---
title: "New Distribution Zhao Bo: When AI Starts Making Decisions for Consumers, FMCG Becomes the Frontline Battlefield"
description: "In the FMCG industry, AI is not just changing a few 'tools' but the consumers themselves: who makes decisions, how they are persuaded, what prices they see, what journeys they take, and to whom they remain loyal. This long article, written entirely from the perspective of FMCG practitioners, systematically deconstructs the five dimensions AI is rewriting, starting from the consumer. Why FMCG is becoming the frontline battlefield for AI rewriting consumer behavior: if AI is reshaping every industry, then in the consumer sector, FMCG is almost the first to feel the change. The reason is not the technology itself, but the structural characteristics of FMCG consumption."
author: "赵波"
publisher: "New Distribution"
email: "zhaobo258@gmail.com"
telephone: "+8615854817671"
published: "2026-02-28"
categories: "Dealer Operations, E-commerce & Instant Retail, Retail Formats"
language: "en"
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original_source: "https://mp.weixin.qq.com/s/eF5AxWxnalUfD_jMu6x4lg"
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attribution: "New Distribution — https://xinjignxiao.com/en/articles/new-distribution-zhao-bo-when-ai-starts-making-decisions-for-consumers-f-73648261/"
citation: "赵波. “New Distribution Zhao Bo: When AI Starts Making Decisions for Consumers, FMCG Becomes the Frontline Battlefield.” New Distribution, 2026-02-28. https://xinjignxiao.com/en/articles/new-distribution-zhao-bo-when-ai-starts-making-decisions-for-consumers-f-73648261/"
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---

# New Distribution Zhao Bo: When AI Starts Making Decisions for Consumers, FMCG Becomes the Frontline Battlefield

> In the FMCG industry, AI is not just changing a few 'tools' but the consumers themselves: who makes decisions, how they are persuaded, what prices they see, what journeys they take, and to whom they remain loyal. This long article, written entirely from the perspective of FMCG practitioners, systematically deconstructs the five dimensions AI is rewriting, starting from the consumer. Why FMCG is becoming the frontline battlefield for AI rewriting consumer behavior: if AI is reshaping every industry, then in the consumer sector, FMCG is almost the first to feel the change. The reason is not the technology itself, but the structural characteristics of FMCG consumption.

In the FMCG industry, AI is not really changing one or two 'tools,' but the consumers themselves: who makes decisions, how they are persuaded, what prices they see, what journeys they take, and to whom they remain loyal.
Below is a long article, written entirely from the perspective of FMCG practitioners, starting from the consumer, systematically deconstructing the five dimensions AI is rewriting.
Why FMCG
Is Becoming the Frontline Battlefield for AI Rewriting Consumer Behavior
If AI is reshaping every industry, then in the consumer sector, FMCG is almost the first to feel the change. The reason is not the technology itself, but the structural characteristics of FMCG consumption.
1. High Frequency, Low Decision Involvement, Strong Context: Most Suitable for AI to 'Take Over'
FMCG products have several typical characteristics:
1. Extremely high decision frequency: Happens daily and weekly, far higher than durable goods like home appliances, 3C, and automobiles.
2. Very low average transaction value: The loss from a wrong purchase is not strong, so consumers do not invest much mental cost.
3. Very shallow decision depth: More habitual choices, picked up casually, rarely with long comparisons.
4. Highly fragmented scenarios: Supermarkets, convenience stores, e-commerce platforms, front warehouses, community stores, vending machines, brand mini-programs, live-streaming rooms... Channels and touchpoints are so complex they are invisible to the naked eye.
What does this mean?
For consumers, FMCG decisions are inherently 'good enough,' naturally willing to simplify and automate decisions.
For algorithms, FMCG is a training ground with massive samples, continuous feedback, and rich data, with new behaviors, orders, and A/B test results every day.
In this structure, AI naturally begins to help consumers do two things: one is to remember 'what you usually buy'; the other is to guess 'what you might want this time.'
Thus, a key question emerges: In high-frequency, low-involvement FMCG, are consumers actively choosing, or just accepting the 'default options' given by the system?
2. Industry Talks About AI Mostly in Terms of 'Efficiency'; the Real Change Lies on the 'Human' Side
Today's industry discussions about AI mostly revolve around:
More accurate demand forecasting, reducing stockouts and overstock; smarter replenishment, improving supply chain turnover; more precise targeting, better ROI; automated content generation, lower creative costs.
