Five ways FMCG and distribution teams can use AI on Monday

FMCG and distribution businesses run on speed and volume: lots of stores, lots of SKUs, lots of people on the road. That makes them a strong fit for AI, but also a place where big, slow projects tend to stall.

Here are five uses that work with the tools most teams already have, and that a manager can start this week.

1. Turn store visit notes into a weekly regional summary

Reps send notes in every format: WhatsApp messages, voice notes, photos of shelves. A regional manager reading all of them every Friday is a full afternoon of work.

Collect a week of notes into one document and ask AI for a summary in a fixed structure: out-of-stocks by product, competitor activity, store requests, issues to escalate. Ask it to list the store for each point, so nothing is vague.

The first week takes some setting up. By the third, it's a ten-minute job.

2. Prepare for a key account meeting in 20 minutes

Before a buyer meeting, give AI last quarter's sales by SKU for that account, your current promotions, and any notes from the field. Ask for:

• the three SKUs most worth discussing, and why;
• the questions the buyer is likely to ask;
• one proposal you could put on the table.

You still bring the relationship and the judgement. AI brings the preparation that usually gets skipped when the week is busy.

3. Write trade marketing briefs and store communication faster

Promotion briefs, planogram instructions, launch packs for merchandisers. These follow the same structure every time, which is exactly where a saved skill helps: your template, your tone, two good past examples. Give it the promotion details and get a complete first draft, consistent across every region.

4. Find the pattern in the complaints

Customer and store complaints often live in an inbox or a call log that nobody has time to read as a whole. Export three months of them and ask AI to group them by theme, count each theme, and quote two examples per theme.

What usually comes out is two or three repeat problems (a damaged pack size, a late delivery route, a confusing promotion) that everybody vaguely knew about but nobody had counted.

5. Ask your data questions in plain English

Many managers have the data they need sitting in a spreadsheet export, but not the time to pivot it. AI tools can now read a spreadsheet and answer questions like "which stores dropped more than 20% on the 500ml this month?" or "which reps have the most out-of-stock reports?"

The check: always ask it to show the rows behind the answer, and spot-check a few. Treat it like a quick analyst, not an oracle.

The data you don't have yet

All five of these get better with more of the right data, and the most valuable data in FMCG is usually the information that never gets captured: what stores asked for, what competitors did, why an order was smaller than expected. We wrote about that gap in the sales data your reps never write down.

Where to start

Pick one of the five, one team and one month. Agree beforehand what "better" looks like, whether that's hours saved, faster responses or fewer stock-outs, and measure it.

If you'd like help setting it up on your own data and tools, that's what our hands-on AI training is for.