Insights

By now, most transportation and supply chain leaders grasp the general value propositions of agentic AI. The technology promises systems that can reason, coordinate work, and take action across multiple business processes. The potential is significant, but it also raises an obvious question: Where do you begin?

The temptation is to start with the technology itself. Organizations evaluate AI platforms, compare models, or look for departments that might benefit from automation. In reality, the best starting point is much simpler. Instead of asking where AI could fit, ask where work consistently slows down.

Google Cloud makes an important distinction between individual AI agents and broader agentic systems. An individual agent can automate a specific task, while an agentic system can coordinate work across processes and reshape how workflows operate. For transportation and supply chain leaders, that distinction matters because many of the most persistent operational problems aren’t confined to a single task or department. They occur when work has to move between them.

Every transportation and supply chain organization has workflows that require multiple teams to stop what they’re doing, gather information from different systems, and coordinate a response. Those moments are often the best opportunities for an agentic approach because the challenge isn’t performing the work. It’s moving the work forward.

Look Beyond Individual Tasks

Traditional automation projects often focus on individual activities. A warehouse may automate inventory counting, or transportation teams optimize routing. Manufacturing plants might streamline production scheduling. Each initiative can produce measurable improvements within a specific function.

Agentic workflows become more valuable when work crosses departmental boundaries.

Consider a delayed inbound shipment. Manufacturing needs to understand how production schedules will be affected. Procurement may need to evaluate alternate suppliers. Transportation has to determine whether delivery commitments can still be met. Customer service may need updated information before customers start asking questions.

Each department already has capable systems and experienced employees. What slows the organization down is the coordination required to connect those decisions.

When evaluating opportunities for agentic AI, leaders should pay close attention to processes that regularly involve multiple departments, multiple business systems, and multiple decision makers. Those workflows often deliver the greatest operational return because improving coordination benefits the entire organization rather than a single team.

A useful way to identify those opportunities is to combine a top-down look at business priorities with a bottom-up look at operational pain points. That’s the approach recommended in Google Cloud’s agentic AI implementation framework, which suggests examining existing KPIs, process maps, customer information, and other operational data to identify potential use cases. 

For a transportation or supply chain organization, that might mean looking for recurring delays, high exception volumes, excess expedited freight, frequent schedule changes, or workflows that consistently require several teams to intervene.

Start with High-Frequency Decisions

Not every business process is a good candidate for an agentic workflow. Strategic decisions that require significant judgment will always depend on experienced people. Routine operational decisions, however, often follow consistent patterns and require the same information every time they occur.

These are the workflows worth identifying.

Production planners may spend hours each week reviewing inventory availability before adjusting schedules. Transportation coordinators repeatedly evaluate carrier capacity when shipments change. Warehouse managers constantly balance labor assignments as inbound and outbound volumes fluctuate. Procurement teams monitor supplier performance and respond to recurring exceptions.

None of these activities are particularly unusual. In fact, they happen every day.

The more frequently a workflow occurs, the greater the opportunity to reduce manual coordination. Rather than replacing human decision making, agentic systems can assemble relevant information, evaluate likely impacts, recommend next steps, and notify the right stakeholders before delays begin to ripple through the operation.

Frequency alone shouldn’t determine what gets addressed first. Google Cloud recommends evaluating potential agentic use cases across three dimensions: business value, technical feasibility, and strategic urgency. 

Applied to supply chain operations, a workflow that occurs hundreds of times a day may look attractive, but it may not be the best first project if the necessary data is inaccessible or the business impact is minimal. A less frequent process that contributes directly to costly production delays or missed customer commitments may deserve higher priority.

Organize Around Events, Not Departments

One of the biggest shifts organizations make when adopting an agentic approach is changing how they think about work itself.

Most enterprise systems are organized by function. Transportation manages transportation. Manufacturing manages production. Procurement manages suppliers. Warehouses manage inventory.

Operational disruptions don’t follow those organizational boundaries.

A weather event, supplier delay, equipment failure, or sudden demand increase immediately affects multiple parts of the business. Each department responds from its own perspective, often creating additional coordination work as information moves from one team to another.

Instead of organizing workflows around departments, organizations can begin organizing them around operational events.

When a shipment is delayed, the workflow should automatically consider every downstream impact. Which production schedules are affected? Is inventory available elsewhere? Are customer delivery commitments at risk? Does transportation need to reroute freight? Which managers need to be notified?

