Is the Digital Agency Model Broken, or Are We Just Calling It the Wrong Thing? With Brent Weaver | Ep #920

Most digital agencies claiming to be "AI-first" have simply made their existing teams more efficient—they haven't fundamentally changed how work gets done. Brent Weaver explains why an AI-first agency and an agentic agency are two completely different operating models, and why trying to retrofit AI into existing teams often fails.

Drawing from E2M's experience transforming a 400-person digital marketing agency, Brent shares why building a dedicated AI-native team is more effective than forcing company-wide adoption, how agentic workflows are already replacing traditional roles, and why founders must protect their own thinking as AI becomes increasingly capable. If you want AI to create leverage instead of simply increasing output expectations, this episode outlines the structural changes required.

What You'll Learn

• The critical difference between an AI-first agency and a truly agentic agency.
• Why increasing AI adoption inside your current org chart rarely creates transformation.
• How E2M built a dedicated AI services division instead of retrofitting existing delivery teams.
• Why small "two-pizza teams" are the best starting point for marketing agencies experimenting with AI.
• The structural reason traditional teams struggle to innovate while maintaining billable utilization.
• How founders can leverage AI without outsourcing the strategic thinking that creates competitive advantage.

Key Takeaways

• AI acceleration is not the same as organizational transformation. Agentic agencies redesign workflows instead of simply speeding up existing ones.
• Existing delivery teams inside a digital agency are optimized to execute today's work, not reinvent tomorrow's operating model.
• Building a separate AI-native team creates the space, incentives, and culture needed for meaningful innovation.
• Start small by giving three or four AI-curious team members ownership of a specific business problem rather than asking everyone to change simultaneously.
• As AI handles more execution, human value shifts toward judgment, strategy, quality control, and contextual decision-making.
• The founders who continue developing original thinking while using AI as leverage—not a replacement—will create the greatest long-term advantage.

Are you running an AI-first agency and telling yourself you have made the shift, when what you have actually done is give everyone on your team a faster version of the same job? After months of AI training, are you wondering why the team still works the same way it always did?

Today’s featured guest currently serves a CEO of a white-label agency serving over 400 partner agencies worldwide. He came into the role specifically to go deep on AI, and what he found was not an upgrade to the existing model. It was a fundamentally different model. In this episode, he draws the line between an AI-first agency and an agentic agency, what that distinction actually means structurally, and what the parallel-build approach looks like inside a 400-person operation.

Brent Weaver is the CEO of E2M Solutions, a white-label digital agency with a team of over 400 people serving more than 400 partner agencies worldwide across web development, SEO, paid media, content, and AI services.

Before E2M, Brent built and sold his own agency, UGURUS, where he spent years coaching agency owners on positioning, sales, and growth. He joined E2M deliberately, partly because it gave him a forcing function to go all-in on AI at scale rather than observe it from the outside. Brent has been on the podcast before, discussing the sale of UGURUS, and how he found his next path.

In this episode, we’ll discuss:

• AI-first agencies vs agentic agencies

• Creating a separate AI services team

• What happens to thinking when you stop doing it?

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Sponsors and Resources

E2M Solutions: Today's episode of the Smart Agency Masterclass is sponsored by E2M Solutions, a web design and development agency that has provided white-label services for the past 10 years to agencies all over the world. Check out e2msolutions.com/smartagency and get 10% off for the first three months of service.

The Difference Between AI-First and Agentic, and Why It Matters

Most agency owners who say they have adopted AI have adopted AI-first. What’s the difference? Everyone on the team has an assistant, things ship a little faster, and the org chart looks the same, yet the billable expectations have quietly gone up.

That is not a transformation. That is acceleration inside an existing structure. An agentic agency is something different:

• The seats themselves are being replaced by agents.

• Front-end development: going to an agent.

• Standard WordPress builds: going to an agent.

• Content production: already 90 to 95 percent there.

The humans remaining in those workflows are the ones responsible for the quality layer and the decisions that require genuine contextual judgment. When describing this structure, Brent is actually describing what E2M is already building inside its own teams, through dedicated agentic squads that operate separately from the traditional delivery team.

Why Retrofitting Your Existing Team Is the Wrong Move

E2M spent nine months doing what most agencies do: mandatory training, dedicated Fridays, internal incentives, and all-hands sessions. The team showed up, but the adoption did not follow.

The problem was structural, not motivational. You cannot ask people who have spent years developing expertise in a specific workflow to simultaneously do that workflow at full speed and fundamentally question whether the workflow should exist. Those are incompatible demands.

Brent uses the Blockbuster parallel: Netflix did not ask its DVD fulfillment team to build a streaming platform. It built a separate team for a separate business. The skills required were different. The mindset was different. The incentive structures were different. What E2M found was identical: the AI services team they launched separately now approaches 100 people, and those people think about work in a way that the traditional web and SEO teams simply do not.

The Two Pizza Team as the Starting Point

For agencies that are not 400 people with the runway to build a parallel operation, Brent's practical starting point is the Jeff Bezos two pizza rule: a team small enough to feed with no more than two pizzas. Take the three or four people in your agency who are most curious about AI, most willing to experiment, and least burdened by how things have always been done. Give them a specific problem to solve. Isolate them from the expectation of full billable output and let them build.

The reason isolation matters is the same reason the parallel builds at E2M matter. When an AI-native team is embedded in a traditional delivery team, the culture of the traditional team absorbs them. They get pulled into existing workflows to cover capacity. The experimental mandate loses to the billable mandate every time. A separate squad with a separate objective is not a luxury for large agencies. It is the structural condition that allows genuine new capability to develop without being smothered by what the business already needs today.

What Happens to Thinking When You Stop Doing It

Before AI, Brent recalls he used to write a thousand words a day. Clear writing, shaped by years of intentional practice including time inside Amazon's writing culture. He has noticed that skill is atrophying as he routes more cognitive work through AI.

A New York Times study of 350,000 college admissions essays found the same pattern at scale: AI-assisted essays read as more polished and were rated higher by reviewers, but contained fewer novel ideas, fewer dynamic arguments, and less of the fringe thinking that produces genuinely new directions.

Should we feel alarmed? Agency founders are creative, resourceful, fast-thinking problem solvers, and those traits make them the people best positioned to direct AI rather than be directed by it. The risk is for founders who hand over their thinking entirely, as they will end up with polished output from borrowed ideas. The ones who stay in the seat as directors, using AI to build what they can envision rather than to do the envisioning for them, are the ones who will compound the fastest in an environment where the tools keep getting stronger.

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