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Monitor, Recommend, Execute: How AI Creates a System of Action
Key Takeaways
- A system of record gives transportation teams real-time visibility into what already happened, but still leaves them reacting after the fact
- Agentic AI turns that visibility into a system of action: monitoring your network in real time and recommending the next best move
- Choosing the right AI partner separates a real system of action from a pilot: look for transportation expertise, proven execution, and true governance
The traditional TMS performs an important function: centralizing your historical data. It provides visibility into events that happen in your supply chain, and the reports and alerts it offers are valuable…but all after the fact.
With advances in AI, the industry is shifting toward a new model where technology doesn’t just report what happens – it analyzes, understands, and offers your team advice on what should happen next. It can even help execute that next action.
The result is a more agile operation with faster decision-making and better performance. And while the transition from a system of record to a system of action may seem daunting, the benefits make the effort more than worth it.
Why Shift from a System of Record into a System of Action?
Your TMS is a system of record. It tells you what has happened in your supply chain and provides reports and alerts on those events, but those reports always lag behind real-time occurrences.
In an environment that keeps moving faster and keeps getting more competitive, that visibility on its own is no longer enough. This model forces transportation teams to spend a lot of time chasing updates, making calls, reconciling information from spreadsheets and emails, and then manually executing corrective actions.
Allowing teams to focus on more strategic parts of their jobs requires a shift from the system of record model to a system of action. This modern approach builds on the traditional TMS with guiding insights for how your supply chain data should inform future decisions.
Monitor > Recommend > Execute: AI Advances Enable a System of Action
While automation and AI aren’t new to the transportation industry, AI has advanced to the point that it can reason across real-time operational context. Instead of just presenting reports, it can now bring together information from multiple systems, evaluate changing conditions and determine what actions should come next. That level of AI action wasn’t possible even a short time ago.
So how does an AI-empowered system of action work?
A system of action does things in stages:
- It monitors all incoming information from across your transportation network.
- It recommends the next best action based on real-time context and defined guardrails.
- It helps execute that action with appropriate human oversight.
A real-world illustration of this in action is missed delivery appointments. In a traditional TMS environment, the system alerts your transportation manager that a truck will miss its appointment, leaving the manager to figure out what to do next.
A system of action, on the other hand, can recognize the risk before the truck misses its appointment, thanks to its continuous monitoring. It can then evaluate the available options, recommend or initiate rescheduling, communicate with the driver, customer and manager, and keep your operation moving smoothly.
The ultimate goal is getting the right goods to the right place at the right time. AI allows you to do that by responding earlier and reducing disruption. With a system of action, you gain more than just speed – you gain the ability to operate more consistently and scale quickly without increasing your manual overhead. For a supply chain organization that wants to become more agile and remain competitive, that’s crucial.
Choosing the Right Partner Is the Key to System of Action Success
Not every AI solution is built for transportation. Because of the unique needs of supply chain organizations, choosing the right partner is critical to the success of your AI deployment.
Look for strong governance, transparency and control. Understand how your information is being used and how your partner and AI tool will make decisions.
Price lists several criteria for evaluating AI providers to ensure you partner with someone who can meet your needs and goals:
- Deep transportation expertise
- A proven ability to execute across transportation workflows
- Governance, transparency and control
- Defined guardrails and human oversight
- An AI solution with high-fidelity, connected operational context
- The ability to tie AI initiatives to measurable outcomes
The effort of finding the right AI partner to support your transportation tech stack pays off in better use of your team’s time. While a generic model can collect data and generate an answer, an AI purpose-built for transportation understands the network, systems, partners, constraints and business rules unique to this industry, so that it can act safely in your operation.
As AI continues to advance, the potential benefits for the transportation industry will become even more exciting. Instead of just telling teams what’s happening (or what’s already happened), it will understand those events in the context of the network, reason through the options to decide what should happen next, and help teams keep freight moving.
That will make getting the right goods to the right place at the right time a much easier goal to hit.
This article has been reformatted for Shipwell’s website. To view the original on Supply Chain Dive view it here.
Frequently Asked Questions
What's the difference between a system of record and a system of action in a TMS?
System of record vs. system of action is the core distinction in modern TMS design. A system of record is a traditional TMS: it centralizes shipment data and alerts you to what already happened, like a missed appointment or a delayed load. A system of action goes further, monitoring your network in real time, recommending the next best move, and helping execute that action with human oversight.
What is agentic AI in logistics?
Agentic AI in logistics refers to AI that reasons across real-time operational context instead of just summarizing it. Rather than generating a report on what already occurred, an AI transportation platform built on agentic AI pulls information from multiple systems, evaluates changing conditions, and determines what should happen next, catching a missed appointment or capacity gap before it becomes a customer problem.
How does real-time transportation visibility differ from traditional TMS reporting?
Real-time transportation visibility differs from traditional TMS reporting in timing. Traditional reporting shows you what happened after the fact: a truck missed its appointment, a shipment is delayed. Real-time transportation visibility, powered by agentic AI, surfaces that same information as it's forming, while there's still time to act, shifting teams from historical reporting to live, actionable context.
How does the monitor, recommend, and execute model work in an AI transportation management system?
In an AI transportation management system built as a system of action, the model works in three stages. First, it monitors incoming information across your transportation network in real time. Second, it recommends the next best action based on current context and predefined guardrails. Third, it helps execute that action with human oversight, whether that means rescheduling a delivery or reassigning capacity.
Can you give an example of a system of action catching a problem early?
A missed delivery appointment is a common example. In a traditional TMS, the system alerts a transportation manager only after the truck is already at risk of missing its window, leaving the manager to react. A system of action recognizes the risk earlier, evaluates the available options, and can recommend or initiate a reschedule, communicating with the driver and customer before the appointment is missed.
What should shippers look for in AI TMS partner selection?
AI TMS partner selection should start with deep transportation expertise, since freight has constraints a generic dataset won't capture. Prioritize a proven ability to execute across real TMS workflows, not just a demo. And confirm the partner offers governance, transparency, and control, so you understand how your data is used and how the AI makes decisions before you deploy it.
Why does governance matter in an AI transportation management system?
Governance determines whether you can trust the decisions an AI transportation management system makes on your network. Without defined guardrails and human oversight, the system can act on incomplete or incorrect context. Strong governance means understanding how your data is used, how recommendations are generated, and where a person needs to review or approve an action before it's executed.
What does TMS modernization mean today?
TMS modernization used to mean moving from spreadsheets to software. Today, it means moving from a system of record to a system of action: an AI transportation management system that reasons across real-time context instead of only reporting on it. Modernizing a TMS now means adding agentic AI capabilities and unified real-time data, not just replacing legacy infrastructure with a cloud-based version of the same reporting model.
