Ninety-five percent of brands say they’re testing AI creative. Fewer than one in five have actually scaled it. That gap, according to Smartly’s newly released 2026 benchmark report, is the defining tension in performance marketing right now. Everyone’s poking at the technology. Almost nobody’s rebuilding their workflow around it.
The Smartly 2026 report surveyed marketing leaders across retail, CPG, and DTC categories, and the headline finding is blunt: AI creative testing has become table stakes, but production-scale adoption remains rare. If you’re a brand still running “pilots” eighteen months after your first AI creative experiment, you’re not early. You’re stuck.
The Adoption Number Everyone’s Citing, and Why It’s Misleading
A 95 percent testing rate sounds like a market that’s basically solved. It isn’t. Testing means someone on the team generated a few AI variants of an ad, ran them against a control, and filed the results in a deck nobody reopened. That’s not adoption. That’s curiosity with a budget line.
Smartly’s data draws a sharper distinction between three tiers: experimentation (running isolated tests), integration (AI creative embedded into a live production pipeline), and scale (AI generating a meaningful share of in-market creative volume, continuously, with performance feedback loops). The 95 percent figure covers tier one. The report puts scaled adoption somewhere between 12 and 18 percent, depending on category, with retail and DTC brands moving faster than regulated categories like finance and healthcare.
Nearly every brand has dipped a toe into AI creative. Fewer than one in five have built the infrastructure to let it run.
Why the gap? It’s not a technology problem anymore. Generation quality has improved fast enough that “the AI output looks bad” is no longer a credible excuse in most categories. The bottleneck is organizational: unclear ownership, brand safety anxiety, and measurement systems that weren’t designed to evaluate machine-generated variants at volume.
Why So Many Programs Stall at the Pilot Stage
Ask any brand marketer why their AI creative pilot never scaled, and you’ll hear some version of the same three excuses. Let’s take them one at a time, because each one is fixable, and each one reveals something about how immature most governance models still are.
- No clear owner. AI creative sits awkwardly between brand, performance marketing, and creative production teams. When a pilot succeeds, three departments claim credit. When it stalls, nobody owns the fix. Smartly’s report found that brands with a named AI creative lead, even a part-time one, were nearly three times more likely to reach the integration tier.
- Legal and compliance friction. Marketing teams are moving faster than their legal counterparts can review. Questions about disclosure, IP rights on training data, and likeness usage stall approvals for weeks. This mirrors what we’ve seen with AI governance roles becoming a real hiring priority, not a nice-to-have.
- Measurement mismatch. Traditional A/B testing frameworks assume a handful of variants. AI creative can generate hundreds. Most brands’ analytics stacks, and their analysts’ time, weren’t built for that volume. This is the same structural gap driving demand for AI assisted marketing mix modeling that can actually process creative-level granularity.
Notice what’s missing from that list: “the technology doesn’t work.” Nobody’s saying that anymore. The stall is entirely operational, which is actually good news, because operational problems are solvable with process, not R&D budget.
What “Scaled” Actually Looks Like
The brands that broke through pilot purgatory did a few things differently, and the pattern is consistent enough across Smartly’s respondent pool to call it a playbook.
First, they treated AI creative as a production pipeline decision, not a marketing experiment. That means integrating generation tools directly into the ad platforms where creative gets deployed, rather than generating assets in a separate tool and manually uploading them. Every extra manual step is a place where scale dies.
Second, they built feedback loops that fed performance data back into generation prompts automatically. Static test-and-learn cycles, where a human reviews results monthly and manually adjusts creative direction, are too slow for how fast AI-generated variants can be produced and served. The brands seeing real lift are running something closer to real-time creative optimization, where underperforming variants get pulled and replaced within hours, not weeks.
Third, and this is the part most reports skip, they didn’t try to automate everything at once. Scoped pilots with a defined success metric, a fixed budget ceiling, and a hard stop date consistently outperformed open-ended “let’s see what AI can do” initiatives. That discipline echoes advice we’ve given before on why teams should scope one workflow before scaling automation rather than boiling the ocean.
The Creator Content Wrinkle
Here’s where it gets specific to influencer marketing, and where Smartly’s report intersects with a broader shift happening in the creator economy. AI creative isn’t just about brand-generated ad variants anymore. It’s increasingly about repurposing creator content into hundreds of derivative assets, personalized by audience segment, without commissioning new shoots.
This is already reshaping production budgets. Instead of paying a creator for a single deliverable and running it once, brands are licensing that footage and feeding it into generation pipelines that produce dozens of localized or segmented variants. We covered this shift in detail when looking at how AI personalization engines multiply a single creator video into hundreds of targeted cuts. It’s efficient. It’s also a compliance minefield if creator contracts weren’t written with derivative use in mind.
That contract gap is not hypothetical. Several brands in Smartly’s survey reported legal delays specifically tied to unclear usage rights on AI-modified creator content, the exact tension playing out in broader disputes over AI licensing and creator compensation that the industry still hasn’t standardized. If your influencer contracts don’t explicitly address AI derivative rights, you’re not ready to scale AI creative built on creator assets, full stop.
Budget Signals: Where the Money’s Actually Moving
Smartly’s report includes a budget allocation breakdown that’s more telling than the adoption stats. Brands in the scale tier are shifting an average of 22 percent of their creative production budget toward AI-assisted workflows, up from roughly 9 percent a year earlier. Pilot-stage brands, by contrast, are holding flat or even trimming AI creative budgets, often because early tests didn’t show clean ROI.
That divergence matters. It suggests the brands getting real value are reinvesting, while the brands stuck testing are treating AI creative as a discretionary line item that gets cut the moment quarterly pressure hits. According to eMarketer’s broader ad spend tracking, overall digital creative production budgets have grown modestly, but the internal reallocation toward AI-assisted tools is happening much faster than total budget growth, meaning it’s coming directly out of traditional production spend.