The event becomes the starting point for coordinated action rather than the responsibility of a single department.

As these workflows become more sophisticated, that coordination may involve multiple specialized agents rather than one agent attempting to manage the entire process. 

A transportation agent might evaluate routing and carrier options while an inventory agent checks available stock and a production agent assesses scheduling impacts. Google Cloud’s Agent Development Kit, for example, supports multi-agent architectures in which specialized agents collaborate and delegate tasks, as well as orchestration that can follow predictable workflows or adapt dynamically as conditions change.

As organizations build more specialized agents, interoperability also becomes important. Google’s open Agent2Agent (A2A) Protocol is designed to allow agents built with different frameworks or by different vendors to communicate and collaborate securely. For a supply chain that already spans multiple platforms, partners, and technology environments, that kind of interoperability could become increasingly important as agentic systems mature.

Keep People at the Center of the Process

One misconception surrounding agentic AI is that organizations should strive for completely autonomous operations. For transportation, logistics, and manufacturing leaders, that is rarely the objective.

Most operational decisions still require experience, business judgment, and accountability.

The value of an agentic workplace comes from reducing the amount of manual coordination required before those decisions can be made. Instead of spending valuable time gathering information from multiple systems, employees receive relevant context, recommended actions, and a clearer understanding of operational impacts. Their expertise shifts from coordinating routine work to evaluating exceptions, balancing competing priorities, and making decisions that create business value.

In other words, the technology changes how work arrives at people, not whether people remain responsible for the outcome.

That makes decision rights and escalation paths important parts of the design. Google Cloud recommends establishing defined roles, decision rights, and escalation paths as part of agentic AI governance. In a supply chain environment, organizations should be explicit about which routine actions an agent can take independently, which require human approval, and which conditions should immediately trigger escalation to an experienced planner or operations leader.

Three Practical Places to Begin

Once a promising workflow has been identified, leaders can make the idea more concrete by mapping three things: the user’s goal, the tasks an agent would need to perform, and the tools and data required to perform them. Google Cloud uses those three components to map what it calls a critical user journey for an agentic solution.

For a late inbound shipment, for example, the goal might be to minimize production disruption. The required tasks could include identifying affected orders, checking alternate inventory, evaluating routing options, estimating downstream delays, and escalating decisions that require human approval. The required tools might include the organization’s TMS, ERP, WMS, supplier data, weather information, traffic conditions, and other relevant operational systems.

Organizations don’t need to redesign their entire supply chain to begin realizing value from agentic workflows. Many can start with operational processes they already know consume excessive time.

Exception management is often one of the strongest candidates. Delayed shipments, supplier disruptions, inventory shortages, and weather events already trigger cross-functional coordination. Agentic workflows can help identify affected operations, assemble the necessary information, and recommend response options much earlier in the process.

Production and transportation scheduling also present significant opportunities. Schedules are constantly adjusted as customer demand changes, equipment availability shifts, or transportation capacity fluctuates. Coordinating those updates across multiple business systems is often more time consuming than creating the schedules themselves.

Finally, customer communication remains a largely manual activity in many organizations. When operational conditions change, customer-facing teams frequently spend valuable time gathering information from multiple departments before providing updates. Agentic workflows can help consolidate operational information and prepare timely communications without requiring employees to track down answers across the organization.

Start with Friction, Not Technology

Organizations that see the greatest value from agentic AI are unlikely to begin with the most sophisticated technology implementation. They will begin with the workflows that consistently create friction across planning, production, logistics, and customer operations.

Those workflows already exist inside every transportation and supply chain organization. Employees know where they are because they experience them every day. Start by identifying three to five recurring workflows where people routinely move between systems, departments, or data sources to resolve the same kinds of operational problems. Then evaluate each one for business value, technical feasibility, and strategic urgency before selecting a focused use case to prototype.

That last point matters. Google Cloud recommends rapid prototyping before committing to full-scale engineering so organizations can test assumptions with real users, validate technical feasibility, and identify problems early. An agentic workplace doesn’t need to begin with an enterprise-wide transformation. It can begin with one high-friction workflow, a clearly defined outcome, and evidence that a better way of coordinating the work actually delivers value.

By identifying where work slows, where coordination consumes the most time, and where information repeatedly moves between departments before action can be taken, leaders can begin building an agentic workplace that delivers measurable operational improvements rather than simply adding another technology platform.