Read enough industry coverage right now and autonomous AI sounds like a settled outcome: robots in warehouses, agents running freight networks, a shrinking workforce to match. For most shippers today, that isn't the operating reality, and nothing in the current generation of tools points to it becoming the reality next year either.
The word "autonomous" is doing more work in the headlines than it is in actual deployment. It does not mean unsupervised or unaccountable; it means a system is taking an action that a person previously handled. The requirements are therefore like those that would apply before handing the task to a new employee: provide clear objectives with defined boundaries that cannot be crossed. This also includes intentional points where a system must stop and return a decision to a person before acting and a way to review the work after the fact.
The Old Optimizer's Failure
Logistics technology has been here before, just under a less dramatic name. In fact, more than half of supply chain leaders in JBF's own Q3 research still call rules-based automation "AI," so even the label confusion isn't new.
Early in my career, I worked inside a TMS platform with a heuristic optimizer built into the routing engine. Given a stack of shipments, it produced a consolidation and routing plan faster and more thoroughly than any human planner could manage by hand. The math behind it held up under scrutiny, yet shippers who bought the module still took years to get value from it, if they ever did.
Adoption stalled at the objective function (don't worry — an objective function is a less intimidating idea than the term suggests).
Strip the math out of it, and it's just the goal you've told a system to pursue, translated into terms the system can act on: which outcomes count as acceptable and which lines don't move no matter how good the trade-off looks.
Someone had to do that translation for the optimizer. Not writing equations, but turning business judgment like which service windows mattered, capacity constraints, and how much cost variance the business would accept before service took priority, into rules a machine could run without a person checking every plan.
Most first configurations left pieces of that translation undone or weak, and an optimizer running against an incomplete objective function hands back a plan that scores well against the rules it was given but fails in the real world. This model also never adjusted on its own. It kept running against whatever objective function it had been handed until a person went in and changed it, and nobody was translating the business into the optimizer's terms as the business itself changed. The optimizer kept optimizing against objectives that had gone stale over time.
None of that translation work starts with the objective function. Before anyone can specify what a system should optimize for, someone must first answer why the process exists and what outcome it is meant to produce. Skip that question and the existing process is likely to be encoded directly into the system’s rules, along with steps that no longer add value, exceptions whose origins no one remembers, and workarounds built around constraints that stopped applying years ago.
The result may be a technically well-specified objective function that automates the current process with total fidelity, including the parts that no longer earn their place. The person best positioned to answer why is usually the one who has run the process the longest, which also makes them the least likely to question whether the process still deserves to exist in its current form. Getting the “why” right is what makes room for a system to take on work nobody has capacity for today, rather than a faster version of the work already being done.
Agentic AI addresses the optimizer’s specific failure: its inability to adjust once the world around it changes.
An agent can adapt how it pursues a goal as conditions change, without someone manually rewriting its rules. That is the real advance this generation of tools represents, and it deserves to be taken seriously on its own terms. However, this adaptability also means the agent’s behavior continues to evolve, whether that evolution moves it toward the intended outcome or away from it. From six weeks into implementation to eighteen months into ongoing operation, the harder question remains whether the objective was defined properly, whether it still holds up at the current volume and complexity, and whether the system is moving toward what you meant.
Teaching a system to distinguish between acceptable and unacceptable outcomes is what reinforcement means in practice: using correction over time to show it what meeting the objective looks like. That correction depends on visibility into how the system arrived at its answer. If a team sees only the output, it cannot tell whether a good result came from sound reasoning or a shortcut that happened to work, leaving little basis for correcting the behavior. Most organizations are not positioned to make that distinction. In JBF’s Q3 survey, only 11% of respondents had documented a baseline and measurable success criteria before starting an AI initiative, which means they had nothing concrete against which to evaluate or reinforce the system’s behavior. Explainability must therefore come first; otherwise, teams are left reinforcing outcomes without understanding what produced them.
The old optimizer’s objective function matters here in a way that is easy to overlook. Defining it required translating a business’s tolerance for risk into rules a machine could execute without someone overseeing every decision. The tighter the objective function, the less risk the business accepted and the less freedom the optimizer had to find a better plan than the one the team already used.
Agents raise the same issue at a different scale. If a narrow agent handling one task works from an incomplete or poorly defined objective, the potential damage is limited. As an agent, or several agents, begins making a sequence of decisions across a process, that exposure grows. There are more opportunities for the system’s interpretation of the objective to drift and more distance between its actions and the person who would ordinarily catch a problem before it reaches a customer or creates a compliance failure.
