AI Advertising Is Spreading Rapidly, But "Knowing How to Use AI" Is No Longer a Rare Capability
The advertising industry has moved past the debate over "whether to use Generative AI."
The IAB 2025 Digital Video Advertising Study shows that 86% of ad buyers are already using or plan to use Generative AI to produce video ads; by 2025, approximately 30% of digital video ad creatives will be generated or enhanced by GenAI, with buyers expecting this proportion to approach 40% by 2026.
By 2026, advertisers' demands for AI are also shifting from experimentation to business results. In IAB's latest survey, 64% of advertising industry respondents listed "cost efficiency" as one of AI's key values, rather than purely pursuing technical innovation.
On the surface, this should make ad production simpler and simpler.
Models are stronger; images, video, audio, and digital humans can all be generated. Work that previously required multiple production stages can now be run with a few tools.
However, another set of data deserves equal attention.
In the IAB 2025 survey on AI governance, over 70% of marketers have already encountered AI-related issues, including hallucinations, bias, or content that does not comply with brand guidelines; yet fewer than 35% of companies plan to increase investment in AI governance or brand integrity.
Putting these two sets of data together highlights the fundamental conflict after AI advertising enters the commercialization stage:
Production capacity is expanding faster than management capacity.
Right now, what an advertising project lacks least is likely generated concepts and drafts.
What is truly difficult is:
Which of these many options is actually correct?
Is the product representation accurate?
Has the character's appearance drifted?
Is the brand tone off-target?
Can it clear copyright compliance?
When finally deployed to large screens, Social, TV, or overseas channels, does it still hold up?
So AIGC does not bring a simple "shortened production workflow."
It actually shifts the main conflict of a project from:
Can we produce it?
to:
Who can guarantee that the final deliverable is correct?
Global Ad Giants Are Also Shifting from "AI Tools" to "End-to-End Systems"
If this observation were merely self-packaging by small-to-medium AI companies, it wouldn't mean much.
However, the organizational changes across global tier-1 advertising groups over the past two years are actually converging in the exact same direction.
WPP's current definition of WPP Open is no longer an AI creative tool, but an end-to-end marketing system covering strategy, creative, media, and production. Official statements explicitly emphasize that unlike point solutions that automate individual tasks, WPP Open's goal is to connect the entire marketing workflow.
In 2026, WPP further announced an organizational integration strategy, consolidating the company into four operating units, all unified and connected by WPP Open. Its new partnership with Adobe directly articulates the industry pain point: brands need to produce more content for more channels, yet many enterprises remain trapped by fragmented tools and workflows. Therefore, what is needed next is a content supply chain capable of coordinating planning, creation, production, and activation.
Publicis follows a similar path. It reorganized its business into three core capabilities: Connected Media, Intelligent Creativity, and Technology, with CoreAI positioned in the center, bridging data, creative, production, media, and technology. Its 2025 annual filings explicitly stress that this structure aims to handle client needs comprehensively—from marketing to digital transformation—using a unified platform.
This is no coincidence.
Because the deeper AI penetrates advertising production, the clearer one fact becomes:
AI can make every individual point stronger, but if the links between them remain fragmented, overall efficiency will not increase proportionally.
With a strategy team on one side, an AI production shop on another, a post-production house brought in later, media arriving last, and legal handling copyright issues at the tail end—every company completes its assigned task, but nobody truly owns the complete outcome.
That is why in the AIGC era, "full-service" is not an old concept rewrapped by traditional agencies.
It is acquiring a brand-new meaning:
Re-integrating decision-making processes—which have become increasingly complex due to AI—back into a unified system.
Why Do Ad Projects Need Full-Service Capabilities Even More in the AIGC Era?
First, Creative and Tech Decisions Can No Longer Wait Until Handover to Meet
In traditional ad production, a creative idea could first exist inside a PPT, then move into execution with directors and production companies.
AIGC rarely works this way.
The moment a creative idea emerges, simultaneous evaluations are required:
Can this character remain consistent?
Is this scene suitable for generation?
Will this shot sequence be more efficient with AI, or is live-action shooting more reliable?
Are there licensing boundaries for this IP element?
Can product close-ups reach the precision required for commercial advertising?
