TL;DR
AI adoption hit 92% in 2025, but the data reveals a significant gap between brands using AI tactically versus strategically across the entire creator lifecycle
Only 15% of brands used AI tools for influencer selection, while 28% leveraged them for concept development and strategy
The most successful AI implementations reveal that AI is a force multiplier, not a replacement for human expertise
Table of Contents
What began as experimental tools for early adopters became operational infrastructure for the majority of brands. According to Later's 2025 State of Influencer Marketing report, 92% of marketers used AI tools for their creator marketing programs. The primary uses were for creator vetting, performance analysis, and budget optimization.
The data reveals that while AI adoption is widespread, there’s still room for brands to apply it more strategically rather than tactically.
What sets successful brands apart came down to how organizations deployed AI across their influencer operations: high performers built intelligence loops that compounded insights across every stage of creator partnerships. The frameworks they developed are operational approaches that any organization can implement to transform complexity into strategic advantage.
The three stages of AI adoption
AI integration in influencer marketing has followed a natural progression. The brands achieving infrastructure-level maturity deploy AI capabilities across three distinct stages, with each stage feeding intelligence into the next. According to the report, brands used AI throughout the entire creator campaign lifecycle. Specifically, brands used AI tools for influencer selection (15%), content performance analysis (16%), and concept development and strategy (28%).
1. Influencer selection
With 15% of brands using AI tools for influencer selection, it’s proving to be a growing yet effective use case. The technology transforms creator discovery from subjective evaluation to data-driven matching. Tools like Later EdgeAI analyze audience demographics, engagement authenticity, content performance patterns, and brand safety indicators across thousands of potential partners simultaneously. The selection process that once required weeks of manual research now produces vetted recommendations in hours.
2. Content performance analysis
Following close behind influencer selection is content performance analysis. Sixteen percent of brands prioritized this AI use case. Marketing leaders understand that AI tracking extends beyond basic engagement metrics to examine sentiment, conversion patterns, audience response by demographic segment, and content format effectiveness. AI-driven performance analysis reveals which creative approaches drive business outcomes rather than just attention. Additionally, performance insights accumulate across campaigns, enabling pattern recognition that informs future creator selection and content strategy.
3. Concept development and strategy
Finally, the data reveals that the largest marketing application of AI tools is concept development and strategy. This application closes the loop. The strategic applications synthesize insights from creator selection data and performance analysis to generate recommendations for campaign timing, budget allocation, creator pairing, and content approaches. The intelligence becomes predictive rather than merely descriptive.
The progression matters because each stage generates data that enhances the others. Selection insights improve when informed by performance patterns, while performance analysis gains context from understanding why specific creators were chosen. Strategic development strengthens when built on comprehensive selection and performance intelligence.
Organizations that deploy AI across all three stages rather than treating them as isolated applications create compounding advantages that widen with each campaign cycle.
Download Later's 2025 State of Influencer Marketing report to see how leading brands are adopting AI into their influencer marketing infrastructure.
Why “intelligence loops” matter
Leading brands structure their AI deployment to create feedback loops across the creator lifecycle. Creator selection algorithms learn from performance data, identifying which partnership characteristics predict strong outcomes. Meanwhile, performance analysis tools incorporate selection criteria to understand why certain creators may have exceeded expectations. Strategic planning systems synthesize both inputs to generate increasingly sophisticated recommendations.
This intelligence architecture transforms how programs operate. Instead of starting each campaign with similar information quality, teams can work from continuously improving baselines. This means the tenth campaign benefits from insights accumulated across the previous nine.
The operational evolution of AI implementation
AI adoption has restructured how teams allocate their time and attention. Later’s report revealed that this shift happened across three dimensions, which collectively transformed program operations.
From execution-focused to strategy-enhanced operations. AI assumed responsibility for tasks such as creator discovery, initial vetting, performance monitoring, and reporting automation, which has ultimately freed teams to focus on strategic innovation. The question changed from “How do we execute this campaign efficiently?” to “What strategic opportunities should we pursue?” Teams redirected energy from operational execution toward relationship development, creative collaboration, and business integration.