This also tracks with what we’ve seen in attribution. Brands that can prove creative-level ROI, down to the individual variant, are the ones willing to keep funding AI production at scale. That’s part of why incremental lift testing has become such a critical companion tool. Without it, AI creative spend is a leap of faith. With it, it’s a defensible line item in next year’s budget review.
Compliance Is the Quiet Blocker Nobody Budgets For
Ask a CFO why an AI creative pilot didn’t scale and you’ll get a shrug about unclear ROI. Ask legal, and you’ll get a very different, much longer answer involving disclosure requirements, synthetic media labeling, and platform-specific policies that shift quarterly.
The FTC’s endorsement guidelines already require clear disclosure when content involves material connections or misleading impressions, and AI-generated or AI-modified creator content raises fresh questions about what counts as misleading. Brands operating internationally have it worse: the UK’s Information Commissioner’s Office has flagged AI-generated content and data usage as an active enforcement area, and EU rules are tightening in parallel. None of this is theoretical anxiety. It’s the actual reason legal teams slow-walk approvals, and it’s rarely reflected in the “why did our pilot stall” postmortems marketing teams write.
Brands that scaled successfully, per Smartly’s data, brought legal into the pipeline design phase rather than the launch approval phase. That single sequencing change, reviewing the workflow instead of just the output, cut approval cycle times dramatically in the report’s case examples.
So What Do You Actually Do Monday Morning?
If your AI creative program has been “testing” for more than two quarters without a scale decision, that’s not caution. That’s drift. Set a hard evaluation date, define what scale would look like for your specific budget and category, and force a yes-or-no decision rather than letting the pilot quietly run forever.
Name an owner. Fix your creator contracts to cover derivative AI use before you scale anything built on licensed footage. And build your measurement stack to handle variant-level attribution now, because you won’t have time to retrofit it once volume actually increases.
Frequently Asked Questions
FAQs
What does Smartly’s 2026 report say about AI creative adoption?
The report found that 95 percent of surveyed brands have tested AI-generated creative in some form, but only a small minority, roughly 12 to 18 percent depending on category, have scaled AI creative into ongoing production workflows.
Why do most AI creative programs stay stuck at the pilot stage?
The main blockers are organizational, not technical: unclear ownership between brand, performance, and production teams, slow legal and compliance review, and measurement systems not built to evaluate high volumes of AI-generated variants.
How does AI creative testing affect influencer marketing specifically?
Brands increasingly use AI to repurpose creator content into many segmented or localized variants from a single shoot. This raises contract and licensing questions, since many creator agreements don’t explicitly cover derivative AI use.
What’s the difference between testing AI creative and scaling it?
Testing usually means running isolated experiments against a control. Scaling means AI-generated creative is embedded into a live production pipeline with continuous performance feedback, representing a meaningful share of in-market assets.
What compliance issues should brands address before scaling AI creative?
Brands should clarify disclosure requirements for AI-generated or AI-modified content, confirm creator contracts cover derivative usage rights, and involve legal teams during workflow design rather than only at launch approval.
Top Influencer Marketing Agencies
The leading agencies shaping influencer marketing in 2026
Agencies ranked by campaign performance, client diversity, platform expertise, proven ROI, industry recognition, and client satisfaction. Assessed through verified case studies, reviews, and industry consultations.
Moburst
-
2
The Shelf
Boutique Beauty & Lifestyle Influencer AgencyA data-driven boutique agency specializing exclusively in beauty, wellness, and lifestyle influencer campaigns on Instagram and TikTok. Best for brands already focused on the beauty/personal care space that need curated, aesthetic-driven content.Clients: Pepsi, The Honest Company, Hims, Elf Cosmetics, Pure LeafVisit The Shelf → -
3
Audiencly
Niche Gaming & Esports Influencer AgencyA specialized agency focused exclusively on gaming and esports creators on YouTube, Twitch, and TikTok. Ideal if your campaign is 100% gaming-focused — from game launches to hardware and esports events.Clients: Epic Games, NordVPN, Ubisoft, Wargaming, Tencent GamesVisit Audiencly → -
4
Viral Nation
Global Influencer Marketing & Talent AgencyA dual talent management and marketing agency with proprietary brand safety tools and a global creator network spanning nano-influencers to celebrities across all major platforms.Clients: Meta, Activision Blizzard, Energizer, Aston Martin, WalmartVisit Viral Nation → -
5
The Influencer Marketing Factory
TikTok, Instagram & YouTube CampaignsA full-service agency with strong TikTok expertise, offering end-to-end campaign management from influencer discovery through performance reporting with a focus on platform-native content.Clients: Google, Snapchat, Universal Music, Bumble, YelpVisit TIMF → -
6
NeoReach
Enterprise Analytics & Influencer CampaignsAn enterprise-focused agency combining managed campaigns with a powerful self-service data platform for influencer search, audience analytics, and attribution modeling.Clients: Amazon, Airbnb, Netflix, Honda, The New York TimesVisit NeoReach → -
7
Ubiquitous
Creator-First Marketing PlatformA tech-driven platform combining self-service tools with managed campaign options, emphasizing speed and scalability for brands managing multiple influencer relationships.Clients: Lyft, Disney, Target, American Eagle, NetflixVisit Ubiquitous → -
8
Obviously
Scalable Enterprise Influencer CampaignsA tech-enabled agency built for high-volume campaigns, coordinating hundreds of creators simultaneously with end-to-end logistics, content rights management, and product seeding.Clients: Google, Ulta Beauty, Converse, AmazonVisit Obviously →