AI Is Rebuilding the Business of E-Commerce

Let me start with a scene.

Before heading out in the morning, you open your phone and order a coffee. The app doesn't ask what you're drinking today. It puts your usual right at the top of the list. You tap it without thinking. By the time you reach the shop, the coffee is ready. Warm, not scalding.

Have you ever wondered what happened in between?

It remembered your taste. It also predicted, nearly to the minute, when you'd walk in. Close enough.

There's no magic behind this. Just AI.

A century ago, electricity rebuilt the factory. Today, AI is rebuilding e-commerce. It's 2026. If you still think of AI as sci-fi robots or forbidding algorithms locked in a lab, you may have already missed a war that's been raging for years.

So what does "rebuilding" actually mean?

Stripped down, it's two things: up front, guessing what customers want on the merchant's behalf; behind the scenes, getting the work done right on their behalf.

Let's start by running a few numbers.

First, Run the Numbers

The first number: the market. According to market research firm Grand View Research, the global AI-in-retail market was worth about $11.61 billion in 2024. By 2030, that figure is projected to reach $40.74 billion. That's a compound annual growth rate of 23%.

The second number: profit. McKinsey has estimated that AI could add as much as $2.6 trillion to $4.4 trillion to corporate profits worldwide, every year.

What kind of money is that? Imagine an entire major economy's annual output materializing out of thin air — for the world's companies to split.

The third number stings the most: the cart abandonment rate, at roughly 74%.

Of every ten full shopping carts, more than seven end up abandoned right at the checkout.

That's money leaking out the door for nothing.

Catch the money everyone else is leaking, and you win.

The Giants Moved Long Ago

McKinsey's 2024 report, The State of AI, contains a telling figure: 65% of surveyed companies were already using generative AI on a regular basis. That share had nearly doubled in just ten months.

IDC's forecast is even blunter: by 2028, global AI spending will top $630 billion. Generative AI's share of it will climb from 17.2% to 32%. Over five years, that's a compound annual growth rate of 60%.

Gartner's Hype Cycle (its annual chart of which technologies are peaking and which are maturing) has been vouching for this two years running: 2023 named generative AI and decision intelligence; 2024 added AI engineering and knowledge graphs. A knowledge graph, plain and simple, is a web of relationships drawn across products, preferences, and purchase behavior — so machines can genuinely "know" your business.

Big companies vote with hard cash. Nothing tells the truth more plainly.

Look at a few of their moves.

Start with Amazon. In its warehouses, AI is tasked with picking out damaged and defective goods. The company's claim: three times as efficient as doing it by hand.

Then Alibaba. Chatbots handle customer service. You can photograph an item to find it. A recommendation engine quietly re-ranks results based on your browsing and purchase history, and prices adjust automatically with demand and preference. One coordinated operation, no human hands required.

eBay goes further, training its algorithms on more than two decades of accumulated data. Every move of your mouse on the platform teaches it a little more. The more you use it, the smarter it gets.

And Starbucks — that's the coffee from the opening scene. It remembers your usual, predicts peak hours, and lets you grab and go.

After all this, you might be thinking: this is a giants' game. What does it have to do with me?

It does. They've blazed the trail — and there's no magic on that trail, only method.

Front End: Taking "Guess What You Like" to the Extreme

Start where customers can see.

First, personalized recommendations.

What does that mean? Based on your browsing, purchases, and preferences, the system proactively brings "what you might want to buy" straight to you.

How startling are the results? Amazon's recommendation engine accounts for 35% of its sales.

Let that sink in. More than a third of the business comes from machine recommendations.

Second, chatbots.

Sephora's chatbot helps customers find products, book beauty consultants, and get styling suggestions. The effect: customers browse more, ask more, and buy more.

Third, dynamic pricing.

Price is no longer a number carved in stone. When demand shifts or competitors move, prices move with them. McKinsey's math: dynamic pricing done well lifts revenue 2% to 5% and gross margin 5% to 10%.

Don't shrug at a few points. Retail runs on thin margins — five points of gross margin can be the difference between profit and loss.

Fourth, searching by image — and buying across languages, worldwide.

eBay has done two very concrete things. One is image search: snap a photo, find the same item. The other is machine translation: buyer can't read the language? The system translates it on the spot.

Three economists — Brynjolfsson, Hui, and Liu — ran the numbers specifically on the translation feature: after it launched, export trade on eBay rose 17.5%.

You see, what AI translates isn't just language — it's the radius of your business.

Back End: Do the Work Right, Save the Money

The front end gets the attention; the back end is where the deeper craft lies.

Inventory. AI demand forecasting uses historical data and trends to work out ahead of time what stock to carry and how much. Merchants on Shopify can already manage inventory with AI tools. Stock too little and you run out; stock too much and your cash sits idle. What AI does is turn "close enough" into "just right."

