How to Stop Asking “How Can We Use AI?” and Ask This Instead

The AI implementations that have the greatest return target specific time-drains and manual processes. By shifting focus from "what can AI do" to "where is my team losing time," supply chain leaders can more easily judge between problems that require a complex Large Language Model (LLM) and those better served by simple, deterministic automation.

Deterministic automation refers to systems that produce the same output every time they receive the same input, ensuring predictable and repeatable results. This type of automation is often used in traditional workflows where rules and formulas dictate behavior consistently.

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Logistics leaders often face mandates to inject AI into a workflow without a clear objective, leading to "pilot purgatory" and obscure outcomes. Real value resides in identifying the repetitive actions that erode team productivity. 

By reframing the question toward time-loss, you move away from vague AI assistant chatter toward functional utility. Addressing these manual hurdles directly ensures that your technology spend generates measurable, worldwide efficiency rather than just hype.

Surfacing the Unknown Through Insight Generation

One big benefit of this reframed approach is insight generation. When using AI to analyze data, sometimes it can bring unknown issues to the forefront without being asked to do so.

A pilot user connected our Model Context Protocol (MCP) server and requested the AI to examine their environment. Within seconds, the tool identified two specific dispatchers with high cancellation rates occurring within 30 seconds of shipment creation. This was not a question the VP of Logistics had come up with, yet it revealed a specific operational process gap requiring immediate attention. This capability transforms data from a static record into a proactive diagnostic tool.

AI-driven insight generation uses natural language queries to look at volumes of information and reveal hidden inefficiencies. This approach allows leaders to identify specific process gaps that traditional analytics might overlook.

From Static Scripts to Agentic Workers

The next step is agentic AI: workers that understand context, monitor workflows, and act with more precision. Shipwell’s Track & Trace AI Worker is one example. Instead of blindly creating more carrier noise, it monitors shipment status, GPS signals, appointment windows, and tracking updates. Then it determines exactly when you need outreach, effectively reducing manual tracking overhead by 70%.

Document Intelligence extends that same idea to freight documents. It classifies BOLs, PODs, invoices, and rate confirmations, extracts key data, detects signatures, validates whether a document is usable, and connects that information to the right workflow. Paired with the Shipwell MCP Server, that document data becomes searchable and actionable by AI, so teams can ask questions, surface exceptions, compare records, and trigger the next step without digging through files.

That is the difference between supply chain automation and agentic AI. One follows a rule; the other understands the work well enough to help move it forward.

Internal & External AI Pillars for Increased Business Value

A coherent AI strategy categorizes initiatives into internal operations (overhead reduction) and external customer experience (speed-to-quote). Using AI to feed email quote requests into a system of record provides visibility into sales success ratios.

To formulate a comprehensive AI strategy, leaders should categorize initiatives into two distinct pillars:

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In the building goods industry, you aren't putting AI in the lumber, but you can use it to improve your "speed to quote." Many organizations struggle to capture quote data because the market moves too fast. By using AI to automatically feed email quote requests into your system, you gain visibility into your success ratio and exactly why you are losing sales.

Getting Started with AI Strategy

When you need a logistics AI strategy for your supply chain by Monday morning (and it’s already Thursday), skip the vendor list. Start with the five most repetitive tasks your team does each day. Success doesn’t require a massive infrastructure overhaul. It requires a safe environment to test the reframing of these problems. 

Start by identifying a single friction point and test it in a sandbox environment. Think simple things, like parsing inconsistent POD formats or identifying why a specific lane has high fallback rates. No one expects you to build a "smart" supply chain out of the gate. The goal is to build a deterministic, efficient operation that frees your people to handle the exceptions that actually require human expertise. 

We built the Shipwell MCP Server with a sandbox-first approach for this reason. It allows you to connect your data in a non-production environment where no live freight is affected. There, teams have the space to experiment with prompts and automated workflows without risk.

Turning pressure from executive leadership into successful operational ROI means walking away from technology hype and focusing entirely on fixing everyday problems. Ultimately, finding the right path forward for your logistics department depends on one simple shift in perspective: instead of asking, "What can we use AI for?", you must ask your team, "Where are you spending the most time, and what do you wish was automated?" Having this focus ensures software investments do not get trapped in endless testing phases, while safely freeing up your team to take on the high-level strategy that actually needs human expertise.