The End of Enterprise TMS: How Agentic Workflows Are Reshaping Supply Chain Technology

As we navigate through 2025, I find myself increasingly puzzled by one trend: new Transportation Management System (TMS) providers continue entering the market just as agentic AI workflows are fundamentally disrupting the entire premise of traditional enterprise software.

The term "Transportation Management System" was first coined in the 1970s and 1980s alongside the rise of enterprise computing, designed to manage the complexities of freight operations through three fundamental modules:

• Transportation Planning - Route optimization, load building, carrier selection, and mode optimization across complex multi-modal networks

• Transportation Execution - Load tendering, carrier communication, real-time visibility, exception management, and shipment tracking from origin to destination

• Freight Audit and Pay - Invoice validation, rate verification, payment processing, and financial reconciliation

But here's what I'm increasingly certain of as a supply chain technology leader: the days of monolithic enterprise TMS are numbered.

The Agentic AI Revolution is Here

According to Gartner, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. We're witnessing 15% of day-to-day work decisions moving to autonomous AI agents. This isn't incremental improvement—it's fundamental disruption.

Let me break down how agentic workflows are dismantling each TMS pillar:

Freight Audit & Pay: Already Obsolete

This was the first domino to fall. Modern freight audit companies have long since moved outside traditional TMS architectures, directly ingesting transaction data from ERPs, rate data from carriers, and invoices from billing systems. AI-powered agentic workflows are now making human-dependent audit processes look archaic—automatically reconciling complex multi-leg shipments, identifying billing discrepancies in real-time, and processing payments without human intervention.

Transportation Execution: The Stone Age of Workflows

Traditional TMS execution feels primitive compared to what agentic AI enables today. Why force users through rigid booking workflows when intelligent agents can:

• Autonomously tender loads to optimal carriers based on real-time capacity, performance history, and rate optimization

• Proactively manage exceptions by automatically rerouting shipments, communicating with stakeholders, and adjusting delivery commitments

• Orchestrate complex visibility across multiple carriers and modes without requiring manual tracking updates

• Handle carrier communication through natural language processing, eliminating the need for standardized EDI formats

The difference is profound: instead of users clicking through predetermined workflows, agentic systems think, plan, and execute transportation decisions with human-like reasoning but machine-scale efficiency.

Transportation Planning: Plug-and-Play Intelligence

For most shippers using LTL, parcel, air, and ocean modes, robust rate engines are increasingly available as API-first, plug-and-play services. Even truckload shippers can access sophisticated rate engines and load optimization algorithms without investing in full TMS platforms.

Agentic AI takes this further by:

• Dynamically optimizing loads based on real-time constraints, carrier preferences, and service requirements

• Continuously learning from shipment outcomes to improve future planning decisions

• Autonomously adjusting to supply chain disruptions, capacity constraints, and customer priority changes

Why New TMS Offerings Mystify Me?

Given this technological transformation, I'm genuinely puzzled by the continued emergence of traditional TMS platforms. We're building bridges while the world is moving to teleportation.

The enterprise software landscape is fragmenting into specialized, AI-native solutions that communicate through APIs and orchestrate through intelligent agents. The monolithic TMS—with its rigid workflows, manual processes, and user-dependent operations—feels increasingly like a relic of the pre-AI era.

The Path Forward

As supply chain leaders, we should be asking ourselves:

• Are we investing in platforms that will be obsolete in 3-5 years?

• How can we leverage agentic AI to eliminate, not just automate, manual processes?

• What does a truly intelligent, autonomous supply chain operation look like?

The future belongs to orchestrated intelligence—where specialized AI agents handle discrete functions while communicating seamlessly to deliver end-to-end transportation management. This isn't about replacing humans; it's about elevating human capabilities to focus on strategy, relationships, and innovation while agents handle operational execution.

The question isn't whether agentic workflows will disrupt traditional TMS—they already are. The question is whether we'll adapt quickly enough to lead this transformation or find ourselves defending yesterday's technology in tomorrow's market.

What's your perspective on agentic AI's impact on supply chain technology? Are you seeing similar disruption patterns in your organization?

#SupplyChain #AI #AgenticAI #TMS #Transportation #Innovation #FutureOfWork