For Logistics and Supply Chain

40 Questions Logistics and Supply Chain Should Ask Before Trusting AI

Q: When your route optimisation system recommends a carrier or route, what disruptions from the last five years were not in its training data?

A key question for Logistics and Supply Chains to ask when reviewing AI outputs.

Q: If your Blue Yonder or similar tool recommends consolidating shipments on a single carrier for cost savings, do you know which relationships or backup carriers that decision erases?

A key question for Logistics and Supply Chains to ask when reviewing AI outputs.

Q: Has your route algorithm ever been tested against a port closure, fuel price spike, or geopolitical shift of the kind that has actually occurred in your supply chain?

A key question for Logistics and Supply Chains to ask when reviewing AI outputs.

Q: When the algorithm suggests avoiding a region or route, is that recommendation based on current conditions or on patterns from years when your business looked different?

A key question for Logistics and Supply Chains to ask when reviewing AI outputs.

Q: Do your team members still make carrier selection decisions themselves, or has algorithmic recommendation replaced the judgement that used to catch when a preferred carrier was struggling?

A key question for Logistics and Supply Chains to ask when reviewing AI outputs.

Your AI system recommends a carrier, warehouse location, or demand forecast with confidence. But that confidence lives inside a model trained on historical data that may not include the disruption you are about to face. Asking the right questions before you act on AI outputs protects your organisation's resilience and keeps human judgement at the centre of supply chain decisions.

These are suggestions. Use the ones that fit your situation.

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Route Optimisation and Carrier Selection

1 When your route optimisation system recommends a carrier or route, what disruptions from the last five years were not in its training data?

2 If your Blue Yonder or similar tool recommends consolidating shipments on a single carrier for cost savings, do you know which relationships or backup carriers that decision erases?

3 Has your route algorithm ever been tested against a port closure, fuel price spike, or geopolitical shift of the kind that has actually occurred in your supply chain?

4 When the algorithm suggests avoiding a region or route, is that recommendation based on current conditions or on patterns from years when your business looked different?

5 Do your team members still make carrier selection decisions themselves, or has algorithmic recommendation replaced the judgement that used to catch when a preferred carrier was struggling?

6 If you switched to a different route optimisation vendor tomorrow, would your team be able to manually select routes and carriers, or has the skill left the organisation?

7 What is the oldest data point in your route optimisation model, and does your supply chain still look like it did then?

8 When SAP AI or your logistics platform recommends a route, can you see the top three alternative routes it rejected and why?

9 Has your algorithm been trained on data from your actual customer contracts and delivery commitments, or only on cost and distance?

10 Who in your organisation can explain why a specific route recommendation was made, and who can overrule it if conditions on the ground contradict the algorithm?

Demand Planning and Inventory Decisions

11 When your demand planning AI predicts a spike or drop in orders, what changed in your market or customers that might not show up in historical sales data?

12 Has your demand forecast model ever encountered a scenario like the one you are planning for now, or are you asking it to predict outside its training distribution?

13 If a major customer shifted their ordering behaviour or a competitor launched a new product, how long before your demand model recognised that shift?

14 When your Blue Yonder or Oracle SCM demand planning tool suggests inventory levels, do you know how confident that prediction is in your slowest-moving or newest product categories?

15 Have you stress-tested your demand forecast against the actual disruptions that affected your supply chain in the past three years?

16 Does your demand planning system account for relationships with major customers whose orders you influence through sales conversations, or only for historical patterns?

17 When demand forecasting produces an outlier recommendation, can your team recognise it and judge whether it is a signal or noise?

18 If you stopped running your demand planning AI tomorrow, could your planners produce a reasonable forecast, or has that capability atrophied?

19 What was the last time a planner manually overruled a demand forecast, and were they right to do so?

20 Does your demand model know the difference between a permanent shift in customer behaviour and a temporary promotion or seasonal event?

Warehouse Automation and Operations

21 When your warehouse automation system or Palantir Foundry assigns tasks to staff or robots, who is checking whether the assignment makes sense for the actual inventory on hand?

22 Has the operational expertise of your warehouse team grown or shrunk since you implemented AI-driven task allocation and picking systems?

23 If your AI warehouse system went down for a day, could your team run the warehouse efficiently by hand, or would they be lost?

24 What happens when your automation system encounters inventory that is damaged, mislabelled, or in a location the system did not predict?

25 Does your warehouse AI have rules that account for employee safety, fatigue, or skill levels, or does it optimise purely for throughput?

26 When an automation system recommends closing a warehouse location or consolidating operations, who is considering the relationships with local carriers and the time it takes to rebuild logistics networks elsewhere?

27 Has your team tested how the warehouse system behaves when demand spikes beyond what the training data included?

28 Who decides when a human supervisor should override the warehouse automation system's decision, and how often does that actually happen?

29 Can your warehouse staff articulate why they are doing specific tasks, or have they become task-followers dependent on the system's direction?

30 If you needed to double warehouse capacity in two weeks, could your team do it, or is the knowledge of how to run operations now trapped in the AI system?

Resilience, Vendor Lock-in, and Recovery

31 If your primary AI vendor went out of business or changed their terms, could you port your supply chain models and decisions to another system?

32 What happens to your route optimisation, demand planning, or warehouse operations if you lose cloud connectivity to your AI platform for 48 hours?

33 Have you documented the decision rules that your AI systems are actually using, or are they opaque even to your team and your vendor?

34 When your AI system fails or makes a bad recommendation, what is your rollback plan, and how quickly can you revert to human-led decisions?

35 Does your organisation have a supply chain resilience plan that does not assume your AI systems will work as expected, or does every scenario depend on algorithmic optimisation?

36 Have you run a crisis simulation where AI systems were unavailable and your team had to navigate a major disruption using only manual processes?

37 What is the cost in staff time and productivity if you had to operate your supply chain without your current AI tools for a month?

38 When you adopted your current AI tools, did you preserve the manual processes that ran before, or did you replace them completely?

39 Do your staff know who to contact and what to do if they believe an AI recommendation is wrong and needs human review before action?

40 Has your organisation measured how much supply chain decision-making capability now depends on specific AI vendors, and what that concentration of risk costs you?

How to use these questions

Ask your AI vendor explicitly: what is the oldest data in your training set, and what major supply chain disruptions from the real world are not represented in that data?
Run a manual scenario test quarterly. Pick a disruption that actually happened to your supply chain. Turn off the AI system and see how long it takes your team to manually make the same decisions.
Document one decision that your team overruled an AI recommendation on in the past month. What did the human judgement catch that the algorithm missed? Use that case to teach others.
Assign one person on your team to stay current with how to run route selection, demand forecasting, or warehouse operations without the AI system. That person is your resilience check.
Before you automate a decision away, ask: who on our team will lose the judgement-building experience, and is that a risk to our resilience?

40 Questions Logistics and Supply Chain Should Ask Before Trusting AI

Route Optimisation and Carrier Selection

Demand Planning and Inventory Decisions

Warehouse Automation and Operations

Resilience, Vendor Lock-in, and Recovery

Cognitive Sovereignty: How To Think For Yourself When AI Thinks For You