Leaders, Laggards, and the Walking Dead: Why Most Agencies Are Already Behind on AI

You cannot always tell which agencies are truly transforming with AI and which are simply talking about it. But new survey data from agency executives reveals a clear reality. While AI momentum looks strong on the surface, two-thirds of agencies remain stuck in early discussion phases or ad hoc experimentation. Only a small minority have embedded AI meaningfully across their operations.

That contrast with last year’s optimistic “full-scale AI adoption” headlines tells a different story. The groundwork, the experimentation, the failed prototypes, and the workflow redesign are largely invisible from the outside. But advantage compounds in quiet, small steps. A gap that feels faint today will be unmistakable within a year and a chasm within eighteen months.

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The Walking Dead Are Not Who You Think

The most vulnerable agencies right now are not the ones ignoring AI. They are what I call the Walking Dead, and they do not know it.

These are the agencies that appear active. They have formed committees, bought enterprise tool licenses, encouraged experimentation, attended webinars, and are monitoring developments. Despite all of that, they have not changed how real work gets done. There is movement, but no transformation.

This matters because once AI agents become reliably plug and play across workflows, the agencies with built-up operational muscle will accelerate. Those still writing readiness assessments or debating use cases will be left behind.

Survey responses from agency leaders reinforce this pattern. While many are experimenting, very few measure AI’s impact in any structured way. Nearly half do not measure AI outcomes at all, and more than half say they only have generic talking points rather than a differentiated point of view on how AI will shape client value.

The Squeeze Zone

AI is democratizing capabilities that once required scale, infrastructure, or specialist teams. This has created a Squeeze Zone, where mid-size agencies face pressure from both ends.

At the top, holding companies are investing heavily in proprietary models and vertically integrated AI capabilities. At the bottom, small AI-native shops of one to ten people are winning business by producing work that rivals far larger teams. In one recent pitch, a freelancer using a few low-cost AI tools outperformed multiple established agencies for a global campaign brief.

Between those extremes sit generalist agencies, usually around twenty to two hundred people, who are too small to compete on scale yet too slow to match AI-enabled specialists. This segment must move fastest or face margin compression and long-term competitiveness issues.

Red Flags: Activity Without Achievement

Several recurring patterns signal when agencies are stalling out:

The Steering Committee to Nowhere.
An AI Council meets regularly, but no live-use cases ever ship. If the group is not accountable for delivering working AI-enabled workflows, it becomes drag rather than propulsion.

Pilot Purgatory.
Teams test tools without clear success metrics or business outcomes. Experimentation without measurement is indistinguishable from motion without progress.

The Optional Learning Fallacy.
Lunch and learns as well as optional training sessions, rarely build real capability. Curiosity-driven adoption leads to uneven skill development that benefits individuals more than the organization.

Shadow AI.
Without clear policies, employees use AI tools on personal accounts or without data governance. This creates risk but does not produce institutional benefit.

The Seniority Flip

A growing dynamic is what I call the seniority flip. Junior practitioners often use AI more fluently, operating at a faster iteration pace, while senior leaders may remain cautious, unfamiliar, or skeptical.

This becomes a bottleneck. Senior leaders have the judgment, institutional knowledge, and client context, but without practical AI fluency, they may slow down work that could otherwise accelerate. The solution is not replacing senior talent. It is equipping them to amplify their experience through AI.

A Twin Track Framework

Agencies tend to fail AI transformation in one of two predictable ways. Some try to leapfrog the fundamentals by buying tools and expecting instant results. Others descend into long planning phases that never translate into real-world adoption.

A more durable approach is what I call the Twin Track model.

Track A: Capability Building
 Upskill practitioners, build experimentation cultures, and develop internal AI champions. This work is methodical and sometimes slow, but it creates lasting capacity.

Track B: Impact Delivery
 Deploy high-impact workflows that deliver measurable outcomes, whether through internal initiatives or external AI-native partners. This track proves value quickly, captures efficiencies, and strengthens competitive positioning.

One without the other fails. Capability building alone is too slow. Impact alone is not sustainable.

The Window Is Open

Nearly all traditional agency work is trending toward commoditization. But the work AI cannot replace, the narrow portion that depends on creativity, strategy, and context, is where differentiation will be built.

Every month of capability building widens the advantage for early movers. Meanwhile, AI is expanding the overall opportunity in advertising and marketing, not shrinking it. Productivity gains are increasing the size of the market itself.

The next several years represent a rare window. Agencies that act now will shape client expectations, strengthen positioning, and attract top talent. AI is democratizing access to capabilities that once required significant infrastructure. Technology will be widely accessible, but operational maturity will not.

The question is not whether AI will transform the industry. It already has.
The question is whether you are among the agencies defining the transformation or reacting to it.

The window is open for those who choose to move.

About The Author Of This Article

David Mainiero is Chief AI Officer at AI Digital Labs

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