AI matters. It is one of the most consequential moments in technology that we have witnessed since the shift to the cloud. What is missing is a credible, practitioner-level answer to the question founders are actually asking: what should we do specifically, and how do we do it without creating costly failures? Drawing on real case studies from companies that got it right and those that got it badly wrong, Andrew and Yanni will deliver a practical framework grounded in operating experience, not theory.

Topics include:

• The Failure Files – A 13-hour AWS outage. A wiped executive database. 95% of enterprise AI generates zero P&L impact. What connects them, and how to avoid the same mistakes.
• Security, Architecture and the ‘Vibe Coding’ Trap – Why ‘vibe coding’ breaks at scale, and what production-grade AI actually requires.
• Organize for AI – Why a ChatGPT license is not a strategy, and how Owner.com rebuilt its entire GTM motion around a centralized AI team.
• New Benchmarks – ARR per FTE, AI cost as % of revenue, and a plain-English LLM decision matrix for operators.
• Margin Expansion Case Study – How Klarna automated 2.3M conversations in month one (700 FTE equivalent) for a $40M profit improvement.
• GTM Case Study – How AI-native growth teams are compressing sales cycles, improving ICP targeting, and running pricing experiments in days instead of quarters; Fyxer’s $1M to $30M ARR growth in 12 months powered by a 4-person team.

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You will leave with a clear self-diagnosis of where your scale-up stands on AI adoption, 3–4 specific actions you can take before your next board meeting, and a practical framework you can bring to your team the next morning.