The Hard Work of AI
Last week I attended #Mopsapalooza, a conference for people in Marketing Operations—the ones who make lead generation and sales conversion programs actually work.
After getting over the thrill of being surrounded by such smart people solving critical business problems, my main takeaway was this: the dominant theme—unsurprisingly—was AI.
Nearly half the presentations mentioned it, and every vendor in the exhibit hall had some AI-powered solution. With all the buzz and hype, it was hard to pin down how these tools actually used AI—or what companies must do to truly take advantage of it—until I sat in on a session titled: “Humans + AI Agents: The New GTM Playbook in Our Org,” presented by Chili Piper.
What they shared was truly eye-opening.
Drinking Their Own Kool-Aid
Chili Piper builds solutions for marketing and sales teams to collect, manage, and convert leads. Like everyone else at the conference, they had a polished demo showing impressive business results.
But here’s the twist: they were their own case study.
And the results were striking. Pipeline performance jumped dramatically while marketing headcount dropped from 20 to just 2. (Folks from Chili Piper—please correct or elaborate in the comments!)
What impressed me most was how they implemented. Chili Piper co-CEO Alina Vandenberghe 🌶️ —an engineer and product leader—stepped in as interim CMO when results lagged, to get hands-on with the very problems her product was designed to solve.
She mapped the end-to-end marketing flow, broke it into discrete actions, standardized them into a small set of repeatable services, and re-engineered the handoffs so work could move cleanly from one service to the next—an object-oriented approach to operations.
The outcome: a library of dozens of modular workflows that can be assembled, swapped, reported on, and optimized instantly through a simple interface anyone can use—vastly improving marketing efficiency and adaptability.
Where the Work Is
Most marketing teams don’t have the luxury of building their own tools—or a CMO who’s also an engineer.
But if we want to take advantage of AI (or even just systems like Chili Piper’s), we need to start by getting our own house in order. That means:
Documenting current workflows
Breaking them into individual steps
Defining standard, reusable processes
Redesigning handoffs so work can actually flow
Yes, it can feel tedious, even painful—but it’s work that can be done offline, on paper, with minimal investment or risk.
The good news: you don’t have to start from scratch. There are already frameworks and models that can shortcut the effort and reduce the risk of trial-and-error.
So… What’s holding you back from starting? Or, put differently—what would make it easier for your team to start?
