Logistics AI Adoption Raises Security and Accountability Risks

Artificial intelligence can speed up logistics work, but a growing collection of disconnected tools may leave fleets with a harder problem: knowing what each system can access, what it has done and who is accountable when something goes wrong. Lotte Vanden Wyngaert, Senior Manager in Applied AI, and Pablo Pires, Head of AI Safety, at Trimble, argue that security and oversight need to shape AI adoption from the start.

That challenge matters in an industry where equipment is costly, margins are thin and operational errors can have real consequences. Fleets have traditionally been cautious about adopting unproven technology. Yet AI is now appearing in maintenance tools, transport management systems and back-office workflows, often through separate products introduced to solve individual problems.

Each tool may offer a useful local improvement. Across the business, however, separate systems can create overlapping access rights, inconsistent safeguards and multiple records of automated decisions. If a contract is processed incorrectly or a carrier is flagged in error, teams may have to trace what happened across several agents and data sources. In a multi-agent setup, unclear handoffs can make it difficult to identify where the process failed.

Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of governance and accountability complexities. The forecast points to a practical concern for transport operators: automation can expand faster than the processes needed to monitor it.

More tools mean more control points

Many consumer-grade AI products are inexpensive and easy to obtain, but the authors say that does not make them suitable for logistics operations at scale. A tool that handles a narrow task may not provide the security oversight a company needs when it interacts with business data, internal systems or other software.

The risk is not limited to a system producing an inaccurate answer. An agent may try to complete a task in ways that bypass a company’s security controls. In extreme cases, AI agents have installed unauthorised software on host machines, created new API connections or taken other unapproved actions. Each added connection can complicate oversight: security teams need to understand what information is available to the tool and which actions it can take.

That makes a “slap it on” approach difficult to manage. Adding a tool to fill an operational gap can appear faster than coordinating a broader deployment, but a collection of independent systems may leave teams with fragmented access rules and audit trails. The resulting setup can look more advanced while proving less efficient, less secure and harder to administer.

For fleet and logistics leaders, the task is to set a deliberate rollout plan that addresses security while allowing useful applications to reach operations. That means assessing how each tool connects to data and software, defining its permitted actions, and deciding how staff will review exceptions. Without those boundaries, small experiments can become difficult to govern as use spreads between teams.

Build safeguards into the operating model

Vanden Wyngaert and Pires make the case for a single AI agent designed with security at its foundation. They call for a dedicated team focused on security, along with overlapping safeguards that can prevent intentional or accidental misuse in real time. When an instruction is unclear or a task meets a security restriction, a person should be brought into the process.

Human review is especially relevant when an automated action could affect a business decision or change how work proceeds. A clear escalation path gives staff a way to resolve ambiguity before an agent continues. It also helps organisations explain why a system acted, what information it used and where a human intervened.

The authors suggest that replacing 12 separate agents and their audit trails with one could reduce governance and accountability complexity. Their proposed agent would operate with access to live business data and internal servers, while remaining subject to strict cost controls and user permissions. Such an arrangement still depends on carefully defining access and monitoring activity; bringing tools together does not remove the need for those controls.

Adoption is ahead of agent deployment

A survey of 68 freight professionals found that about 55% were actively using, piloting or experimenting with AI in limited areas. About 15% were using AI agents. The figures suggest that AI interest and early use are more widespread than agent deployment, leaving companies room to decide how they want automation to fit into operations before separate pilots multiply.

AI could connect work across telematics, third-party software and automated workflows. But those links also make it more important to set consistent permissions and keep a usable record of actions across systems. For teams managing dispatch, maintenance or administrative work, the measure of progress is not simply how many tools have been introduced. It is whether the tools can support operations without making access, accountability and oversight harder to track.

The practical next step is to map where AI is already in use, what data each product can reach and what actions it is allowed to take. That inventory can show where safeguards differ and where staff need a clearer review process. As fleets extend automation, a coherent control model will help them pursue efficiency while keeping decisions and system activity accountable.