By Marat Bolatov
AI is transforming e-commerce, offering tools to enhance user experience, streamline operations, and drive revenue. Yet, hype-driven “vibe coding” can only prototype solutions. True value emerges when AI integrates with reliable backend and frontend systems, enabling scalable automation, personalized experiences, and strategic insights – turning experimentation into sustainable growth.
Artificial intelligence is changing the way we think about e-commerce. It offers features that improve user experience – and, in turn, boost conversions and revenue.
Many retailers are investing in AI as a promising technology. But in today’s AI boom, it’s important to remember that only around 5% of AI startups are actually profitable. That doesn’t mean you shouldn’t invest – it’s more a reminder that AI integration requires a thoughtful, holistic approach. Choosing partners who can deliver custom solutions adapted to your business is key.
In this piece, I’ll explore some of the challenges retailers face when integrating AI and how to navigate them effectively.
Vibe Coding: opportunities and limitations
The AI boom is pushing retailers to integrate AI just to avoid looking behind the curve. The race to follow trends can lead business owners to vibe coding. That’s not necessarily a bad thing, but it does carry certain risks.
Vibe coding is AI-driven development. Users don’t need knowledge of code or libraries – just plain language to describe a task to a language model.
Today, companies can use AI to create prototypes of tools, paying only for a subscription to something like ChatGPT, Lovable, Claude or Replit.
How is vibe coding used in eCommerce today?
There are many ways prototypes created by AI can optimise business. You can vibe code an interactive tool for dashboards and reports. Or automate a range of processes: AI can generate invoices and documents, verify addresses, and more. Another popular scenario is prototyping AI agents – AI can communicate with customers while operators are busy.
But vibe coding has limitations. Relying on it as the core of your business infrastructure is risky. AI can generate a prototype, but it doesn’t guarantee real integration with your ERP, CRM, payment systems, inventory databases, or warehouse operations.
Plus, AI-generated solutions don’t always scale in real-world conditions. You might create an AI agent that works perfectly for, say, 10 users. But then a big shopping spike happens, and hundreds or thousands of customers interact at once – and the system slows down and can’t handle the load.
AI vibe coding also cannot see your physical warehouse, optimise operations on the ground, or anticipate the operational complexity of handling thousands of orders. It won’t automatically understand that your business needs to scale smoothly under pressure. These seemingly simple realities become critical as you grow.
In the end, vibe coding allows you to quickly prototype ideas, automate routine tasks, and save on development costs, but without a solid backend, these solutions remain experimental and don’t scale.
How does a strong backend solve this problem?
When it comes to AI agents, the backend makes it possible to manage requests properly and distribute them in a controlled way, so they don’t overload the system during peak traffic.
With a strong backend, AI systems are also much better synchronised with company data and internal rules. For example, before providing any information, it is checked in terms of access rights and distribution permissions. After validation, the information is passed to the customer.
And of course – memory. A prototype is limited in how much of the conversation it can remember. The backend stores sessions and user data, knows order statuses, preferences, and other useful information that can be leveraged when communicating with customers.
However, AI agents are just one such example. A strong backend is critical for all AI systems. Without it, your data won’t be properly synchronised or updated. As a result, AI won’t function reliably. You end up with AI for the sake of AI – even though the potential is enormous.
Most importantly, business data already lives in the backend, including product catalogs, pricing, customer preferences, inventory data, access and permissions. They are the operational core of the company.
When AI solutions are built separately through vibe coding, they often start from scratch, outside this system of record. This creates duplication, fragmented data flows, and eventually difficult migration processes.
The role of the frontend
Obviously, we can’t forget about the frontend either. Without it, no backend will save your service.
The frontend is critically important: this is how a customer gets a first impression of your website. If your page takes too long to load, a user can just leave. If it’s not clear where your catalogue is or how to put products in the cart, you’ve lost them. The easier the navigation, the more pleasant the experience your customers have, and therefore the more likely they are to come back.
Poor design or structure reduces trust. Good design makes the product usable for people with different devices, screen sizes, and accessibility needs. Thoughtful frontend means a wider audience.
When the frontend has a clear structure and is tightly integrated with a scalable backend, that’s the right time to plug in AI features.
