In supply chain, I do not care about hype. I care about whether something helps teams make better decisions, faster, with fewer mistakes.
That is where AI is becoming useful.
When I talk about AI, I am not talking about replacing people. I am talking about tools that help professionals analyze more information, see patterns sooner, and make thoughtful decisions instead of reacting under pressure.
In this post, I want to share how I see AI actually being used today in planning, forecasting, logistics, and operations. Not theory. Not marketing language. Just practical examples and lessons.
AI helps teams see problems before they become fires
One of the most valuable changes AI brings to supply chain is earlier visibility.
Traditional systems tell you what already happened.
AI can highlight what is likely to happen next.
For example, I have worked with teams that use AI models to scan historical demand, promotions, seasonality, weather, macro trends, and even customer behavior patterns. The tool surfaces items at risk of underforecasting or overforecasting before the numbers become obvious.
Instead of discovering a problem after orders spike or inventory piles up, the team gets a signal early.
They can ask questions. They can adjust. They can prepare.
The professional is still responsible for the decision, but the guesswork is reduced.
Practical example: improving demand conversations, not replacing planners
A planning team I supported implemented an AI-assisted demand forecast alongside their existing process. Initially, people were worried.
“Is this going to replace planners?”
It did not.
What happened instead was more collaborative conversations. The AI forecast became one more lens. Planners compared:
• their statistical baseline
• their business insights
• the AI recommendations
When numbers did not align, it created healthy discussion.
Why is AI showing growth here? What assumption is the model making? What do we know from customers that the model cannot see?
The forecast improved. The thinking improved. The planners became more strategic.
AI did not make the decision. It helped the team think more clearly.
AI helps reduce variability in logistics and transportation
Logistics is full of uncertainty. Traffic. Port delays. Weather. Carrier performance. Routing decisions that impact cost and service.
AI tools can analyze thousands of movement patterns at once and suggest routes that balance speed, cost, and risk better than manual planning.
I have seen teams use AI to:
• predict transit delays before they occur
• recommend alternative carriers based on performance history
• suggest consolidation opportunities
• identify shipments likely to require intervention
Again, the human is still in control.
But instead of spending time hunting through spreadsheets, the professional spends time interpreting insights and making decisions that matter.
Example: preventing repeated freight issues
A company struggled with recurring delivery failures in a particular region. Traditional reporting showed late deliveries, but it did not clearly explain why.
Their AI-enabled logistics platform surfaced something simple but powerful.
Most failures were tied to a specific combination of factors.
• certain product categories
• specific delivery windows
• one particular carrier
Once they saw the pattern, the solution became straightforward. Re-route those shipments and renegotiate expectations.
Without AI, it may have taken months to notice. With AI, the pattern appeared in minutes.
This is what I mean by reducing guesswork.
AI supports scenario planning, not predicting the future
One area I am most optimistic about is scenario planning.
In supply chain, we constantly ask “what if.”
What if demand increases faster than expected. What if a supplier goes offline. What if fuel prices rise. What if we launch earlier than planned.
In the past, running scenarios was time-consuming. Many teams simply did not do it.
Today, AI-powered tools can simulate multiple scenarios quickly and show how inventory, capacity, cost, and service levels would be affected.
This does not give perfect answers. It creates better questions.
Do we have enough buffer? Where are the biggest risks? What decisions would make us more resilient?
AI becomes a decision support tool, not a crystal ball.
Where teams go wrong with AI
I also want to be honest about mistakes I see.
Some teams expect AI to solve leadership and process problems. It won’t.
If forecasting discipline is weak, AI will not fix that. If master data is poor, AI will amplify bad signals. If roles are unclear, AI can even create confusion.
I always remind teams.
AI is powerful only when the fundamentals are strong.
Clear ownership.
Reliable data.
Sound processes.
Willingness to learn and adjust.
Without these, AI becomes another tool that looks impressive and delivers little value.
How professionals can work effectively with AI
If you are early or mid-career in supply chain, learning how to partner with AI is a meaningful advantage.
Here are practical ways to start.
Stay curious about the models
Do not just accept outputs. Ask questions.
What data is the model using?
What assumptions drive the recommendations?
When does it tend to be wrong?
The more you understand, the more wisely you can apply the insights.
Compare AI recommendations to your experience
Use AI as a second opinion. If it contradicts your judgment, explore why.
Sometimes AI reveals blind spots. Sometimes your context reveals limitations in the model. Either way, you learn.
Focus on decision quality, not tool performance
Leadership cares about outcomes, not whether AI was involved. Use technology to enhance thinking, not replace it.
Develop strong fundamentals first
Forecasting basics. Inventory principles. Root cause analysis. Communication. AI is most useful when layered on top of real capability.
A story from mentoring
I worked with a young professional who was excited about AI. He learned new tools quickly and started automating reports and building predictive dashboards.
But he felt overlooked in meetings.
When we talked, I explained something simple.
Tools are impressive. Judgment is invaluable.
So we focused on strengthening his ability to explain what the insights meant, why they mattered, and how they should influence decisions. Over time, leaders started asking for his opinion, not just his reports.
AI became part of his toolkit. Not his identity.
That shift accelerated his growth.
Bringing it together
AI is not going to replace thoughtful supply chain leadership. It is going to reward teams that know how to use it responsibly.
For operations professionals, this means:
• understanding what AI is actually good at
• using it to reduce guesswork, not to avoid thinking
• combining technology with experience, discipline, and good judgment
When used well, AI helps us see patterns earlier, test ideas faster, and make smarter decisions with more confidence. It does not remove the human element. It raises the level of conversation.
If you want support learning how to use AI effectively in your role or thinking through where it fits in your supply chain work, I am here to help.
