Supply chains generate mountains of data. Every shipment, contract, and milestone creates decisions — and distractions. AI is promising, but knowing where to start is the real challenge.
The reality is:
- Broad, vague prompts don't help your team
- Narrow repeatable tasks are where AI shines
The best way forward? Use LLMs to handle the boring, repetitive, and rules-based tasks that pull your teams away from more valuable work.
Here’s a few practical prompts that supply chain practitioners can start experimenting with immediately.
Exception Management and Shipment Monitoring
AI excels at scanning large data sets and pulling out the “need to know” moments. Use it to help your team spot risks faster:
Prompt:
“Given this list of shipments with origins, destinations, and ETAs, which are at risk of missing delivery deadlines?”
Prompt:
“Review this delivery report and highlight guaranteed deliveries that missed SLAs. Prepare a summary so we can file claims.”
Prompt:
“Summarize today’s shipment exceptions into 3-5 issues for my team to prioritize.”
Why this matters:
Exception management is a daily grind. AI helps teams spend less time hunting for issues and more time solving them.
Contract, Rate, and Claim Analysis
Contracts and rates can hide costly mistakes. AI can help audit the fine print.
Prompt:
“Analyze this carrier contract and flag clauses that don’t align with our standard terms.”
Prompt:
“Review freight rates across lanes. Where are rates more than 10% higher than last year?”
Prompt:
“List deliveries that missed guaranteed dates and draft summaries ready to submit for claims.”
Why this matters:
AI won’t replace your contract team — but it can be the first pass filter to catch discrepancies and reduce missed opportunities.
Document Comparison and Data Cleanup
Supply chain documents pile up fast. AI can help keep them organized and useful.
Prompt:
“Compare these two SOP documents and list all differences or additions.”
Prompt:
“Organize this set of instructions by priority and remove outdated versions.”
Why this matters:
Organizing documents isn’t high-value work — but disorganized documents can cause high-cost mistakes.
Audit and Compliance Support
AI can make your audits faster and more accurate.
Prompt:
“Review these product classifications and flag any that appear incorrect or risky.”
Prompt:
“Summarize audit findings from this report with recommended next steps.”
Why this matters:
Audits are resource-intensive. AI helps flag areas that require deeper human attention.
Decision Support and Daily Prioritization
AI can help keep teams focused on what matters most.
Prompt:
“Review this transit report and list shipments needing immediate intervention.”
Prompt:
“Turn these meeting notes into an action plan with deadlines and owners.”
Why this matters:
When everything feels urgent, AI helps cut through noise and guide decision-making.
Training and Knowledge Sharing
AI is also a fast way to create or refresh process documentation.
Prompt:
“Draft onboarding instructions for new employees on how to use our shipment tracking system.”
Prompt:
“Explain demurrage and detention in simple terms for new hires.”
Why this matters:
Keeping teams aligned and trained is always a challenge. AI makes sharing knowledge easier.
Workflow Automation Ideas
AI can suggest process improvements based on patterns in your data.
Prompt:
“Suggest repetitive tasks from this shipping process that could be automated to save time.”
Prompt:
“Draft business rules for handling late shipments that could be automated.”
Why this matters:
When you’re ready to go from reactive to proactive, AI can help identify automation opportunities.
Getting Started
Before jumping in:
- Start small: Focus on internal use cases first, not customer-facing ones.
- Work with IT and Legal: Understand your data types and define safe zones.
- Set clear guardrails: Always keep humans in the loop, especially on exceptions and sensitive data.
- Don’t boil the ocean: AI works best today when solving narrow, specific problems.
Data Security with AI Tools
At Chain.io, we classify information carefully to protect our customers and operations. Here's how we handle data in relation to AI use:
Customer Confidential: Data about customers that must remain private (e.g. shipment records, rates, and business processes). This type of data may only be processed using approved AI applications while logged into Chain.io accounts to ensure protection under our privacy policy.
Company Confidential: Internal Chain.io information, like business plans and source code, which should not be shared outside the company. Only AI applications approved by the IT department may process this data.
Non-Confidential: Public or non-sensitive data that may be processed using any AI application, subject to general IT security standards.
This is how we do it—and you might want to think about it too.
If your organization doesn’t have AI data handling policies in place yet, now is the right time to start. Define data categories, align AI use accordingly, and check in with legal and leadership to ensure responsible and compliant use of generative AI.
Getting Started With AI Experiments
The opportunity isn’t in giving AI control of your supply chain.
It’s in giving your team relief from tedious, time-consuming tasks — and freeing them up to focus on what actually drives value.
Supply chain leaders who treat AI as a tactical co-pilot (not a magic solution) will see the most success.
