Building and Scaling AI Product Teams: Lessons from Kristina Walcker-Mayer at Just Product 2024

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In the fast-paced world of AI, building and scaling product teams demands a completely new approach. Kristina Walcker-Mayer, CPO at Aily Labs and former product leader at Zalando and N26, took the stage at Just Product 2024 to share her insights on what it really takes to manage AI-driven products. Her key message? Throw the traditional playbook out the window—AI is a different beast.

Let’s dive into the key lessons from Kristina’s talk on building and scaling AI product teams.

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1. Redefining What a Product Is

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At Zalando, product managers might have defined the digital shopping window as the product, while the marketing team viewed the “Nike shirt” as the product. This distinction makes sense in traditional tech companies. But with AI, the concept of a product gets even more complex.At Aily Labs, the product is essentially a virtual consultant. It’s not just about features, but about how this AI-driven tool analyzes and stitches together messy data across various systems to deliver hyper-personalized insights. Defining the product means dealing with unpredictability and stitching together data from across the board.

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2. AI vs. Traditional Tech Products: The Big Differences

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AI-driven products don’t follow the same rules as traditional tech products. Kristina highlighted some key challenges:

• Data Sensitivity: Designs might fail when real, messy data is introduced into the system.

• Unpredictability: AI outputs can vary, making user experience planning a challenge.

• Innovation Source: In AI, innovation often stems from how data is processed and interpreted rather than from adding new features.

In AI product management, it’s not about crafting perfect features; it’s about shaping user experiences around data. As Kristina emphasized, data defines the product.

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3. Setting Up Product Teams for AI

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When it comes to structuring AI product teams, Kristina discussed two approaches Aily Labs tried:

• Domain-Specific Teams:

These teams specialize in areas like finance, HR, and R&D. They bring deep expertise but come with the risk of each team “reinventing the wheel.”

• Overarching Teams:

This structure fosters more efficiency and scalability but lacks the deep business domain knowledge crucial for AI products.

Both structures have pros and cons. However, in the AI world, domain-specific knowledge is critical. Teams need to be organized in a way that leverages this expertise while maintaining efficiency.

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4. Addressing AI’s Unique Challenges

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One of the core challenges Kristina shared was about ownership of quality in AI products. In AI development, it’s easy to encounter this situation:

• The PM, AI team, tech team, and UX team all say the feature is “done,” yet the product doesn’t seem complete.

The root cause? No one is responsible for the end-to-end quality. In AI, the role of ensuring data, usability, and outcomes are all aligned falls into a gray area if not clearly defined.

Key takeaway: Product managers must rethink who owns quality. AI isn’t just about technology—it’s about ensuring everything comes together seamlessly, from the data to the user experience.

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5. Skills and Mindset for AI Product Managers

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AI product management demands a unique set of skills. Kristina outlined a few essential ones:

• Systems Thinking: Understanding the entire system and how each part connects.

• Complex Problem-Solving: AI products are inherently complex and require a love for solving open-ended problems.

• Leadership in Ambiguity: There is no clear playbook in AI. You need to lead confidently in uncertain situations.

AI product teams need to embrace quick iterations, cross-functional collaboration, and a high level of ownership. Traditional methods simply won’t work in this fast-evolving space.

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Final Thoughts: Align, Adapt, Evolve

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Kristina’s final advice for AI product managers: Align, Adapt, Evolve. In AI product management, success isn’t just about scaling teams; it’s about fundamentally rethinking how products are built and scaled in a data-driven world.

By combining domain-specific knowledge, cross-functional collaboration, and agile methodologies, AI product teams can rise to the challenge of delivering impactful, scalable products in an unpredictable landscape.

Kristina’s candid, practical approach to AI product management left the audience with a clear takeaway: the future of AI demands flexibility, humility, and a deep understanding of how to harness data to create meaningful products.

This blog post should now be ready to publish, with a dynamic tone that fits Kristina’s key points. Let me know if you need any further adjustments!

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