The Agency Retainer Review: How to Evaluate Whether Your Marketing Agency Is Delivering Value in a World Where AI Has Changed the Discovery Landscape

The quarterly agency retainer review follows a familiar script in most marketing organizations. The agency presents coverage data, campaign metrics, and activity reports. The client team reviews the deliverables against the agreed scope of work. Discussion focuses on what went well, what underperformed, and what changes should be made to improve results next quarter. The retainer is renewed or renegotiated based on the agency's performance against last quarter's objectives.

This review process has one fundamental flaw in the current marketing environment: it evaluates the agency's performance against objectives that were defined before the AI discovery landscape became commercially significant — and it doesn't ask whether the agency's program is building authority in the channels where the brand's best prospects are conducting their research today and increasingly tomorrow.

The backward-looking retainer review gives passing grades to agencies that are executing their traditional programs competently while the discovery landscape shifts beneath them. The forward-looking retainer review asks a different set of questions — and the honest answers to those questions reveal significant gaps in most agency programs.

Question 1: What is our current AI Visibility Score, and how has it changed since we began our agency relationship?

If your agency cannot answer this question — if they don't have AI Visibility Score data, don't track what ChatGPT, Claude, and Gemini say about your brand in response to target queries, and don't have competitive benchmarking against your category peers — then they have not been managing your brand's presence in the fastest-growing discovery channel during your entire agency relationship.

This is not a minor gap. It means that for every month of the agency relationship, your brand's AI visibility has been either improving or declining without your agency knowing which, why, or what to do about it.

Question 2: What percentage of our agency investment is producing AI training data authority, and how does this compare to the share of target audience research that is AI-mediated?

If 20% of your target audience's product or vendor research is now AI-mediated and trending toward 40% — and if your agency investment produces less than 5% of its value in AI training data authority — then the allocation mismatch between your agency investment and the discovery channel mix your audience is using is significant and growing.

Question 3: Which specific editorial publications are we consistently appearing in that carry high domain authority and AI training pipeline coverage for our target query categories?

Not all media placements contribute equally to AI training data authority. Your agency should be able to identify the specific publications in your editorial program that carry the highest AI training data authority for your category — and demonstrate that a meaningful proportion of their placement activity is in those high-authority publications.

Question 4: What is our Share of AI Voice relative to our top three competitors, and what is our trajectory?

Share of AI Voice — how often your brand appears in AI responses to category queries relative to competitors — is the competitive intelligence metric that tells you whether your discovery position is improving or declining. Your agency should be tracking this metric. If they're not, you don't know whether you're winning or losing the AI discovery competition.

Question 5: What specific changes has the agency made to our program in response to the shift toward AI-mediated discovery?

The agency's answer to this question is diagnostic. If the answer is "we've added AI monitoring to our reporting" or "we're watching the AI space closely," the agency has not materially adapted its program. If the answer includes specific changes to publication targeting strategy, content architecture for AI extraction, knowledge infrastructure investments, and Share of AI Voice tracking — the agency has begun the adaptation that AI-era marketing requires.

After conducting a forward-looking retainer review using these questions, most marketing teams will reach one of three conclusions:

Outcome 1: The agency is adapting. A small minority of forward-thinking agencies have built GEO capability alongside their traditional disciplines — adding AI visibility monitoring, adjusting publication targeting for AI authority, and developing editorial content programs specifically designed to build AI training data. These agencies are building the programs their clients need for AI-era discovery. Renew and deepen the relationship.

Outcome 2: The agency is willing to adapt. Some agencies don't yet have GEO capability but are genuinely willing to build it. If your agency relationship is strong and the traditional program has delivered value, working with the agency to integrate GEO into their program — potentially with AHOD as the specialized GEO platform they operate — can be the most efficient path to closing the AI visibility gap.

Outcome 3: The agency is not equipped to adapt. Many agencies are structured around traditional disciplines and compensation models that are not compatible with the GEO capability their clients increasingly need. If your retainer review reveals that the agency has no path to GEO capability and no genuine commitment to developing it, the case for replacing or supplementing the agency relationship with a specialized GEO platform is clear.

AHOD works with marketing teams in all three situations — as a standalone GEO platform for brands transitioning from traditional agency models, as a specialized GEO capability layer alongside existing agency relationships, and as a measurement and strategy resource for the forward-looking retainer reviews that are increasingly common at brands serious about AI-era discovery.

Conduct your forward-looking agency retainer review with AHOD data →