I recently ran a morning session at the ICOM World Meeting in Porto on the new operating model for advertising agencies. ICOM is the global network for independent agencies and the room was full of agency founders and leaders from markets around the world. A big part of the session focused on a scenario modelling exercise where we used Claude to map potential future dynamics for independent agencies and which led to some fascinating conversation. But for this post I’m going to focus on one of the big themes of my talk – how an operating model can be designed to bring AI and human capability together in a way that compounds value over time.
Sam Altman said this in 2024: ‘95% of what marketers use agencies, strategists, and creative professionals for will easily, nearly instantly, and at almost no cost be handled by the AI’. It is, I think, a classic example of something I wrote about here, the seeming inverse relationship between confidence in automation predictions and proximity to the actual work. Looking at industries/occupations from a distance often leads to a tendency to underestimate the hidden complexities involved in that work. In an oft used example, Geoffrey Hinton (the so-called ‘Godfather of AI’) said in 2016 that in a few years we wouldn’t need Radiologists any more because AI would be able to look at X-rays and CT scans and recognise patterns that could lead to an effective diagnosis. But there are more Radiologists now than ever before. And that’s because the job of a Radiologist is not only about looking at X-rays. They have to deal with patients, make them comfortable, triage signals from multiple sources to generate an effective diagnosis, work with other health practitioners to manage the patient’s progress. The role of a Radiologist is a bundle of tasks, some of which may be able to be automated by AI but some of which won’t be. Jobs that have specific tasks which are less dependent on other parts of the job can more easily be unbundled and so are more likely to be heavily impacted. Jobs where there is a high coordination cost to unbundling (the market buys all the interdependent parts of the job as a bundle) will stay human.
The same is true of an ad agency. An agency is a bundle of tasks, some of which can be automated, and some of which can’t. For decades, the agency model worked because the links in the chain held together. But the problem for agencies is that AI is breaking several links in the chain at once. Channel expertise is becoming software, production capacity is becoming commoditised, the talent pipeline is coming under pressure as AI is replacing tasks that were once done by juniors. Richard Rumelt called this a chain-link problem. An organisation’s performance is limited by its weakest subunit or link in the chain, and making improvements to other links in the chain yields little benefit if the weakest link isn’t fixed. The classic illustration Rumelt uses is American carmakers in the 1970s and 1980s, trying to compete with the Japanese on quality. Detroit couldn’t match Toyota by improving any single link in the chain (design, components, assembly, supplier relationships, dealer network, management). Each link was tightly coupled to the others, and so improving just one didn’t produce better cars. That’s why Detroit struggled for so long despite spending heavily on individual improvements, and why Toyota’s quality position was so durable.
A personal view here but for agencies, I think that the weakest link in the chain is the pricing/remuneration model. Selling time creates a perverse logic in the age of AI. If an agency can compress a two-hour task into thirty minutes you’re earning less for potentially better outcomes. The faster you get, the less you make. This holds back almost everything else. Agencies can invest in AI tooling, redesign workflows, hire hybrid talent, and reposition around judgment and orchestration, but while revenue is tied to hours, those gains leak straight back to the client through lower fees. Margins compress. Reinvestment gets harder to justify. The operating model can’t evolve because the commercial model won’t let it. Pricing isn’t the only link that needs rebuilding, but it’s the one that determines whether rebuilding the others actually pays off. Get pricing wrong and the gains you make everywhere else leak straight back to the client.
But pricing on its own doesn’t get you there. If AI is disrupting multiple links in the chain you have to rebuild the whole chain or you get nothing. That’s why AI is not an efficiency story, it’s a remodelling story. So what does a new, AI-enabled, operating model look like for agencies? For my ICOM talk I chose to focus on the difference between AI as consumed value and AI as compounding value. The former is where most of the focus is right now – staff and teams giving inputs to AI and getting outputs. AI as productivity gains, but with little or no accumulation of value over time. If agencies are ever going to get the true benefits from AI they need to design operating models that compound capability.
This begins with an appreciation of how AI can be brought together with human capability in a way that is greater than the sum of the parts. LLMs and humans learn and create value in fundamentally different ways. The former uses pattern recognition and predictive sequencing from huge datasets, excels at inductive reasoning from many examples, learns from explicit knowledge and literal understanding. It has no fundamental motivation to learn, and no accountability. Humans learn in multi-sensory ways by interacting with the world around us, excel at abductive reasoning and continuous adaptation, develop tacit knowledge and are good at handling complete novelty, abstraction, semantic meaning, competing objectives. This means that there needs to be deliberate design choices in workflows (and agent design) about the specific role of humans and AI but also in how hybrid teams of humans and AI agents are set up to succeed. An automated workflow that requires little or no human intervention should be designed a lot differently to a process where the human is the driver and AI is playing specific roles at key points. Equally, a hybrid team of humans working with AI agents will fail if there is no trust in AI results, no ability to challenge outputs, no alignment around clearly defined, shared objectives, no explicit ownership for monitoring, correcting and learning from AI behaviour, and the whole system isn’t focused on value and results rather than just velocity.
But building compounding value into the system is not just about how you bring humans and AI together, it’s also about the operating model itself. In Craig Hepburn’s five layer ‘intelligence stack’ he noted that it was the context layer (or how businesses organised their knowledge for AI to draw on) where the real value and advantage lived. Knowledge is now not something to be managed. It needs to be architected as a living system.
People bring judgement, taste, creativity, orchestration. Agents bring automation, sequencing, execution, support. Together they work in project spaces or collaborative tools, drawing on two kinds of persistent context: agency-level and client-specific. The agency level context feeds the agency’s unique methodologies, ways of working, IP, historical learning into every brief, every pitch, every strategy, every project. As agency capability and learning expands and evolves, so the value of this context compounds. The next project begins with a richer understanding of how to improve ways of working, the one after richer still.
A client or brand brain is a persistent context that contains everything needed to seamlessly incorporate brand guidelines, audience insights, tone of voice, performance data and even client stakeholder personas into everything that the agency does. This makes it systemically easier for agency teams to draw client-specific context and understanding into their work, but it also compounds in value over time. The more work that gets done and put out into the world the more context there is to build on over time, and the more unique understanding is inherently built into the agency’s work for that client. Every project leaves your brand brain richer than it found it. The next brief starts from a smarter foundation, the one after smarter still. The agency that has been running hundreds of projects over months and years for a client develops a depth of understanding that is very hard for another agency to replicate. And that’s competitive advantage.
This is also why the pricing question matters so much. Compounding capability is only valuable to the agency if you can actually capture it. Build richer brand brains, deeper context, smarter workflows, and then bill by the hour, and you’re handing all that accumulated advantage straight to the client.
This is an operating model but it’s also a philosophy for how humans and agents can work together to reinforce value and build momentum over time. The key shift in thinking is to go beyond seeing AI as simply a route to productivity and efficiencies and to start seeing it as an asset that compounds value and capability over time.
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Photos by Edge2Edge Media, Evgeni Adutskevich on Unsplash
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