---
title: "AI Is Reorganizing Consumer Decision-Making"
description: "As AI enters search, comparison, and recommendation, the structure of consumer decision-making is being reorganized."
author: "Zhao Bo (赵波)"
email: "zhaobo258@gmail.com"
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published: "2026-05-20"
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---

# AI Is Reorganizing Consumer Decision-Making

> As AI enters search, comparison, and recommendation, the structure of consumer decision-making is being reorganized.

Author | Zhao Bo  Proofreading | Ge Chang **Layout** | Yin Chuanjia

Over the past two years, AI has been talked about endlessly.

When many companies bring up AI, their first thought is still copywriting, poster design, short-video scripts, or helping customer service answer a few messages. That direction is certainly useful — at the very least it saves some headcount and speeds up content production.

But if that is all you see, you will underestimate AI's impact on the consumer industry.

What I care about more is something else: AI has already begun to enter the steps a consumer takes before placing an order.

In the past, consumers had to search on their own, browse on their own, compare on their own, judge on their own, and finally place the order on their own. Now, part of those steps is starting to be handed over to AI. It can understand what a person needs, screen products for them, assemble the order, and even carry on through payment and fulfillment.

For consumer goods, retail, platforms, and distribution channels, the impact of this will run deep.

**Ordering coffee with one sentence is not about fewer taps on a phone**

Start with a very small scenario.

In the past, someone who wanted a cup of coffee had to go through a series of actions: open a food-delivery app, search for coffee, tap into a store, check the price, the delivery fee, and the discounts, then choose the size, hot or iced, the sweetness level, and finally place the order.

Everyone knows this routine by heart. So well, in fact, that we no longer find it tedious.

But once AI enters, the path changes.

The user might say just one sentence: order me an iced Americano from somewhere nearby that can deliver fastest.

From there, the AI interprets the need, evaluates nearby merchants, compares prices, delivery times, and the user's preferences, generates the order, and waits for the user to confirm.

On January 15 this year, the Qwen app (Alibaba's consumer AI assistant) announced integrations with Alibaba businesses including Taobao (its e-commerce marketplace), Alipay (its payment service), Taobao Instant Commerce (its rapid-delivery service), Fliggy (its travel platform), and Amap (its mapping and navigation app), enabling it to order food delivery, shop, and book flights, and opened these features to users for testing. The launch-event demo was "order me 40 cups of Chagee's Boya Juexian" — Chagee being a popular tea-drink chain and Boya Juexian its signature milk tea — with Qwen calling Taobao Instant Commerce to place the order and completing payment through Alipay's AI Pay.

The point of this is not that food delivery gained one more entry point.

What really deserves attention is that AI has begun to enter real transactions. The user no longer just asks "what's good to eat nearby" and then goes hunting in an app. The AI can keep going — straight into ordering, generating the transaction, and paying.

Once this step is proven to work, the entry point of the consumer industry will change.

**It used to be people finding goods; now the need comes first**

The consumer goods industry used to run on a very clear chain.

Brands bought advertising, platforms supplied traffic, retailers stocked the shelves, and consumers, once they saw a product, would search, compare, and buy.

So what companies fought over in the past was equally clear: whoever could be seen, be remembered, rank at the top of search, and hold the shelf was the one most likely to be bought.

When e-commerce arrived, the shelf moved from offline to online.

When content platforms rose, the shelf moved again, from the search page into the feed.

But the underlying behavior did not change much. Consumers still had to find the goods themselves.

With AI in the picture, the consumer may not lead with a brand, or even with a category. They may simply express a life need.

For example, instead of saying "I want to buy C&S tissues" (C&S being a major Chinese tissue brand), they say:

There are elderly people and kids at home. The tissues need to be soft, no linting, not too expensive — buy me a case.

Is there a brand in that sentence? No.

Is there a category? Yes, but an incomplete one.

What really carries value are the conditions that follow: elderly, kids, soft, no linting, not too expensive, one case.

