The Transformative Role of AI in Transportation Management

Summary: This article delves into the significance of artificial intelligence and agentic AI in enhancing decision-making within businesses. We highlight insights from Infios’s Chief Supply Chain Strategist, Steve Blough, and Aadil Kazmi, the Head of GenAI, in a dialogue featured in Supply Chain Management Review.

Table of Contents

• Maximizing Data Utility in Business
• AI’s Role in Transportation Management
• Defining an AI Agent
• AI Agents in Transportation Management
• The Impact of AI on Transportation Operations
• Risks Associated with AI Deployment
• Overview of Infios’s Purposeful AI Strategy
• Practical Applications of Agentic AI

Maximizing Data Utility in Business

In an era marked by an overwhelming influx of data, many businesses find themselves hamstrung by their inability to effectively leverage this information. While organizations have heavily invested in data collection, a lack of actionable insight renders those resources largely ineffective.

AI’s Role in Transportation Management

The introduction of artificial intelligence offers a solution for companies seeking to capitalize on their data investments. By transforming traditional supply chains into intelligent execution systems, these organizations can respond proactively to disruptions and optimize decision-making in real-time.

In a recent conversation featured in Supply Chain Management Review, our experts discuss how Infios is reshaping transportation management through AI and modular technology. Watch the full conversation here to discover how we prepare supply chains for future challenges.

Defining an AI Agent

An AI agent is fundamentally a self-contained autonomous system, often based on a large-language model (LLM). It is equipped with prompts, a knowledge base, and a predefined set of actions, allowing it to interpret data and make informed decisions based on specific business logic.

AI Agents in Transportation Management

In transportation management, AI agents act as integrated systems that enhance real-world operations. They can:

• Aggregate data from various sources
• Analyze data to produce actionable insights
• Determine logical next steps based on real-time conditions
• Automate decision-making processes

The speed and accuracy of these agents often surpass human capabilities, driving greater efficiency.

The Impact of AI on Transportation Operations

Agentic AI revolutionizes access to real-time data in transportation management. By integrating AI with transportation management systems (TMS), organizations can make rapid, informed decisions across operational workflows—from carrier procurement to freight audit and payment processes. This integration frees human teams to focus on strategy rather than repetitive tasks.

Risks Associated with AI Deployment

• Data Quality: AI effectiveness is tethered to the quality of input data. Clean and accessible data is crucial for sound decision-making.
• Integration Complexity: Moving data effectively across systems can pose significant challenges without proper integration strategies.
• Change Management: Teams must be prepared to rethink workflows and integrate AI successfully within established operations.
• Upskilling: As AI automates repetitive tasks, human teams need training to transition into more strategic roles.
• Bias: AI systems can develop biases that affect outcomes, necessitating human oversight to ensure alignment with business objectives.
• Governance: Establishing clear accountability and security measures is vital to mitigate risks associated with AI deployment.

Aadil Kazmi, Head of AI at Infios: “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 tools to monitor and optimize them continuously.”

Overview of Infios’s Purposeful AI Strategy

At Infios, our approach to Purposeful AI prioritizes real-world outcomes over technological complexity. We focus on helping clients achieve practical results, whether accelerating operational efficiency or enhancing customer experiences, especially during disruptions.

Our strategy includes:

• Precision in Workflow: We design AI use cases that produce measurable results, transforming common processes into strategic advantages.
• Human-AI Collaboration: We work closely with clients to rethink workflows that harmonize human capabilities with AI efficiencies, unlocking insights and enhancing resilience.

Practical Applications of Agentic AI

In traditional workflows, human operators handled tasks like monitoring carrier status. However, agentic AI can automate these processes. For instance, an AI agent can manage communications proactively, reducing the operational burden on human staff. This allows workers to shift their focus to exceptions and strategic decisions that drive operational value.

Conclusion

As supply chain networks continue to evolve, the need for smarter, more efficient transportation management becomes critical. By leveraging artificial intelligence, businesses can find new ways to turn data into actionable insights and enhance workforce capabilities. This transition not only optimizes operations but also prepares organizations for future challenges.

For further insights on AI in transportation management, see our interview with Supply Chain Management Review.

Additional Resources

• Building an AI Strategy to Leapfrog Tech Debt in Supply Chain Execution
• How to Save Time Using TMS Automation, Machine Learning, and AI
• Inside the Intelligent Supply Chain: How AI Transforms Visibility into Action
• State of Supply Chain: Truth or Trend
• The Intelligent Supply Chain Execution Playbook
• Intelligent Supply Chain Execution
• Infios Transportation Management