In Conversation with Chad Musick: Practical AI in a Design-Driven Business

In this episode of The Tale of Two AIs, Razi Raziuddin talks with Chad Musick, Chief Data Officer at Bellroy, the globally recognized Australian brand known for beautifully designed carry goods. From an unconventional entry into software to a PhD in mathematics and now leading data strategy at Bellroy, Chad brings a unique blend of curiosity, abstraction, and pragmatism to the conversation.

Their discussion spans the evolving role of AI, practical business use cases, and what it means to lead a forward-thinking data team in a world full of hype and transformation.

1. From Data Engineer to Chief Data Officer

Chad’s rapid rise from data engineer to CDO in just two years is a testament to the ability to abstract problems, connect technical work to business value, and think strategically about scale. Rather than getting stuck in the weeds of dashboards and ETL pipelines, Chad focused on asking “why?”—what’s the purpose of the data, and how does it help solve a real business problem?

This mindset makes it possible to build systems and teams that don’t just deliver data, but deliver outcomes.

2. Mathematics as a Lens for Abstraction and Innovation

Chad’s academic background in topology and complexity theory plays a significant role in shaping a strategic approach to data and AI. The training emphasizes abstraction, generalization, and reframing of problems—skills that are essential when navigating evolving data systems and rapidly shifting technologies.

By approaching challenges with this mindset, it’s possible to move beyond short-term fixes and build flexible, scalable systems designed to support both current needs and future opportunities.

3. The Dual Role of GenAI: Automation and Amplification

At Bellroy, Chad sees generative AI not as a replacement for humans but as a tool for amplification and learning. In customer service, GenAI helps generate draft responses that align with the brand’s tone. In engineering and data science, it speeds up onboarding and experimentation, especially when helping team members transition from R to Python or automate code generation.

Importantly, Chad emphasized that AI should serve humans, not the other way around, and noted that “vigilance is exhausting.” AI can help us notice what matters faster, freeing humans to focus on decision-making.

4. Balancing Precision and Practicality in AI Applications

Not all AI use cases are created equal. Chad made a compelling case for aligning AI deployment with utility and risk tolerance. In areas with low risk and high volume (like customer service), automation is a win. But when accuracy and trust are paramount, such as in logistics optimization or decision-making workflows, human judgment remains essential.

He also cautioned against using LLMs for strategic thinking: “If you rely on a large language model for strategy, you’ll get the same strategy everyone else has.”

5. Design Patterns for Application Development in the GenAI Era

Chad sees GenAI democratizing application development. Employees across Bellroy now use AI tools to prototype their own solutions in tools like Google Sheets—even if the code isn’t perfect. These “desire paths” show the tech team where to invest next. Meanwhile, developers use GenAI to iterate faster, treating it as a higher-level programming interface that accelerates—not replaces—good engineering.

6. What’s Ahead: Visualization, Guardrails, and Responsible AI

Looking to 2025, Chad is most excited about advances in visualization tools that help users see patterns, gaps, and insights across large datasets. Also important in the short term is the need for guardrails, versioning, and error tolerance—especially when AI is embedded into core business processes.

Chad’s closing advice is that AI tools should be judged not just by their averages, but by their edge cases. One mistake—especially one scaled across thousands of users—can turn a barrel of wine into a barrel of sewage.

Listen to the full episode of The Tale of Two AIs  for more on AI strategy, data leadership, and what it takes to build useful AI systems: