The 4 AI Steps for Every Category Manager: Now or Later?

Yet first, we want to address the concept of AI. AI is not the same as ChatGPT. AI is not just a language model (LLM).

What is it then?When we talk about AI in our industry, we distinguish between Generative AI (like ChatGPT, which creates text and images) and Analytical AI (Machine Learning). For you as a Category Manager, much of the power lies in the latter.

Think of it as a super-powered calculator that learns. Where an LLM excels at language, Analytical AI masters patterns. It can scan millions of data points, from historical sales and promotions to weather data and price elasticity, in seconds. It sees connections the human eye would never discover in Excel. So it's not about writing a nice email, but about hard mathematics that helps predict what happens to your demand tomorrow.

In short: Analytical AI calculates tomorrow, Gen AI tells the story.

Here are 4 steps on how you can embrace AI in Category Management

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1. Build the Right Foundation

Let's be honest: AI isn't magic, it's mathematics. And that mathematics only works if the foundation is right. Many FMCG companies are drowning in retail datasets: different platforms, sales figures in one place, market research in a PDF, and promotion plans in separate spreadsheets.

Before you can fly, you need to build a runway. The first step toward AI-driven category management is centralizing and harmonizing all that data. Captain automates this "boring" work. We provide one single source of truth (SSOT), specifically configured for your category, that is leading. No more manual matching or updates, but a solid foundation you can build on. Do ensure a human-in-the-loop for edge cases. Not entirely clear, or can't be matched on EAN? Then always have a category manager review it. This way you stay completely in control of your data quality.

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2. From Rearview Mirror to Proactive Category Management

Traditional category management looks backward. "How did the promo perform last month?" "What did revenue do in Q1?" That's safe, but too late. It's like driving a car by only looking in the rearview mirror.

With AI, you change from a reactive analyst to a proactive strategist. Instead of post-mortem analyses, you use predictive modeling. Tools like Captain simulate scenarios before you execute them. What happens to your margin if you raise the price by 5%? What's the effect on volume if you run a 2+1 promotion? You stop guessing and start knowing.

This way you can steer category plans much more proactively and adjust more frequently. Because it takes much less to no time to run the analyses. Not hitting your volume? Then adjust your promo calendar mid term and decide to run a promo together with your retailer.

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3. The Battle for Margin and Against Waste

Nobody likes waste. It's bad for the planet and devastating for your margins. Yet overproduction or out of stocks is often the result of poor forecasting based on gut feel.

By combining external factors (like weather data, trends, and events) with historical data, AI creates forecasts that are much more accurate than your manual models. This means your supply chain runs tighter and you have enough inventory to meet demand. The result: less waste, better availability (higher deliverability score) and a healthier margin.

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4. The Right Insights Meet the Right Moment

In a fast-moving market, you don't have time to wait three days for your insights. You need an answer now.

This is where the AI Agent changes the playing field. Imagine being able to ask questions to your retail data like you would to a colleague. "Which promotion delivered the highest ROI in the Snacks category last month?" or "Predict the impact of a price increase on SKU X." Within seconds you have the answer, clearly visualized. No digging through dashboards, but direct insight at the right moment.

This is especially useful for all your non-category manager colleagues, your marketing, sales, and finance teams. How much time would it save you if they stop asking these questions?

The Ideal Collaboration

AI doesn't take over your work, it's the collaboration that puts you in the captain's seat of your data (quality). It gives you the freedom to focus on big-picture strategy, using your creativity and emotional intelligence in ways AI simply can’t.

The ideal vision is a seamless partnership where AI and people work hand in hand. By combining human ingenuity with AI's power to analyze massive datasets, suppliers and retailers can make smarter and more innovative decisions. Some retailers are already there, it's time suppliers get those same tools in an affordable way. We suggest not to wait, start at the beginning, build an AI (future)-proof data foundation.

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Become a Category Captain.

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