11 things NOT to do when scaling AI

AI promises efficiency, but for most Ops teams, the real challenge isn’t building models. It’s building the foundation. From missing KPIs and poor data quality to underestimating context engineering and cost volatility, small oversights can derail even the most promising AI initiatives. In this session, we’ll break down the 11 most common “don’ts” that stop AI projects before they scale and show you how to avoid them. You’ll learn how to set realistic success metrics, ensure data accessibility and quality, and build the orchestration layer that makes AI work across your GTM systems.

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Key takeaways

• Identify and avoid the 11 most common operational pitfalls in AI projects.
• Learn how to design AI initiatives with measurable KPIs, trusted data, and scalable workflows.
• Build an orchestration framework that keeps AI aligned with your GTM tech stack and business goals.

Speaker

• Ed King, Openprise