These are all important, but they remain from the enterprise perspective. What truly determines the future shape of the industry are the following fundamental changes in consumers.
Who is making consumption decisions for him? How is he influenced and persuaded? What does price become in his eyes? Is the consumption journey he takes self-explored or pre-orchestrated? Is his loyalty to a specific brand or to an entire algorithm-driven experience system?
When the answers to these questions are quietly rewritten by AI, many of the marketing, channel, and brand methodologies we are familiar with will lose their original premises.
The Shift of Decision-Making Power:
From 'People Choosing Products' to 'AI Choosing Products for People'
Among all changes, the most profound and easily overlooked is the structural shift in 'who makes the decision.'
1. New Role Emerges: AI Agents Enter the Consumer's Shopping Scene
In the past, the logic was: People have needs → they search and browse themselves → they compare on shelves or pages → they decide what to buy.
Now, the chain increasingly looks like this:
People express a vague need in natural language: 'Buy more of the drink I usually have,' 'Help me restock the tissues and laundry detergent I use at home.'
The AI system does the 'dirty work': checking order history, looking at current prices, comparing promotions, calculating delivery times.
The system gives one or two 'suggested plans,' and the consumer only needs to click 'confirm.'
On the surface, this is just user experience optimization; in essence, it is a redistribution of decision-making power:
  * Consumers go from 'choosing on the shelf themselves' to 'choosing from the options the system gives.'
  * The starting point of the decision shifts from 'I suddenly thought of what to buy' to 'the system reminds me I should buy this.'
For FMCG, because many purchases are repurchases and routine replenishment, this 'decision agency' happens almost without resistance.
E-commerce platforms' 'one-click reorder' and 'frequently bought list'; platform or brand app 'restock reminders'; smart speakers and voice assistants' 'order my usual milk and cat food for me'; community group buying and front warehouse apps' 'frequently bought list.'
In these scenarios, the consumer's active thinking gradually takes a back seat, and AI's 'presets' and 'recommendations' become the true starting point of decisions.
2. Decision Participation Declines, Decision Efficiency and 'Fit' Rise Rapidly
For consumers, this change has obvious benefits:
  * Search costs drop significantly: No need to flip through categories page by page; just confirm the system's plan.
  * Risk of wrong choices decreases: The system references historical preferences and reviews, filtering out extremely unsuitable options.
  * Time value increases: Limited attention is used for more important, complex consumption decisions.
But at the same time, the depth of consumer participation in decisions continues to decline.
Many purchases become subconscious clicks on the same option, with almost no comparison. New brands and new SKUs enter his field of vision only if the system is willing to give an exploration slot. The shelf is no longer a linear display but a small world filtered by algorithms, presented as a recommendation page.
Practitioners must realize that you are less and less directly persuading 'that person,' but rather persuading 'the system that gives him the plan'; if you are not in the recommendation list, for some consumers, you have already 'exited the competition.'
3. Decisions Are 'Better' but 'Less Like One's Own Choice': A New Paradox
AI does make many decisions objectively better:
Comprehensively considering price, delivery, reviews, ingredients, and other dimensions; continuously learning from user feedback and gradually adjusting recommendations; avoiding 'impulse buying things you will never use a second time.'
But the subjective experience may become:
'It all seems to happen naturally, but I am not clear how I was persuaded'; 'I know the recommendation fits me well, but I also vaguely feel I don't have as much initiative.'
This brings a long-term challenge: FMCG brands must both use AI to improve decision quality and give consumers enough sense of 'making their own choices' at key moments. This is both experience design and a psychological topic.
The Rewriting of Experience:
Personalization, Emotion, and the Redistribution of Attention
If the shift in decision-making power is a structural change, then in daily perception, what consumers feel most strongly is that the experience has changed.
1. From Audience Profiles to Individual Profiles: Everyone Walks a 'Exclusive Script'
In the traditional marketing era, we were used to categorizing people into groups: mothers, workers, students, middle-class men...
These were coarse segmentations based on demographics, occupation, and family stage.
In the AI era, high-frequency consumption, represented by FMCG, begins to see another way of segmentation.