If the creative team has zero understanding of AI production boundaries, it is easy to design concepts that look visually stunning but are extremely expensive or even impossible to execute stably.
Conversely, if the technical team only starts from "what the model does best," the content easily turns into a tool demo.
Thus, the first shift in the AIGC era is:
Creativity isn't finalized and handed over to tech; strategy, creative vision, and technical judgment must happen simultaneously from Day 1.
This does not restrict creative freedom.
It incorporates "whether it can truly hold up in reality" into creativity itself ahead of time.
Second, As Generation Capabilities Grow Stronger, the Core of Projects Shifts from "Production" to "Control"
An AI advertisement can generate dozens of character iterations, hundreds of storyboards, and numerous dynamic tests.
But commercial advertising isn't about who generates the most.
What brands truly need to control is:
Whether the character remains the exact same person throughout;
Whether the product packaging is deformed;
Whether Logos, color schemes, and visual guidelines drift;
Whether temporal, spatial, and emotional continuity is maintained between shots;
Whether different aspect ratio versions still communicate the same brand feeling.
When WPP launched its AI Production Studio, it directly defined the problem it aimed to solve: how to maintain brand-compliant and product-accurate standards during large-scale generation.
The industry shift behind this statement is critical.
Before AI adoption, the main bottleneck was capacity.
After AI adoption, the new bottleneck became:
Maintaining correctness amidst vast capacity.
Therefore, "how many models we can use" is not the key metric for evaluating full-service capability.
The real question to ask is:
What mechanism do you use to ensure the 50th version still belongs to the exact same brand as the 1st version?
Third, Copyright and Compliance Cannot Remain Post-Production Checkpoints
AI advertising rapidly expands creative boundaries while making asset sources, likeness rights, music, IP, and generated asset permissions far more complex.
If copyright is checked only at the very end, a counterintuitive problem emerges:
The faster AI produces, the higher the sunk cost in the wrong direction.
This is especially pronounced in IP co-branding, celebrity endorsements, sports events, and global market campaigns.
This is why, in previous discussions on IP co-branding, we emphasized:
"Generable" and "commercially usable" are two entirely different issues.
A truly complete AI workflow must confirm during the creative phase which assets are clear for use, which expressions require approval, and which content must adhere strictly to original standards—rather than waiting for legal to decide whether it "can be published" after the work is completed.
Thus, the third value of full-service delivery is not "offering an extra copyright service."
It is:
Integrating commercial risk into the production workflow, rather than leaving it outside.
Fourth, What Brands Ultimately Buy Is Never Just an MP4 File, But Content That Succeeds in the Real World
This is the layer most easily overlooked in many AI production projects.
A video that looks great on a laptop screen doesn't mean it is ready for delivery.
Real commercial content enters drastically different environments:
Social media must hook attention in the first few seconds;
City giant screens require a powerful visual focal point;
Elevator media relies on passive viewing;
TV and streaming demand brand narrative depth;
Global versions involve local language, cultural nuances, and regional approvals;
Naked-eye 3D and ultra-wide screens completely alter composition principles.
FansAI previously formed a key insight during TVC content creation:
Multi-media adaptation is not a post-production cropping issue; it is a content design problem that must enter during the strategy phase.
Therefore, the endpoint of full-service capability should not be:
"The video file is finished."
It should be:
Once this content enters the real deployment environment, it remains effective and valid.
A Real Project: Why in Complex Projects, "Speed" Comes from Making Fewer Wrong Judgments Earlier
When FansAI completed the 2026 Winter Olympics brand film for TCL, this challenge became tangible.
The project involved multiple concurrent variables:
Global brand ambassador Eileen Gu, live-action shooting in Italy, AIGC production, bilingual Chinese-Italian versions, International Olympic Committee site approvals, and an immovable Winter Olympics campaign deadline. The content also needed to simultaneously convey Olympic sponsorship status, technological leadership, and humanistic value that consumers could feel.
The final project was delivered in 14 days:
4 days of live shooting in Italy + 5 days of AIGC hybrid editing + 5 days of communication & review, delivering final Chinese and Italian master cuts and passing venue approvals.
Yet what makes this project worth highlighting isn't just "how fast 14 days was."
It was why the 14-day turnaround succeeded.
The single most critical judgment was not viewing "live-action shooting" and "AIGC" as opposing camps.
Where real people, physical space, and brand credibility were required, live shooting was used;
Where spatial expansion, visual imagination, and schedule pressures dominated, AIGC took over;
Creative, production, and AI post-production were not handled by three separate vendors doing three isolated tasks, but worked together under a single narrative judgment.
Similarly, instead of creating three separate video segments for "technology, Winter Olympics, and product" and stitching them together in post, the team first identified a unified narrative axis capable of bridging all three demands upfront.
This project validated an essential reality of full-service delivery:
What truly wastes time in complex projects is rarely production itself, but repeating work after making wrong judgments.
AI compresses execution timelines.
However, that efficiency can only be realized when strategy, creative, tech, line production, and approvals operate inside a single decision-making system.
So, What Defines a True "Full-Service AI Advertising Agency"?
When brands look for AI vendors today, the easiest thing to see is a technical feature sheet:
Which models they can use;
Whether they can generate video;
Whether they can train LoRAs;
Whether they possess proprietary workflows.
These are important, but insufficient to prove full-service capability.
What truly deserves evaluation are four fundamental criteria.
| Evaluation | What Brands Should Really Ask |
|---|---|
| Integrated Judgment | Are strategy, creative, and tech evaluated together from Day 1, or subcontracted step-by-step downstream? |
| Consistent Production | Are there systems for consistency, product accuracy, brand guidelines, and human QC—rather than relying on getting lucky with a good generation? |
| Commercial Constraint Handling | Can they handle copyright, IP, celebrities, languages, multi-media, and launch windows simultaneously? |
| Final Accountability | When issues arise in the real deployment environment, who takes ultimate responsibility for the result? |
This final point is particularly crucial.
Many so-called "full-service" offerings are just longer vendor lists:
One agency for strategy, one for production, one for AI, one for post-production, one for media.
If the brand still has to act as the master producer to stitch five or six vendors together, it has not reduced the brand's management burden.
True full-service capability isn't "being able to do everything," but having a single entity take full accountability for the complete outcome.
What AIGC Truly Changes Is the Agency's Boundary of Responsibility
In the past, production companies were judged by craft quality.
Creative agencies were judged by strategy and ideas.
Media agencies were judged by placement efficiency.
These specialized divisions will not disappear.
However, AI is making the boundaries between them increasingly difficult to separate.
Because strategy determines what is worth generating;
Creative determines what must be controlled;
Technology determines what can be achieved;
Copyright determines what can be published;
And media determines how the creative idea should ultimately present itself.
This is why top global groups like WPP and Publicis are building AI into a unified system across business functions, rather than adding isolated "AI departments."
Behind this lies a clear industry insight:
AI won't just reshape production; it will reshape "who takes responsibility for the whole thing."
For brands, the value of choosing a full-service AI advertising agency shouldn't merely be:
Liaising with fewer people.
The real value lies in:
Reducing repeated information translation between different teams;
Reducing high-speed production along incorrect directions;
Reducing copyright re-works post-production;
Reducing distortion of core creative ideas across different media formats.
In other words:
AI full-service delivery truly optimizes the decision chain, not just the production chain.
Conclusion: When "Being Able to Make It" Gets Easier, "Daring to Take Responsibility" Becomes Rare
More companies will enter the AI advertising space.
Model capabilities will continue to advance.
Techniques that look highly specialized today may become basic baseline features within six months.
Therefore, if brands continue using questions like:
"Can you make AI videos?"
"Which model do you use?"
"How much per video?"
to judge an AI agency, it will become increasingly difficult to discern true capability.
Because these capabilities are rapidly standardizing.
What will not be standardized so quickly is:
When the Brief is unclear, who can clarify the core problem;
When creative and technology conflict, who knows what to compromise;
When brand, copyright, time, and media press simultaneously, who can still make the right calls;
And finally, when content hits the real market, who is willing to take responsibility for the outcome.
AIGC lowers production barriers, but raises delivery thresholds.
Generation capability determines whether a company can enter the industry; the ability to take responsibility for complex commercial outcomes determines whether it stays at the table.
The next phase of AIGC advertising will not just be a competition of models.
It will be a competition of responsibility and execution capability.