What This Looks Like Today
In JBF's Q3 2026 survey of 215 supply chain and logistics leaders, only 10% report governance that's active and maintained once an AI system goes live, and only 8% say the supply chain or operations team itself owns that job after launch.
Ownership isn't unclear because organizations are careless. It's unclear because nobody decided whose job it was before the system went into production.
The most visible AI operator in freight shows the same pattern from the other direction. C.H. Robinson has disclosed workforce-reduction charges in each of its last three earnings releases, most recently in its Q2 2026 release filed July 29, 2026, alongside a separately verifiable productivity signal: North American surface transportation volume grew 1.5% year over year in that quarter while headcount in that segment fell 11.6%, the thirteenth straight quarter volume growth has outpaced the broader freight market.
This is a company with every reason to describe full autonomy if it had reached it. What it describes instead, in its own investor materials, is a named roster of agents, each scoped to a single function in the quote-to-cash process: quoting, order entry, appointment scheduling, load tracking. The filings disclose no AI-specific spend, and nothing in them claims the process runs without a person involved at any point. The company's own language for the strategy is human insight and Lean AI working as one, not AI operating on its own.
Most agentic AI capabilities in logistics technology remain narrow by design, not because the technology cannot support broader action. A narrow objective is easier to define completely, observe in operation, and correct when the system’s behavior begins to drift. Vendors are choosing narrow deployments because that is what most customers can govern today.
In an AI-driven operation, executing a task by hand transforms into defining a correct outcome, watching for drift in the system's read on it, and correcting that drift before it compounds. A lot of operations haven't assigned that job to anyone yet, for close to the same reason a lot of shippers never assigned someone to keep tuning the optimizer twenty years ago, and the job stayed unowned until a service failure or a cost overrun made the gap visible.
The question worth bringing into the conversation now is narrower than what an agent can do. It is whether anyone asked why the process exists before deciding what the system should optimize for, defined that objective clearly, and determined whether it can hold up as the business and its operating environment change.
It is also whether the team can see how faithfully the system is pursuing that objective today, rather than relying on how well it performed in a demo months ago. That requires knowing which guardrails encode the organization’s compliance, service, and operational requirements, where the system must stop for a person’s decision, and how drift in its method of reaching the objective will be detected. These questions are harder to answer than they appear because they ultimately depend on someone who understands the operation well enough to recognize when a technically sound result is wrong. That judgment is what the entire approach rests on.
About the Author
Tara Buchler is Principal, Strategy at JBF Consulting, bringing more than 20 years of experience at the intersection of logistics operations and enterprise supply chain software. She partners with shippers to design and implement pragmatic, high-impact strategies that align business goals with advanced technology solutions.
Tara’s unique perspective blends vendor-side product leadership, hands-on implementation expertise, and operational insight—allowing her to provide objective advisory services rooted in real-world experience. Her background includes senior roles at e2open, BluJay Solutions, and LeanLogistics, where she helped shape TMS, visibility, and parcel execution capabilities for global shippers.
FAQs
No. Even the company that talks about its AI program the most publicly, C.H. Robinson, describes agents scoped to single tasks: quoting, order entry, appointment scheduling, load tracking. Its own language for the strategy is human insight and Lean AI working as one, not AI operating on its own.
It's the translation of business judgment, which outcomes are acceptable and which lines can't move, into rules a system can act on. Getting that translation wrong, or leaving it unchanged as the business evolves, is the most common reason automation underdelivers, whether that's a TMS optimizer from twenty years ago or an AI agent deployed this year.
Both depend entirely on how completely someone translates business rules into system logic before turning the system loose. An incomplete translation produces a plan or a decision that scores well on paper and fails in practice. An agent compounds that failure faster than an optimizer did, because it keeps adapting instead of holding still until someone notices.
Per its Q2 2026 earnings release, filed July 29, 2026, C.H. Robinson runs a named roster of single-function agents while North American surface transportation volume grew and segment headcount fell over the same period. The company frames this as augmentation, not autonomous operation.
Whether anyone defined why the process exists before deciding what the system should optimize for. Whether that objective is complete and still current. Where a person has to approve before the agent acts. And how drift from the intended outcome gets caught before it reaches a customer or a compliance line.
