AI is reshaping retail planning, but there’s a lot of noise right now.
This guide is meant to do the opposite, it’s grounded in the actual work planners do every day.
No promises that “everything changes tomorrow.”
Just straight to the point: here’s what’s happening with AI in retail, here’s why it matters, here’s how to start.
Why AI Is Suddenly Everywhere in Retail
For years, planning tools evolved in small steps:
Instinct → spreadsheets → connected system → systems that learn → systems that act.
The part that feels new right now is not the math, the math has been here.
It’s the accessibility.
Systems can now:
- Understand a question
- Explain their reasoning
- Run simulations
- Suggest actions
This moves the planner’s job away from “clicking through” and toward directing the work.
AI Acronyms, In a Way That’s Actually Useful for Planners
Acronyms tend to make this space feel more complicated than it is.
Machine Learning (ML)
This is pattern recognition and prediction.
Most planners already rely on this without calling it ML.
Practical examples:
- Baseline forecasting
- Clustering doors based on buying behavior
- Understanding lift/drag from promotions
- Predicting demand curves for newness
Where ML helps you: it catches patterns you’d eventually see, just faster and at scale.
Where ML does not help you: choosing strategy, reading taste, knowing the “why” behind a merchant’s directive.
Large Language Models (LLMs)
These models interpret language, respond in full sentences, and explain things.
This matters because it changes how you interact with data.
You don’t have to:
- Track 17 tabs
- Run pivots to find out what happened
- Guess which exception report applies
You can ask directly:
- “Why is denim forecasted up in week 12?”
- “Which stores are underperforming relative to the plan and why?”
- “What changed in the last 48 hours?”
LLMs make the system understandable. They turn your planning tool into something you can talk to rather than work around.
Where LLMs help you: speed, clarity, context.
Where LLMs don’t: they don’t know your brand objectives unless you give them that context.
Agents
This is where planning starts to feel different.
An agent uses ML + LLMs to do the “heavy lift” part of planning:
- Finds exceptions
- Simulates fixes
- Drafts the action
- Waits for your approval
Think of it as an assistant planner who handles the setup work so you can focus on the decision.
Important:
Agents take repeitition away. They don’t take autonomy away.
Where agents help you: All the things you usually do before doing the real work, pulling numbers, recalculating, validating, setting up files, the agent handles those.
You stay focused on the decision itself.
Where agent don't: deciding what your business should prioritize, only planners can do that.
How AI Modern Models Learn (In Plain Terms)
Older forecasting was a loop:
history → calculation → output.
Useful, but narrow. Newer models consider more signals, often dozens or hundreds, and learn which ones matter for predicting demand.
Examples:
- Weather
- Marketing intensity
- Price point shifts
- Inventory availability
- Product type
- Lifecycle stage
- Region, store behavior, digital behavior
It’s not your job to fine-tune the model. Your job is to make sure it’s fed the information that actually reflects how your business works.
Modern systems should also tell you:
- How confident they are
- What influenced the prediction
- What the possible range looks like
Planning becomes more about understanding the range of what could happen and prepare for it instead of finding one “perfect” forecast.
The ML You Should Already Be Using
None of this is new or experimental:
- Clustering to group stores based on how they actually behave
- Causal forecasting to understand what’s driving demand up or down
- Price sensitivity models to quantify how much lift or drop comes from a price or promo change
These are meant to give you clearer inputs so you can make better decisions, rather than thinking for you.
Moving From Insight to Action (In Practical Terms)
This is the part of retail planning that’s changing the most.
The work becomes faster because the steps between “I see something” and “I fixed it” get tighter.
A simple, clear cycle:
1. Identification
The system flags what needs your attention: risks, outliers, gaps, upside. Not every alert, just the ones that are actually worth acting on.
2. Simulation
You can test changes before you make them. The system shows the impact on revenue, units, GM$, stockouts, and sell-through in one place so you can compare options quickly.
3. Execution
If the scenario makes sense, the system prepares the work:
- Price file
- Allocation run
- Buy or receipt adjustment
- Assortment change
You’re not rebuilding everything manually.
4. Approval
You review the change, edit if needed, and approve.
You stay in control of the decision, you just avoid the repetitive setup.
Practical shift: You’re reviewing, deciding, and moving on instead of doing every step by hand.
How to Start with AI (Even If You’re Busy, Which You Are)
You don’t need to become “technical.” You just change the way you work with the system.
Start with curiosity.
Ask questions in plain language:
- “Why is this trending up?”
- “What changed since last week?”
- “Which stores are driving the miss?”
See where the system can give you a quicker read than manual digging.
Move into collaboration
- Give the system small corrections and context as you go.
- If something looks off, nudge it in the right direction.
- If it missed a key detail, add it.
- This helps the model reflect your judgment more accurately.
Build confidence through simulation
- Let the system test scenarios before you act.
- Let it show you the upside, downside, and impact on your KPIs.
- You still make the decision — you just have cleaner options and less prep work.
The Planner’s Advantage in the AI Era
The next era of planning still needs the human element. AI simply empowers planners to do work that matters, faster.
The skills that matter most stay the same:
- Judgment
- Prioritization
- Pattern recognition
- Knowing when something “feels off”
- Knowing when to take the risk, and when to hold back
AI can’t replace those skills. It simply gives you more time and clarity to use them.
A question to consider:
What’s the first task you’d hand to an agent if it freed up time for the decisions that actually move the business?
At NRF, we’ll share an early look at a planning agent we’ve been building, designed to help planners see around corners, anticipate risks sooner, and make confident decisions with far less manual setup.
If you want to understand what this could look like for your team, or how to start applying these ideas inside your current planning process, Speak with an Expert!
