AI Product Leadership — Insights & FAQs

What does an AI Product Manager actually do (and how do you work)?

I bridge cutting-edge models with business outcomes: define the problem, prove feasibility fast, ship value, then scale responsibly. My approach blends user research, LLM evaluation, and lean experimentation. At Red Bull, I led a 10+ team across engineering, design, and legal to launch a global GenAI product; at Google Ads/AdSense I owned a self-serve A/B testing API used by 1M+ publishers and drove a $12M ARR uplift within 6 months.

How do you take an AI idea from 0→1?

Start narrow, validate quickly. I synthesize interviews + data (e.g., 20+ interviews & 100+ signals at Red Bull) into a crisp PRD shaped by example prompts and acceptance criteria. I prototype with LLMs early, run smoke-tests, and instrument activation/retention from day one. This approach delivered a 30% view→activation conversion on our v1.

How do you measure and improve generative AI quality?

I design an LLM evals framework that tracks task accuracy, safety, and UX fit. At Red Bull we improved iteration speed 4× by automating offline/online evals, prompt/version control, and A/B gating. I pair system metrics (latency, cost/token) with user metrics (NPS, task success) so “better” means better for users and for the business.

What results have you driven at scale?

• Red Bull: first global AI product from 0→70K users in 40 countries (20× YoY).
• Google Ads/AdSense: $12M ARR uplift from experimentation platform improvements; designed monitoring for services used by 1M+ publishers.
• Fraud/Data integrity: reduced invalid applications by 21% via safety, policy, and data checks with legal & security.

How do you keep AI products compliant, safe, and brand-aligned?

I partner early with legal, security, and brand to define guardrails (policy filters, PII handling, content controls, auditability). We ship with human-in-the-loop paths where needed and clear disclosures. This is how we scaled globally at Red Bull without surprises.

Which models and partners have you worked with?

I’ve collaborated with Microsoft, AWS, and Suno research teams on model training/evaluation (including audio). I match model choice to the job: quality, latency, cost, data boundaries, and deployment surface (server, edge, or vendor API).

What’s your playbook for AI infra & cost optimization?

Right-size the model, cache aggressively, batch where possible, and monitor token + latency budgets. I’ve owned AI infra budgets and tuned usage to keep unit economics positive while maintaining quality. If quality requires a larger model, I scope it to the highest-value flows first.

How do you work with founders and hiring managers?

Founders: I help validate use cases, build a lean AI MVP, and architect an evals/experiment loop to find pull in the market fast.
Hiring managers: I lead cross-functional roadmaps, align execs on measurable outcomes, and de-risk launches with staged rollouts and A/B tests.

Can you share frameworks or talks I can review?

Yes — see Insights for my LLM product playbooks (0→1 scoping, evals, safety, and scale) and case studies. If you’d like links to specific talks or articles, I’m happy to send them — or check /talks and /articles if published.

What’s your view on the future of LLM products?

Products will shift from tools to collaborators: agents that learn, adapt, and act with oversight. Winning teams will master behavior design (prompts, memory, tools), trust (safety, transparency), and unit economics (quality×latency×cost). I help teams operate at that intersection.