Artificial intelligence is not on the horizon. It is already embedded in freight operations, reshaping how Shippers and 3PLs manage procurement, communication, documentation and execution. As demand grows for faster and more accurate service, AI is helping teams transition from reactive processes to proactive, data-driven execution.
AI is not about replacing people. It is about refocusing their time on strategy, relationship building and solving high-impact problems while technology handles the repetitive work.
Automate Load Booking. Accelerate Results.
In freight, time is everything. A few minutes can mean a lost deal. Carrier sales teams are often overwhelmed by calls once a load is posted, with each call requiring manual attention and negotiation.
AI-powered virtual agents now manage those calls simultaneously. These agents can verify credentials, negotiate rates and finalize bookings. When integrated with a transportation management system (TMS), they also log interactions and update capacity in real time. The outcome is improved Carrier responsiveness and faster load execution.
This advancement represents a major leap in freight efficiency. AI-powered virtual agents not only eliminate hold times and missed calls, they also ensure every opportunity is captured without delay. By syncing directly with a transportation management system (TMS), these agents close the communication gap, reduce manual data entry and accelerate load execution.
The automation of routine tasks leads to measurable cost savings by lowering labor expenses, minimizing errors and maximizing asset utilization, giving 3PLs and Shippers a tangible edge in a time-sensitive, capacity-driven market.
The Future of AI-to-AI Negotiation
As virtual agents become more common on both the broker and Carrier side, freight procurement may evolve into a landscape where AI tools negotiate directly with one another. These AI-to-AI exchanges eliminate the need for voice or email, instead relying on digital protocols to share information and finalize terms.
“You’ll have a virtual agent representing the broker and another representing the carrier. And at that point, it’s just AI talking to AI,” said Boza.
While this reduces friction and increases efficiency, it also raises the importance of transparency and auditability. Logs and dashboards play a critical role in monitoring transactions and ensuring that when issues arise, human intervention can occur promptly.
Transparency and auditability are essential in AI-to-AI freight negotiations because they provide the accountability and oversight needed in an automated environment. Without human interaction, it's easy to lose visibility into how decisions are made or why a particular rate was accepted.
Logs and dashboards create a verifiable record of each transaction, helping teams trace actions, resolve disputes and identify anomalies. This not only builds trust in the technology but also ensures compliance, reduces risk and gives human operators the ability to intervene quickly when needed.
Why Data Is the Foundation
At its core, AI operates on probabilities. Give it enough quality data, and it can outperform humans in tasks like document classification, image recognition and predictive modeling.
“If I gave it 9,000 images of dogs and 1,000 of cats, the model is going to be much better at recognizing dogs. That’s data bias,” said Boza.
In freight, this translates to models trained on thousands of bills of lading, proof of delivery documents or rate confirmations. But companies just getting started may only have a few hundred samples, so Boza uses a technique called data augmentation to synthetically generate more training material.
“Machine learning is all about big data. But if you don’t have it, you’re not out of luck,” said Boza. “We can use AI to generate new, synthetic samples that mimic your real data.”
Data augmentation is important because it levels the playing field for companies without massive data sets. AI models thrive on large volumes of quality data, but many Shippers and 3PLs may only have limited historical records.
By generating synthetic samples that mimic real-world data, data augmentation fills those gaps, enabling effective training without waiting to accumulate years of information. This approach accelerates AI adoption, reduces barriers to entry and allows smaller or newer organizations to gain meaningful insights and automation from the start.
Exception Handling: Where Humans Still Matter
AI excels at repetitive, rules-based tasks. But exceptions still occur, including miscommunications, errors or edge cases, and human oversight is essential for managing those scenarios effectively.
Some AI systems now include conversation scoring tools that evaluate sentiment, tone and emotional context to identify calls or exchanges that may be going off track. These signals can trigger human review before small issues escalate. The key is not to rely on AI alone but to build a system where people manage the exceptions while AI handles the rest.
“You don’t want to turn your back on the system,” said Boza. “AI should handle the repetitive stuff, but humans are still needed for exception handling and oversight.”
While AI is powerful in handling routine tasks, it lacks the nuance and judgment required in complex or sensitive situations. Exceptions — like miscommunications or unusual requests — can quickly disrupt operations if not caught early. By integrating tools like conversation scoring and sentiment analysis, companies can proactively flag potential issues for human review.
This hybrid approach ensures that AI increases efficiency without compromising quality or customer experience. It reinforces that AI should augment, not replace, human insight, keeping people in control where it matters most.
Accuracy vs. Perfection
AI can often exceed human accuracy in specific tasks, especially in structured environments like document classification or load matching. However, perfection is not the goal. Striving for 100 percent accuracy can lead to diminishing returns by consuming time and resources that could be better spent optimizing overall performance.
In logistics, where speed and consistency matter, a model that reliably delivers in the mid to high 90 percent accuracy range is typically more than sufficient when supported by safety checks and human oversight.
Establishing clear benchmarks or key performance indicators (KPIs) ensures that AI implementation stays aligned with business goals. It shifts the focus from chasing flawless outcomes to delivering meaningful results, such as reduced error rates, faster response times and improved operational visibility. Ultimately, success should be defined by how well AI supports risk management, decision-making and customer service, not by theoretical perfection.
The Real Value: Reclaiming Time
Beyond cost savings and operational efficiency, the most valuable return on AI in freight procurement is time. AI enables Shippers and 3PLs to focus on high-value work such as strategy, problem solving and relationship building, rather than being buried in repetitive tasks.
By automating load matching, documentation, tracking and other routine functions, teams gain the capacity to think ahead instead of constantly reacting.
This shift is not about replacing people. It is about empowering teams to do more with less, scale faster and respond intelligently. Freed from manual processes, logistics professionals can drive innovation, improve service levels and uncover new revenue opportunities.
In an industry where responsiveness defines success, the ability to redirect time toward meaningful work is a true competitive advantage.
Explore the Tire Tracks Podcast AI Mini-Series
Banyan Technology recently launched a six-part AI mini-series on its Tire Tracks® podcast. The series explores how AI is redefining every stage of freight procurement, from forecasting and load execution to cybersecurity and beyond.
Each episode brings a fresh perspective from industry and technology leaders working at the cutting edge of AI in logistics. Whether you are just starting to explore AI or looking to scale your existing strategy, the series offers practical insights, real-world use cases and future-forward thinking you can act on today.
Click here to watch episode 1 of our Impact of AI on Freight Procurement mini-series.
Stay tuned for upcoming episodes covering predictive analytics, risk management, fraud detection, data security and how AI is transforming shipper and 3PL operations across the supply chain.
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