AI agents are beginning 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 administer complexity, control costs, and shorten production cycles as campaigns run constantly across more channels. A recent funding round for adtech startup Fluency shows how AI-driven marketing agents are starting to tackle that operational role inside enterprise marketing teams.
Fluency has raised $40 million to expand an AI-driven platform designed to automate performance marketing workflows across major promoting channels. While the corporate positions its technology around “AI agents,” the underlying value proposition is more familiar to enterprise leaders: reducing manual work, standardising processes, and improving consistency across teams operating at scale.
Marketing operations under strain
For large brands, performance marketing has change into an increasingly operational function. Campaigns run constantly across 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 depend on specialised operators for every channel, supported by layers of tooling and manual processes.
This structure creates friction. Campaigns are often rebuilt from scratch, optimisation decisions rely on individual expertise, and small changes can require multiple hand-offs between teams. At scale, the difficulty just isn’t creative output but coordination, repeatability, and time spent on routine work.
Enterprises are now trying to AI to handle those constraints, not by replacing marketing strategy, but by absorbing repetitive tasks that slow execution. That places tools like Fluency’s in a special 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. These agents handle tasks comparable to campaign setup, testing, optimisation, and iteration across platforms, operating inside parameters defined by the organisation.
The distinction matters for enterprise use. Instead of marketers actively managing every adjustment, AI systems tackle day-to-day execution, while humans deal with oversight, performance review, and higher-level decisions. This is consistent with patterns appearing in other enterprise sectors, comparable to IT operations, finance, and customer support, where AI is increasingly acting as a background operator relatively than a front-end interface.
For marketing leaders, this approach guarantees a strategy to scale output without scaling headcount at the identical rate. It also reduces reliance on channel-specific expertise, making teams more resilient as platforms and formats proceed to alter.
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 inside 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 resolve how much authority to delegate, how often outputs are reviewed, and the way exceptions are handled.
This places governance on 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 across large organisations. Many enterprises have moved past early experimentation with AI and are now under pressure to point out tangible returns. At the identical time, marketing budgets are facing closer scrutiny, at the same time as the variety 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 concerning the promise of AI in promoting and more about where enterprises see practical value emerging. Marketing is becoming one other function where AI is predicted to operate quietly within the background, handling routine work while humans retain strategic control.
What AI agents signal for enterprise marketing
What stands out just isn’t the technology itself, however the framing. AI is not any longer positioned as an add-on for creative teams or a specialised optimisation tool. Instead, it’s being integrated into core operational systems that support large-scale execution.
That shift mirrors developments in other enterprise domains, where AI is increasingly judged by how well it suits into existing workflows and governance models. For marketing leaders, the lesson is obvious: AI adoption is moving away from experimentation and toward infrastructure.
As enterprises reassess how work gets done across digital functions, marketing is following the identical path as IT, finance, and operations. The focus is not any longer on what AI can do in theory, but on the way it reduces friction in practice.
(Photo by Carlos Muza)
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