For example, the same 'young female white-collar worker' is split into countless micro-profiles in the system. Office workers who like sweet drinks but are wary of sugar; single renters who order takeout frequently at night; fitness enthusiasts who often buy sports nutrition products.
Even further down to specific individuals: someone likes buying small packages, trying new flavors, and is extremely price-sensitive.
The result: Each person sees a different category ranking on the e-commerce homepage; the content and messaging the brand uses to reach him are different; the same brand presents a completely different face on different people's phones.
The consumer's subjective experience is: 'How can I always see what I want to see'; 'It seems all pushes and recommendations have become more targeted'; 'The longer I browse, the more the system understands me.'
Attention is redistributed here. From one-time exposure bought from mass traffic to long-term, multi-touchpoint, multi-dimensional personalized companionship. The relationship between brand and consumer changes from a series of brief encounters to a story line that is continuously written.
2. Emotion-Driven Content and Advertising: From 'Grabbing Attention' to 'Tuning Emotions'
With AI's involvement in content production, FMCG advertising is undergoing three layers of change:
Layer 1: A leap in content production efficiency
Massive versions of posters, short videos, and copy can be generated quickly; different stories and visual languages greet different groups and interest circles.
Layer 2: Precise triggering of 'emotional buttons'
The system not only knows 'what you like to drink' but also 'what moves you.' Some people resonate more with childhood memory content; some are more sensitive to health anxiety information; some care deeply about social identity and trend labels.
AI tests different emotional narratives, finds the one that best drives clicks and conversions, and reinforces it on you.
Layer 3: The boundary between content and advertising disappears
Many 'grass-planting videos' and 'lifestyle content' are backed by AI-assisted brand stories and placement strategies.
Consumers see content that suits their taste, but may not realize they are being guided along a highly designed persuasion path.
For FMCG, this emotional impact is especially important. Low unit price and low trial cost give emotion-driven decisions more weight; a bottle of drink, a pack of snacks, a lipstick are given emotional value like 'rewarding oneself, fighting anxiety, expressing attitude.'
In other words, advertising no longer just occupies your eyeballs but your emotional channels; AI continuously optimizes behind the scenes, using what plot, what colors, what music, at what time of day, to pull you into that consumption path.
3. Algorithmization of Shelves and Packaging: Design Tamed by Data
In physical stores, the most intuitive experience for consumers is that the shelf 'seems to understand me more and more.' New products update quickly, packaging styles always hit the current aesthetic, and information layout just follows reading habits.
Behind this is: Packaging design is no longer a creative shot in the dark but is incorporated into continuous testing and iteration.
Copy A vs. Copy B; warm vs. cool main color; functional information prominent vs. emotional information prominent; e-commerce click-through rates, conversion rates, browsing dwell time, offline sales and display performance all become the basis for the next round of design optimization.
Thus, what consumers face is no longer the inspiration of a single designer but a version 'most likely to make people buy' filtered by algorithms and data; the shelf itself is a continuously trained model output.
For brands, creativity is no longer unquestionable 'art' but must accept being quantified and tested. At the same time, collaboration between design and data teams becomes a key capability affecting consumer perception.
The Intelligence of Pricing and Promotion:
Consumers Enter the 'Algorithmic Pricing Era'
If the previous changes are relatively 'soft,' then at the price and promotion level, AI's impact on consumers is 'hard.' The same bottle of drink, the same person, at different times and scenarios, may see different prices.
1. Dynamic Pricing: Price Changes from 'Label' to 'Variable'
In the traditional retail era, prices were relatively stable. Changing price tags in stores had costs, so they could not change all day; brands and channels set promotion schedules together, with relatively fixed rhythms; consumers could use 'historical memory prices' and 'shop around' to make judgments.
With AI, prices begin to have the following characteristics:
1. High-frequency fine-tuning: Automatically adjust prices based on real-time sales, inventory pressure, proximity to expiration, weather, holidays, and time periods;
2. Multi-scenario differences: Online and offline, different cities, different channels, the same SKU presents a 'price cloud map';
3. Model-driven optimization: The goal shifts from 'maintaining a certain list price' to 'using the most appropriate price structure to achieve overall profit and turnover optimization.'
Consumers' intuitive feelings: 'How did the price quietly change when I wasn't paying attention'; 'The original price no longer has reference meaning, since there are always promotions'; 'I have to buy according to the promotion rhythm, not my own rhythm.'
