Many product teams are still in an early, largely ad-hoc phase of AI adoption. Individual PMs may rely on generic chatbots for occasional tasks, but this often remains fragmented and limited in impact. At La Centrale, we took a step back to consider whether meaningful change requires more than simply providing access to AI tools, and whether it calls for a deeper evolution of the Product Management operating model.It’s easy to fall into what we call the “chatbot trap”: using AI mainly for isolated, low-value activities such as summarization, without a shared vision or clear strategy. This raised a simple but important question for us: how can we move from merely using AI to thoughtfully augmenting the Product Management operating system?In this session, we will share our initial attempts to assess how our teams work today and where AI can realistically support them. We will introduce a set of “AI superpowers” mapped to the product management journey, with the aim of shifting the focus from basic text generation toward more meaningful support.We will also present the prioritization framework we are experimenting with at La Centrale, designed not to chase every new tool, but to focus attention on the use cases where AI can generate meaningful impact.Finally, we will reflect on an emerging idea around the “Builder PM,” and consider how Product Managers might be better supported in prototyping and engaging more directly with technical execution.

We are witnessing a fundamental shift in how users interact with digital products. Static interfaces are giving way to dynamic, conversation-based experiences that mimic human dialogue. Most product teams already build AI agents. The challenge is no longer how to create an agent, but how to make it feel like a coherent, reliable product surface.In this 25-minute talk, I'll share a practical framework to set up AI conversational agents for success, whether they are internal tools or end-user facing products. Drawing from UX writing, linguistics, and content operations, I'll show how teams can design, govern, and assess conversational AI in a way that is scalable, measurable, and aligned with product goals.We'll explore why some AI interactions feel natural and trustworthy while others feel confusing or frustrating, connecting linguistic principles (Grice's maxims, pragmatics, Jakobson's functions) to concrete design decisions. Then, I'll walk through a two-part methodology: first, how to prepare your conversational agent (documentation, identity, principles, and prompt design); second, how to assess output quality through both quantitative and qualitative approaches.Poorly designed conversations increase cognitive load, break trust, and create hidden costs through rework, escalations, and user frustration. Well-designed conversational AI, on the other hand, improves efficiency, adoption, and long-term product value. This talk gives product designers and PMs the tools to achieve the latter.

Criteo is exploring how AI and agentic capabilities can be integrated into our ad-tech platforms. But how do we decide what should be powered by AI or agents, and how can we ensure that what we build remains useful, coherent, and consistent across all platforms—without slowing down our pace of innovation?

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To tackle this challenge, we created the Core AI Design taskforce — a multidisciplinary team bringing together UX Research, UX Design, Content Design, UI Design, and UX Ops. Our mission: to build a robust AI Design Playbook, grounded in research and collaboration, that provides UX Designers, Product Managers, and R&D teams with the tools, principles, and frameworks needed to design AI experiences that truly make sense for our users.In this talk, we’ll share how we built our AI Design Playbook, a framework that bridges human-centered design principles with AI-driven innovation.

We’ll discuss how we evolved from fragmented, product-specific efforts to a cohesive and scalable system that ensures every AI experience—no matter the product or platform—feels consistent, meaningful, and trustworthy.

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We’ll also reveal how we structured our collaboration, from defining processes and workflows to training and communicating with internal teams, ensuring that the Design System is not only well-crafted but also actively used and maintained across the organization.Our journey is structured around four key pillars:

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- Design Principles: The ethical and experiential foundations guiding every AI design choice.

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- Research Insights: Continuous discovery work that grounds our system in real user needs and behaviors.

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- Automation Matrix: A framework for determining the right balance between automation and human agency.

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- AI Design Patterns: Reusable, research-backed solutions for common human–AI interaction challenges.

By the end of this session, you’ll learn how a collaborative, structured, and organization-wide design approach can turn fragmented AI initiatives into coherent, cross-platform, and human-centered experiences—and how such an approach can help scale AI design consistency, adoption, and impact across complex ecosystems.

You rolled out AI tools. Some people love them, some ignore them, and you're drowning in support requests. Sound familiar?Here's what nobody tells you: organizations don't adopt AI, they evolve through it. And each phase needs a completely different playbook.In this talk, I'll share the framework we built navigating three distinct phases, from Ignition through Acceleration to Orchestration, while growing our people.

In this talk, Anne-Sophie will share Back Market's 5-year journey building AI capabilities—from a single supply optimization algorithm to 10+ production use cases across supply, finance, and customer care.She'll reveal the strategic decisions behind their success: designing every AI feature with an obsession for business impact, navigating build-versus-buy decisions, choosing between rule-based approaches, genAI, and classical Machine Learning, and having the discipline to sunset underperforming projects. You'll learn how their organization evolved from one AI squad to three, and discover their incubation model for validating impact before scaling.You will also learned how Back Market empowered non-AI teams to build AI POCs autonomously—creating a virtuous cycle where squads validate impact independently before graduating to full production.A practical blueprint for scaling AI with both rigor and autonomy.

