AI in e-commerce is not about yet another smart tool, but about using your data more wisely so that the webshop becomes more relevant, efficient, and scalable. With a solid foundation in tracking, structure, and UX, AI can enhance personalization, product recommendations, and conversion optimization, and help you prioritize the efforts that actually drive revenue in your webshop.
Artificial intelligence in e-commerce: what it is and what it requires
Artificial intelligence in e-commerce fundamentally involves using data to make better decisions faster. This can range from improving onsite search to prioritizing which products should have the most visibility and which segments should receive which messages. When it works, the webshop feels more relevant to the customer and more efficient for the team.
The most important thing is to understand that AI does not save a webshop that lacks direction, data, and a clear goal. If tracking is inaccurate, the information architecture is messy, or product data is incomplete, then the output from AI will be correspondingly uncertain. Therefore, a good AI effort typically starts with solidifying the foundation.
What AI can and cannot realistically do
AI is strong at finding patterns in large amounts of data and making decisions more consistent across channels. But it is not a replacement for a healthy e-commerce setup. This means that you should clarify what is data, what are assumptions, and what is a concrete goal before implementing anything.
In practice, we often see that the greatest effect comes when AI is closely linked with structure, integrations, and development, so that data flows correctly and output can be used in everyday life. Read more about our approach to webudvikling, if you want to build a foundation that makes AI more value-creating.
AI personalisering i webshop: relevans frem for gimmicks
Personalization is one of the areas where AI can become very concrete. It's not about inserting a first name in an email, but about avoiding showing the same thing to everyone when behavior clearly shows that customers shop differently. The goal is to make the path to a relevant product shorter and more intuitive.
AI can, for example, help customize content and recommendations based on behavior such as:
- Areas of interest and categories that are visited repeatedly
- Previous purchases and repeat buying patterns
- Navigation patterns, filters, and searches on the site
It still requires that you know what you want to optimize for, and that you measure the effect continuously. Otherwise, you risk creating variation without direction, where the customer does not experience any real improvement.
Personalized UX requires discipline
If you want personalization that doesn't feel random, UX and structure must be spot on. Otherwise, you'll end up creating different versions of the same confusion. If you want to work more systematically with user experience and information architecture, you can read about our approach to UX design and see how we typically build a clearer customer journey.
AI product recommendations: make them contextual and measurable
AI product recommendations are popular because they are easy to understand. Show relevant products, increase the cart, and move on. But the quality depends on data and context. Recommendations rarely create value if they are generic or if they do not fit the page where they are displayed.
A good practice is to test recommendations at the most important touchpoints and link them to specific KPIs. This could be:
- Product list: helps recommendations the customer narrow down the choice?
- Product page: does it increase the add to cart without creating noise?
- Cart: do they help with relevant add-ons that actually match intent?
Before you increase complexity, it is often worthwhile to remove friction in the purchasing flow itself. As inspiration, you can see Planet Nusa-casen, where the focus on the product page and clear choices helped to improve performance.
AI and conversion optimization: continuous improvements, not a one-time project
AI and conversion optimization are closely related because both are about getting smarter about what works. Conversion optimization is not a one-time exercise. It is ongoing improvements where you test, learn, and adjust based on data and clear hypotheses.
AI can especially help you become quicker at prioritizing where the effort has the most impact. This can include:
- Summarize behavior patterns across sessions, devices, and channels
- Point out friction points in the flow, where many drop off
- Propose hypotheses that you still need to validate through tests.
When AI is used correctly, it becomes an accelerator for a CRO program, not a replacement for the work. If you want to see how we work with continuous improvements, you can read more about konverteringsoptimering and how we prioritize tests in practice.
AI i Shopify: apps, custom funktioner og integrationer
AI in Shopify can be apps, custom functions, or integrations that better leverage your data. The important thing is not whether something is called AI. The important thing is whether it makes your webshop faster, smarter, and more relevant for customers, without creating unnecessary complexity.
When AI is to be integrated into a Shopify webshop in a scalable way, it typically requires that strategy, design, and development work in the same direction. If you want an overview of what Mercive does across disciplines, you can start with our overall services.
If you want to assess where AI makes the most sense for you, start with three questions: What decision will we make better? What data needs to be reliable to do that? And how do we measure effectiveness so it doesn't just become another tool that no one opens after 14 days?
If you have a webshop where AI should create business value, you can contact us at contact@mercive.com or call us at +45 61 60 29 83.
