Madvertising by AdQuick — Advertising, Marketing & Ad Tech Podcast · AdQuick

Britney Muller - the state of AI for marketers | E61

·41 min·2 clips

Marketers get AI wrong when they treat it as a buzzword, not a task-specific tool.

1. Madvertising by AdQuick features Brittany Muller on the state of AI for marketers. 2. Adam welcomes Brittany Muller, an international keynote speaker and former Moz and Hugging Face employee, because she works at the intersection of SEO, AI, and marketing. 3. The episode asks what marketers should actually do with AI as the technology moves from trend to workflow. 4. Brittany says she knew the marketing world would eventually embrace AI, but not as quickly as it did. 5. She recalls 2019 conferences where people asked her to stop talking about machine learning and featured snippets. 6. Brittany says the marketers getting AI right are intentional about specific applications rather than broad 'AI powered' claims. 7. She says AI works well for reporting, summarization, and surfacing key insights that save time across a year of daily tasks. 8. Brittany warns that using AI as a research tool is unreliable because these models are probabilistic language systems, not fact systems. 9. She says repeated prompts can return 'drastically different' numbers, which is why she advises against using AI for advertising data or competitive data. 10. Brittany says hiring or consulting talent should involve specific examples of AI experimentation, model testing, and practical use cases. 11. She compares learning AI to the early iPhone era, when people needed hands-on use before they understood the product. 12. Brittany says people who pivot from Web3 or metaverse branding to AI too quickly can be revealing their lack of focus. 13. She is reading April Dunford's positioning book and uses it to critique OpenAI's investor-facing positioning strategy. 14. Brittany says OpenAI has the 'biggest graveyard of announcements' she has ever seen and thinks many moves are aimed at VCs. 15. She says model naming is confusing, but she remembers early GPT-2 as more whimsical because it was trained on Shakespearean text. 16. Brittany says marketers need basic statistical knowledge, but she puts more weight on knowing the North Star and using data to drive action. 17. She argues that the best marketing teams stay scrappy, learn from startup-style feedback loops, and avoid perfectionism and bureaucracy. 18. The conversation is conversational and opinionated, with Adam pushing examples from recipe sites, Tesla, Google accessibility, and brand safety. 19. Marketers building AI workflows, SEO teams, and startup-minded operators would get the most from this discussion. 20. Listeners looking for technical product reviews or deep engineering detail may skip this one.

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