AI is reshaping product management: it changes how we prioritize, operate, and design our products. The boundaries between Product Manager, Designer, and Engineer are blurring in favor of profiles capable of experimenting quickly, every day. In this new paradigm, coordination is no longer enough: we need builders with strong research, strategy, and go-to-market muscles. Bpifrance shares the discovery tactics that build confidence in its AI bets—and allow it to invest in the right place, at the right time.

Conversational AI is transforming how people interact with our products. It's the famous shift from "browsing" to "asking": users no longer navigate, they formulate an intention and expect an immediate and contextualized response (via chat, voice, or even video). In this new paradigm, how do we create a product that really makes people want to talk? And how do we make that conversation useful and engaging? I will share two concrete cases of AI-native products I designed as Founding Designer at Hexa, the startup studio behind 50 startups including Aircall, Spendesk, and Front: Rose, a marketing agent for B2B sites where the challenge was to move beyond the image of "useless chat support" to create real business value and an interaction that engages and converts. Verso, an audio/video interview agent where the challenge was to establish enough trust and fluidity to obtain qualitative insights... with an AI opposite you. For each, I will detail the design choices we made over the iterations, and how these micro-interactions directly improved engagement and the quality of exchanges: animation, follow-up questions, reformulations... The approach is deliberately focused on practice, illustrated with real examples and field results. The talk is mainly aimed at designers and PMs who want to understand how to design more engaging conversational AI interactions. This is a major turning point in how we think about interaction design.

At Spotify, we launched Vedder, an internal tool that transforms natural language questions into reliable SQL queries (text-to-SQL), used every day by data teams.

This project was born from a simple need: to make data accessible to everyone, reliably and with a real guarantee of quality answers. Text-to-SQL is a well-known subject, but mastering it at the scale of all a company's data is a considerable challenge. Explaining how we built Vedder is telling the story of how to build an AI product based on large language models (LLMs).

In this talk, I will share the experience of a PM on an AI product and a few key messages:

• Start small, learn, then scale: how we transformed an experiment into a widely adopted product.

• Adding certainty to LLMs: setting up continuous evaluations to ensure reliability of results.

• Involving users in learning: collaborative data curation by business experts.

• Measuring the ROI of an AI product: productivity gains, new types of users, and organizational adoption.

Finally, I will address possible developments and how to project them:

• The perspectives of context retrieval and the connection with the company's collective knowledge to move from text-to-SQL to text-to-insights.

• And the strategic question: how these internal AI products can differentiate themselves sustainably from major LLM providers like OpenAI or Anthropic.

AI has changed product building far more than it has changed engineering.A single founder can now design, prototype, validate, ship, and iterate at a pace that used to require full teams.This talk explains how to build modern products when you’re one person operating in an AI-native environment.

Key insights:

- Why the AI era changes the structure of product work, not just the toolingHow to run the entire product loop solo: discovery, research, prototyping, delivery, iteration

- How to use agents, automation, and lightweight infra to replace traditional team overhead

- How to move from feature shipping to system-building with AI-powered services

In short, this talk shows how the modern AI product stack lets a single founder compete with traditional teams.

Today, you can go from idea to testable product in just a few days thanks to "vibe coding." But, with the speed of generative AI's evolution, new tools come out every day and you end up getting lost... In this talk, I will share a simple and effective setup: Lovable for the interface, n8n for API interactions, and Supabase for the backend. We'll see how to use these three building blocks to deliver a 0-to-1 product very quickly. After this talk, you will have no more excuses not to test your ideas!

July 14, 2025: The Hôtel Molitor M Gallery signs. Our product is only 30 days old. 10 years in the product (Logitech, Ledger, Fairmat), no line of code before Uniforms. We could have waited 6 months to find a CTO. I chose to build it myself to meet the need identified during our many field interviews: hospitality lacks digitalization and loses money on what it doesn't measure. Uniforms: the first intelligent inventory management digital platform in hospitality. We make the invisible visible. 6 months later: → 5 daily paying customers → 28 customers in the pipeline → 10 convinced investors → €250k raised in pre-seed → French Tech BPI Grant → A CTO recruited to scale The (non-linear) journey: - Start: euphoria with low-code tools: Bolt.new, V0, Lovable - Month 2: total crash, hardcoded code - Month 3: burned credits, uncontrollable tool - Month 4: technical debt, complete overhaul with a first significant but decisive expense - Month 6: multi-tenant SaaS, +1500 movements recorded I learned React in 1 week on YouTube. Created a Figma + prompt engineering framework that divided my credits by 4. Pivoted the tool at the right time. What you will discover: - The 8 phases: tool exploration → live prototyping with clients → crashes and learnings → optimization → migration → multi-tenant → scale with a CTO. - Concrete learnings: Figma/Notion/prompt engineering framework, signals for changing tools or recruiting, how to steer AI instead of being steered. - Why it's unique: A complete B2B SaaS in production, built with low-code AI. Concrete, not theoretical. Actionable today. - My message: AI tools have not replaced my skills. They have amplified them. - PM + Design + Curiosity + Resilience = Product in production. If I could do it, you can do it. I'm here to show you how.

