AI agents are starting to reshape how enterprise marketing teams operate, shifting the technology away from creative support tools and toward operational infrastructure. Across large organisations, marketing leaders are under pressure to manage complexity, control costs, and shorten production cycles as campaigns run continuously in more channels. A recent funding round for adtech startup Fluency shows how AI-driven marketing agents are beginning to take on that operational role inside enterprise marketing teams.
Fluency has raised $40 million to expand an AI-driven platform designed to automate performance marketing workflows in major advertising channels. While the company positions its technology around “AI agents,” the underlying value proposition is more familiar to enterprise leaders: reducing manual work, standardising processes, and improving consistency in teams operating at scale.
Marketing operations under strain
For large brands, performance marketing has become an increasingly operational function. Campaigns run continuously in search, social, short-form video, and retail media platforms, each with its own formats, optimisation rules, and reporting requirements. As a result, marketing teams often rely on specialised operators for each channel, supported by layers of tooling and manual processes.
The structure creates friction. Campaigns are frequently rebuilt from scratch, optimisation decisions depend on individual expertise, and small changes can require multiple hand-offs between teams. At scale, the issue is not creative output but coordination, repeatability, and time spent on routine work.
Enterprises are now looking to AI to address those constraints, not by replacing marketing strategy, but by absorbing repetitive tasks that slow execution. That places tools like Fluency’s in a different category from earlier generations of marketing automation software.
From tools to embedded systems
Rather than acting as a standalone optimisation layer, Fluency’s platform embeds AI agents directly into campaign workflows. Agents handle tasks like campaign setup, testing, optimisation, and iteration in platforms, operating in parameters defined by the organisation.
The distinction matters for enterprise use. Instead of marketers actively managing every adjustment, AI systems take on day-to-day execution, while humans focus on oversight, performance review, and higher-level decisions. This is consistent with patterns appearing in other enterprise sectors, like IT operations, finance, and customer support, where AI is increasingly acting as a background operator rather than a front-end interface.
For marketing leaders, this approach promises a way to scale output without scaling headcount at the same rate. It also reduces reliance on channel-specific expertise, making teams more resilient as platforms and formats continue to change.
AI agents as a governance challenge
The rise of agent-based systems also raises familiar enterprise questions around control and accountability. While AI agents can act autonomously in defined limits, organisations still need clarity on how decisions are made, what data is used, and when human intervention is required.
In marketing, these concerns are particularly acute. Campaigns operate in public environments, budgets can change quickly, and performance signals are often noisy or delayed. Enterprises deploying agentic AI systems must decide how much authority to delegate, how frequently outputs are reviewed, and how exceptions are handled.
This places governance at the centre of adoption. Rather than asking whether AI agents can optimise campaigns, enterprises are asking how those agents fit into approval structures, compliance requirements, and reporting frameworks already in place.
Why timing matters
The interest in operational AI for marketing reflects broader shifts in large organisations. Many enterprises have moved past early experimentation with AI and are now under pressure to show tangible returns. At the same time, marketing budgets are facing closer scrutiny, even as the number of channels and formats continues to grow.
AI systems that minimise cycle times and operational overhead are more easily justified than tools that only provide incremental performance gains. They are also more closely aligned with how enterprises measure success: reliability, predictability, and efficiency over time.
In this context, Fluency’s funding round is less about the promise of AI in advertising and more about where enterprises see practical value emerging. Marketing is becoming another function where AI is expected to operate quietly in the background, handling routine work while humans retain strategic control.
What AI agents signal for enterprise marketing
What stands out is not the technology itself, but the framing: AI is judged by how well it fits into existing workflows and governance models. For marketing leaders, the lesson is clear – AI adoption is moving away from experimentation and toward infrastructure.
As enterprises reassess how work gets done in digital functions, marketing is following the same path as IT, finance, and operations. The focus is no longer on what AI can do in theory, but on how it reduces friction in practice.
(Photo by Carlos Muza)
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