What is the true potential of AI in e-commerce?
One of my favourite examples is continuous inventory updates. AI can take into account user behaviour, trends, external factors, and current stock levels – and generate inventory restocking forecasts. At the same time, it considers not only widely available data, but also indirect data. For example, trends across different regions, weather forecasts, holidays, as well as workforce workload and warehouse status. All of this represents massive volumes of data. Simple prototypes are not designed to handle this consistently. A solid backend infrastructure is required. With it, the system can operate reliably at scale.
As a result, such systems save massive amounts of money: forecast accuracy improves, and the number of unsold products sitting on shelves goes down.
Interesting that in this kind of architecture, models can be improved and replaced. But the backend infrastructure remains the foundation that allows the business to scale.
But AI assistance can go further than forecasting. It can act as a kind of co-pilot that recommends, for example, where and when to expand, using data about demand, purchasing power, and customer behaviour in different regions. AI, supported by a backend system, can identify emerging product trends by considering location, seasons, and other market signals. It can help optimise your business by providing insights into marginal gains that can increase profit or improve efficiency across different areas, from picking and packing to delivery, logistics, and even fulfilment.
It’s essentially an ecommerce co-pilot. The idea of an AI business partner tailored specifically for ecommerce inspires our clients who use Native Commerce’s AI capabilities and are already benefiting from it. The AI-enhanced innovation can offer strategic recommendations, support you when making important decisions, and help teams test and launch new products and solutions.
What’s more, it can help you build a fully working front end in minutes, drawing on patterns and learnings from hundreds of already successful e-commerce sites. Instead of starting from scratch, teams can begin with something proven and refine it to fit their brand.
Backend unlocks new business ideas
One of the most underestimated effects of a mature backend infrastructure is the ability to create new business models. And it’s not happening in retail only. You can see how AI tools that started as lightweight vibe-coding platforms are investing in reliable backend infrastructure. Some well-known examples, such as Lovable, Base44, or Replit, initially let users build apps through prompts. They offered speed rather than depth.
But as their user bases grew, the technology stack developed as well. To keep up with clients’ needs, they went beyond code generation and started offering functions like cloud hosting, model access, data storage and portability, pipeline security, and more.
Thus, platforms like Base44 evolved from simple vibe-coding tools into systems that support real market workloads, simply because people are not just experimenting with AI anymore but integrating it fully into their businesses and demanding infrastructure that can support their data and processes.
As for the e-commerce industry, many of my clients use AI to explore new revenue streams.
Some successful cases I’ve seen include dynamic bundles. AI can assemble personalized sets for a particular user and/or a specific time period. Not just a generic “Valentine’s Day bundle,” but one that takes into account customer interests and behaviour, warehouse availability, and delivery timing.
Personalized experiences and consumer expectations have improved dramatically thanks to AI, with 91% of consumers more likely to shop with brands that provide personalized offers and recommendations.
Beauty retailers and brands offer some of the most interesting examples of this new kind of customer experience.
For instance, the British company Boots offers Smart Skin Check, an online AI-powered skin analysis tool that recommends products based on the results. The same is done by Garnier, Sephora, and other well-known names.
Another strong example is dynamic delivery pricing. With a strong backend, AI systems always have access to up-to-date data. This makes it possible to factor in external conditions. For instance, how busy are couriers right now? What’s the weather and time of day? Are there other orders in a given area?
Taking these factors into account, AI can generate optimal routes and schedules. But this also opens the door to entirely new services – for example, premium “delivery within one hour” as a standalone product.
Which is almost what Amazon is already doing.
“Amazon is using the technology to optimize delivery routes, make more intelligent warehouse robots, create more-ergonomic environments for employees and better predict where to stock new items,” said Steve Armato, Amazon’s vice president of transportation technology and services, to CNBC.com
The opportunity space here is vast. Data accumulated over years of operating your business can be directly monetized with AI – if it is applied strategically.
A strong backend turns AI from a tool for incremental improvements into a mechanism for creating new businesses. It enables fast idea validation, rapid launches of new formats, and – critically – the ability to turn them into fully fledged, scalable products. Without this foundation, even the most compelling AI ideas remain experiments. With it, they become sources of sustainable growth.
