Whenever Agentic AI comes up in agency conversations, the same pattern follows: interest, quickly followed by hesitation. Where’s the proof? What does it actually improve? How do we know it won’tcreate complexity instead of removing it? These are fair questions. Media buyers are responsible for outcomes, not experiments. They need to justify every decision, pound of investment, and any shift in strategy.
But buyer scepticism creates a Catch 22: agentic advertising needs adoption to prove its value, but adoption depends on proof already being there. The assumption is that the industry is still waiting for that proof to emerge. But that’s not the case, and it’s independent agencies who prove it.
The proof is already on the table
First, let’s define what we mean by “agentic”, as competing definitions surround this emerging technology. Agents are AI solutions that can independently execute decisions across complex workflows, capable of coordinating multiple functions in sequence rather than performing a single action in isolation.
For advertisers, this might mean an agentic system that can set up a campaign based on a brief delivered in natural language, monitor its performance over time, and automatically surface (or even action) recommendations to steer it towards defined KPIs. Agentic advertising takes the steering wheel for routine tasks, but the direction of travel is chosen by the user.
This streamlining is especially valuable for independent agencies that have historically been at a technical and resource disadvantage compared to their holdco peers. But agentic AI doesn’t just help independent agencies make up for their shortcomings, it also plays to their strengths.
Independent agencies build their reputations by providing individualised services delivered by specialist teams. This has often meant smaller teams and closer client relationships, along with a willingness to test approaches that larger organisations might take longer to validate. It’s an operating model well placed for putting agentic AI to work.
Today, cutting-edge independent agencies now regularly use agentic workflows to streamline campaign management and respond to performance signals while campaign activity is live. Work that once required multiple touchpoints and manual adjustments is now supported by systems that analyse signals and recommend changes as campaigns evolve. Buyers stay close to the campaign while execution accelerates. Early adopters are already reporting meaningful gains, including cost efficiencies of around 10% compared to traditional DSP economics, campaign set-up times reduced by as much as 87%, and CPMs coming in significantly below forecast.
Why smaller teams are moving faster
Part of this success is a simple matter of size. Big agencies have naturally more complex processes in place to effectively function at the scale that they do, which maintains operational unity but means decisions need to pass through more approvals. This is often also reflected in the operations of the large brand clients they work with.
Independent agencies, meanwhile, have the benefit of agility. Having lightweight teams (which may themselves have a lot of operational independence) makes it easier to experiment and implement new technologies, while smaller campaigns carry less complexity and risk than a multi-million pound contract.
In short, it’s the classic case of a big ship turning slower than a small one.
There is also a practical advantage in how these agencies approach technology. Rather than building systems internally, many choose to work with external partners that specialise in specific capabilities. That approach reduces the time between identifying a need and implementing a solution. It also allows agencies to adopt evolving technologies without committing to one partner and long development cycles.
This has clear implications for agentic AI. These systems are still developing, and their value often depends on how well they integrate with existing workflows. Agencies that are comfortable collaborating with multiple external partners are finding it easier to test, refine, and scale these capabilities.
Agentic advertising also depends on the quality of the inputs it receives. Agencies, technology partners, and media owners all contribute to the signals that inform decision-making.
Independent agencies have shown a willingness to engage closely with partners in this process through strategic data collaborations which are, again, easier to negotiate when there are fewer stakeholders. Those that have pursued more direct supply paths, connecting their own data infrastructure to supply-side partners without intermediaries, are finding that budgets go further, pricing becomes more transparent, and performance data becomes more reliable.
By doing so, they are able to access more transparent supply paths, gain clearer visibility into pricing, and work with more reliable performance data. That foundation supports more informed decisions and improves confidence in the outputs generated by agentic systems.
Different operating models come with different strengths. In this case, agility is helping some agencies move earlier and pick things up more quickly.
What agentic AI is actually changing
There is still a tendency to think of AI in advertising as a layer of automation. In fact, agentic AI introduces a new approach to decision-making.
Instead of relying on static rules or periodic optimisation, agentic systems continuously evaluate performance, identify opportunities, and suggest actions. The buyer defines the parameters, and the system operates within them, surfacing recommendations that can be reviewed and approved.
Crucially, this changes how time is spent across a campaign. Routine work, whether pulling reports, checking numbers, or making small adjustments across campaigns, becomes less time-consuming. That then creates more time to strategically shape a campaign’s direction or properly prepare for conversations with clients, making them more valuable.
Buyers don’t lose visibility either. Actually, they gain a clearer view of what is happening. The data is clear, and the reasoning behind each decision is fully traceable and therefore easier to understand.
The industry does not need to wait
What’s emerging from early examples is a clearer picture of what agentic AI can do in practice. Teams are finding they can work more efficiently, with less spend lost in the supply chain, while staying firmly in control of decisions.
What happens next will largely depend on who chooses to get involved now and how they choose to approach it. Those who take the time to understand how these systems work in real situations will have a stronger influence on how they evolve and will be able to capitalise on their return faster.
Some agencies will engage now, shape how these systems develop, and build a working advantage in the process. Others will adopt later, once the approach is familiar and the first-mover advantage has gone. Right now, it’s the independent agencies who are blazing the trail.
