AI solutions have advanced across the supply chain, helping shippers and carriers carry out a myriad of tasks in just a matter of minutes that previously may have taken hours or days.
Today’s AI solutions are most effective at solving “volume and velocity” challenges facing human teams. In areas where teams are increasingly overwhelmed (e.g. carrier selection, compliance, fuel tax reporting, exception management), task-based AI solutions such as agents can automate high-volume, high-velocity tasks to reduce risk and allow human teams to focus their scarce resources on judgment, reasoning and decision-making – areas at which humans (currently) outperform AI.
Because of AI’s potential benefits, Gartner predicts that by 2030, 50% of supply chain solutions will incorporate autonomous decision-making, marking a significant shift from executing tasks to pursuing outcomes.
But that future is still years ahead of us. Although 36% of shippers have moderate or basic AI capabilities in their transportation management systems, only about 1% are currently using advanced autonomous decision-making such as goal-based agentic AI systems, according to Trimble’s 2026 Transportation Pulse Report. However, the momentum for change is building: McKinsey’s State of AI in 2025 report found that 23% of organizations are already scaling agentic AI systems and another 39% are experimenting with them.
In the meantime, preparing and positioning/repositioning today’s supply chain workforce for the AI future means evolving roles, dedicated training, and strong governance practices to ensure the success of the AI-human team of colleagues.
AI as a colleague
The expression “AI as a tool”, which applies to reactive and task-based AI solutions such as generative AI solutions and AI agents, is starting to give way to “AI as a colleague.” When AI acts as a proactive independent member of the team taking initiative and managing and automating complex workflows and repetitive tasks, it’s more than just a tool - it’s a non-human member of your team. This is reflected in the fact that two-thirds of shippers and more than half of carriers see this as AI's primary role. To accommodate this change, companies will need to shift their thinking about their teams from individuals to identities — humans and non-humans working side by side to get the work done and drive success.
Agentic AI takes automation to a new level beyond task-based outcomes following pre-programmed rules (e.g. “if X happens, then do Y”). Agentic AI solutions plan and execute multiple workflow steps on their own. They're goal-oriented, they monitor situations, make decisions and take action within the boundaries you set for them.
This shift is already tangible. Agentic AI systems are becoming fully-fledged parts of the workforce.
As a result, companies are no longer asking whether AI can help. Instead, they’re increasingly asking: “Can AI do it, and how quickly can it deliver?”
Where are shippers putting agentic AI solutions to use? Spot buying, carrier vetting, and real-time ETA monitoring and disruption management top the list of priorities.
These more complex AI solutions may not require as much training for team members to understand prompt engineering best practices, but do require an understanding of what agentic systems can or can’t decide, when to intervene and how to work across departments and functions.
Governance is essential
Even as agentic and other AI systems become more autonomous, the “human-in-the-loop” approach remains an essential governance requirement. That means treating agents like new colleagues that need to be supervised, and not just software. Like with any new hire, AI agents need clear job descriptions, continuous feedback and ongoing evaluation to become effective, reliable workers and partners.
The more decisions AI makes on its own, the more critical strong AI governance becomes. AI is often talked about as a multiplier, but the focus is most often on multiplying the positive benefits. The converse is also true - autonomous tasks can multiply problems and risks as well. Managing both positive and negative AI multiplication effects means setting clear boundaries: what can your AI agents do and what's off limits? Those guardrails enable safe AI use that stays perfectly in line with your intentions.
The key is to establish effective AI governance and guardrails before you scale, not after things break down. You need to track how AI tools and agents, and agentic AI solutions, perform at each step of the workflow and not just examine the final results. This enables you to catch errors early and keep refining, giving you a level of visibility that becomes critical as you move beyond pilots. If you’re using AI in high-risk situations (as defined under various AI laws), additional governance and compliance steps may be required. Working with market-validated platforms and a trusted network, and your compliance and legal teams, can help you keep your deployments on target.
Looking ahead
There’s no question that AI colleagues will be a critical part of tomorrow’s supply chain teams. The technology has already proven its reach: in the United States, research shows AI can already handle tasks representing 11.7% of the workforce. By the end of the decade, the global potential for efficiency gains and cost reduction will be substantially higher.
Roles are already evolving. Dispatchers and planners are shifting from handling every task manually to overseeing intelligent agents, still responsible for the decisions, but with AI handling the execution.
While AI is excellent at flagging exceptions, for example, companies may lack the internal protocols and skilled personnel needed to review those flags efficiently and apply the necessary regulatory judgment to them, leading to alert fatigue instead of effective risk reduction.
To provide the “fastest win,” first identify a problem that can be solved by data-driven decisions. To find the AI solution to help address that problem, supply chain stakeholders should start by looking at the AI capabilities in their existing software solutions. Many software providers have been adding AI capabilities into their existing products - fleet managers and shippers may not need to invest in new third-party solutions to take advantage of AI.
Alternatively, vendors may offer add-on modules with AI capabilities that can be easily added to an existing solution. Companies must be able to integrate agentic AI solutions into what they already have, not rebuild everything from scratch. This approach lets organizations adopt agentic capabilities incrementally, matching their pace to their resources and technical readiness.
The measure of excellence won’t be automation levels alone, but also the business results that human and AI teams achieve together. The companies that build the right infrastructure, governance and culture to support that partnership, will shape the next era of supply chain leadership.
