Summary In today's episode, I explain how to build a blended scoring rubric by combining different stakeholder definitions of quality so AI can balance competing objectives like marketing appeal, technical depth, and legal safety. Here's what this means for you. You'll learn to push stakeholders into quantifying what success looks like rather than relying on…
When times are good, people buy ideas. When times are bad, people buy action. When times are good, people buy strategy. When times are bad, people buy tactics. When times are good, people buy brand. When times are bad, people buy price. When times are good, people buy growth. When times are bad, people buy…
Summary In today's episode, I walk through how to safely feed unstructured customer feedback like reviews and support tickets into language models to extract marketing insights without compromising data privacy. Here's what this means for you. You'll get a clear three-step approach that protects sensitive customer data while still letting you mine feedback for actionable…
Summary In today's episode, I walk through how to run an objective AI search visibility audit without letting personal search histories and custom profiles skew the results. Here's what this means for you. You get a practical, low-cost playbook for figuring out what AI models actually know about your brand and what they see when…
“when everyone’s super… no one will be!” – Syndrome, the Incredibles When everyone has access to state of the art models, your AI is no longer any kind of advantage, and depending on you or your team’s level of skill, might even be a disadvantage. So, if AI isn’t an advantage any more, then what…
Summary In today's episode, I break down why most AI brand visibility tools are unreliable and what you should actually measure instead. Here's what this means for you. You can stop paying for snake oil dashboards and instead use free, vendor-sourced data to track how AI systems actually interact with your site. You'll also learn…
Pangram, the AI that powers the new Substack AI validation system, is proud of its 0.02% false positive rate. Let’s frame that appropriately. First, call it what it is: a false accusation rate. In any situation that matters (not Substacks, but things like students being kicked out of school for academic dishonesty, professors losing tenure,…
Summary In today's episode, I explain how to decide where to draw the line for human review in AI marketing tasks based on risk levels. Here's what this means for you. You'll calibrate your review process to match the consequences of each AI output so you scale low-risk work without exposing your brand to high-risk…
Summary In today's episode, I break down why chasing the right LLM for each stage of your marketing funnel is usually the wrong problem to solve. Here's what this means for you. You'll get far better results by sharpening your prompts, guardrails, and surrounding workflows than by swapping between Claude, ChatGPT, and Gemini. You'll also…
