7 posts in this category
I keep seeing AI tools pitch agencies on content volume. But if you've ever managed real client relationships, you know the harder problem is trust: isolation, permissions, context, and not leaking one client's thinking into another's.
Most conversations about AI and team design start with headcount. I think that is the wrong starting point. The better question is which functions your team needs — and those turn out to be the same whether you have four people or forty.
I spent years in advertising watching teams confuse motion with progress. Then I started building AI marketing tools and realized the problem was getting worse: faster execution, weaker judgment.
AI can now produce media plans, performance summaries, measurement frameworks, and campaign setups at impressive speed. The problem is not that the output is obviously bad. The problem is that it is often good enough to pass a casual review while missing the business context that actually matters.
Most teams still ask which model to use. From my experience, that is no longer the main question. If your AI system forgets the client or the brand, the category, and what good looks like, the smartest model in the world still starts every conversation from zero.
In 2023, I thought generative AI would flood search with cheap content and reduce the return on SEO. Three years later, that happened. But the bigger shift is that content production is no longer the moat. Structure, trust, QA, localization quality, and answer-engine visibility are.
The narrative that AI replaces entry-level work misunderstands what entry-level people actually do. In platform-heavy disciplines like activation, a junior isn't doing busywork — they're configuring targeting, QA-ing tracking, managing bid strategies. The real question is: when AI raises the floor for everyone, where does the advantage come from? Depth.