In FMCG, this change is especially evident: short shelf life, large sales volume, and strong demand elasticity give dynamic pricing ample space. Numerous promotional names mask the algorithmic price adjustments, making it hard for consumers to form stable judgments.
2. Personalized Promotions: Everyone Lives in 'Their Own Price Universe'
Before AI, FMCG promotions were more like a 'public welfare.' The same supermarket, at the same time, all consumers faced the same promotional posters and discount prices.
After personalized promotions emerged, the situation changed: The system first creates a 'price profile' for you: How price-sensitive are you? How loyal are you to this brand? Do you often buy only when there is a discount?
Then, based on this profile, it distributes different coupon combinations and discount levels to different consumers. Some get 89% off, others 79% off; some get a coupon for 50 off 99, while others only have 20 off 199.
For consumers:
  * Advantages: 'There is always a discount that suits me,' high promotion hit rate; feeling 'taken care of,' increasing satisfaction and repurchase intention.
  * Hidden changes: Price is no longer a 'public information' openly displayed on the shelf but a 'private experience' mixed with personal behavior history; the uncertainty of 'did I pay more?' rises, but due to high decision costs, most people choose 'forget it, it's good enough.'
This makes the FMCG price system shift from a transparent market mechanism to a semi-transparent algorithmic mechanism.
3. Promotion Rhythm Algorithmized: Consumers' Price Expectations Systematically Reshaped
Before AI, promotions had several fixed rhythms. Major periods like Spring Festival, National Day, Double 11, 6.18; fixed periodic 'monthly/quarterly big promotions' by category.
After AI, promotions become: 'Countless micro-promotions spliced into a whole year': Continuously generating new small activities based on real-time data; whatever tests effective is immediately amplified. If an activity performs well, it is automatically extended or replicated; if not, it is quickly shut down.
Moreover, different people see different rhythms; some have specific periods strengthened, others have more small promotions on regular days.
Consumers are slowly trained into a new mindset: 'There will always be another activity anyway'; 'No activity, no buy; if the activity is not strong enough, no buy'; 'I only buy when the system thinks I should buy.'
In other words, AI is not just adjusting prices themselves but reshaping consumers' overall mental model of 'when it is worth buying.'
The Reconstruction of Relationship Structure:
From Brand Loyalty to Experience Loyalty
When decision-making methods, experience paths, and price mechanisms are all rewritten by AI, the relationship between consumers and brands naturally is no longer as simple as 'see ad—form preference—repeat purchase.'
1. The Object of Loyalty Shifts from 'a Name' to 'a Whole Experience System'
In many FMCG categories, consumers' loyalty to brands is initially emotional. Childhood memories, ad stories, spokesperson images, social identity, etc.
After AI intervenes, another dimension is added: Does this brand/platform remember me consistently? Does it 'think ahead for me' at every key moment? Does it 'stand up to solve problems immediately' when issues arise?
Consumers gradually form a new 'experience loyalty.' Loyal to the brand or platform where 'you always buy right, buy fast, buy comfortably.' Loyal to 'this system that always knows what I will need next and prepares it in advance.'
The result is that the substitutability of a specific SKU may be stronger, but the substitutability of the entire experience system becomes weaker. The competition between brands is not just about product strength and advertising strength but also the smoothness and thoughtfulness of the entire AI-driven experience chain.
2. Psychological Expectations Upgrade: Personalization and Intelligent Experience Become 'Basic Features'
Once, personalized recommendations, intelligent customer service, and cross-channel consistent experiences were seen by consumers as delightful bonus points.
But as more and more leading brands and platforms do it well, consumer expectations have changed.
He begins to default that you should remember his history; default that you should give the right content and discounts at the right time; default that you should have a responsive online service entry.
When this expectation becomes the baseline, any brand that returns to 'one template for all' or 'no personalized communication' will appear backward in experience.
Brand images without memory are seen as 'doesn't understand me'; channels without intelligent service are seen as 'inefficient' and 'inconvenient'; touchpoints that are homogeneous and lack personalized communication are seen as 'noise.'
A direct insight for FMCG practitioners: 'Intelligence and personalization' are no longer an edge innovation to try slowly but a new requirement gradually written into the infrastructure of consumer minds.
