Isn't every vendor going to say they are AI-native?

Yes, which is exactly why the RFP questions above matter. The answer to "are you AI-native?" is meaningless. The answer to "what is your published autonomous decision accuracy for carrier allocation?" is not.

What if my incumbent TMS adds AI features next year?

It will. The question is whether those features are bolted on or whether the TMS is being re-architected. Re-architecture takes 3–5 years and most incumbents won't undertake it. Feature bolting takes 6 months and almost all incumbents will do it. You can usually tell the difference by looking at the vendor's engineering org size and recent hires.

Does AI-native mean I need to rip out my TMS?

No. Shipsy's AgentFleet sits on top of existing TMS, WMS, and control tower deployments — reading data, taking actions via API, writing outcomes back. The re-architecture question is about the decision layer, not the record layer. Your TMS of record is probably fine.

How do I get my procurement team aligned to this?

Show them the compounding argument and ask them to run the same five questions through three vendors on their current shortlist. The answers will separate the field faster than any feature matrix. We've helped 20+ enterprise procurement teams run this exercise.

AI-native vs AI-wrapped: the five questions that spot logistics vendor washing

By Soham Chokshi, CEO

In the last 18 months, every logistics software vendor has become an “AI company.” Most of them bolted a model onto a 2018 architecture and updated the pitch deck. Here is how a CXO tells the difference in a single 45-minute RFP meeting.

What most CXOs believe

The standard buyer assumption in 2026 is that AI is now a table-stakes capability — every serious logistics vendor has it, and the real differentiator is functional fit, price, and implementation risk. AI itself has been commoditized, the story goes, because the underlying foundation models are shared across the industry.

This is half right and almost entirely misleading. Yes, GPT, Claude, Gemini, and open-weight models are accessible to every vendor. No, that does not mean every vendor’s AI is comparable. The gap between AI-native software and AI-wrapped software is not in the model — it is in the architecture. And buyers who treat the two as interchangeable will end up paying for capabilities that look identical in a demo and perform nothing alike in production.

The reason this matters: AI-wrapped products plateau. They improve for six months, hit the ceiling of what a model can do bolted onto a legacy schema, and stall. AI-native products compound — because every decision, every exception, every outcome feeds back into the architecture and sharpens the next decision. Year two of an AI-native deployment looks nothing like year one. Year two of an AI-wrapped deployment looks identical.

What’s actually happening

We have sat across the table from enough competitive evaluations to know the pattern. Vendors position one of three postures:

Posture 1 — “We added AI.” A control tower with anomaly detection. A TMS with a natural-language query bar. Invoice OCR with a model behind it. The underlying system is unchanged; a model was bolted on. These capabilities are real, but they are Stage 1 (see the agentic maturity curve) and they don’t compound.

Posture 2 — “We have an AI module.” A separate AI product sold alongside the core suite. Often acquired or partnered, loosely integrated. Looks better in a demo than it runs in production because the integration seams break under real exception volume. This is the most common posture in 2026 — it is what most of Shipsy’s competitors are doing.

Posture 3 — “Our core decision layer is AI-native.” The system is architected so that every meaningful decision — allocation, routing, exception handling, settlement — is taken by an agent with bounded authority, an accuracy SLA, and a feedback loop. The AI is not a feature, it is the substrate. This is what AgentFleet is, and honestly, it is what maybe 5% of the logistics software market has today.

The tell is not the demo. The tell is what happens after year one. AI-wrapped products produce a flurry of dashboards, a handful of suggested decisions, and the same operational headcount you started with. AI-native products produce measurable autonomous decision volume — Vera resolved $25M+ in disputes at Heineken without a human in the loop for most of them; Astra is taking routing decisions at DPD Poland that contributed to $37M in unit economics recovery; Clara is answering CX queries at Aramex without escalation for the majority of cases. That volume is architectural, not feature-driven.

What to do in the next 90 days

Rewrite your RFP. Specifically, add these five questions and disqualify any vendor who deflects on three or more.

1. What percentage of decisions in your system are taken autonomously, and what is the published accuracy? A Stage 3 vendor will have numbers. An AI-wrapped vendor will pivot to “we provide recommendations” or “we surface insights.” Both are signals the answer is zero. Ask for the decision taxonomy, not a percentage of “automated tasks” — the latter includes anything that was already scripted.

2. Show me the escalation protocol for a single agent. If a vendor cannot walk you through the exact rule, confidence threshold, and human routing for one agent decision in five minutes, the agent doesn’t exist the way they described it. This is a 60-second filter.

3. What does the feedback loop look like? How does the system learn from a wrong decision? Who tunes confidence thresholds? How often? An AI-wrapped system typically has no answer — a human edits a rule somewhere. An AI-native system has a defined re-training cadence and an auditable log.

4. Can I see the decision log for a real customer, anonymized? The ability to produce a log of agent decisions with timestamps, confidence scores, and outcomes is a proxy for whether the architecture actually works that way. Vendors who cannot produce this log in any form are pattern-matching to what you want to hear.

5. What is your roadmap to reduce human headcount in my ops? The rudest but most revealing question. AI-wrapped vendors will say “our product augments your team.” AI-native vendors will give you a 24-month plan with a headcount trajectory. You do not have to pursue the full plan — but the vendor who cannot articulate it has not thought seriously about what AI does to your operation.

The fifth question is the one most buyers flinch from asking. Ask it anyway. The vendor’s answer tells you more about their architecture than any RFP response document will.

Why this matters now

AI procurement mistakes compound. If you sign a 3-year AI-wrapped deal in 2026, you are locked out of the compounding curve that your AI-native competitors will be on by 2028. This is not a reversible decision at the scale most enterprises sign these contracts. The cost of getting it wrong is not the contract value — it is the two years of operational gap you can’t close after.