Agentic AI and the Future of Agency Leadership

Agencies are racing to announce agentic AI capability. Omnicom has already executed live media buys for clients through an agent-to-agent framework, negotiating directly with publishers and bypassing part of the intermediary chain that has always reduced ad tech margin. Stagwell unveiled The Machine as an agentic operating system. Every major holdco has a version of this story now.

The conversation about what this means rarely gets past the demo. The question that interests me is what happens to agency operating models and client relationships once agents take on real operational work, not just assistance with a task, but ownership of outcomes, inside organisations that still run on people, hierarchy, and trust. The structural and relationship implications are much bigger than the press releases suggest.

The potential transformation of junior roles

If agents start taking on real operational work, agency structure has to change with it, and the group most exposed is junior staff. Historically, juniors carry the manual, repetitive load, while mid-level managers get stretched between strategic work and people management. Both groups have real efficiency opportunity ahead of them, but only if it's handled deliberately.

For juniors, the answer is not fewer entry points into the industry, it's a different one. Give them the education to build and manage their own agents, and their expertise shifts rather than disappears. In practice, the junior role becomes one of monitoring and staying current, tracking a technology landscape that is moving fast enough that keeping up with it is a genuine, valuable specialism in its own right. Juniors become the internal subject matter experts the rest of the agency relies on, which is a stronger position than the one they're in today, not a weaker one.

For middle management, the opportunity is different: a huge amount of what stretches this layer thin is reporting, scheduling, and coordination overhead. An agent that can look across an organisation's meetings and workflows and identify what's necessary, what's functioning and what isn't, hands time back for the strategic and creative work that got squeezed out in the first place.

What changes for leadership

Handing operational work to agents does not remove the leadership job, it changes what the job consists of. Governance has to happen earlier: what agents are permitted to do, what data they can access, and who controls that. Not everyone in an organisation should be able to see a client's budget or financial targets, and if juniors are building their own agents, that access boundary has to be designed deliberately, before anyone starts using them.

The more interesting shift is in how that governance gets built. Rather than leadership dictating the answer, junior and mid-level staff can be commissioned to go out and understand what the technology can currently do, where the existing process breaks down or runs inefficiently, and bring back ideas for what agents could fix. Leadership's role becomes assessment: applying the experience of what has and hasn't worked operationally, guiding the ideas that get built, and making sure all of it serves the client's interest first. It's the same leadership principle that's applied for years, that a confident leader doesn't need to know everything themselves, their job is to set direction, incentivise the team, and get everyone working toward the same goal. Agentic AI doesn't change that job. It just gives leadership a new, considered focus.

How can agencies work with clients using AI?

Clients want to see impact on their objectives first, tied clearly to their own timeframes, and that doesn't change because agents are involved. What does change is the trust conversation sitting underneath it: data security, and transparency about what's being built and used on their account.

This opportunity extends beyond risk management. Building custom agents specifically for a client can deepen the relationship rather than threaten it, pulling the client further into the agency's process and making the relationship harder for either side to walk away from. Clients increasingly understand that AI isn't just a mechanism for cutting people, and that doing it well still requires people, just doing different work than before. What clients need from their agency is confirmation that this is being handled openly rather than kept from them, that risk is being managed thoughtfully, and that all of it stays oriented around their goals as the primary aim.

From sequential process to concurrent workflow

The day-to-day shape of an agent-augmented team looks different depending on the type of agency, media, creative, or production, but one pattern holds across all of them: work becomes more cross-functional, and more oriented around the client relationship than it has ever been.

Advertising has historically run as a linear chain: brief, ideation, production, media execution, in that order, with the media plan ideally arriving earlier but often not. The recurring failure in that model is that problems which should have been caught early only surface at the end. A multi-market campaign discovers a regulatory conflict or a cultural misstep in one territory only once the work is nearly finished, because nobody checked early enough.

An agentic operating system changes that by making the process concurrent instead of sequential. An operator can pull a media plan, share it with a cultural-advisory agent, flag concerns up front, and have media and content teams working the same problem at the same time rather than in a queue. That requires a new role: an operator who pilots the agentic system at campaign level, sitting across functions rather than owning one of them, reducing errors and catching problems before they become expensive.

Patience and intentionality

The obstacle to making this work is not the technology, it's what agencies chronically underestimate: the time and financial investment required to build it properly.

There are three further barriers beyond that one. Creative and media functions have historically operated in silos, even inside agencies that have tried to integrate them, and getting all of those parties to contribute to a shared agentic system takes more political work than the historical model ever demanded. Staff need to understand what their own role becomes in this new structure, so the change doesn't read as replacement, and they need to be brought into the process rather than have it done to them. And clients need a realistic understanding of the timeline. Building this safely, confidently, and effectively takes real setup time, and that runs against the fast-paced habits the media and advertising industry has trained everyone into.

Where to start

The temptation is to start by deploying agents somewhere visible. The better starting point is diagnostic: understanding the agency itself properly first. Where are the strengths and weaknesses, where is the agency making money and where isn't it, where are clients genuinely happy and where are they not. That diagnostic work should be happening anyway, agentic AI or not, and it's what lets an agency identify real quick wins rather than guessing at where automation might help.

What success looks like

Three to five years out, the agencies that get this right will have a clear, integrated proposition they can explain to clients: who is doing the work, agent or human, and why. The normal indicators of a healthy agency still apply, client retention, growth, low escalation rates, because the industry remains intensely competitive and clients will leave if the service they're getting isn't right.