Marketing AI Blog
Insights and strategies for marketing leaders exploring AI
What AI Made Super Bowl Ads Reveal About CMOs and Brands
The Super Bowl doesn’t just showcase ads. It exposes how CMOs are making decisions about AI, brand, and leadership in public. As AI becomes more visible in marketing, the real differentiator is no longer the technology itself, but the judgment behind it. This piece explores what AI made Super Bowl ads reveal about brand alignment, human oversight, and the leadership choices CMOs are already making, intentionally or not.
Top 10 Shifts that CMOs and Marketing Leaders Should Plan for in 2026 & Beyond
In this podcast episode of the Marketing AI Pulse Monthly Brief, Aby Varma and Matt Cyr break down the top 10 shifts CMOs and marketing leaders must prepare for in 2026 and beyond. Moving past hype, the conversation explores how AI is reshaping agencies, brand discovery, creative, media, analytics, and leadership itself. The episode offers a clear-eyed look at what is already changing and where human judgment, governance, and decision quality will matter most.
Five Signals from Davos that CMOs and Marketing Leaders Should Really Pay Attention To
As AI took center stage at Davos, the conversation shifted from hype to execution. Leaders focused on results, operating models, workforce readiness, and human accountability as prerequisites for value. For CMOs and marketing leaders, the message was clear: AI success now depends on disciplined execution, not experimentation.
How Lean Marketing Teams Can Use AI to Do More With Less
Lean marketing teams are expected to deliver more without additional headcount. This blog explores how AI can help when applied with clarity and intent. It covers practical ways AI supports marketing writing, creative production, email, video, and automation, while highlighting why defined ICPs, real customer pain points, and thoughtful workflows matter. The focus is not on chasing tools, but on using AI to improve execution, reduce friction, maintain quality, and support sustainable marketing operations over time.
Why CMOs Should Pay Attention to Advertising Inside AI Models
As AI platforms like ChatGPT begin testing in-app advertising, CMOs face a critical new frontier in brand visibility and trust. This blog breaks down what’s changing, how major AI platforms are approaching ads, and what steps marketers must take to stay relevant in AI-driven discovery. With search, social, and display giving way to conversational intent, it’s time to reframe your strategy for the age of AI-powered attention. Relevance, not reach, now wins.
AI Resolutions for CMOs: What Marketing Leaders Should Commit to This Year
Many AI initiatives stall because they are treated as tools rather than leadership commitments. This article outlines the AI resolutions CMOs should commit to this year to move beyond pilots and experimentation. It focuses on clarity before capability, disciplined prioritization, governance that enables speed, role-based enablement, and fixing operational friction. The goal is to help marketing leaders embed AI into everyday workflows with confidence, reduce adoption anxiety, and deliver measurable business impact.
What Do CMOs Get Wrong About AI Adoption in Marketing
Many CMOs recognize the importance of AI but struggle to turn adoption into meaningful impact. The issue is rarely technology. It is how AI is framed, led, and embedded into marketing operations. This piece outlines five leadership mistakes that quietly derail AI adoption, from treating AI as a tool rollout to expecting ROI without changing workflows, and explains what successful CMOs do differently to make AI a durable marketing capability across teams and decision making.
Why Governance Enables Responsible AI in Marketing
Governance is the foundation that enables responsible AI use in marketing. It protects brand trust while giving teams clarity about what is allowed and how to move forward with confidence. This post explains why CMOs must own AI governance, how a marketing governance council operates, and the five pillars that ensure AI tools, use cases, inputs, outputs, and disclosures are managed responsibly and at scale.