Segmentation. Customer segmentation used to be a gut call. Now AI reads behavior and segments on its own — marketing lands more precisely, and conversion rates climb.

Decisions. Tools like Google Analytics have added predictive metrics: they don't just tell you what happened, they flag what's coming next.

Security. PayPal uses machine learning to watch every transaction in real time, blocking fraud patterns the moment they surface.

Logistics. DHL uses computer vision to automate inventory counts, optimize delivery routes, and forecast demand peaks. Last-mile efficiency is squeezed out exactly like this — one small gain at a time.

None of this is sexy. Every bit of it saves money.

Half of the business hides where customers never look.

Tools Are Easy to Buy. Trust Is Not.

By now you might think: adopting AI just means buying tools.

Here's what most people miss.

At B2B Online Europe, a B2B e-commerce industry conference held in Düsseldorf, people from HEINEKEN and Virto Commerce shared what deploying AI actually looked like for them. John Kelly, HEINEKEN's global e-commerce director, shared one telling detail.

Their new AI tool couldn't get traction at first.

Who finally pushed it through? The sales reps. These were people clients had dealt with for years, and clients trusted them. Once the reps tried the tool and saw real benefit, the clients followed — and so did their trust.

So the most critical move at that stage was getting the most trusted people to stand on AI's side first. Deploying the system could wait.

The first mile of AI adoption runs on trust, not algorithms.

Why do I say that? Consider: when AI comes to the table, it shakes up four things.

Data. AI can only work with data that's clean and connected. Many companies can't even stitch their own data together; the best tool just spins its wheels.

People. When employees see AI, their first thought is "is this here to take my job?" Until that knot is untied, the tool ships and nobody uses it.

Money. Infrastructure, training, headcount — all of it costs real cash. Smaller companies have to weigh it carefully.

Boundaries. How is customer data collected, stored, used? Without transparency, trust collapses.

HEINEKEN's path, in the end, came down to four steps:

1. Understand customer pain points thoroughly before choosing any tool. No AI for AI's sake.
2. Find partners who understand both the technology and the business. The collaboration with Virto Commerce is exactly that: outside experts shore up the gaps while the customer experience never wavers.
3. Roll out in phases. Small steps, fast cycles — win one round first, and bank trust with results.
4. Stay approachable. Make it safe to ask questions and voice complaints, so the team feels this is "our tool," not "something handed down from above."

Slow? A little. But every step counts.

Looking Ahead: Three Lines to Watch

So where does the curve go from here?

First, some context: in Q3 2024, B2B Online surveyed 100 B2B e-commerce leaders. 99% of their companies already had a clear AI implementation strategy.

Practically everyone is holding a map. What's left to compete on is who moves fast — and who moves steady.

Three lines are worth watching.

Line one: generative AI, moving from "writing copy" to "holding a conversation."

It writes product descriptions, marketing copy, and visual assets. Amazon is exploring conversational search: no need to think up keywords — just ask.

B2B goes deeper still. Virto Commerce's Virto Portal embeds content like certificates, research papers, and video tutorials directly into the buying flow. Why? Because B2B purchasing runs on information, not impulse. Whoever feeds information most smoothly gets closest to the order.

An even more concrete one: Virto Commerce built a tool called SmartCapture. The customer photographs a handwritten purchase list — quantities and prices included — and uploads it. The system recognizes the products automatically, matches them to the catalog, and generates the order. You never even have to fill in SKUs.

Line two: personalization, shifting into the "real-time" gear.

More than half of respondents say personalized sales outreach is the form of personalization with the biggest impact on customers.

Further out lie experiences like AR try-ons and "placing" furniture into your own living room. The endgame of personalization: everyone sees a store opened just for them.

Line three: AI moving from "giving advice" to "making decisions."

It forecasts inventory and adjusts the supply chain automatically. The checkpoints that need a human nod will keep shrinking.

But there's one line I'd advise you to keep gripped in your own hand, always: the more the machines automate, the more people are worth.

What automation can copy is process. What it can't copy is the trust between one human and another. Automation that overreaches drives away exactly the customers who are most demanding — and most loyal.

Finally, Back to That Cup of Coffee

Remember that cup of coffee from the beginning?

All you felt was "just right." Behind the scenes, an algorithm had arranged everything.

There is nothing loud about the way AI is rebuilding e-commerce. It hides in the first row of a recommendation list, inside that 35% of sales, along an optimized delivery route, in every single "just right" moment.

Whoever can produce "just right" moments one after another will lay hands on the next generation of business first.

Here's to bringing AI to the table early. More than that: here's to letting trust go first.