The AI's job is to translate those words into a product choice.

At that point, the problem facing brands changes. Before, you needed consumers to remember you. Now you also need the AI to know: in which scenarios should you be placed on the candidate list.

It is a subtle shift, but a critical one.

**The shelf will go from a fixed position to something generated on the fly**

When we talked about shelves in the past, they were essentially fixed.

> Offline stores have end caps, pallet displays, and the spots beside the checkout counter;
>
> E-commerce has search results pages, homepage recommendations, and campaign venues;
>
> Content platforms have feeds, influencer seeding, and livestream rooms.

These are all positions you can see.

The shelf AI brings is different. It is not necessarily a fixed page, and not everyone necessarily sees the same thing.

Take tissues again. One user says "there's a baby at home," another says "office procurement," a third says "for elderly use, nothing too rough" — and the selection the AI generates may differ each time.

Which means that what a brand competes for in the future is not just a particular shelf slot.

It has to get into many specific scenarios:

family packs, infant use, elderly use, office procurement, value-for-money, gifting, stocking up, instant replenishment.

In the past, a brand could say "the family's first choice," and it sounded fine.

To an AI, that line is not enough.

The AI needs clearer information: how many plies, how many sheets, scented or not, suited to whom, cost per sheet, whether reviews mention linting, whether there is stock nearby, how fast it can be delivered.

Much of what brands used to communicate was written for people.

From now on, part of that information also has to be readable by machines.

This is not something a few lines of polished copy can solve. Product information, user reviews, price justification, scenario tags, fulfillment data — all of it has to become clearer.

**Instant retail will be rewritten first**

Why will this change happen first in food delivery, coffee, milk tea, and instant retail?

The reason is simple: these needs are high-frequency enough, and the cost of deciding is low.

> Hungry — order a meal;
>
> Thirsty — order milk tea;
>
> Out of tissues at home — buy a case;
>
> The kid has a school outing tomorrow — grab some snacks last minute;
>
> Friends coming over — restock a few bottles of drinks.

These needs are naturally suited to being expressed in a single sentence.

For buying a house, a car, or insurance, users can hardly hand the decision entirely to AI. But for a cup of coffee, a takeout meal, a case of tissues, a bag of rice — users are willing to give it a try.

Instant retail has another trait: fulfillment capacity is already in place.

Whether there is stock nearby, how long delivery takes, what the delivery fee is — the platforms already have that data. AI simply connects "the user expressing a need" with "the platform organizing the order."

Meituan, China's leading local-services and food-delivery platform, is running similar experiments. In September 2025, its first AI agent product, "Xiaomei," entered public beta, positioned as a personal assistant for daily life; public reports say it can complete food-delivery orders, restaurant recommendations, table booking, navigation, and other local-life services through natural language.

JD.com, one of China's largest e-commerce companies, is moving in the same direction. Public reports show its AI shopping assistant surpassed 150 million annual active users in 2025, with user penetration above 20%, driving billions of yuan in GMV.

Put these moves side by side and the picture is clear.

Platforms are pushing AI from "answering questions" toward "handling consumer needs."

**Brand owners need to rethink what recommendation means**

In the past, brand owners drove recommendation mainly through a few channels.

> One, advertising;
>
> Two, influencer seeding;
>
> Three, platform search and recommendation;
>
> Four, offline display;
>
> Five, in-store staff and promotions.

What these have in common is that the brand influences the consumer, directly or indirectly.

Once AI enters, a layer of judgment is inserted in between.

When a user says "buy me a case of tissues suitable for elderly people," the AI first interprets the need, then screens products. Whether a brand makes it into the selection depends on many pieces of information:

  * whether your product information is clear;

  * whether your user reviews can support this scenario;

  * whether your price has explanatory power;

  * whether your inventory and fulfillment are stable;

  * whether your brand has enough consumer feedback behind it.

So brand competition gains a new question: can my product be surfaced by AI?

