AI Doesn’t Make Agencies Less Relevant. It Makes Them More Necessary.              

By Calvin Nichols, Go Fish Digital, SVP-Product Development

There’s a familiar refrain happening inside marketing organizations right now: “We need to use more AI.”

The client point of contact hears it, nods, and then goes back to doing the other 14 things already on their plate.

That’s the problem.

Marketing leaders and client POCs are being asked to “scale with AI,” “find efficiencies with AI,” and “just use AI” without necessarily being given the time, training, infrastructure or expertise to make it part of how the organization actually works. The expectation is to do more with less, but the resources required to change how work gets done haven’t necessarily kept up.

That’s where agencies have an advantage.

Companies have been hiring agencies for decades because they can’t, or don’t want to, maintain every specialized capability in-house. Media buying is one example. Website development, creative production, analytics and content are others. The agency model works because clients can bring in deep expertise when they need it, without having to build and constantly maintain that expertise themselves.

AI readiness is simply the next version of that model.

The agencies that are leaning into AI aren’t just adding another tool to the stack. They’re figuring out how AI fits into workflows, technology, data, talent and decision-making. They can lean in heavily when a client has a specific need and lean back when it doesn’t.

And agencies have had a particularly strong incentive to figure this out quickly.

They felt the pressure early. Clients wanted more output, faster. Budgets were under pressure. “Do more with less” stopped being a slogan and became an operating requirement. That made forward-looking agencies an early testing ground for AI: trying tools, connecting platforms, automating workflows, breaking things, fixing them, and constantly reevaluating what actually works.

That last part matters.

Because using AI is easy. Using it well is not.

We’ve already seen what happens when organizations approach AI as a collection of shiny new tools rather than as part of an operating model.

Dashboards can produce different answers from one week to the next, leaving both stakeholders and the person who built the dashboard wondering what changed. Teams use sophisticated AI models for tasks that a cheaper, faster and more reliable tool could handle. Sensitive information gets put into systems without enough thought about permissions, privacy or personally identifiable information.

None of these are futuristic problems. They’re happening now.

And there’s another issue that tends to get overlooked: cost.

Yes, relatively nominal AI costs have been a boon for experimentation and adoption. We’ve  been living through a subsidized golden age of AI pricing. But it can’t last forever. We’re already hearing frontier stories of companies blowing their budgeted allocation and getting hit with exorbitant bills wholly unexpected. Building an important business workflow around the assumption that tokens will always be cheap is not a particularly resilient strategy.

The smarter question isn’t, “How cheaply can we do this with AI today?”

It’s, “Would this workflow still make sense if the economics changed tomorrow?”

That’s where an experienced agency can bring something different to the table. Agencies are building AI-ready workforces because the market is forcing them to. They’re not just teaching people how to prompt. They’re figuring out where human judgment belongs, where automation belongs, which tools should connect to which systems, what data can safely move where, and when AI simply isn’t the right answer.

The opportunity isn’t to replace the in-house team. It’s to give that team an operating partner that has already been through more of the experimentation curve.

And this is where the agency model may prove surprisingly durable in an AI-first world.

The companies that get the most value from AI won’t necessarily be the ones with the most tools. They’ll be the ones that make AI a foundational part of how they work—across people, processes, technology and decision-making—without turning every employee into an AI systems architect.

That’s a workforce and operating-model challenge, not just a technology challenge.

And it’s one agencies are increasingly equipped to help solve. At least the ones who are already visualizing and implementing what an AI-ready workforce actually looks like—and how companies can structure people, processes and resources to make AI a foundational capability rather than another item on the to-do list.