultant at NexaFlux Inc., a Canadian firm helping growing businesses turn data into stronger, more resilient supply chains. With over a decade of experience across transportation, healthcare, and consumer goods, he applies a constraint-first, systems-thinking lens to every engagement. Parth focuses on translating operational data into clear insights, measurable performance, and long-term competitive strength. Parth can be reached at pdave@nexafluxinc.com.