---
title: "How Distributors Can Use AI to Manage Business, Read the Market, and Lead Teams"
description: "Many distributors have been experimenting with AI, initially using it for writing copy. However, the real value of AI for distributors lies not in content generation but in helping owners identify business problems earlier and decide what to manage first. By exposing hidden wastes in resources, manpower, inventory, and opportunities, AI can transform from a content tool into a business management tool that improves revenue, profit, and efficiency."
author: "赵波"
publisher: "New Distribution"
email: "zhaobo258@gmail.com"
telephone: "+8615854817671"
published: "2026-05-12"
categories: "Dealer Operations, Management & Methods"
language: "en"
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original_source: "https://mp.weixin.qq.com/s/PCFPGAjY0PnWxL1R_90eSg"
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citation: "赵波. “How Distributors Can Use AI to Manage Business, Read the Market, and Lead Teams.” New Distribution, 2026-05-12. https://xinjignxiao.com/en/articles/how-distributors-can-use-ai-to-manage-business-read-the-market-and-lead-4fa0b6b3/"
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---

# How Distributors Can Use AI to Manage Business, Read the Market, and Lead Teams

> Many distributors have been experimenting with AI, initially using it for writing copy. However, the real value of AI for distributors lies not in content generation but in helping owners identify business problems earlier and decide what to manage first. By exposing hidden wastes in resources, manpower, inventory, and opportunities, AI can transform from a content tool into a business management tool that improves revenue, profit, and efficiency.

