AI Agents Are Changing How Media Work Gets Done

AI agents are often framed as an efficiency layer. In practice, their real impact is more fundamental. When media specialists are given the ability to build no-code automation workflows on top of an enterprise-level LLM, execution is no longer locked into certain tools or fixed processes. The way campaigns are built, checked and activated can change as conditions change – without waiting for new tools, development cycles or platform updates.

It isn’t about replacing practitioners. It’s about removing unnecessary steps between a decision being made and that decision being implemented. When specialists can translate intent directly into action, they’re able to respond at the same pace as the platforms they operate within.

Most media teams already use automation, but it is typically embedded within platforms or constrained by rigid tools. While this reduces manual effort, it also limits flexibility. Decisions are automated, but the underlying logic remains fixed.

AI agents built on an organisation’s enterprise LLM change that dynamic. Instead of relying on platform defaults, teams can encode their own standards, frameworks and guardrails into workflows – and update them as requirements evolve.

This gives teams direct control over how automation is applied, rather than inheriting logic defined by platforms or tools.

The real step-change comes when AI agents are accessible through no-code interfaces. Media specialists no longer need to brief developers or wait for technical support to adapt workflows. They can build, test and refine automation themselves.

Take campaign creation as an example. A specialist can communicate directly with an AI agent using prompts aligned to brand guidelines, channel strategy and market-specific requirements. The agent builds campaigns using bespoke templates – applying naming conventions, structuring ad sets, assigning budgets and selecting formats – ready for the specialist to review and activate.

The specialist remains accountable for the strategy and campaign decisions. The agent removes the executional effort. 

Once embedded, these agents can support a wide range of execution tasks that typically slow teams down:

• Campaign QA and governance: Agents can check campaigns against internal standards before launch, flagging issues early rather than relying on post-launch fixes.

• Surfacing insights: Instead of waiting for scheduled reports, agents can monitor performance continuously and surface meaningful changes, anomalies or emerging patterns as they occur.

• Scenario testing: Media specialists can ask practical “what happens if” questions – adjusting budgets, formats or creative mix – and receive directional guidance based on historical and live data.

Not all AI agents are equal. The distinction between generic tools and agents built on an organisation’s enterprise LLM is critical.

When agents are trained on internal data, frameworks and measurement logic, they reflect how the organisation actually operates. Decisions align with established standards, commercial realities and performance principles – rather than platform assumptions or generic best practice.

As platforms accelerate execution and reduce manual control, advantage shifts toward teams that can adapt how work is done without delay.

Providing media specialists with no-code AI agents doesn’t just increase efficiency. It shortens the distance between insight and action, allows teams to adjust workflows as conditions change, and reduces reliance on rigid tools that struggle to keep up.

In an environment where speed is no longer optional, that shift becomes decisive.