This is not only a big-brand problem; small and mid-sized brands face it too.

In the past, some brands sold reasonably well on low prices, on channel strength, on shelf interception. If AI starts participating in recommendation, those advantages may be recalculated.

A cheap price is still an advantage, of course.

But how cheap, suited to whom, whether the reviews are good, whether it can be delivered on time, whether returns are a hassle — all of it enters the judgment.

Going forward, brands cannot just talk in concepts. They have to explain the product clearly, explain the scenarios clearly, and explain clearly why users buy again.

**Retailers must shift from selling goods to organizing life needs**

For retailers, the change is just as direct.

In the past, retailers mainly organized products.

Rice, flour, grain, and oil; snacks; personal and home care; beverages and dairy; fresh produce — laid out category by category.

With AI, users will not necessarily search by category.

They might say:

> Guests are coming tomorrow — get me some drinks and snacks;
>
> My kid has a school outing — buy some food for the road;
>
> I'm cutting sugar lately — pick a few suitable breakfast options;
>
> My parents are coming to stay for a few days — restock some household supplies.

None of these are standard categories, but every one of them is a consumption task.

A retailer that can only display categories will gradually be put on the back foot. It needs to connect its capabilities — products, inventory, membership, pricing, delivery — so that AI can call on them.

For supermarkets, convenience stores, and home-delivery operations, this change matters especially.

Because they sit closest to consumers' daily lives, and their SKUs are the most miscellaneous. That used to be a management burden; in the future it could become an advantage. As long as they can organize those products around life needs, they have a chance to capture the new traffic.

**Distributors should not assume this is far away from them either**

Many distributors may feel that AI shopping is a matter for platforms and brands, with little to do with them.

Be careful with that judgment.

Once the front-end consumer entry point changes, back-end inventory, distribution coverage, pricing, and trade spend all change with it.

If platforms start recommending products by task, brands will ask distributors to support more scenarios: family stock-up packs, instant-replenishment packs, office-procurement packs, holiday bundles. Behind each scenario are different requirements for inventory, in-store display, price structures, and delivery lead times.

What distributors used to watch was: is this month's target hit, which customers have paid, which SKUs are overstocked.

From now on they will also have to watch: which products are being called on by more scenarios, which products have demand in instant retail, which stores have stock but are not being recommended, in which regions slow delivery is costing orders.

AI will not make the channel business simpler. It will expose many management problems that used to hide in the back — earlier.

Inaccurate data, unclear inventory, chaotic pricing, weak sell-through at the terminal: in the past, people could paper over these. Once the front end speeds up, the back end will fall behind that much more easily.

**This is what the consumer industry really needs to watch**

So the impact of AI on the consumer industry cannot be judged by whether it can write a Xiaohongshu post (Xiaohongshu, also known as RED, is China's leading lifestyle-sharing platform) or generate a promotional poster.

Those things are useful, of course, but they are not the core change.

The core change is that the way consumers express their needs is shifting.

Consumers used to find products through a search box.

In the future, they may hand a single sentence to an AI to handle.

That will set off a chain reaction.

> When the entry point changes, the shelf changes;
>
> When the shelf changes, the logic of brand recommendation changes;
>
> When the recommendation logic changes, product information, review data, inventory and fulfillment, and price structures all get re-examined;
>
> Further down the line, sales organizations and distributor management get pulled along too.

That is why I say AI is reorganizing consumer decision-making from the ground up.

Not every category will change overnight, and not every consumer will change habits immediately. The consumer industry is vast and slow, and many old logics will persist for a long time.

But once one more entry point exists, competition will gradually shift.

Whoever enters consumers' life needs earlier, whoever's products can be explained clearly, whoever's goods can be found in time, whoever's fulfillment is more stable — that is who gets placed into the next round of selection.

For brand owners, retailers, platforms, and distributors, this has only just begun.

But it is already more than a technology question. It is becoming a business question.

---

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