AI in Retail Ecommerce, with Benoit Jacquemont, CTO and co-founder of Akeneo

Artificial intelligence has moved from buzzword to boardroom priority. For retail and ecommerce leaders, it’s no longer about whether to adopt AI, but how to do it responsibly, strategically, and at scale. From personalized recommendations to operational efficiency, AI in retail ecommerce is rewriting the rules of customer experience and competition.

To explore this topic further, we spoke with Benoit Jacquemont, CTO and co-founder of Akeneo. During the conversation, Jacquemont briefly discussed the role of AI in retail/e-commerce and how it benefits both businesses and customers. He even highlighted the common pitfalls of going all-in too fast with AI and the impact it can have on customers’ trust and loyalty. Moreover, by illustrating the significance of AI governance in business, he gives strategic advice for future tech leaders.

Q. Welcome to CTO Magazine! To get started, would you like to introduce yourself to our readers? You can share some key highlights about your role as a CTO and Co-founder of Akeneo.

Jacquemont: As Akeneo’s co-founder and CTO, I’ve had the privilege of orchestrating our technology strategy from day one, building the AI-powered Akeneo Product Cloud offering that helps brands and retailers deliver better product experiences.

Prior to coming to Akeneo, I spent more than a decade working on different high-traffic  systems, building ecommerce websites. That’s where I met my cofounders as well as the pain of managing product information.

Q. As a tech professional, how do you think AI is transforming the retail/ecommerce space?

Jacquemont: AI is already revolutionizing everything. In retail/ecommerce today, AI is driving personalization, powering intelligent search, automating enrichment, and shaping how products are surfaced to shoppers.

However, from my perspective, AI’s real power in this space lies in how it transforms product data into elevated product experiences. One of the most obvious applications of generative AI is creating engaging product experiences. By starting with a set of technical information, the risk of hallucination is significantly reduced because the data is highly constrained. You can then define the retailer’s tone of voice in the prompt to generate accurate content that matches their positioning.

Consumer demand for better, more convenient e-commerce experiences is at an all-time high, and retailers must use AI to scale their product catalog and speed time to market. For example, furniture retailer JC Perreault expanded its online catalog from 3,000 to 12,000 products and cut time to market from two weeks to 24 hours using Akeneo’s AI functionality.

Q. But amidst the AI hype, most businesses are blindly rushing to implement AI without having a strategy. What are the pitfalls in such a scenario? What could go wrong?

Jacquemont: As with any new technology, one of the major pitfalls of rushing with technology lies in losing consumer trust. AI is only as good as the product information that powers it.

Clean, consistent, and enriched data ensures that AI tools – from chatbots to recommendation engines – deliver relevant, trustworthy experiences. Without that foundation, AI risks eroding consumer trust rather than strengthening it.

In fact, Akeneo research shows consumers are increasingly cautious: while they want better personalization, 60% are concerned about how their data is being used, and only 45% trust AI tools to suggest relevant products.

Q. In customer service, how is AI transforming interactions? Is it possible to attract more customers with AI?

Jacquemont: AI is transforming customer service by making interactions faster, smarter, and more personalized.

AI chatbots and assistants are now guiding shoppers through discovery, returns, and post-purchase questions. In fact, according to the above-mentioned research, 75% of U.S. consumers have already encountered AI recommendations or chatbots in their shopping experiences. This reduces friction for consumers and allows service teams to focus on more complex, human-driven interactions.

When it comes to attracting more customers, AI can be a growth driver – if used responsibly. AI-powered discovery (such as recommendations in Google Shopping or through generative AI shopping assistants) helps brands be discovered faster and in more relevant contexts.

Q. In your opinion, how important is customer trust and transparency when it comes to AI implementation? Any strategies businesses can leverage to maintain customer trust when increasing AI agent implementation.

Jacquemont: Customer trust and transparency aren’t just important in AI implementation; they’re non-negotiable.

Akeneo research shows that while shoppers want the convenience of AI, 60% are concerned about how their data is used, and only 27% believe brands are honest about AI-driven recommendations. If consumers feel AI is a “black box,” they’ll opt out – even if it means sacrificing convenience.

When looking to maintain customer trust, companies must employ the following strategies:

• Prioritize data quality and integrity. AI tools only deliver valuable, trustworthy experiences if they’re fueled by clean, consistent, and enriched product information. This product information should then be enriched with customer feedback, context, and reviews to ensure AI outputs are reliable and explainable. Without that foundation, errors creep in, recommendations feel irrelevant or misguided, and trust erodes.
• Showcase transparency. Clearly communicate when and how AI is being used in product discovery, recommendations, or customer service.
• Keep humans in the loop. Use AI to enhance – not replace – human support, ensuring complex or sensitive issues are escalated.
• Measure and adapt. AI strategies aren’t one-and-done. Use analytics to monitor how AI-powered recommendations perform across channels and refine them based on real-world signals.

Q. Any key considerations for AI governance in retail?

Jacquemont: AI governance in retail starts with data governance. AI can only enhance the shopping journey if it’s built on accurate, trustworthy product information.

That’s why businesses must implement data governance policies that clearly define roles, responsibilities, and procedures for managing product data. This ensures information stays accurate, complete, and up-to-date over time, and not just at launch. At Akeneo, we like to say AI may be the engine of modern commerce, but data is the fuel. In this scenario, data governance would be the steering wheel.

With strong governance frameworks, policies, roles, and procedures, retailers can confidently scale AI while maintaining the accuracy, transparency, and trust that keep customers engaged.

Q. In your opinion, how will AI shape the retail/e-commerce space in the future? Any key trends to watch out for?

Jacquemont: In the future, AI will transform the customer journey in retail/ecommerce, moving beyond simple keyword searches to a more conversational, intent-based experience.

The current process of a customer using keywords to find a product will evolve. Instead, shoppers will be able to explain their needs and intentions to AI-powered chatbots or generative AI shopping assistants, which will then recommend suitable products. This shift will make customer interactions faster, smarter, and more personalized.

The relevance of these AI shopping assistants hinges on the quality of product information that powers them. Clean, consistent, and enriched data is the foundation. Without it, AI shopping assistants and chatbots are not going to recommend the right products. Therefore, companies must prioritize data quality and integrity to ensure that the AI outputs are reliable and explainable.

Even more so than today, in the future, if a company doesn’t have the right product information, their product will be invisible.

Q. Any advice you would like to give to future tech leaders?

Jacquemont: To the future tech leaders, my advice is to master the fundamentals of technology. A solid grasp of the basics – from how the internet works and the principles of HTTP to low-level computer programming and mathematics – is crucial.

This foundational knowledge allows you to navigate the complexities of higher-level concepts, such as Generative AI, and understand their underlying mechanisms. The ability to move up and down this “abstraction level scale” is mandatory for effective problem-solving and innovation. Beyond a deep technical understanding, it’s essential to have a clear vision. A leader must not only comprehend the current technological landscape but also be able to articulate a compelling vision for the future.

This vision is a powerful leadership tool, inspiring teams and guiding strategic decisions. By combining foundational knowledge with a forward-looking perspective, you can confidently lead your organization through an ever-evolving technological world.