Summary In today's episode, I introduce the Model Context Protocol and explain how these extensions enhance generative AI capabilities. Here's what this means for you. You can expand the functionality of your AI tools beyond their native limits to handle specialized tasks like math or data retrieval. You'll also learn these concepts: the difference between…
Summary In today's episode, I walk through how to use AI as a sales co-pilot to transform raw CRM data into actionable pipeline insights. Here's what this means for you. You gain a practical workflow for building dashboards, scoring deals, and generating next-best-actions without expensive consulting or complex software. You'll also learn these concepts: how…
Summary In today's episode, I explain the importance of building a solid foundation for AI and introduce a free marketing strategy kit. Here's what this means for you. You can prepare your organization for successful AI implementation by mastering essential basics before chasing buzzwords. You'll also learn these concepts: why data governance and process management…
“Why doesn’t AI sound like me?” The recent academic paper “From Tokens to Thoughts” explains exactly why, albeit in a technical way. LLMs – the engines that power tools like ChatGPT – are compression engines. They take big data and compress it into small data so that AI can efficiently predict the next set of…
Summary In today's episode, I introduce the first foundational principle of generative AI. Here's what this means for you. You can master AI by applying it to every possible task until you discover its limits. You'll also learn these concepts: how to test AI across various workflows, how to manage data privacy risks, and why…
Can you build a three layer cake by starting with the third layer? Can you deploy code before writing it? Can you publish an ugly first draft and get great results? One of the biggest flaws I see in AI implementation today is rushing ahead. Lack of patience. Taking shortcuts. Cutting corners. If your idea…
Predictive analytics in marketing is broken. More specifically, predictive analytics at the macro level – like what consumers are going to purchase, how they plan, anticipating demand, etc. is badly broken. And it has been since March 2020. Here’s why. By its very nature, forecasting and predictive analytics rely on historical data. The more stable…
AI struggles with low probability. When you understand the inner workings of AI – namely, that it’s always trying to predict the highest probability sequence, as a prediction engine – then it becomes logically clear that AI struggles with generating low probabilities. Doing so goes against its very nature, the way the system is designed.…
“Garbage in, garbage out.” This truism from the earliest days of computing is still true. In fact, it’s more true than ever; generative AI models are built using massive amounts of data. And not all that data is good. When we talk about hallucinations, or weird outputs, or models doing unexpected and unwanted things, certainly…
