Summary In today's episode, I tackle Alan's question about how to stop people from using AI for harmful purposes in the workplace. Here's what this means for you. You discover that no technical safeguard stops AI misuse, so you must focus on people, values, and data governance instead. You'll also learn these concepts: why generative…
Summary In today's episode, I walk through how to upload sales demo calls into AI to synthesize common objections and generate SDR sales scripts. Here's what this means for you. You'll gain a step-by-step framework that turns raw call transcripts into actionable sales intelligence without producing misleading results. You'll also learn these concepts: why labeled…
In today’s episode, you’ll explore the intriguing paradox of why marketers, despite having access to vast amounts of data, often struggle to extract meaningful insights. You’ll discover the crucial role that well-defined questions play in guiding data analysis and learn why simply having data isn’t enough. I’ll share a practical framework that helps you transform…
Summary In today's episode, I explore why marketers sit on piles of data yet rarely surface meaningful insights. Here's what this means for you. You'll discover that defining clear questions before touching your data is the missing link between raw numbers and real answers. You'll also learn these concepts: how a kitchen analogy maps data…
Summary In today's episode, I walk through the infrastructure and security steps you need to take to keep your data out of generative AI training pipelines, as part two of a five-part series. Here's what this means for you. You'll see how bringing critical services in-house puts a physical wall between your data and third-party…
In today’s episode, we’re tackling a truth that can be unsettling for many marketers: sometimes, your data is just plain wrong. You’ll learn why discrepancies in your analytics are more common than you might think and why that’s not necessarily a reason to panic. I’ll explain the difference between “reliably wrong” and “unreliably wrong” data…
Summary In today's episode, I explore why business data is often wrong and how to tell whether that wrongness stays consistent enough to trust. Here's what this means for you. You gain a practical framework for diagnosing your analytics data and deciding whether you can still rely on it for forecasting. You'll also learn these…
Summary In today's episode, I break down a core maxim of generative AI: that specificity means scarcity, and why it dictates how much data you must supply to get useful results. Here's what this means for you. You gain a clearer mental model for judging when to lean on a model's built-in knowledge and when…
Almost Timely News: 🗞️ Building a Synthetic Dataset with Generative AI (2024-04-28) :: View in Browser 👉 Did you miss my webinar this past week on generative AI for agencies? Go catch the free replay! Content Authenticity Statement 100% of this week’s newsletter was generated by me, the human. Learn why this kind of disclosure…
