Among many industries, it is marketing where AI is no longer a side project in an “innovation lab” but integrated into briefs, production pipelines, approvals, and media optimization. A WPP iQ post published in December based on a WPP and Stability AI webinar shows what the implementation of AI in daily work looks like.
Here we’re talking about focusing on the practical constraints that determine whether AI changes daily operations or simply adds layers of complexity and tools.
Brand accuracy and repeatable functionality
Marketing agency AI treats brand accuracy as an engineering subject. WPP and Stability AI point out that off-the-shelf models are “not trained on a brand’s visual identity,” so their output often looks generic. The solution for companies is fine-tuning, or training the model on brand-specific datasets so that the model learns the brand’s strategies such as style, look, and color. That way you can consistently reproduce these elements.
WPP’s Argos is a prime example. After fine-tuning the model for the retailer, the team explained how the model would recognize details beyond the characters, such as lighting and subtle shadows used in the brand’s 3D animation. Recreating these details can take time during production in the form of re-rendering and multiple approvals. As the AI output nears “done,” teams spend less time revising and more time shaping the narrative and adapting media to different channels.
Cycle time collapse (and calendar changes)
WPP and Stability AI note that traditional 3D animation may be too slow for reactive marketing. After all, cultural moments require immediate content, not cycles defined by weeks or months. In the Argos case study, WPP trained a custom model on two 3D toy characters to learn how the models look and behave, including details such as proportions and how the characters hold objects.
As a result, “high-quality images…are produced in minutes instead of months.”
Accelerated workflows move rather than eliminate production bottlenecks. As variations are generated faster, review, compliance, rights management, and distribution become constraints. These issues have always existed, but the speed and efficiency of AI in this context shows the difference between what is possible and the systems that are being integrated into workflows and accepted. Government agencies that want to transform their daily operations with AI need to go beyond simply adding technology as a new tool to redesigning their workflows around AI.
“AI front end” is essential
WPP and Stability AI point to a “UI issue” where creative teams are wasting time because interfaces to common tools are “disconnected, complex, and confusing,” forcing them to work around and constantly move assets between tools. The response is often a bespoke, brand-specific front end, with complex workflows taking place on the back end.
WPP positions WPP Open as a platform that encodes WPP’s proprietary knowledge into “globally accessible AI agents” that help teams plan, produce, create and sell media. Clearer handoffs between tools deliver operational benefits as work moves from brief to production, assets to activation, and performance signals back to plan.
Self-service capabilities transform agency operations
AI-powered marketing platforms are also becoming customer-enabled. On the operational side, agencies need to focus on parts of the workflow that clients can’t easily do self-service, such as designing brand systems, building tweaks, and ensuring governance is built in.
Governance from policy to workflow
Routine use of AI requires governance to be built into where work is done. Dentsu describes building a “walled garden,” a digital space where employees can safely prototype AI-enabled solutions and commercialize their best ideas. This reduces the risk of sensitive data being leaked and allows experiments to be moved to production systems.
Compress plans and insights
The operational impact is not limited to production environments. Publicis Sapient describes an AI-powered content strategy and plan that “turns months of research into minutes of insight” by combining large-scale language models with contextual knowledge and prompt libraries (PDF). Research and easy development compress work schedules, resulting in more client work and allowing agencies to respond quickly to changing culture and platform algorithms.
What will change for people?
Throughout these examples, the impact on marketing professionals is one of rebalancing and changing job descriptions. Spend less time on mechanical drafting, resizing, and versioning and more time on brand management. New operational roles will expand, including model trainer, workflow designer, and AI governance lead.
AI makes the biggest operational difference when agencies have customized models, a usable front end that enables smooth adoption (especially by clients), and an integrated platform that connects planning, production, and execution.
While the biggest advantages are speed and scale, the deeper change is that marketing delivery is becoming more like a software-enabled supply chain: standardized, flexible where needed, and measurable.
(Image source: “Solar Wind Workhorse Marks 20 Years of Science Discoveries” by NASA Goddard Photo and Video is licensed under CC BY 2.0.)
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