Sébastien Debon, a leading CRM and loyalty expert and AI consultant, shares a powerful vision for transforming customer relationships. At the core of his philosophy lie three strategic applications of AI—predictive, generative, and agentic—each designed to enhance engagement, conversion, and brand loyalty. Combined with Behavior Design principles, these technologies help create intelligent, adaptive, and emotionally resonant CRM programs.
Table of contents
2. The Three Pillars of AI in CRM
3. Implementing Agentic AI: A Step-by-Step Approach
Introduction
In a world where artificial intelligence is no longer a futuristic concept but a daily reality, businesses across the globe are redefining how they interact with their customers. From Paris to San Francisco, brands are now expected to deliver real-time, personalized experiences that feel intuitive, contextual, and even emotional. AI is at the heart of this transformation.
As marketing teams embrace tools like predictive analytics, generative content engines, and autonomous agents, one question remains central: how can companies use AI not just to automate, but to build stronger, more human relationships with their customers?
In this exclusive interview, we speak with Sébastien Debon, a CRM and loyalty expert with international consulting experience. Drawing on projects from global brands and the latest AI innovations, he shares his vision of how companies can combine technology, strategy, and behavioral science to future-proof their customer engagement models.
From predictive intelligence to generative creativity and autonomous AI agents, Sébastien takes us on a deep dive into what the next generation of CRM and loyalty programs will look like—and why now is the time to act.
The Three Pillars of AI in CRM
Fourseeds : Sébastien, as a CRM and loyalty expert and AI consultant, what is your vision for integrating artificial intelligence into CRM and loyalty programs today?
Sébastien Debon : My vision revolves around three main pillars for integrating AI into CRM. These are predictive AI, generative AI and agentic AI. Combined with the Behaviour Design approach, it helps transform customer relationships, leading to greater engagement, increased conversions and stronger customer relationships while freeing marketing teams from repetitive tasks so they can focus on strategy and creativity.
PILLAR 1 – Predictive AI
Fourseeds: Could you tell us more about the first pillar, predictive AI?
Sébastien Debon: Of course. Predictive AI aims to anticipate, classify and recommend. The idea is to predict scores, quantities or purchasing behavior thanks to AI, using statistical models, deep learning or machine learning in particular. This enables us, for example, to identify customers who are likely to churn or make a next purchase, and adapt strategies accordingly.
Fourseeds: Can you share a real-world example?
Sébastien Debon: Absolutely. Let’s say a fashion retailer notices that a segment of customers hasn’t purchased in over 60 days—predictive AI flags these as at-risk. The system then recommends a personalized offer, such as a discount or early access to a new collection, to win them back. It’s not just about automation—it's about smart anticipation.
PILLAR 2 – Generative AI
Fourseeds: Moving on to generative AI—how is it used in CRM and loyalty strategies?
Sébastien Debon: The aim of generative AI is to create content and interpret instructions to generate texts or images. In the CRM context, this translates into the creation of personalized scripts for each customer segment and the integration of Behaviour Design into the AI tools of Marketing Automation platforms. Think of solutions like Einstein for Salesforce, Assistant AI for Adobe or Copilot for MSD365, which leverage machine learning and the multimodal approach to personalize communications. This enables hyper-personalization on a large scale, generating relevant and engaging messages for each individual.
Fourseeds: Could you walk us through some concrete use cases?
Sébastien Debon: Take Nespresso, for example. They can segment users by coffee machine type, intensity preferences, or buying frequency. Using generative AI, they can send different email sequences: some emphasizing rarity (limited edition flavors), others playing on exclusivity ("members-only" offers), or using progressive engagement like tutorials on mastering latte art at home.
Fourseeds: Any use cases outside of retail?
Sébastien Debon: Sure, for example, a brand like Nespresso can segment its campaigns based on purchasing habits, machine type or intensity preference. Their emails can exploit behavioural design levers such as exclusivity (e.g. “members only” offers), rarity (limited edition capsules) or progressive engagement (tips for perfecting your coffee-making at home). Another example: by regularly interacting with the driver, the Volkswagen chatbot adapts to driving style, memorizes preferences and provides personalized suggestions: more “scenic” itineraries, reminders to maintain the vehicle or playlists adapted to the weather and the dynamics of the journey.
PILLAR 3 – Agentic AI
Fourseeds: The third pillar—agentic AI—sounds promising and perhaps less familiar. Can you explain its function?
Sébastien Debon: Agentic AI focuses on taking action, not just analyzing or generating. These are autonomous AI agents that operate across your CRM, executing tasks based on predefined goals and customer inputs. Think of them as digital team members that don’t sleep—they monitor, decide, and act in real time.
Implementing Agentic AI: A Step-by-Step Approach
Fourseeds : How does a company begin to implement agentic AI?
Sébastien Debon : Firstly, the AI agent platform must provide APIs and integration modules for seamless data exchange between the CRM platform, such as SFMC, MSD365 or Adobe Campaign Manager, and the AI agent platform. Secondly, it's crucial to configure the AI platform to build and refine the agent, defining its goals, use cases and the types of personalization actions it should perform, such as content recommendations or automated responses.
Next, we insert the AI agent into customer journeys, for example using SFMC's Journey Builder. This enables the agent to trigger messages, recommend the best actions and personalize content based on customer behavior in real time. Fourthly, the use of AI-driven segmentation is essential to group customers according to their behavior, interests and engagement, ensuring the relevance and timeliness of the campaigns generated.
Finally, control and optimization are paramount. We examine CRM platform dashboards and AI platform tools to track engagement, conversions and customer satisfaction. We also implement continuous learning by regularly updating agent training data and refining prompts for continuous improvement and alignment with objectives.
AI in Action
Fourseeds: In a nutshell, how do these three pillars of AI transform CRM and loyalty programs in concrete terms?
Sébastien Debon: These three pillars of AI enable unprecedented personalization and intelligent automation. Predictive AI helps us anticipate customer needs and behaviors, generative AI enables us to create hyper-personalized experiences at scale and agentic AI offers us the ability to act and interact autonomously to optimize every customer touchpoint.
Conclusion
Fourseeds: If you had to summarize the transformation AI brings to CRM and loyalty in one sentence?
Sébastien Debon: By combining these approaches, CRM and loyalty programs can not only enhance the customer experience, but also generate sustainable value for the business, maximizing margin and retention over the long term. It's a strategic investment that redefines customer relations on the basis of real Life Time Value.