3. Between 'Being Understood' and 'Being Calculated': Trust Becomes the New Key Variable
When AI deeply intervenes in consumer life, a subtle but critical psychological tension emerges.
On one hand, consumers enjoy being understood, remembered, and taken care of; on the other hand, they vaguely worry about 'being seen through too clearly,' even being exploited or manipulated.
This tension exists in FMCG as well: You always see drink discounts when thirsty, coffee and energy drinks when staying up late, electrolyte drinks after exercise.
From a shopping experience perspective, it is indeed good, but if even your emotional fluctuations, sleep habits, and social relationships can be precisely targeted by ads, many people will feel the discomfort of 'being monitored.'
This means that in the AI era, FMCG brands must answer two layers of questions when handling consumer relationships.
  * Functional layer: Are you really using AI to help him make better decisions and get products more suitable for him?
  * Value layer: To what extent do you respect his rhythm, boundaries, and right to know, rather than just trying to sell more?
Truly sophisticated brands will continuously reinforce this feeling in their narrative:
'We use AI to make you more relaxed and at ease, not to squeeze more out of you'; 'You can choose more personalization at any time, or keep it simple'; 'You can turn off certain recommendations at any time, or tell us not to push this type of content again.'
When consumers believe this, AI is no longer just a cold efficiency tool but becomes part of the brand promise—a promise to use technology responsibly.
Final Thoughts:
From Studying Consumers to 'Studying Consumers + Algorithms'
Before AI, FMCG professionals studied consumers with more concern about: Who is he, where is he, what does he want, why does he buy from me and not others.
After AI, the questions to answer have increased by half:
How does the layer of algorithms behind him understand him? Who decides what he sees and does not see? Who sets his price universe and promotion rhythm? Who is quietly rewriting his decision path and experience expectations?
You will find that part of the real competition has shifted from 'between brands' to 'between brands and algorithms.'
You must win consumers in the classic sense, and also win the entire AI system that carries his experience in the new sense: platform recommendations, channel rankings, and the logic of your own systems.
For FMCG consumers, the big trend brought by AI can be condensed into one sentence: Decisions become easier, experiences become more personal, prices become smarter, but he also unknowingly hands over part of his 'self-determination' power to invisible algorithms.
For today's FMCG practitioners, the question truly worth continuously asking may be: In this world where 'algorithms are getting smarter,' are you also making your brand more human?
At the CFC 11th China FMCG Conference on March 16-18, 'New Distribution' specially held the 'Digital AI Reconstructing Growth' forum, inviting benchmark enterprises and technical experts who have taken the lead in reaping AI dividends to gather and share real landing cases, pitfall experiences, and the latest applications of AI Agents.
Su Longfei (General Manager of Tencent Smart Retail Vertical Industry): Adding Wings of WeChat Ecosystem AI to Omnichannel Growth
Guo Ling (Deputy General Manager of Qingtu Data, Research Director of Tsinghua University Data Governance Research Center): Knowledge Sharing: AI Decoding the Password for FMCG Growth
Xu Haiyan (Deputy General Manager of Ximai Food Group Special Zone): The Era of Omnichannel User Operations: How Ximai Food Uses Digitalization to Reconstruct Brand Growth
Ding Peng (Co-founder of Baique Intelligent): AI Reconstructing Overseas Growth: From Lead Acquisition to Buyer Matching
Zhao Zhe (Founder of Qixing AI, Chief AI Expert of Qingtu Technology): Efficiency First, Then Growth: The Landing Logic of Enterprise AI Applications
Zhang Huan (Founder and CEO of Yishu Intelligent): AI Technology Reshaping Instant Retail: From Single-Point Breakthrough to Intelligent Operations
Saibo Dafu (Top Marketing AI Blogger, AI Artist, Founder of Ersheng San Consulting): AI-Driven Marketing: From Content Efficiency to Business Conversion


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## Citation metadata

- Publisher: New Distribution
- Author: 赵波
- Published: 2026-02-28
- Canonical: https://xinjignxiao.com/en/articles/new-distribution-zhao-bo-when-ai-starts-making-decisions-for-consumers-f-73648261/
- Original source: https://mp.weixin.qq.com/s/eF5AxWxnalUfD_jMu6x4lg

## Copyright and AI use

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