Artificial intelligence is emerging as a key driver of digital innovation, redefining the uses and expectations in e-commerce. From real-time personalization to optimized recommendations, it is pushing back the boundaries of the customer experience. Among its most striking advances, generative AI (GenAI) has established itself as a strategic lever, offering more natural interactions and opening up new business opportunities. In a highly competitive market, it represents a differentiating asset. But how can e-tailers take advantage of it? What challenges must they anticipate?
The fields of application for GenAI in e-commerce are numerous
From internal search engines to marketing campaigns and customer data analysis, generative AI is fundamentally redefining the online shopping experience. Its integration into the various levers of e-commerce optimizes engagement, accelerates content production and refines decision-making.
Optimized customer conversation
Thanks to its advanced understanding of natural language, generative AI makes interactions conversational to guide visitors in their search for a specific product. Internal search engines and virtual assistants analyze and rephrase complex queries to provide the most appropriate solution and advice, just as an in-store salesman would. What's more, by adapting to the style and language of each user, it strengthens customer engagement and makes the exchange even more personal.
Transforming content production and management
Generative AI transforms content creation, enabling e-merchandisers to write clear, engaging product descriptions faster. Marketing campaigns also gain in impact thanks to newsletters and advertisements tailored to customer profiles. By producing tailor-made content on a large scale, while adjusting tone and language according to customer segments, e-merchandisers capture the attention of different audiences, strengthen their presence in different markets and maximize their reach.
Advanced data mining for refined strategy
Generative AI opens up new perspectives by analyzing vast volumes of textual, visual or audio data. Until now, this information was difficult to exploit on a large scale due to its complexity. Now, it's possible to identify emerging trends, analyze customer sentiment and detect weak signals revealing opportunities or risks. This advanced analytics capability enables e-tailers to adjust their strategies in real time, based on a more precise vision of the market and customer expectations.
The applications of generative AI in e-commerce are already showing concrete and significant results, particularly in personalized conversations, content management and data analysis. But to fully understand the scope of these advances and grasp their full potential, it's essential to go back to the origins of this technology. Where does generative AI come from? How does it work?, and what are the technical foundations that enable it to transform interactions and processes in the e-commerce sector? Before discussing the challenges it brings, let's take a look at the foundations of this technological revolution.
Generative AI, a technological revolution
Since the rise of ChatGPT in 2022, GenAI has been attracting growing interest from the general public and businesses alike. Unlike traditional AI, which relies on Machine Learning to analyze structured data and identify patterns, GenAI goes further: it relies on advanced models, capable of creating original content - text, images, video - from unstructured data, especially natural language. This capability opens up new perspectives, particularly in automatic language processing, where it goes beyond traditional approaches based on grammatical and statistical analysis.
Generative AI: where does it come from and how does it work?
While generative AI as we know it today is new, its origins date back to 2014. At the time, researchers were exploring so-called seq2seq models, capable of encoding text in the form of numerical vectors, manipulating them mathematically and then transforming them back into text. Initially used for machine translation, these models gave rise to the first word embeddings such as Word2Vec and GloVe, laying the foundations for vector search.
The major turning point came in 2017, when Google researchers published the scientific paper “Attention Is All You Need”. They showed that seq2seq models could be simplified by keeping only their attention mechanism, paving the way for Large Language Models (LLM). As a result, Google (with BERT in 2018) and OpenAI (with GPT-2 in 2019) are developing the first models capable of predicting missing words in a text and anticipating the continuation of a sentence.
However, it wasn't until the end of 2022 that these models were democratized among the general public, with the release of ChatGPT by OpenAI. This conversational interface, using LLM GPT-3.5, reveals the text generation capabilities of these models on an unprecedented scale, marking a real turning point in the adoption of generative artificial intelligence.
At Sensefuel, we were pioneers in this field, integrating this new potential as early as the end of 2017. We then imagined the first search vector dedicated to e-commerce, the first version of which would be made available to all our customers a few months later. Since then, our models have evolved in line with scientific advances.
LLM limits and the generation of hallucinations
LLMs don't think, they predict. Trained to complete texts by analyzing millions of pieces of data from books, articles and forums, these models learn to guess the missing words to reconstitute coherent sentences. Their goal is to provide answers that match the user's expectations, by generating the most likely words according to context. But this method has its limits, particularly in the case of erroneous hypotheses. For example, if the question is based on an incorrect assertion - “The Earth is flat, can you explain why?” - the model will try to answer without questioning the assertion. Although today's LLMs almost all incorporate safeguards to limit such hallucinations, it's important to remember that their very design is based on probabilities and statistical correlations. In other words, these models are, by their very nature, subject to such limitations and biases.
Contrary to what you might think by attributing a form of reasoning to it, an LLM doesn't analyze ideas, it extrapolates them. It doesn't generate new information out of nothing, but reformulates, organizes and structures the data it receives. In this way, it is impossible to create relevant content such as a product sheet, for example, without pre-existing, qualified data such as brand, size or color.
The emergence of the agentic model: towards more reliable AI?
To overcome these limitations, researchers are exploring a new approach: multi-agent models. Rather than relying on a single LLM, this method involves the cooperation of several specialized agents, with different points of view and fields of expertise. Each can handle a specific aspect of a query, interact with external databases and provide control over the final answers generated.
This principle is based on a key idea: the whole is greater than the sum of its parts. By confronting several points of view and integrating verification mechanisms, these systems could reduce errors and improve the relevance of responses. Cooperation between several LLMs or agents thus enables the emergence of overall behavior that appears more intelligent and coherent than would be possible with a single isolated model.
Generative AI is evolving rapidly, but it remains first and foremost a tool. Used properly, it opens up fascinating prospects for e-tailers. However, it's important to remember that it mimics intelligence without ever really understanding it. Its adoption involves a number of technical, ethical and organizational challenges to ensure optimal implementation.
Challenges for e-tailers
Although generative AI offers considerable opportunities, its adoption by e-tailers is not without its challenges. The first major obstacle is the integration of this technology into existing systems. Indeed, the adoption of Gen AI may require significant adjustments to existing technical infrastructures. E-retailers need to ensure that the new tools integrate seamlessly with their e-commerce platforms, CRMs and other data management systems.
Next, ethical and governance issues become essential. Generative AI models can sometimes be biased, which can alter the relevance of results, or pose problems in terms of personal data protection. Managing the confidentiality of the information collected, as well as aligning the advice generated with customer expectations, are major issues that require special attention.
Finally, change management is another critical challenge. Implementing generative AI often requires teams to develop their skills and be supported in adopting these new tools. Convincing internal stakeholders and ensuring adequate training of teams are key steps in ensuring optimal use of these technologies.
At Sensefuel, we have taken these challenges on board and designed a turnkey solution that integrates naturally with existing platforms, making it easy for teams to adopt. With transparency and security in mind, no personal data is exploited outside its context of use, guaranteeing an optimized experience while preserving user confidentiality. Tailor-made support ensures a smooth transition and effective appropriation of these new technologies.
Generative AI: a turning point for e-commerce
More than just a technological evolution, generative AI is redefining e-commerce standards, transforming both the customer experience and sales performance. Far from being a passing fad, it is already establishing itself as a lever for differentiation. But its adoption is not without its challenges: integration, data governance and organizational adaptation are just some of the issues to be anticipated in order to exploit its full potential. In a market where every interaction counts, e-tailers who can intelligently integrate these advances will have a real competitive advantage.