Podcast

AI Product Design: Algorithm-Ready UX, Rapid Experiments & Data-Driven Roadmaps

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AI Product Design: Algorithm-Ready UX, Rapid Experiments & Data-Driven Roadmaps

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Episode Overview

How do you design products that are “algorithm-ready” while running rapid experiments and building data-driven roadmaps? In this episode, Liesbeth Dingemans—strategy and AI leader, founder of Dingemans Consulting, former VP of Revenue at Source.ag and Head of AI Strategy at Prosus—walks through pragmatic approaches to AI product design that bridge vision and execution.

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Liesbeth Dingemans

Chapter Summary

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0:00 - Episode Introduction & Guest Overview
1:18 - Guest Background: Strategy, Product and AI Trajectory
3:41 - Interdisciplinary Perspective: Physics Meets Humanities
5:07 - Design as a User-Centered Product Process
6:43 - Algorithm-Friendly Product Design & Signal Collection
10:04 - Interaction Design Case Study: TikTok vs Instagram Signals
12:12 - Double Diamond Framework: Problem Framing to Solutions
14:32 - Problem Discovery: Scoping and Prioritizing User Problems
16:02 - Solution Exploration: Parallel Experiments & Proofs of Concept
18:21 - Timeframes for Research, Prototyping and Scaling
20:17 - Design Thinking Overview & Google PAIR Resources
23:16 - Design Sprint Structure: One-Week Prototyping Approach
25:00 - Cross-Functional Participation: Designers, Data Scientists, PMs
27:13 - Engineering Involvement: Building Algorithm-Ready Interfaces
28:18 - Data Scientists in Problem Definition: Avoiding Rework
31:04 - Scoping Documents: Challenging Assumptions with “Why”
33:25 - Organizational Miscommunication & Backtracking Problems
37:15 - Product Managers’ Role in AI Roadmaps and Prioritization
39:33 - Innovation vs Quarterly OKRs: Making Space for Long-Term Bets
43:19 - Radical Innovation Example: Second-Hand Car Trust Solutions
46:30 - Building Evidence: Data-Driven Pitches for Big Ideas
49:16 - Task Force Model (Jet Ski): Rapid Experimentation Teams
52:45 - Innovation Workflow: From Discovery to Investment Case
54:11 - Experimentation Culture: Prioritization Through Measurability
56:36 - Measurement Mindset: Data-Guided Product Decisions (Citrix)
58:20 - Skill Building: Learnable Design & Innovation Practices