AI-Driven Product Experiences: Personalization, Trust & Data Accuracy | Romain Fouache, Akeneo

1. Given that nearly one-third of consumers complete purchases based on AI recommendations, how is your organization evolving its AI capabilities to influence decision-making across the customer journey?

Based on our data, we know that about 33% of consumers have completed a purchase based on AI recommendations. We also know that 84% of them were satisfied with the purchase – a significant success rate. This tells us that the majority of people are benefiting from these recommendations that are relevant and personalized to their needs, which is why we are always looking for ways to evolve and mold our AI capabilities to go beyond the basics, such as “you previously purchased a similar item so you might like…” and focus on helping to ensure that recommendations and product information are complete, consistent, and contextually relevant for every shopper no matter where they are in their journey. It’s not just about nudging a sale, it’s about building and fostering a greater level of trust, reducing friction, and helping consumers feel more confident in their purchases.

2. How do you assess the current maturity of your product information systems to support AI-driven personalization across your digital commerce channels?

Product information maturity is a critical foundation for any successful AI strategy, especially when it comes to personalization. Akeneo helps brands assess this by providing the right foundation of technology, and through a unique blend of data audits, system diagnostics, and customer journey mapping to better understand where content is falling short. Most of the time, the challenge isn’t the lack of data; it’s that the data is siloed, inconsistent across channels, or doesn’t have the right context that AI needs. Looking at key indicators such as readiness, completeness, and consistency helps evaluate maturity. Once there is a baseline, we help customers move up the maturity curve and automate where possible to scale AI personalization efforts.

3. How is your team measuring the impact of AI implementations on key metrics such as product return rates, customer satisfaction, and conversion efficiency?

AI isn’t valuable unless it’s working to drive business impact, so it’s important to track key metrics to ensure efficiency and accuracy. We are always looking to tie our implementations and product offerings to our clients' success metrics that matter, and customer satisfaction, conversation efficiency, and return rates fall into that category. For example, when product information is incomplete, we know it leads to confusion and frustration, AKA more likelihood of returns. So, using AI to automatically flag gaps, suggest improvements, scan reviews for common themes, and generate missing content allows brands to enrich their product content with the help of our AI tools.

4. With trust in AI-powered features still emerging, what measures is your organization taking to ensure transparency around how AI is used in customer interactions and data handling?

Increasing trust in AI is an issue that every company is facing. Without trust, the technology will fall flat, so it’s top of mind to increase. At Akeneo, our approach is always a transparency-first mindset. That means we are crystal clear with our customers, and ultimately their customers, about how, when, where, and why AI is being used and incorporated into the product experience. For example, if an AI model is working to enrich product descriptions or recommending alternative options, we make sure that users know its AI-driven and provide that context. Or if AI is scanning reviews to highlight themes, we outline that clearly to consumers.

5. In what ways is your organization investing in improving product data accuracy and enriching descriptions to support AI applications such as improved search results, summaries, and personalized recommendations?

AI is only as smart as the data that it’s fed. For Akeneo, that means the product data that it’s given. A major aspect of our investment is going toward helping brands not only clean up their plethora of data and information, but also to ensure it’s AI-ready. Our PIM platform incorporates AI capabilities that can detect inconsistencies, suggest category-specific improvements, and generate richer, more contextual descriptions at scale. This is essential for powering better search results, more accurate summaries, and ultimately, recommendations. Because when marketers and product teams can collaborate and enrich the product data faster, they’re able to provide a strong customer experience.

6. How is your leadership balancing the pursuit of AI innovation with the need to establish ethical boundaries that prioritize user consent, data privacy, and transparent value exchange?

Our roots as an open-source company have instilled a deep commitment to transparency, openness, and user trust, which are values that continue to guide our approach to AI innovation. As we develop and integrate AI capabilities across our platform, we remain committed to upholding ethical principles, particularly around user consent, data privacy, and transparent value exchange. We believe that innovation should never come at the cost of trust, which is why we prioritize building AI features that are explainable, auditable, and respectful of customer data boundaries, while ensuring users understand how value is being created and shared. Our commitment to openness is the foundation for how we shape the future of AI at Akeneo.

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