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In our new AI-First Agency model, we are selling systems that create unprecedented efficiency. This efficiency is our greatest asset, but it is also our greatest liability. Why? Because it creates a gap of understanding. Our clients are no longer
As an agency owner, I’m obsessed with finding and eliminating “drains”—the low-value, high-time tasks that kill your profitability. For years, my biggest drain was end-of-month client reporting. It was a 3-4 hour manual process per client that my team dreaded.
For my entire career as an agency owner, the billable hour was my foundation. It was a simple, “fair” way to trade my team’s time for a client’s money. Today, that foundation has crumbled. An AI agent can now perform
In my last article, I drew a hard line between the “AI-Assisted” agency (a commodity) and the “AI-First” agency (the future). The assisted agency’s tech stack is simple: a subscription to a frontier AI model. But as I argued in
As an agency owner, you are in one of two camps right now. You are either using AI to do your old job faster, or you are inventing a new job entirely. This is the most important strategic distinction of
For the last five years, the marketing agency model has been built on a simple, profitable lie: that time equals value. We sell our services in billable hours, retainers based on blocks of time, and project fees calculated by how
In my work, I’ve found that once leaders understand the theory of RAG, their next question is immediate: “Great. How do we build one?” The good news is that you no longer need a team of Ph.D.s in machine learning
In my strategy work, the most common misconception I encounter from finance and operations leaders is that “RAG is the cheap option.” On the surface, it makes sense. You are just using an off-the-shelf AI model and pointing it at
In my conversations with technical leaders, the discussion around AI architecture is often framed as a binary choice: RAG or fine-tuning. Teams spend weeks debating which path to take, treating them as mutually exclusive options. From my perspective, this is
In my strategy sessions with executives, “fine-tuning” is one of the most misunderstood terms in AI. It is often presented as a magic button to create a “smarter” AI. This leads to a dangerous question: “How much does it cost