Five of six. That’s how many top marketer priorities now run through AI, according to IAB’s latest Outlook Study. Not “supported by.” Not “enhanced with.” Driven by. If your influencer program, media plan, or measurement stack still treats AI as a side project, the data suggests you’re already behind, and the gap is widening faster than most budget cycles can keep up with.
What the IAB Outlook Study Actually Found
The study surveyed senior marketers and agency leaders heading into the new planning cycle, asking them to rank their top six strategic priorities for the year. Five of those six, spanning media buying, measurement, content production, audience targeting, and creative testing, now have AI embedded as the primary mechanism for execution, not just an optional accelerant.
That’s a structural shift, not a trend headline. A year or two ago, AI showed up in these surveys as a “tool we’re piloting.” Now it’s the operating layer underneath budget allocation decisions. Marketers aren’t asking whether to use AI anymore. They’re asking which workflows still make sense without it.
When five of six core marketing priorities run through AI, the real competitive question shifts from “are we using AI” to “are we using it better than the next brand in our category.”
This mirrors findings from other recent industry data. AI-driven budget decisions are already standard practice across APAC markets, where 70% of marketing leaders say algorithmic recommendations directly shape spend allocation. The IAB numbers suggest North American and European markets are catching up fast.
The One Priority Still Resisting Full Automation
So what’s the sixth priority, the one holdout? Brand strategy and long-term positioning. Marketers still want a human hand on narrative, tone, and the kind of category-defining bets that don’t reduce cleanly to a dataset. That makes sense. AI is excellent at optimizing within a strategy. It’s far less reliable at inventing one from scratch, especially when the goal involves cultural nuance, brand voice, or genuine differentiation in a crowded category.
This isn’t a knock on AI capability. It’s a reminder that judgment and pattern recognition are different skills. A model can tell you which creator format converts best this quarter. It can’t tell you whether entering a new vertical aligns with where your brand wants to be in three years. That’s still a leadership call, and the IAB data backs that distinction up cleanly.
Why Now? The Budget Pressure Behind the Shift
None of this happened because marketers suddenly fell in love with automation. It happened because CFOs stopped accepting vague ROI explanations. 61% of CMOs still can’t cleanly measure ROI even as spend climbs, and that accountability gap has become untenable in boardrooms where every line item gets scrutinized.
AI tools promise something finance teams actually want: traceable inputs, measurable outputs, and faster iteration cycles. Whether that promise fully delivers is a separate conversation, but the appeal is obvious. When a platform can show attribution modeling, audience overlap analysis, and creative performance scoring in near real time, it’s a much easier budget conversation than “trust the brief.”
There’s also a scale problem AI happens to solve well. Running hundreds of micro-influencer campaigns, testing dozens of creative variants, or personalizing messaging across fragmented audience segments used to require headcount brands couldn’t justify. Now it requires tooling. That’s a cost structure shift as much as a technology one, and it’s reshaping how agencies price their services too.
Where the Five AI-Driven Priorities Actually Show Up
- Media buying: Programmatic and influencer platform algorithms now handle real-time bid adjustments and creator matching at a scale manual teams can’t replicate.
- Measurement: Multi-touch attribution models increasingly rely on machine learning to connect creator-driven engagement to downstream conversion, something last-click attribution has failed to do credibly for years.
- Content production: AI-assisted editing, captioning, and localization tools compress production timelines without proportionally increasing headcount.
- Audience targeting: Lookalike modeling and predictive segmentation now run continuously, adjusting as campaign data accumulates rather than at the start of a flight.
- Creative testing: Automated A/B and multivariate testing across formats lets teams find winning creative in days instead of weeks.
Where Brands Are Getting This Wrong
Here’s the uncomfortable part. Adopting AI tools and adopting AI-driven operations are not the same thing. A lot of brands have bought the software. Fewer have rebuilt the workflows, approval chains, and talent structures around it. That gap is exactly what’s creating risk right now.
73% of brands show a measurable adoption gap between the AI tools they’ve purchased and the tools their teams actually use in daily creator workflows. That’s not a technology failure. It’s a change management failure, and it’s the single biggest reason AI investments underdeliver against the promises made in the pitch deck.
Buying the tool is the easy part. Rebuilding the workflow, the approval chain, and the team’s muscle memory around it is where most AI investments quietly stall.