In the past two years, many distributors have been experimenting with AI. Initially, the most common use was writing copy.
Writing a WeChat Moments post, a promotional poster, a livestream script, a franchise recruitment pitch, or meeting minutes.
These are certainly useful. What used to take a salesperson half a day to write can now be done in minutes. What used to require the marketing department to stay up late for a last-minute event plan can now be outlined by AI first.
But for most distributors, this is not where AI's greatest value lies. Because the real pain point for distributors is that daily business problems are becoming increasingly difficult to judge.
The boss doesn't know what to prioritize today; no one warns in advance which customer is becoming risky; which SKU sells a lot but isn't profitable is only discovered at month-end; which salesperson is running every day but not visiting the key stores; which stores are worth continued investment and which should be stopped—the team can't articulate this.
So, the greatest value of AI for distributors is not to write a few sentences for you, but to help the boss see earlier what problems are emerging in the business and what should be managed today.
**Distributors Don't Lack Reports; They Lack Judgment**
Nowadays, most distributors have systems in place.
They have inventory management, financial software, ordering systems, salesperson visit systems, and various reports pushed by brand owners.
But why do bosses still feel it's not enough?
Because reports only tell you what happened, not why the results occurred.
Sales dropped yesterday—was it due to weather or customer churn? Inventory is high—is it normal stocking or slow sell-through? Gross margin declined—are prices chaotic or were promotional expenses eaten up? A customer didn't order—is it a temporary fluctuation or have they already been poached by competitors? A salesperson visited 20 stores—were these effective visits or just clocking in?
Reports give results; business requires judgment.
In the past, distributor bosses relied on experience to judge.
Which customers are reliable, which regions have problems, which salespeople are dependable, which brands are worth stocking up on—the boss had a rough idea.
During periods of rapid growth, this approach worked well. With ample market space, brand resources, and distributors relying on hard work, relationships, and channel coverage, they could drive performance.
But now the environment has changed.
Terminals are more fragmented, channels are more numerous, prices are more transparent, budgets are tighter, and manufacturers demand higher sell-through and profits.
At this point, relying on experience alone is no longer sufficient. It's not that bosses aren't working hard; it's that there's too much information and change happens too fast.
**What AI Really Needs to Solve: Four Types of Waste in Operations**
In a distributor's business, waste isn't just inventory waste; there are many invisible wastes.
First, resource waste.
Brands provide budgets, and distributors invest people, but resources are spread too thin. Every store gets a little, every customer gets a little attention, and in the end, no one is truly penetrated.
Second, manpower efficiency waste.
Salespeople are on the road every day, but they're not visiting the stores they should be. Supervisors hold meetings daily, but they're not addressing the most critical issues.
Third, inventory waste.
Some products that aren't selling are being restocked, while others that are about to run out get no warning. By the time the boss sees the inventory report, problems have piled up.
Fourth, opportunity waste.
Some stores have growth potential, but no one identifies it. Some areas are suitable for development, but they've never been included in sales routes.
The real value of AI is to expose these wastes early and turn them into actionable daily tasks.
In other words, AI is not a "content tool" but should become a business management tool for distributors.
**From "Looking at Sales" to "Looking at Growth Quality"**
Many distributor bosses start their day by checking sales figures. How much did we sell yesterday? How much of the monthly target is complete? How far are we from the goal?
This habit isn't wrong, but looking only at sales can easily lead to misjudgment.
Some sales are driven by low prices; some are driven by channel stuffing; some are achieved at the expense of gross margin; some sales look like they meet targets, but customer inventory is already dangerous.
AI should help bosses see not just how much was sold, but the quality of growth.
For example, which customers saw sales growth but declining gross margin? Which SKUs saw volume growth but also higher expenses? Which stores increased purchases but without corresponding sell-through? Which regions maintained sales but saw a decline in customer numbers? Which customers used to order regularly but are now ordering less frequently?
These issues can be seen by humans, but slowly. AI's value is to help bosses screen out these anomalies in advance.
Bosses don't need to flip through ten reports daily; they need a judgment: which three problems should be managed first today.
**C&S Paper's Methodology Provides a Good Reference**
C&S Paper has been doing digital intelligence in recent years, and one of its approaches is worth noting for the FMCG industry.
It doesn't keep AI at the level of writing content, customer service, or marketing materials; it embeds AI into business judgment.
In C&S Paper's 2025 annual report, it explicitly mentions the strategy of "data-driven, decision-first," using decision AI as the core engine to promote digital transformation across the entire industry chain.
In marketing, it uses AI and big data to reconstruct "people, goods, and places," identify high-potential stores and consumer profiles, and build profit analysis, price system monitoring, industry insights, and marketing agents to support market judgment and strategy formulation.
In 2025, C&S Paper achieved operating revenue of 8.78 billion yuan, a year-on-year increase of 7.72%; net profit attributable to shareholders was 319 million yuan, up 312.80% year-on-year.
The key point of this case is not that C&S Paper used AI, but that it answered a more specific question: how should offline business grow?
In the past, many companies' first reaction to offline growth was to open more stores, distribute more products, invest more in promotions, and set higher targets.
But C&S Paper's high-potential store strategy has a core judgment: not all stores are worth investing in; finding the right stores is the key to growth.
It combines external store data, consumer profiles, store models, product selection models, and execution tasks to determine which stores are more worth investing in, which are risky, and which products should be matched to which stores.
This is very inspiring for distributors.
The biggest problem distributors have faced in the past is also limited resources. Limited people, limited vehicles, limited budgets, and limited boss energy.
Since resources are limited, you can't spread them evenly.
**Not Running More Stores, but Running the Right Stores**
In the past, much sales management emphasized diligence. More visits, more distribution, more stocking, more displays. This is certainly important; the FMCG industry can't do without diligence, but only emphasizing diligence can also cause problems.
If a salesperson visits 30 stores a day, and 20 of them have no growth potential, that diligence is inefficient.
If a supervisor requires the team to increase visit volume daily, but doesn't tell salespeople which types of stores to visit first, what products to push, and what problems to solve, the higher the visit volume, the greater the waste.
So, in C&S Paper's high-potential strategy methodology, there's a very key sentence: **Not opening more stores, but opening the right stores; not investing more resources, but investing in the right places; not relying on individual ability, but replicating the ability of top performers; not putting out fires after the fact, but predicting in advance.**
Applied to distributors, this means not making salespeople busier, but making them busy in key areas; not making bosses look at more reports, but making them see key anomalies; not making supervisors chase processes daily, but making them know which processes affect results.
This is where distributors should use AI.
**Distributor Bosses Need an Operations Workbench**
For distributor bosses, AI should first solve five problems.
First, whether there are anomalies in today's sales.
Not just looking at ups and downs, but seeing where the anomalies are.
Which brand dropped? Which region dropped? Which customer dropped? Which SKU dropped? Is it a random fluctuation or a continuous decline? Is it a major customer ordering less, or a large number of small customers being lost?
Second, whether there are inventory risks.
Which products are about to run out? Which products are tying up cash? Which products have rising expiration risk? Which products were pushed by the manufacturer? Which products are driven by real sell-through?
Many distributors have inventory on the surface, but it's actually "dead inventory." The warehouse looks full, but not many products actually make money.
Third, whether profits are being eroded.
Sales growth doesn't mean making money.
Delivery costs, promotional expenses, personnel costs, returns and exchanges, and price inversions all eat into profits. AI should help bosses see clearly which customers look like big sellers but aren't actually profitable.
Fourth, whether there's a risk of customer churn.
Customer churn for distributors often doesn't happen suddenly.
Slower order frequency, smaller order sizes, fewer core SKUs, less participation in promotions, fewer salesperson visits—these are all signals.
Fifth, what to focus on today.
What bosses need most is not a pile of metrics, but priorities. Which customer should be talked to first today? Which inventory should be cleared first? Which price should be adjusted first? Which salesperson should be followed up with first? Which brand should be handled first?
AI's value is to turn business problems into items the boss can handle that day.
**Frontline Supervisors Need a Process Workbench**
Distributor bosses look at operations; supervisors look at processes.
Many teams' problems are not a lack of goals, but a loss of process control.
Stores that should be visited aren't; visits happen but products aren't shelved; products are shelved but displays aren't done; displays are done but prices are wrong; prices are right but restocking doesn't happen; restocking happens but there's no sell-through; sell-through happens but there's no repurchase.
This is the most realistic chain in the FMCG industry.
AI entering frontline management isn't about replacing supervisors' meetings, but helping supervisors see each salesperson's problems today.
For example, which key customers weren't visited today? Which stores were visited but didn't place orders? Which stores have orders but lack core SKUs? Which stores' display photos don't meet standards? Which stores have price anomalies? Which stores have competitor promotions? Which salesperson's route is clearly unreasonable?
Yang Senlin, CIO of C&S Paper, mentioned at the New Distribution conference that the system connects decisions, tasks, and execution. The front end has high-potential store identification, consumer profiling, and people-goods-place matching; the middle generates tasks, routes, and priorities; the back end uses display photo recognition, price tag detection, competitor information collection, and execution process monitoring to ensure actions can be tracked and assessed.
This logic is exactly what distributors need most.
In the past, supervisors chased processes through WeChat groups. Now, the system should directly tell supervisors who hasn't done their job, where it wasn't done, and which action affected results.
**Regional Managers Need a Regional Workbench**
If bosses look at profits and supervisors look at actions, regional managers look at structure.
Which city is declining? Which distributor is weakening? Which brand can't break through in a certain area? Which project spent money but didn't generate sell-through? Which region is not just a short-term fluctuation but a structural decline? These issues determine whether a region should add resources, adjust strategy, or reorganize channels.
