Marketers Adopt AI for Social, Retail Media; Lag on Influencer, CTV in Q1 2026
Serge Bulaev
New data from early 2026 suggests marketers are quickly using AI for social and retail media, but are slower to use it for influencer and connected TV (CTV) marketing. Social media has the highest AI use at 49 percent, while only 18 percent of CTV marketers are using AI. Many marketers say AI helps with speed and cost, but concerns about trust, measurement, and internal skills remain. Generative AI is mostly used for making creative content now, but there may be a shift toward campaign management and analytics in the near future. The survey indicates that while there are challenges, marketers might increase spending on new AI-driven marketing tools soon.
New Q1 2026 data reveals that marketers are rapidly adopting AI for social and retail media, but adoption lags significantly for influencer and connected TV (CTV) marketing. A survey of over 100 industry professionals by Modern Retail+ Research highlights an uneven implementation of automation, with adoption highest in channels offering clear performance metrics. While the primary drivers for this shift are speed and cost-efficiency, each channel presents unique challenges, creating a fragmented landscape for brands to navigate.
Adoption gaps by channel
Marketers are adopting AI most rapidly in social media and retail media, where performance data is readily available. In contrast, adoption is lower in influencer marketing and connected TV due to concerns over authenticity, measurement complexity, and platform fragmentation.
The survey's adoption rates reveal a sharp contrast between channels. Social media marketing leads AI integration, with retail media following closely behind. Meanwhile, influencer marketing shows lower adoption, and connected TV lags significantly. A recent Modern Retail article analyzing the survey connects this gap to the clear ROI attribution available in social and retail. Currently, generative AI is the dominant application, with most marketers using it for creative production. For retail media specifically, many use generative AI for content tasks, though future plans indicate a strategic shift toward campaign management and analytics.
Why influencer and CTV trail
In influencer marketing, the primary obstacle is authenticity. A significant majority of brands steer clear of virtual influencers due to consumer trust concerns. Among the marketers using AI in this space, most limit its use to data analysis, with fewer using it for content creation or outreach. A crowded creator market also contributes to this caution.
For CTV, the main challenges are measurement and fragmentation. Research shows many marketers are concerned about platform fragmentation, and a significant portion find cross-channel optimization difficult. Despite these issues, more than half of advertisers plan to increase their AI media budgets for streaming, indicating strong interest pending better attribution models.
System-wide headwinds
Beyond channel-specific issues, several systemic barriers are slowing widespread AI implementation across marketing organizations. Data from Modern Retail highlights key technical and organizational hurdles:
- Brand Safety: A significant portion of marketers are concerned about maintaining brand safety with increased automation.
- Skills Gap: Many cite a lack of internal expertise to manage AI tools effectively.
- Data Quality: A substantial number report that poor data quality undermines the performance of AI-driven decisions.
The report notes that employee training on new AI systems is not keeping pace with tool deployment, emphasizing that success depends as much on people and processes as it does on technology.
Strategic adjustments underway
Despite the challenges, marketers are making strategic adjustments. For many social and retail media teams, generative AI is already foundational. The focus in retail media is expected to shift from creative generation toward campaign management and personalization in 2026. In the CTV space, advertisers are exploring attention-based metrics to connect ad exposure with business results, with many believing these could become a new standard.
The survey also reveals early investment in "AI media," or ads placed within AI assistants. With a majority of marketers planning to increase spending in this nascent area, the industry is clearly preparing for a future where AI dictates both content creation and media placement.
Which marketing channels saw the highest AI adoption rates in Q1 2026?
According to the Modern Retail+ Research survey, social media led adoption, followed closely by retail media. These channels became early proving grounds for AI-driven efficiencies, with many retail media marketers using generative AI primarily for creative and content production. The survey highlights a clear pattern: marketers gravitate toward AI where automation can streamline high-volume, data-rich workflows without compromising the core consumer experience.
Why are marketers hesitant to adopt AI for influencer marketing?
The primary barrier is consumer authenticity. An overwhelming majority of brands with no plans to work with virtual influencers cite consumer trust issues as their reason for caution. Among those who do use AI for influencers, most limit it to data analysis rather than content creation or outreach. The challenge extends beyond virtual influencers to the broader "validation gap" - brands trust AI to find creators but rely on humans to verify them due to rising fraud concerns. Significant losses to influencer fraud make human oversight a reputational necessity rather than a preference.
What explains the low AI adoption in CTV advertising?
CTV shows the steepest adoption cliff, with most respondents not using AI for streaming marketing. The barriers here are technical fragmentation and measurement complexity. Unlike social and retail media, CTV operates across disparate platforms, devices, and operating systems, creating what industry observers call "attribution and measurement chaos." While many marketers plan to boost spend in AI media broadly, CTV's infrastructure challenges require more foundational solutions before AI can scale effectively. However, many marketers believe attention-based metrics will become the standard for CTV measurement, suggesting future growth potential as these frameworks mature.
How are marketers using AI differently across channels?
The data reveals channel-specific AI strategies rather than uniform deployment:
| Channel | Primary AI Use | Adoption Level |
|---|---|---|
| Social Media | Campaign optimization and creative | High |
| Retail Media | Creative/content generation | High |
| Influencer Marketing | Data analysis only | Lower |
| CTV | Minimal adoption | Lowest |
In retail media specifically, marketers are shifting from creative production toward campaign management, analytics, and personalization as their 2026 priorities. This evolution suggests AI tools are maturing from experimental to operational across the marketing stack.
What systemic challenges are slowing broader AI adoption?
Beyond channel-specific barriers, platform fragmentation tops the list of marketer concerns, followed by cross-channel measurement worries. Brand safety and accuracy concerns and loss of human oversight round out the major obstacles. Notably, training employees on AI tools lags behind overall adoption rates, creating an expertise gap that limits strategic deployment. For CTV specifically, the sunset of third-party cookies has forced a pivot to synthetic data modeling and contextual intelligence - AI-driven but privacy-safe alternatives that require new technical competencies.