56: From Orchestra Conductor to AI Product Leader (with Brian Diller)

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THE GUEST

Brian Diller is an AI product leader focused on turning complexity into clarity in higher education. At Watermark, he’s leading the design and launch of student success and course evaluation products, thoughtfully integrating AI into workflows that help institutions better support learners and make more informed decisions. With a rare blend of systems thinking and creative empathy, Brian brings a unique perspective to product leadership. Before stepping into the world of AI and product development, he spent years as a music professor—an experience that continues to shape how he approaches collaboration, problem-solving, and leadership today. Known for translating complex ideas into practical solutions, he focuses on building tools that are not only powerful, but genuinely useful for the people who rely on them. Brian is especially passionate about the responsible application of AI in real-world decision making—ensuring that emerging technologies support human judgment rather than replace it. And in this episode, we’re diving into how AI can meaningfully improve student success, the challenges of designing for higher education, and what it really takes to bring responsible AI into everyday institutional workflows.

THE SUMMARY

AI product leadership often starts with curiosity, not expertise: Getting involved in AI initiatives doesn’t require deep technical skills upfront. Asking to participate in projects, raising your hand early, and being willing to learn in public can quickly position you as the internal expert in emerging AI workflows.

AI works best as a thinking partner for product managers: Tools like Gemini and ChatGPT are incredibly effective for brainstorming product features, exploring competitive strategies, and refining ideas. Instead of replacing PM judgment, AI amplifies creative problem-solving and structured thinking during product discovery.

One of AI’s strongest use cases is synthesizing overwhelming data: Large lecture classes can generate hundreds of course evaluations, making manual analysis nearly impossible. AI can summarise patterns, detect recurring themes, and highlight actionable feedback, allowing educators to quickly understand what students are actually saying.

AI can transform fragmented student data into meaningful stories: Academic advisors often manage hundreds of students with scattered records across multiple systems. AI can aggregate these signals—grades, advising notes, life challenges, and historical context—to produce a coherent narrative that helps advisors respond with empathy and better guidance.

Giving product managers control over prompts is powerful: When PMs own the prompting strategy instead of engineers, they gain direct influence over how AI interprets data and solves user problems. This shifts AI development closer to product thinking—where the focus is storytelling, user pain, and the outcomes the system should prioritise.

Prototyping AI products with synthetic data accelerates innovation: Using generated datasets allows teams to experiment safely, test hypotheses, and validate whether AI can detect meaningful signals. It also enables colleagues to explore prompts, break the system, and collaboratively refine how AI behaves.

AI adoption inside organisations often starts with one brave experiment: Many teams are still figuring out how to work with AI. Jumping into a messy, ambiguous project—despite uncertainty—can rapidly build credibility and create momentum for wider AI adoption across the company.

THE SHOW

Weekly conversations with the AI’s top product leaders. Join Polly Allen as she discovers th