Regional managers hate listening to reports because reports can be polished.
When subordinates say the market is under pressure, is it a market problem or an execution problem? When they say competitor activities are fierce, is it that competitors are strong or that we invested resources incorrectly? When they say distributors aren't cooperative, is it that distributors aren't cooperating or that the manufacturer's strategy isn't suitable?
AI can break down regional operations for analysis.
Look at cities, areas, customers, stores, SKUs, expenses, prices, and sell-through.
The goal isn't to create a beautiful dashboard, but to help regional managers judge:
Should this city add resources or change tactics? Should this distributor be supported or adjusted? Should this project continue or stop? Is this supervisor a capability issue or a resource issue?
This is shifting from listening to reports to looking at data points.
**AI Should Replicate the "Top Performer Ability"**
In a distributor organization, who is the scarcest person?
Not the average salesperson, but the top performer. Top performers know which stores to visit, what the store owner cares about, which products fit which store, when to stock up and when to replenish, and how to respond when competitors come.
The problem is that top performers are few, and their experience is hard to replicate.
In the past, companies often had top performers share on stage. After the sharing, everyone was excited, but they still didn't know what to do when they got back.
What AI can do is break down top performer experience into standard actions. Which types of stores deserve priority visits? What products suit which consumer groups? Which stores should have displays? Which customers should be maintained as key accounts? Which anomalies should be handled the same day?
We can call this "smart brain + strongest limbs." The front end uses data to identify opportunities, the back end uses standard actions to ensure execution, and feedback continuously adjusts.
This is what distributors want.
Bosses can't rely on a few capable people to conquer the world; they need to turn capable people's judgment into systems, systems' judgment into tasks, and task completion into results.
**Business Tools Must Ultimately Return to Performance**
AI can't just stay at "looking advanced."
For distributors, whether a tool has value ultimately depends on three things:
> Has revenue grown?
>
> Have profits improved?
>
> Has efficiency increased?
The value of AI can be summed up in three things: revenue, profit, and efficiency.
Through AI, identify high-potential stores and concentrate resources on stores with higher output; through AI, match people, goods, and places to reduce product mismatch; through standardized execution and real-time tracking, reduce resource waste.
In summary: AI doesn't make distributors do more; it helps distributors put limited people, money, and goods where they're more likely to produce results.
In the past, distributor growth relied on distribution, stocking, promotions, and relationships. In the future, these will still be useful, but not enough.
Because the market no longer gives much room for extensive management.
Whoever can identify high-potential customers earlier, handle abnormal inventory faster, invest expenses more accurately, and manage salesperson actions more effectively will be more likely to survive and make money.
**This Is Not a Technical Problem; It's a Business Problem**
Many distributors, when they hear AI, think it's a matter for the brand company's technical department.
Actually, it's not. Distributor bosses don't need to study model parameters or understand complex algorithms.
Bosses only need to think clearly about one question: What do I want AI to help me see, remind me of, and drive every day?
For example, which three customers need key management today? Which five SKUs are at risk? Which salespeople have process anomalies? Which brand's profits are being eaten by expenses? Which store deserves more investment? Which customer is not worth extending credit to? Which region needs route adjustments?
These questions are the real AI entry point for distributors. Don't start with "I want to implement AI"; start with "What am I most afraid of missing every day?" Distributors aren't most afraid of problems arising; they're afraid of knowing about them long after they've arisen.
AI's value is to make problems visible earlier, actions arranged faster, and results easier to review.
So, the greatest value of AI for distributors is not writing copy. Writing copy only improves one person's work speed; looking at business can improve a company's ability to make money.
In the future, what distributor bosses really need is not a chatty tool, but a business assistant that can accompany them in reviewing the market, warning, assigning tasks, tracking processes, and calculating profits.
It doesn't make decisions for the boss, but it helps the boss miss less, misjudge less, and do less post-hoc remediation.
This is what truly relates to performance.
On May 27-28, in Hangzhou, at the **2026 China FMCG Conference AI Application Forum and Baidu NARA AGENT Launch**, we have specially invited many brand executives and AI experts to present the real paths of AI implementation in FMCG companies.
In addition, in the pre-conference warm-up, I will focus on how to use AI in FMCG sales management, covering five modules: AI-ification of management actions, AI workbenches for four positions, business diagnosis, red-light warnings and task flow, and organizational implementation, to help everyone truly apply AI to sales management.
**For conference details, please scan the QR code to add the enterprise WeChat and contact the organizing committee.**


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## Citation metadata

- Publisher: New Distribution
- Author: 赵波
- Published: 2026-05-12
- Canonical: https://xinjignxiao.com/en/articles/how-distributors-can-use-ai-to-manage-business-read-the-market-and-lead-4fa0b6b3/
- Original source: https://mp.weixin.qq.com/s/PCFPGAjY0PnWxL1R_90eSg

## Copyright and AI use

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