At PayFit, we've moved beyond single-purpose AI tools to create an intelligent ecosystem where specialized AI assistants work in concert to solve complex HR and payroll challenges—serving our customers, their employees, AND our customer service agents.Our Two-Pillar AI Strategy1 - PayFit Copilot: 24/7 Autonomous Support for customersAvailable around the clock, PayFit Copilot empowers users to run their payroll autonomously.Key Use Case: Enabling customers and their employees to independently navigate and complete payroll processes—from routine declarations to complex multi-jurisdictional scenarios—without waiting for support.2 - CS AI Assistants Ecosystem: Orchestrated Intelligence for AgentsOur orchestration layer coordinates specialized AI agents to supercharge CS team effectivenessKey Use Case: Transforming CS agents from ticket responders into strategic problem-solvers, equipped with AI that handles complexity so they can focus on empathy and judgment.Our 2026 Vision: We're evolving from answering questions to taking autonomous action—AI that doesn't just guide but executes, doesn't just react but anticipates. This session will showcase live orchestration flows, reveal our architectural principles, and share lessons from deploying production AI that genuinely augments human capability at scale.

Feedback isn’t a tooling problem—it’s a culture problem. In this talk, I’ll share a culture-first Product Ops approach to make feedback truly actionable: what teams do (and don’t do) to turn raw input into clarity, alignment, and momentum across Product and GTM. Lessons from what I built at Contentsquare, and how we’re now scaling it at Gorgias with AI (tagging, clustering, summaries, retrieval). I’m genuinely passionate about this—no magic recipe, just concrete practices you can pick up, adapt, and make your own.

When an organization starts multiplying AI agent use cases, the question is no longer merely technical: it's a matter of governance. Who creates the agents? Who validates the prompts? How can we avoid misuse, black box effects, and security risks? I will share our approach to governing an internal agent platform: responsibility models, validation workflows, quality and risk management." Key points: - Responsibility model: who owns what (platform, agents, data, operational risks). - Governance of prompts and tools (tooling): nomenclature, versioning, review, deprecation. - Control mechanisms: safeguards, usage limits, monitoring. - How to integrate risk/compliance functions into the loop without blocking innovation. - Examples of incidents or near-incidents that led to the evolution of governance.

AI agents are reshaping how people discover and buy products, moving ecommerce beyond websites and search into a world where conversations, autonomous agents, and vertical shopping models become the primary interface. As agents take over discovery, comparison, and checkout, the foundations of e-commerce are being rewritten.This talk explores why agents outperform traditional ecommerce UX, how product discovery is shifting from pages to semantic entities, and why structured, enriched product data is becoming the core interface for commerce. We will break down the emerging Agentic Commerce Stack, from catalog foundations and GEO metadata to agent-facing APIs and checkout orchestration, and outline concrete strategies for preparing catalogs and commerce infrastructures for agent-driven discovery and transactions across multiple AI ecosystems.

In an era where building software is cheaper and faster than ever, AI brings the biggest boost yet.

But speed doesn’t solve the hardest problem in product: understanding the market and customers deeply enough to build the right thing.

In this session, product leaders will explore how to avoid feature bloat and low adoption by sharpening the most important capability: deciding what to build and for whom.You’ll walk away with practical strategies you can start using immediately, including approaches to prioritize features that truly matter, techniques for validating customer needs before building, and ways to strengthen human judgment in an AI-driven world.

If you’re ready to turn effortless execution into real product impact, this talk will show you how.

At Pennylane, we make sure that AI is as much a matter of culture as it is a business objective.

Rather than seeing AI as a block to add to product design, we have integrated it into the very core of how we work.

Our goal is to share our experience and our challenges around this "virtuous loop": a continuous "feed" between our internal use of AI and the design of our AI-based product features.

We have discovered that these two worlds do not just coexist, they mutually feed each other:

-> From design to use: getting our hands dirty to develop our own AI features allows us to better understand their mechanics. This technical background makes us more informed and intentional internal users.

-> From use to design: by relying on AI in our daily lives, we directly experience the user experiences we are trying to build, whether they are "aha moments," frictions... This empathy now guides our choices to create more relevant and human experiences.

Design and deploy agentic systems for the general public on the scale of a French systemic bank. Take into consideration the unprecedented characteristics of a new raw material.

3 years, 15+ AI features added. Our product has never been so powerful.

And yet, faced with the AI-first experiences emerging in our industry, one thing is clear: "if we had to create a new product today, we wouldn't make PlayPlay as it is."

6 weeks to create PlayPlay Design. Founder mode.

Outside of Process. Radically different product mindset. I will share our double challenge: combining startup agility and scale-up assets, then managing the return to constraints without losing momentum.