Guru Rao, CEO & Founder, nuVizz

Listen to how people talk about artificial intelligence in logistics right now and you often hear a
strange tone of finality. Companies ask whether they have “finished” their AI roadmap or
whether their platforms are now “fully autonomous.” That framing is misleading. What we are
living through is a transition from classic automation and predictive models to agentic systems
that can watch processes, decide what to do next and act across applications. It is an important
step, but it is still the middle of the story, not the last chapter.

For the last decade, logistics AI mostly meant better forecasting and optimization inside well
defined boxes. Routing engines and planning tools made smarter suggestions, but they still
waited for humans to trigger them. Agentic AI moves closer to a junior dispatcher or planner. An
agent can read an email, look up relevant orders, schedule a dock slot and send confirmations
without a person clicking through multiple systems. It can monitor events and decide when to
escalate. That extra agency is powerful, yet it is still narrow. Most deployments today are
wrapped around specific workflows with clearly bounded risk, not around the full chain of
decisions that moves a shipment from origin to destination.

The adoption data underlines how early this phase still is. A recent survey of transportation and
logistics executives across North America found that only a small portion of companies had live
agentic AI deployments at the end of 2025, even though nearly a quarter planned to pilot agents
in the next twelve months and more than forty percent were deliberately holding back. Their
hesitation was not about whether AI works in principle. They worried about integration
complexity, data quality and the need to redesign processes before giving software more control
over everyday decisions.

Retail and omnichannel businesses are in a similar place. A 2025 study reported that more than
two thirds of retailers have piloted or partially implemented AI agents and that most expect
efficiency gains in the near term. Yet only a small fraction consider their AI systems mature and
fully optimized. The biggest obstacles are not models or computers, but trust, regulatory
concerns, data integration problems and skills shortages. Today’s agents are concentrated in
customer service and marketing, while supply chain and delivery decisions still rely on a
patchwork of legacy tools and human judgment.

If you look at some of the leading 3PL providers, they’ve established an “agentic middle” by
deploying conversational AI to handle millions of operational and customer inquiries annually,
managing tasks like booking confirmations, tracking updates and documentation requests.
Similarly, large global shipping and logistics companies have begun deploying autonomous AI
agents across multiple regions to handle high-volume operational communication. These agents
schedule appointments, make driver follow-up calls, respond to transport status inquiries and
coordinate high-priority warehouse tasks. They manage hundreds of thousands of emails and
millions of minutes of phone time annually, freeing human teams from repetitive coordination work and improving response times and service level consistency. Crucially, these organizations
still treat these agents as part of a supervised operating model, where human teams set the
rules, oversee performance and step in when something unusual happens.
That is where many serious players are today, and it is not a bad place to be. But the long term
vision for logistics AI is much more ambitious.

I like to think in terms of an Autonomous Delivery Ecosystem, a world where a shipment can
move from order capture through cost calculation, partner selection, work assignment, route
optimization, pickup, delivery, exception management, tracking, communication and final
settlement without humans nudging every step. In a mature ADE, agents would not just send
emails or answer calls, they would also decide which carrier to use, how to replan around a
disruption and when to trigger a claim or a rebill, all while collaborating with other agents across
the network.

For that kind of ecosystem to emerge, some foundations have to be designed deliberately.
Participants need to operate as part of a shared network so that operational data can move
quickly and consistently across shippers, carriers, warehouses and partners. Business
processes and exception rules have to be standardized enough that similar events are handled
predictably regardless of who touches them. Stakeholders have to participate on roughly equal
terms, contributing and consuming data instead of protecting their own partial view. And the
technical architecture has to be open, making it easy for different systems and agents to
exchange information rather than trapping logic inside closed boxes.

Seen in that light, the agentic middle should be treated as a design phase rather than a
destination. Every new agent is a chance to rehearse how humans and software should share
work and to decide which decisions we are truly ready to hand over. Some organizations
already think in terms of explicit tiers of agency: decision support only, where the agent
analyzes and recommends; agent proposes and human approves, where the machine drafts
actions but people still press “go”; and full execution, where the agent acts on its own but
humans monitor outcomes and adjust policies.

This period can look messy. Workflows are split between people, rules and agents. Guard rails
feel heavy. Progress arrives as dozens of small use cases instead of one cinematic leap to
autonomy. It may be tempting, especially when budgets are tight, to freeze the current tech
stack and declare AI work finished. That would be a mistake. The value of the coming decade
will not come from being able to say you have agents in production, it will come from using this
developmental phase to harden the data foundations, standardize processes, build the right
networks and learn which decisions you are willing to entrust to software.