How AI Can Transform the Client-Agency Compensation Model

By Meredith Cuevas, Archer, Chief Growth Officer

So far, most of the chatter about generative AI in Adland has settled around the impact on job security and creativity, with a fair amount of deserved hand-wringing about the potential of broader societal harm. Much of the commentary has been anthemic, even alarmist in some cases. It is time for our industry to shift the conversation to street level or perhaps suite level, as in C-Suite. This includes thoughtful discourse on how AI will impact agency infrastructure and resources, and how, in turn, those impacts will affect agency-client compensation models.

Let’s use this inflection point of technological innovation to formulate and accelerate the move away from full-time equivalent (FTE)-based hourly compensation — historically charging clients according to the total hours put or estimated against a client’s business — to more transparent and accountable outcomes-based pricing. This would necessitate agencies incorporating the value and cost of evolving AI-infused tech stacks into these new pricing models.

Forrester Research has presented one version that is referred to as a HumanTechnology Equivalents (H/TEs) model — one that accounts for human plus technology that will actually be more cost-effective for clients while also helping agencies manage their P&Ls. Another FTE alternative would be to adopt a subscription model like some ad tech companies have done, and that Wall Street has rewarded because of the consistent, recurring revenue generated. This would put more emphasis on the tooling and tech and less on the human element than the H/TE model. Whatever “value-based pricing” approach your agency adopts, the goal is to break free from being commoditized in the FTE model.

The key to making any new model work is, of course, what is always central to any client-agency relationship: better communications and dialogue that are transparent and in good faith. This means agencies being more forthcoming around what is required to meet client objectives and KPIs, and not being shy about protecting agency profit margins. On the flip side, clients need to be more willing to have an honest appraisal of what is necessary to invest to fulfill those KPIs. This means clients cannot assume that AI will automatically lower agency costs. In fact, many, like veteran agency search consultant Joanne Davis, posit that AI will be a catalyst to generate more billable value per FTE and not necessarily reduce FTE headcount.

Another key consideration that could be critical to a successful transition to a value-based pricing model is deciding who will lead this effort organizationally. So far, from what I’m hearing, AI is being folded into the remit of the Chief Technology Officer or into the scope of IT departments. Because of the emerging legal and compliance challenges associated with AI, we can expect the drumbeat for a single “Head of AI” to get louder. Whoever it may be, it is critical that they are brought to the table in early discussions with the client to ensure technology requirements are well-documented, considered and priced appropriately.

These decisions need to be made thoughtfully and in a way that is organic to the ethos, culture and infrastructure of your particular organization. While AI as a practical construct will be disruptive, the integration of it should be done with the least amount of disruption possible to create the best outcomes for clients and agencies.