From manual decisions to data-supported choices. Access to comprehensive performance data, audience insights, and predictive analytics changed the foundation for decision-making. Teams used AI to evaluate creator partnerships against historical performance patterns rather than subjective assessment. Budget allocation reflected predicted ROI rather than intuitive distribution, while campaign timing aligned with data-supported seasonal patterns rather than conventional wisdom. The decisions themselves remained human-driven, but the intelligence supporting those decisions achieved a sophistication that manual analysis could never match.
From post-campaign analysis to real-time optimization. AI-powered monitoring enabled teams to identify campaign performance patterns and make strategic adjustments in real time. Underperforming content approaches could be modified mid-campaign, while budget could shift toward highest-performing creators before campaigns concluded. The feedback cycle that previously operated on campaign timeframes shifted into continuous optimization.
In each case, AI removed complexity so marketers could focus on higher-value activities.
AI as a multiplier (not a replacement)
The most successful AI implementations share a common characteristic: they amplify strong teams and processes rather than compensating for gaps.
The data indicates that organizations with clear creator vetting criteria used AI to execute those criteria at scale. Teams with established performance frameworks deployed AI to generate insights that refined those frameworks, while programs with strategic clarity leveraged AI to identify opportunities aligned with existing priorities. The takeaway is clear: technology multiplied the effectiveness of existing capabilities rather than creating capabilities from nothing.
This reality creates an adoption requirement that many organizations may underestimate. AI tools deliver maximum value when integrated into mature operational processes. They surface insights that teams must interpret and act upon, while also generating recommendations that require strategic context to evaluate properly.
The brands achieving enterprise-level capacity through AI deployment invested in both technology and the operational maturity required to leverage that technology effectively. This requires building clear processes before automating them, and establishing measurement frameworks before deploying predictive analytics.
How small teams achieve enterprise-level capacity
The most significant shift AI enabled wasn’t giving large teams more power. Instead, AI gives small teams the capabilities that previously required enterprise resources. The operational gap between a two-person influencer marketing team and a ten-person department compressed dramatically when AI handled the complexity that once demanded increased headcount.
The brands achieving this transformation, regardless of team size, approached AI implementation through the same operational lens that characterizes mature influencer programs. They didn’t just adopt tools, they rebuilt processes around intelligence rather than manual effort.
For example, small teams historically struggled with strategic investment and budget allocation because they lacked data to support decisions. Where should the budget concentrate across platforms? What seasonal campaigns produce the strongest returns?
AI-powered analytics transformed this constraint into an advantage. Performance forecasting enables confident budget allocation based on predicted outcomes rather than intuitive guesses. Historical data reveals which investment patterns produced strongest ROI, informing planning even with limited campaign history. Real-time monitoring allows reallocation toward highest performers, maximizing returns from every dollar.
Another practical application of AI for small teams is to transform operations through intelligent automation. AI solutions like Later EdgeAI handle creator discovery, surfacing qualified candidates based on specific criteria. It manages initial vetting, flagging potential issues before teams invest in relationship building. It also monitors content performance across platforms, alerting teams to optimization opportunities, and generates performance reports that once required hours of manual data compilation.
Every automated process returns time to the team, which can be redirected toward activities that AI cannot handle: strategic thinking, creative collaboration, relationship building, and business integration.
To apply this framework in 2026, teams should audit their current workflows to identify which activities consume time without requiring human judgment. Those activities become automation candidates. The time saved compounds across campaign cycles, enabling small teams to manage program complexity with fewer resources.
Download the full report to access deeper insights on adopting AI for your influencer marketing program.
The brands that committed to intelligence-powered operations throughout 2025 built advantages that widen with each campaign. Leading brands use AI to optimize entire systems, predict future outcomes, and operate from predictive intelligence that significantly changes how influencer marketing campaigns impact business outcomes.
The opportunity to build these advantages into your influencer marketing program requires moving beyond viewing AI as a tactical efficiency tool toward recognizing it as an operational foundation.
Ready to scale your operations through strategic AI adoption? Schedule a call with Later to discuss how our platform’s AI capabilities can empower your team with intelligence-driven insights.
