Agencies are drowning in tools, dashboards, and AI gimmicks, but John Saunders has spent years building something that actually works. Nova started as an internal fix and grew into an operating system that strips away noise, delivers context with every number, and gives AI a cockpit filled with real operational data.More
Category Archives: AI
186: Olga Andrienko: Ex-VP at Semrush left her 35-person brand team to build AI for marketing ops
Olga thought she was ahead of the AI curve, but a weekend course on autonomous systems showed her she was thinking too small. She pitched a shared internal AI stack at Semrush, built systems off APIs, skipped procurement by using already-approved tools, and tracked hours saved instead of promising vague ROI.More
183: Kevin White: Building a super IC role to escape management burnout and fixing the broken promise of AI SDRs
Kevin rebuilt his career around the work that fuels him. After years leading teams at Segment, Retool and Common Room, he walked away from politics and board decks to create a “super IC” role focused on experiments, product evangelism, and hands‑on growth. He applies that same mindset to go‑to‑market: strip out the bloat, ditch templated outreach, and use real buyer behavior to build small, personal campaigns.More
182: Simon Lejeune: Wealthsimple’s VP of Growth on 2 keys to be a top 5% marketer
Simon Lejeune learned early that chasing small wins keeps growth teams stuck, a lesson that landed hard when Hopper’s CEO dismissed his price‑point test as a “local maximum” and pushed him toward ideas bold enough to reshape the business. That experience drives how he leads at Wealthsimple, where he tells teams to stop polishing the same hill and start climbing new mountains.More
181: Alison Albeck Lindland: Climb the AI literacy pyramid and stand out as a customer‑first marketer
Alison believes marketing careers thrive when you stay close to the people who buy from you, and at Movable Ink she has built that into the culture with a customer strategy team, advisory boards, and events that create real connections customers carry into new roles. More
180: István Mészáros: Merging web and product analytics on top of the warehouse with a zero-copy architecture
István built a warehouse-native analytics layer that lets teams define metrics once, query them directly, and skip the messy syncs across five tools trying to guess what “active user” means. Instead of fighting over numbers, teams walk through SQL together, clean up logic, and move faster.More
179: Tiankai Feng: The comeback of data quality and how NLP is changing the data analyst role
Data governance feels like the Jedi Council, steady with its rules, while marketing ops moves like the Rebel Alliance, quick to adapt when perfect data never arrives. Tiankai believes progress comes from blending discipline with curiosity, bringing data in early as a partner, not a critic.More
178: Guta Tolmasquim: Connecting brand to revenue with attribution algorithms that reflect brand complexity
Brand measurement often feels like a polite performance nobody fully believes, and Guta learned this firsthand moving from performance marketing spreadsheets to startup rebrands that showed clear sales bumps everyone could feel. When she built Purple Metrics, she refused to pretend algorithms could explain everything, designing tools that encourage gradual shifts over sudden upheaval.More
177: Chris O’Neill: GrowthLoop CEO on how AI agent swarms and reinforcement learning boost velocity
Chris explains how leading marketing teams are deploying swarms of AI agents to automate campaign workflows with speed and precision. By assigning agents to tasks like segmentation, testing, and feedback collection, marketers build fast-moving loops that adapt in real time. More
176: Rajeev Nair: Causal AI and a unified measurement framework
Rajeev believes measurement only works when it’s unified or multi-modal, a stack that blends multi-touch attribution, incrementality, media mix modeling and causal AI, each used for the decision it fits. At Lifesight, that means using causal machine learning to surface hidden experiments in messy historical data and designing geo tests that reveal what actually drives lift. Attribution alone can’t tell you what changed outcomes.More