A speakers bureau that represents the best original thinkers,
writers, and doers for speaking engagements.
AI can analyze data. But it can't tell a story. Those who lead in the age of AI will be the ones who can turn information into a decision that sparks real change.
Every company now has the same AI, the same models, the same dashboards. When the analysis itself becomes a commodity—something any tool can spit out in seconds—what’s left that makes a person, a leader, or an entire organization valuable? The answer, says Sarah Evangeline Norman, is data storytelling: the human ability to turn complex data into simple stories that convince people to act. Sarah spent four years as TikTok’s Head of Analytical Sales. She has advised a presidential campaign, served as a Senior Fellow at Wharton Analytics, and sold to Fortune 50 CMOs as an analytical consultant at Google. She argues that the leaders who thrive in the AI era won’t be the ones with the best models, but the ones who can do the one thing the machines still can’t: make meaning. In brilliant, practical, funny talks and workshops, she shows audiences how to find their edge in a world where analysis is free. “Technology will keep changing,” she says. “The ability to make decisions is what will last.”
Sarah Evangeline Norman is the founder of Ambler Labs, a stealth start up focused on building businesses that support family and community, as well as a revenue and data leader who helps companies turn analytics into business growth.
She spent four years leading TikTok’s largest sales team and analytical sales. Prior to TikTok, she spent six years at Google as an analytical consultant to Fortune 100 companies. While at Google, Sarah served as Senior Advisor to Digital and Data to the Kamala Harris 2020 Presidential campaign.
Known as a data storyteller, Sarah teaches AI and data storytelling through Section AI and serves as a Senior Fellow at the Wharton AI & Analytics Initiative. Sarah lives in New York City with her husband and daughter.
Imagine trying to out-compete a calculator by getting slightly better at arithmetic. That’s what most knowledge workers are doing with AI right now. It’s a losing game, says Sarah Evangeline Norman, because the future doesn’t belong to the fastest analyst. It belongs to the best decision-maker.
Sarah spent four years as TikTok’s Head of Analytical Sales and six years as an analytical consultant to Fortune 50 CMOs at Google, and now teaches data storytelling at Wharton and Section AI. She knows better than anyone how to turn analysis into judgment, action, and persuasion.
Using her “Woo, Do, Accrue” framework, Sarah shows audiences how to turn information overload into ideas people actually act on:
Leaders walk away knowing how to get support for their best ideas, and how to build the taste, judgment, and trust that AI still can’t manufacture.
Facebook missed the shift to short-term video, handing market share to TikTok. Blackberry dismissed the iPhone as a threat. Blockbuster laughed at the idea of buying Netflix. Why do big companies with the world’s best data miss such big consumer behavior shifts? And how do you find and exploit those shifts? In this hands-on workshop, former TikTok and Google executive Sarah Evangeline Norman offers you a data analysis framework that uses AI at every step to find the shifts big companies miss.
Sarah spent four years as TikTok’s Head of Analytical Sales and six years as an analytical consultant to Fortune 50 CMOs at Google. She’s spent her career inside the rooms where these calls get made.
In this customizable workshop, Sarah walks participants through her four-phase AI analysis framework—Prep, Analysis, Insights, Story—using Facebook’s blind spot on short-form video as the working example. Participants then run the same prompts live, on their own business problem, to surface the shift their own company might be dismissing right now. You’ll leave with a repeatable framework that can and should be used to analyze your business and identify your biggest opportunities, both today and well into the future.
