2025年数据现状_AI在媒体广告活动中的当下_近期与未来演进_36页_8mb

State of Data 2025: The Now, The Near, and The Next Evolution of AI for Media Campaigns

Core Content Overview

This report, State of Data 2025, explores the current and future state of AI adoption in the media campaign lifecycle, focusing on the evolution of AI from a tool for optimization to a strategic enabler of full-scale media campaign execution. It provides insights into the current status, perceptions, challenges, and future opportunities for AI in the digital advertising industry.

Main Points

1. AI is Transforming Media Campaigns

• AI is evolving beyond simple optimization to enable full-scale execution of media campaigns, including planning, activation, and analysis.
• New AI forms such as generative AI and agentic AI are enabling unprecedented levels of automation, strategy, and execution.
• AI can now be used for tasks like audience segmentation, media mix planning, performance forecasting, synthetic data generation, and customized sales packaging.

2. Current AI Adoption Status

• 70% of agencies, brands, and publishers have not yet fully scaled AI across the media campaign lifecycle.
• Agencies and publishers are leading AI adoption, while brands are still in the pilot stage.
• Brands cite cost as the main barrier to AI adoption, while agencies and publishers are more advanced in implementation.

3. AI Use Cases by Stakeholder

• Agencies are using AI for audience segmentation, real-time bid adjustments, market intelligence gathering, automated campaign management, and advanced analytics.
• Publishers are leveraging AI for inventory forecasting, cross-device attribution, audience behavior analysis, and ad placement recommendations.
• Brands are experimenting with AI for audience identification, anomaly detection, campaign setup automation, contextual targeting, and RFP creation.

4. AI Perceptions

• Agencies and publishers are highly satisfied with AI, with 70% or more reporting it meets or exceeds expectations in terms of efficiency, effectiveness, and reliability.
• Brands view AI as slightly less effective, likely due to slower adoption and pressure to demonstrate ROI.
• AI is most effective in audience segmentation and data aggregation, but less effective in areas like ad fraud prevention, brand safety, and contract management.

5. AI Challenges

• The primary challenges to AI adoption are data readiness, security, and tool fragmentation.
• Job security is the least concerning at 37%.
• Only 49% of companies are actively addressing AI challenges with key solutions like strategic roadmaps, training, and governance.

6. Future AI Opportunities

• AI is expected to advance measurement and agility in the media campaign lifecycle.
• Agencies are focusing on real-time budget shifts and automated campaign setup.
• Brands are testing anomaly detection and data integration.
• Publishers are looking to optimize inventory and measure campaign impact.

Key Insights

• 70% of agencies, brands, and publishers are not fully integrating AI across media planning, activation, and analysis.
• Agencies and publishers are more advanced in AI adoption than brands.
• Challenges include data quality, security, and tool fragmentation.
• Only half of companies are taking action to overcome AI adoption hurdles.
• Clear AI adoption plans and strong governance are essential for successful integration.

Recommendations

1. Build a phased roadmap for full-scale AI adoption, mapping out all potential use cases and setting clear milestones.
2. Ensure data readiness by auditing data quality, security, and governance.
3. Implement organization-wide training and best practices for AI usage.
4. Strengthen industry collaboration, trust, and standardization.
5. Prioritize key use cases that align with business goals and can drive measurable outcomes.

These steps are critical to ensuring that AI is not just adopted, but also integrated effectively into the media campaign lifecycle to unlock its full potential.