The FMCG industry doesn't lack AI that can chat. In the past two years, AI has been red-hot. Whether it's ChatGPT, DeepSeek, or other general large models, they've reshaped how many industries work. Writing copy, summarizing, generating plans, analyzing spreadsheets—these tasks have indeed become faster. But the FMCG industry is a bit special. FMCG business doesn't just happen in offices; it happens on a street, in a store, in a freezer, on a shelf, during a visit, in a promotion. Whether a brand can grow often comes down to very specific questions:
How many stores suitable for me are still available in this city?
Should this area prioritize convenience stores or small shops?
Is this store surrounded by residential areas, office buildings, or schools?
Is this product suited for stock-up consumption or immediate consumption?
Which stores should sales reps visit today, what should they discuss, and what results should they bring back? General AI can help you organize your thoughts on these questions, but it can never directly give you reliable answers. Because it lacks the most fundamental and critical thing in FMCG: real channel data. Without store data, no matter how smart AI is, it can only talk;
Without format judgment, no matter how fluent AI is, it's hard to guide distribution;
Without regional granularity, no matter how complete AI is, it can't translate into frontline actions. So, AI that truly works for FMCG can't just be a chatbot. It must know where the real market is. Channel operations can no longer rely on gut feeling. The difficulty in FMCG today isn't just slower growth; it's that after the infinite fragmentation of channel structures, relying on intuition to do business no longer works. In the past, FMCG companies faced a relatively high-certainty market. Hypermarkets, supermarkets, traditional trade shops, and distributor networks were complex but had general patterns. The playbook was relatively clear: recruit distributors, push inventory, negotiate fee points, first get products on shelves, then rely on people to monitor displays, relationships, and payments. But now it's different. Traditional hypermarkets are shrinking, while convenience stores, bulk snack stores, front warehouses, flash warehouses, community shops, and special channels keep growing. In a city, different areas may have completely different channel structures; for the same category, the priority of stores near office buildings, schools, communities, or transport hubs varies. This brings a new problem: companies don't know where to precisely invest their limited people, money, and products. Distribution can't just look at store count; it must also look at store quality;
Spending can't just look at city budgets; it must also look at surrounding scenarios;
Strategy can't just look at national trends; it must also look at the real channel structure in the region;
Team management can't just look at month-end results; it must also know if daily actions are off track. What FMCG companies truly lack is a system that can provide reliable business recommendations and action directives based on market and terminal changes. Useful AI must understand stores. FMCG companies don't lack digital systems. On the contrary, over the years, they've implemented many systems: SFA, DMS, CRM, BI, channel data platforms, expense management platforms. The problem is, the more systems, the more fragmented the information. BI can tell you which region had low achievement this month, which sales department declined year-over-year, or which channel has insufficient distribution. But it often can't answer more upstream questions: How many expandable stores are outside this region? Which stores are suitable for this category? Should sales reps first cover small shops or convenience stores? If adding 300 new stores, how should they be allocated across districts and people? So, AI that truly works for FMCG isn't about being better at chatting; it's about understanding stores better. It needs to know if a store is still operating, what format it belongs to, whether it's a chain or independent, its approximate size, whether the surrounding area is residential or office, if there are subway entrances, schools, hotels, transport hubs, and whether it's suitable for selling water, snacks, frozen products, or better for immediate consumption. This information sounds trivial, but it's precisely the foundation for FMCG channel decisions. On May 27, a new name will appear in the FMCG industry: Nara. Nara is not a general Q&A tool, nor is it a BI plugin that generates charts after importing company data. Precisely, it's a channel operations agent for the FMCG industry. Nara's foundation is connected to a national channel master database. This database covers 366 cities, 2,810 districts/counties, nearly 30,000 streets/townships, and includes over 7,800 chain brands, updated monthly and archived monthly. It doesn't just record store names and addresses; it also includes format, chain attributes, area range, O2O integration, surrounding facilities, consumption scenarios, demographic characteristics, and category relevance. This is crucial. Because for FMCG companies, a store isn't just a point on a map; it's a basic unit of growth. A convenience store, a small supermarket, a small shop near a school, or a chain convenience store under an office building—each has completely different significance for different categories. With this layer of data, AI can move from "being able to answer" to "being able to judge." Useful AI gives correct action directives. Nara's more important value for the FMCG industry is its ability to break down a vague business problem into executable actions. For example, a frozen food brand wants to know how many operating stores in Beijing's Xicheng District are non-chain, over 500 square meters, and suitable for frozen product turnover. In the past, this might have required a team to do street surveys or buy data from a third party with uncertain update schedules. Now, Nara can directly provide store counts, names, addresses, area ranges, format classifications, and priority scores. Another example: a snack food company wants to enter a new city and needs to compare the channel structures of Chongqing and Kunming. A typical analysis might stop at a sentence: Chongqing is more supermarket-oriented, Kunming is more convenience-store-oriented. But what truly guides business is further answering: How much do the formats differ? What are the differences in chain vs. independent structures? If it's snack food, should the two cities adopt the same playbook? Nara can break down these channel differences and provide channel strategies for different cities accordingly. There's also a more typical scenario: for example, Wuhan's market coverage needs to increase by 10% in three months. In the past, this goal was often roughly split into "add about 300 stores per month," and then it was left to city managers and sales reps to judge. But Nara can continue breaking it down: Which districts/counties have more gaps?
Which store categories should be prioritized?
Which stores are suitable for first distribution?
Which are suitable for repeat purchase boosting?
Which sales reps should take on more new store tasks?
Which process metrics should be reviewed each month? The goal is no longer just a number, but a set of action lists. This is the AI the FMCG industry truly needs. It's not about asking one question and getting one answer, nor is it about speaking nicely to provide emotional value. It's about moving from market judgment to channel strategy, from channel strategy to store selection, from store selection to personnel actions, and from personnel actions back to KPI review. It can't replace sales reps in negotiating displays in stores, nor can it replace distributors in maintaining relationships. But it will redefine where frontline time should be spent. In the past, sales reps relied on experience to find opportunities; in the future, the system will first identify opportunities, and then people will go seize them. Nara is rewriting the underlying logic of channel operations. In the past, FMCG companies relied on people to run the market. As channels become more fragmented, scenarios more numerous, and changes faster, relying solely on experience can no longer support refined growth. Future channel competition is no longer just about whose team can run harder, whose spending is bolder, or whose distributor network is deeper. It's also about who can identify opportunities faster, allocate resources more accurately, break down actions more finely, and detect deviations earlier. This is the significance of Nara's emergence. It's not adding a "chatty tool" to the FMCG industry, but providing a new possibility: putting market insights, channel strategy, store selection, personnel actions, and KPI review into the same operational loop. AI that works for FMCG can't just be a chatbot. It must understand channels, stores, regions, categories, and frontline actions. It must be able to turn a plan that a regional manager might take two days to piece together into business actions that can be calibrated, broken down, and executed. It must ensure that headquarters' strategies don't just stay in PPTs, but become the stores that sales reps will actually visit tomorrow. On May 27, at the AI Application Forum of the first CFC China FMCG Conference in Hangzhou, Nara—China's first FMCG-specific Agent—will be officially released. What the FMCG industry needs is definitely not an AI that's better at chatting, but an AI that can truly step into the channel scene.