By Guru Rao, CEO, nuVizz

Everyone in last-mile delivery – and every other industry – is talking about AI. There are new tools, new agents, and new announcements every quarter. The technology is impressive, but I keep watching companies deploy AI on top of infrastructure that was never designed to support it, and then wonder why the results don’t match the press release.

Here’s the problem: AI is a multiplier. It takes whatever is already in your system and makes more of it. If your data is fragmented, AI will surface that fragmentation faster. If your processes are inconsistent, AI will automate the inconsistency. You cannot upgrade your way out of a foundation problem with a software layer.

The last-mile delivery market is approaching $180 billion globally. The pressure to modernize is real. But the answer isn’t to buy AI, the answer is to build the right base first, then let AI do what it was built to do.

What “Foundation” Actually Means

When I say foundation, I don’t mean having APIs or being cloud-native. I mean something more specific.

Every stakeholder in the delivery chain needs to exist on a shared network. Not a series of point-to-point integrations, but a real network where data flows between participants without a custom connector built for every handoff. Layered on top of that, business rules and workflows need to be consistent. Variance is the enemy of automation. When exceptions live in people’s heads instead of systems, AI hits a wall.

The network also only works if every participant contributes. A carrier who guards their data or a shipper who won’t share delivery outcomes breaks the loop. AI needs the full picture to optimize anything real. And the architecture underneath all of it has to be open. Closed systems are the opposite of network value. If your platform can’t exchange data freely, you’re not building a network. You’re building a silo with a better user interface.

None of this is glamorous. But without it, you’re asking AI to navigate a maze with no map.

The Problem With How Most Platforms Are Deploying AI

I see two patterns in how last-mile platforms are approaching AI today, and only one of them works.

The first pattern: AI as a feature. A company identifies a specific pain point, builds a narrow AI model to address it, and ships it as a product update. Route optimization. Address validation. Order status. These are real problems and AI helps with them. But they’re solved in isolation. The AI doesn’t know what the rest of the system is doing. When conditions change, or when edge cases stack up, the model degrades. The humans step back in. The efficiency gain disappears.

The second pattern: AI as a system capability. This only works when the foundation is already in place. The AI isn’t solving one problem in one corner of the platform. It’s operating across a connected data environment where every decision informs the next.

According to recent industry analysis, only about 12% of logistics companies have meaningfully adopted AI, and 37% of transportation companies were still heavily manual as of 2025. The gap isn’t capability. It’s infrastructure readiness. The companies that deployed AI on top of fragmented data and disconnected workflows are the reason those numbers look the way they do.

The companies getting this right started with infrastructure before they touched AI. Network-first platforms, built on open APIs, designed for stakeholder connectivity from the start. Before adding any AI, they built the rules engine: business processes pre-defined, exception scenarios accounted for, every action mapped to a decision path.

Then they layered in AI, starting with bounded, high-confidence use cases: document reading, automated order capture, route optimization, appointment scheduling, real-time order status agents. These are not autonomous systems. They are automation systems. They run within defined constraints and hand off to humans when something falls outside those constraints.

That distinction matters. “Automation engine” and “autonomous engine” are not the same thing. The first executes pre-defined rules very fast. The second makes independent decisions in novel situations. Building toward the second requires full clarity about where you are in that progression.

Today’s agentic AI tools are still operating with guardrails. Human-in-the-loop is not a limitation, it is the honest acknowledgment of where the industry actually stands. The companies that pretend otherwise are taking risks they do not fully understand.

The Autonomous Delivery Ecosystem is not a product you can buy. It is what emerges when the foundation is solid enough to support real AI agency.

Here is what that looks like when it arrives: every stakeholder on a shared network, every exception rule pre-defined, every data point accessible and clean. At that point, AI stops being a feature that reduces manual work in one department. It becomes the operating system of the entire delivery process, from order capture to billing, with no human intervention required.

We are not there yet. But the distance between where we are and where that ecosystem becomes real is shorter than most people assume. Computational capacity is advancing fast. Learning models are improving fast. The maturity curve for AI is steeper than anything we have seen in logistics technology before.

The bottleneck is not the AI. It is the readiness of the infrastructure underneath it.

Before Your Next AI Investment

Run an audit of your foundation. Is your network truly connected, or are you maintaining a web of point-to-point integrations? Are your processes standardized enough to be automated, or do key decisions still live in individual judgment calls? Can your architecture exchange data without a custom build every time you add a partner?

If the answers are no, the AI will surface those problems faster. It will not solve them.

Build the foundation.Then let AI do what it is actually capable of doing.

Guru Rao is CEO of nuVizz, located in Atlanta, GA. nuVizz is a cloud-based, AI-driven delivery management and Transportation Management System (TMS) SaaS platform that focuses on real-time delivery orchestration, network visibility, and last-mile logistics. The platform connects all stages of transportation—from the first mile to the final mile—to help enterprises optimize route planning, decrease transportation spending, and improve the overall customer experience.