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Building an AI strategy to leapfrog tech debt in supply chain execution
Overcome legacy limits with agentic AI. Discover how supp...
AI in Transportation Management: 8 questions shaping what’s next
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Summary: This blog explores the role of artificial intelligence and agentic AI in helping businesses unlock better decisions and elevate their teams, featuring insights shared by Infios’s own Steve Blough, Chief Supply Chain Strategist, and Aadil Kazmi, Head of GenAI, in a discussion with Supply Chain Management Review.
In this article:
- Are you using your data to improve your business?
- How do AI capabilities fit transportation management?
- What is an AI agent?
- What is an AI agent in transportation management?
- How is Infios using AI in transportation management?
- What are the risks of deploying AI in transportation management?
- What is the Infios purposeful AI approach?
- How does agentic AI work in action?
In a world saturated with data, global businesses have invested heavily in extracting it, compiling it and storing it. But without using that information in meaningful ways, those data investments are wasted.
With the evolution of artificial intelligence (AI), those businesses can finally leverage data management investments to transition their traditional supply chain into an intelligent execution system—one that responds proactively in real-time to help you adapt during disruptions.
In a recent conversation with Supply Chain Management Review, we discussed how Infios is transforming transportation management with AI and modular technology. Watch the full conversation here to learn how we’re helping customers prepare their supply chain for what’s next.
This blog highlights key perspectives from that discussion. Here, we focus on artificial intelligence and agentic AI, and how businesses leverage these solutions to better utilize data resources and augment their human workforce. We also explore how purposeful AI innovation creates practical, measurable impact in transportation management.
Are you using your data to improve your business?
Most businesses sit on an enormous volume of data but struggle to act on it. Data lakes and terabytes of storage lack value if you can’t use that information to improve supply chain decisions.
Chances are, all that accumulated supply chain data is raw and difficult to normalize across systems. Or there is so much that there will never be enough time and resources to effectively use it, especially for a large business.
How do AI data capabilities fit transportation management?
Within transportation management, data inflows come from many places—internal systems, partners, external platforms. Effective execution relies on generating and using that information in a timely manner to meet business goals.
That’s where artificial intelligence comes in. AI fits transportation management for two reasons:
- You can apply AI as a decision engine on top of your data, turning massive volumes of information into insights at a scale that exceeds human capabilities. AI can make sense of data extremely rapidly and, more importantly, cost-effectively.
- Insights generated with AI support can be converted into action through the application of an AI agent.
What is an AI agent?
An AI agent, regardless of industry, is a large-language model (LLM) that has been given a prompt, a knowledge base and a defined set of actions that the agent can take.
The premise behind an agent: it’s a self-contained autonomous unit that can make sense of what’s happening, reason through the next best step, and act—all within your business logic and guardrails.
What is an AI agent in transportation management?
AI agents in transportation management are systems agents that are deeply integrated within a business’s real-world operations and supply chain management platforms, such as the TMS/WMS/OMS, invoicing solutions, and email applications, to name a few.
An AI agent for transportation management is designed to execute operations in the same way a human would:
- Combine data from individual operating platforms
- Make sense of the data
- Reason to determine the next action
- Take that action
However, agentic AI solutions can execute these activities at a scale and pace unrivalled by humans.
How is AI transforming transportation management?
Agentic AI is changing how transportation operations access and act on real-time data. By integrating AI directly with a transportation management system (TMS), these agents can rapidly analyze information and determine the next best action based on current conditions.
This approach enables faster, more informed decision-making across critical workflows—from carrier procurement and rate negotiation to appointment scheduling, driver check calls and freight audit and payment. By combining AI capabilities with human oversight, organizations can scale operations, reduce repetitive tasks and focus their teams on strategic decisions.
What are the risks of deploying AI in transportation management?
