Splice Insights
Technology, Logistics and Supply Chain Perspectives

Most logistics providers and shippers are now paying a "Hybrid Tax" because of API and EDI integrations. In 2026, the path forward is "protocol agnosticism," where integration providers like Splice seamlessly marry EDI and API together and apply AI as needed. The goals are to reclaim the time to innovate within your IT and development teams by reducing, if not eliminating, time on integrations and to build a competitive advantage in connectivity.

While the concept of AI "agents" is generating significant buzz, it's critical for logistics professionals to understand the difference between agent-based AI and the more widely adopted workflow automation platforms that dominate today’s enterprise stack. While agent-based AI holds promise, regulated logistics workflows require a cautious, transparent approach. Today’s compliance best practices use workflow automation platforms that support compliance.

In logistics, the "integration problem" is a cocktail of two specific issues. First, the Information Gap: "I don’t know what I need to know." Second is the Connection Gap: "I can’t get the data to where it needs to be." In logistics, these gaps are rarely isolated. They combine to create a black hole that results in demurrage fees, missed sailings, and frustrated customers. Splice offers a suite of compatibility tools, including AI, designed for the fragmented world of freig

LLM hallucinations: We’ll explain what they are, how they impact logistics workflows, and why deterministic automation platforms like ours are still the most reliable approach for real-world operational integrity.

Splice breaks down the core cost drivers of agentic operations, how to model them accurately, and how to evaluate whether an agentic approach is cost-effective for your logistics use case.

To build truly AI-powered logistics workflows, we need a universal translation engine capable of bridging the gap between rigid legacy data and the chaotic, unstructured reality of the supply chain. Rules-based translators and GenAI agents are the combination to drive tremendous results.

In logistics, agents shouldn’t replace humans, they should empower them. human-in-the-loop agent design offers the best of both worlds: Speed and intelligence from AI, and safety and oversight from human experts

As agentic AI adoption grows in logistics, a new reality is becoming clear: many APIs need to be reimagined for use by autonomous agents, not just humans or scripts. This article proposes a practical design spec for agent-compatible APIs — drawing from current LLM limitations, developer feedback, and real-world logistics use cases. Whether you’re building new APIs or retrofitting legacy systems, these best practices will help make your tools understandable and usable by AI ag

Most logistics APIs were never designed to be used by an AI agent. We break down the core incompatibilities between typical logistics APIs and agentic AI, and how to adapt your systems to overcome them.

It’s feasible to create agentic AI for logistics powered by large language models (LLMs). Still, going from concept to a working logistics AI agent is not trivial. It requires a different architecture, and in this article, we break down the components required to build an AI agent for logistics, including what current limitations you need to plan for.

Data integrity is essential for AI in logistics. "Garbage in, garbage out” (GIGO) is more relevant than ever, and logistics data is known to be messy and siloed. That's where an integration platform like Splice has a key role in automating logistics and supply chain processes.

As logistics companies explore artificial intelligence (AI) to increase efficiency and reduce costs, a critical question emerges: What kinds of problems are well-suited to agentic AI? And equally important: Which are not? Agentic AI is most useful when workflows involve unstructured data, unclear instructions, or frequent exceptions. In other cases, use workflow orchestration, a capability inherent in Splice's platform.

As artificial intelligence gains traction in the logistics industry, many supply chain leaders are asking: What is the difference between traditional automation and agent-based AI? More importantly, when should you use one over the other? In this article, we'll explore the distinction between workflow orchestration (deterministic automation) and agentic AI (autonomous, adaptive systems), helping you understand when each approach is best suited for your logistics operation.

Explore what agentic AI actually is, how it differs from traditional workflow automation, and where it fits within the broader landscape of supply chain and logistics technology.

AI in Logistics - Who's wearing rose-colored glasses about? And exporters continue to see challenges managing export receiving dates, and no wonder as 1 in 3 ships are late per Sea Intelligence. We address the systemic issues that go beyond tardy vessels and share a few key reads to keep you on top of resin exports.

There’s a growing belief in logistics that once you’ve built a few APIs, you’re ready for AI. But here’s the truth: AI in logistics doesn’t just need access to data—it needs the right structure, timing, and context. That's where Splice fits in.

On June 17, 2025 at the Agriculture Transportation Coalition (AgTC) Annual Meeting, Kevin Speers had the opportunity to share an update on one of the most persistent challenges in containerized agricultural exports: Changes to Earliest Receiving Dates (ERDs) and Cut-offs.