There’s also a compliance dimension brands underestimate. When AI tools generate or heavily edit influencer content, disclosure requirements don’t disappear. The FTC’s endorsement guidelines still apply regardless of how much automation touched the final asset, and regulators are paying closer attention to AI-generated marketing content than most legal teams have budgeted time for. Pharma and other regulated categories are feeling this especially hard, as seen in how AI field force tools are reshaping compliance expectations in healthcare marketing specifically.
Operationalizing AI Without Losing the Plot
If five of your six priorities are supposed to run through AI, the operational question becomes: how do you actually build that without creating a mess of disconnected point solutions? A few things separate teams that are pulling this off from teams that are just accumulating software licenses.
First, systems thinking beats tool hoarding. Teams that map how AI touches the entire campaign lifecycle, from briefing through measurement, see materially better ROI than teams layering isolated tools onto old workflows. This is the core argument behind why systems thinking outperforms prompting as a strategic framework; the prompt is never the bottleneck, the workflow integration is.
Second, reusable infrastructure matters more than one-off automation wins. Brands building reusable creative libraries are cutting production costs significantly because they’re not rebuilding AI prompts and templates from scratch every campaign cycle. That’s the kind of compounding efficiency that actually shows up on a P&L statement.
Third, treat this as an operating model shift, not a software rollout. The brands that will look back on this cycle as a turning point are the ones restructuring teams now, not waiting for next year’s budget to catch up. That’s consistent with what IAB’s own regional research has flagged, including findings from the IAB Hong Kong C26 session, where creator budget resets were explicitly tied to AI readiness rather than AI adoption alone.
Finally, don’t treat the sixth priority, brand strategy, as an afterthought because it resisted automation. If anything, protect it more deliberately. Teams moving toward AI-first creator operations as the default model still need a clear human point of view steering the strategy underneath all that automation, or the output starts to look identical to every competitor running the same tools.
What This Means for Influencer and Creator Budgets Specifically
Influencer marketing sits squarely inside the AI-driven priorities IAB identified, particularly in measurement and audience targeting. Platforms are increasingly using AI to match brands with creators based on predicted conversion likelihood rather than follower count or engagement rate alone. That’s a meaningful upgrade over the vanity-metric era, but it also means brands need cleaner first-party data feeding those models, or the matching quality degrades fast.
Third-party research from eMarketer and benchmarking data available through Statista both point to accelerating AI integration across creator platform infrastructure, reinforcing that this isn’t an isolated IAB finding. It’s a cross-industry pattern showing up in multiple independent datasets.
Practically, that means influencer teams should be auditing which parts of their process, from creator discovery to content approval to performance reporting, still rely on manual spreadsheet work that AI tooling could handle with better accuracy and far less turnaround time.
Next Step
Audit your six top priorities against IAB’s framework this week: if AI isn’t the primary driver behind at least four or five of them, you’re not behind on technology, you’re behind on operating model, and that’s the gap worth closing first.
Frequently Asked Questions
What did IAB’s Outlook Study actually measure?
The study surveyed senior marketers and agency leaders on their top strategic priorities for the coming planning cycle, then assessed how central AI is to executing each one, rather than simply asking whether respondents use AI tools generally.
Which marketer priority remains least driven by AI?
Brand strategy and long-term positioning. Marketers still rely on human judgment for category differentiation, narrative, and cultural nuance, areas where AI can support execution but struggles to originate genuinely novel strategic direction.
Why is AI adoption outpacing actual AI integration in workflows?
Many brands purchase AI tools without restructuring the approval chains, team roles, and daily processes needed to use them effectively, creating a visible gap between tool ownership and tool utilization.
Does AI-driven influencer marketing change FTC disclosure requirements?
No. Endorsement and disclosure rules still apply regardless of how much AI was involved in creating or editing the content, and brands remain responsible for compliance even when automation handles parts of production.
How should a mid-sized brand start operationalizing this shift?
Start by mapping AI touchpoints across the full campaign lifecycle rather than adding isolated tools, then build reusable templates and workflows that compound efficiency gains over multiple campaign cycles instead of resetting each time.
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
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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 → -
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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 → -
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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 → -
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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 → -
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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 → -
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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 → -
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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 →