- Data quality
AI agents rely on your underlying data to make a decision. Poor or unusable data can compromise decision-making, highlighting the importance of accurate, accessible information. An AI vendor with direct access to that data through the TMS minimizes data quality risks. - Integration complexity
Even when data is clean, moving it to the right systems at the right time can be challenging. Flexible, modular integrations make it possible for AI agents to operate effectively across supply chain platforms, supporting timely decision-making. - Change management
Scaling AI adoption requires rethinking how humans and agents work together. Success depends on helping internal teams understand the logic, capabilities and potential of AI-driven workflows. - Upskilling
As AI takes on repetitive tasks, human teams must be empowered to train, monitor and interact with AI agents—transforming their roles from execution-focused to strategic decision makers. - Bias
AI agents can develop predictable patterns, such as favoring lower-cost carriers. Human oversight remains critical to ensure decisions align with broader business priorities. - Governance
AI introduces new considerations for security and accountability. Clear ownership, audit trails and operational guardrails help protect against errors or malicious interference.
“No one is going to deploy an AI agent into their businesses and let this AI agent run wild. It's extremely important to partner with a vendor that not only builds high-fidelity and high-impact agents, but provides you with the tools to monitor, observe and continuously optimize your AI agents that are in flight.”
What is the Infios purposeful AI approach?
Purposeful AI is redefining what it means to leverage artificial intelligence in supply chains. It's not about flashy technology—it's about outcomes that truly matter to businesses.
Some businesses want to do more with less. For a broker, it means managing four or five times as many loads without adding headcount. For a shipper or 3PL, it means maintaining a consistent, reliable customer experience, especially in times of supply chain disruption. Purposeful AI starts with these real-world challenges, not with algorithms.
Meeting our customers where they are to help solve their challenges is purposeful AI. Infios uses a two-pronged approach to make this possible:
- Precision in the workflow: developing use cases and agents that drive measurable results across industries, turning common processes into strategic advantages.
- Human-AI collaboration: we work closely with customers to rethink how humans and AI work together, creating workflows that unlock insights, efficiency and resilience.
Purposeful AI is more than technology adoption, it’s the new standard for how supply chains achieve outcomes. By aligning intelligence with business priorities, Infios helps organizations transform data into actionable insight and potential into performance.
“AI has become a buzzword in many ways across industries. Purposeful AI, to Infios, means helping our customers make the jump from “Will AI help me?” to “How do we use AI to transform?”
How does agentic AI work in action?
Traditional business workflows were built entirely for humans. As the workforce evolves into a hybrid of humans and AI agents, businesses are forced to rethink those workflows.
Take the burdensome task of call and status checks. In one agentic AI use case, when a load is late, an AI agent automatically contacts the carrier and determines the next action.
For a single operator managing one carrier, checking status by phone or email is tedious but manageable. Scale that workload across multiple carriers during a demand surge or business growth, and it becomes unsustainable. Failures here can limit growth, especially as new customers often require more frequent status checks.
Automating check calls has an outsized impact: humans shift focus from repetitive tasks to monitoring, managing exceptions and making high-level decisions, work that truly drives operational value.
Learn more about AI in transportation management in our interview with Supply Chain Management Review, covering:
- Supply chain trends that are creating new challenges in transportation management
- Where do you draw the line between human decision-making and AI decisions within transportation management?
- Emerging technologies that will continue to advance transportation management forward
- Why it is important to get started today
Additional resources:
Solution page: Intelligent Supply Chain Execution
Solution page: Infios Transportation Management
How artificial intelligence augments human problem-solving
Supply chain networks generate massive amounts of data. Transportation teams rely on that information to control costs, fulfill customer expectations and support sustainable growth.
Yet much of this data goes underutilized. Humans alone can struggle to compile, contextualize and analyze it at scale—let alone act on it quickly. Artificial intelligence changes that equation.
At Infios, we’re innovating human-assisted AI, where AI agents are responsible for traversing through endless terabytes of data to make sense of it all, generate actionable insights and deliver them to the right person at the right time. The result is a workforce augmented by AI, empowered to make better, faster decisions at scale.
This is more than automation—it’s about amplifying human problem-solving with intelligence that drives outcomes across the supply chain.
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