AI:ROI Insights
A Section Blog

Why you can’t get your AI automations to work
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Why we’re opening up unlimited access to sprints
I was talking recently with a Section4 student, and I let him know that we would soon be giving members access to all our sprints for $995, about 1% the annual tuition of an MBA.
“That’s incredible,” he said. “I’m so excited – my mind is blown.”
Then he paused.
“Why are you doing that?”, which is a polite way of asking “What’s the catch?”
There isn’t one. And here’s the answer as to why.

Your step-by-step guide to a winning product strategy
Former Netflix VP of Product Gibson Biddle explains how to build a product that delights, makes money, and beats the competition.

How to make your competitors look bad without even mentioning them
Laddering (as defined by Scott Galloway) means highlighting your strengths in a way that inherently points out your competitor’s weaknesses. We’ll explain how to use laddering to deposition your competitors, using Writer, one of our favorite AI case studies right now.

The 4 priority strategies for every marketer in Q1
This week, we heard from 8 marketing experts from companies like Estee Lauder, HubSpot, and Zapier on THE AI strategies that need to be in every CMO’s 2026 plan. Our own Head of Marketing, Rachel Fields, is sharing the 4 tactics on her own to-do list.

85% of workers don’t use AI for business value
Our latest AI proficiency research was released this week – and if you’re leading a team through AI transformation, you’re going to want to keep reading.

Squishy vs. hard ROI: Why leaders need both
One of the hardest parts of AI ROI is figuring out how to report qualitative wins to execs who want quantitative reports. At our AI:ROI Conference, Michael Domanic shared the framework for approaching these two disparate kinds of ROI.

AI’s progress may be holding your team back
On paper, the rapid release of new AI models and features looks like a win for knowledge workers. In reality, teams are drowning in AI overwhelm. Here’s how leaders can help them scale an ever-steeper learning curve.





