Oleksandr Khimiak
Artificial intelligence is rapidly transforming numerous sectors, and ecommerce is no exception. With its reliance on data, automation, and user engagement, AI is becoming a driving force behind the next generation of online purchasing. Industry leaders among online merchants and software platforms are already embracing AI as a core part of their strategies, and this is only the beginning.
Let’s explore how AI is revolutionizing ecommerce and what we can expect in 2025 as these changes unfold.
Several explanations for AI’s revolutionary trend:
1–Abundant high-quality data allows for hyper-personalization.
When shopping at every given website, a buyer’s every move (and lack of movement) is being watched closely. The amount of data collected is truly staggering, and this enables the creation of complex structured datasets that result in high-quality avatars of customers.
A-B testing can be performed fairly painlessly, allowing for a feedback loop called reinforcement learning. This further strengthens the reliability of AI judgment. Studies suggest that about 80% of content discovery at Netflix and 35% of Amazon web sales come from recommendations.
While ecommerce benefits from abundant data collection, ensuring its quality and structure remains critical for achieving accurate customer insights and hyper-personalization. So, players that will benefit most are those who have centralized and well-structured data pools.
2–The virtual environment creates nearly perfect conditions for AI usage and enables it.
In every given situation, compared to the physical world, only a few scenarios are possible. The more unpredictable the environment, the less we can rely on AI for correct judgement — at least at this stage of its technology readiness level, or TRL.
Let’s use a customer complaint case as an example. With high probability, the case will be concluded in 3-4 ways: refund, return, no further action or escalation. Data on customer complaints is abundant and well understood, the amount of behavioral patterns is limited, and statistics are publicly available. This allows for using GenAI to resolve customer complaints efficiently, especially for low-risk cases.
AI excels in handling structured, repetitive processes like resolving simple customer complaints but may falter in ambiguous or high-stakes scenarios requiring human judgment.
3–Last but not least, ecommerce is highly competitive but has a high barrier to entry and rather high margins.
So the opportunity to improve customer satisfaction through AI is seen as a need, not a desire. This is a race to the bottom, of sorts.
Every additional step of a customer journey is a risk of losing the customer. So every improvement that can shorten and make it more seamless and enjoyable is highly desirable.
Where do I see the trends for AI in ecommerce?
1. Further advances in hyper-personalization, both for recommendation engines, dynamic pricing and tailored marketing campaigns.
2. Visual search-scaling functions similar to Google Lens allow for quick searches of items from photos or screenshots and interactive in-content purchases. When you see some items in the video and have the ability to interact with the screen and search for the item in question, you will be able to stop the video and ask Youtube/Netflix/HBO to search for the item on the screen and buy it seamlessly.
3. Further improvements of inventory management: AI excels in pattern recognition and trend prediction, meaning it can predict demand and adjust stock levels accordingly, reducing waste from overstocking or lost orders due to understocking.
4. AI-generated content creation: The elephant in the room, this track has a lot of room for growth, especially in cases where nice-looking visuals are needed, not a reliable depiction of the object. But even there, we see quick progress, particularly with image-to-video use cases (e.g., models like Runway, Sora and Luma). Another example of AI-generated content that will gain traction will be high-quality product descriptions prefilled based on scripts and product visuals.
5. Proliferation of fraud attempts: After the publication of GenAI tools like ChatGPT, we observe an explosion of fraud attempts. We already have seen numerous successful deep fake fraud attacks, and these will only become more sophisticated in the near future.
6. Last but definitely not least: Application and conversational AI agents — both voice and text — for customer-facing and back-office applications. Large language model (LLM) agents are trained on collective human knowledge, and their responses are refined with reinforcement learning from human feedback (RLHF). This fact makes them very nimble at interacting with humans, showing great efficiency in conversation, especially when it comes to empathy.
There are a few catches, however.
Agentic AI is still an emerging technology, so these tools are used by early adopters. We can expect some inconsistencies and failures at first until architecture matures and organizations adapt.
Ethical consideration: people should know and agree to converse with AI and let it act upon their request.
Hallucinations: GenAI has a proclivity to make things up. This weakness can be overcome by using RAG security layers and escalation rules.
Data privacy: personal data fed into AI systems may be compromised. So it’s crucial to ensure data grounding and build a proper architecture around LLMs.
Key takeaway
AI is fundamentally reshaping the ecommerce landscape — enhancing customer experiences, streamlining operations, and addressing emerging challenges. From hyperpersonalization and conversational agents to visual search and smarter inventory management, these advancements will define the next era of online shopping.
However, as AI adoption accelerates, businesses must also address ethical concerns, data privacy, and security risks. Balancing innovation with trust and reliability will be key to sustaining success in this evolving landscape.
So stay tuned as soon we’ll see further major AI advances and developments in ecommerce in 2025.
About the author:
Oleksandr Khimiak is an advisor at Sigma Software, focusing on establishing and advancing the AI/ML Center of Excellence. The CoE drives AI innovation by creating best practices, developing governance frameworks, and ensuring that AI/ML initiatives deliver measurable value across regulated industries. He also serves as vice president of membership at the Project Management Institute’s PMI Sweden Chapter.
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