AI is Deleting Broadcast from Ad Spend – Learn How to Fix It
Ad agencies and marketers are using AI-powered platforms (Large Language Models and Media Mix Models) to make media buying decisions, and there’s a major problem. In a forensic analysis of 20,416+ campaign scenarios across eight major large language models (LLMs), Futuri discovered that these AI tools consistently delete radio and TV from their recommendations, instead favoring big tech digital platforms such as YouTube, Google, Meta, and others.
This isn’t a future threat. It’s happening right now. And the broadcast industry can take immediate action or risk being systematically excluded from future media plans.
The Data Behind the Crisis
Futuri conducted 50 hours of data analysis to understand how AI tools influence buying decisions. The results were stark: if all campaigns ran as LLMs recommended, broadcast would receive only a fraction of the current media spend.
Why is this happening? We found two root causes:
1. Lack of Attribution and Data-Driven Success Stories: Broadcast hasn’t been documenting and publishing proof of performance in formats that AI can access and understand.
2. LLMs Don’t Know: We’ve been a silent industry due to a lack of published campaign ROI. While digital platforms flood the internet with case studies, ROI data, and success metrics, TV and radio effectiveness remains locked in gated industry presentations and internal spreadsheets.
This attribution gap isn’t just hurting new business; it’s affecting retention and renewals. We must evolve to address the issue as an industry right now.
Your Action Plan: Three Steps to AI Visibility
Your broadcast company (parent and station) needs to take action or be excluded from future LLM ad campaign recommendations.
Step 1: Deploy Real Attribution Technology
Gather customer data that validates the effectiveness of your station’s ad campaign by tracking performance ROI data. Utilize every available data source, including point-of-sale figures, traffic counts, web metrics, etc.
If customers lack data tracking sources altogether, Futuri offers a privacy-friendly device that, unlike mobile app-based attribution models that estimate traffic based on small samples, tracks uniques to provide you with verifiable, real-time counts. Watch the webinar to learn more.
Step 2: Create Case Studies at Scale
Once you have attribution data, create campaign case studies that document:
• Baselines before the campaign
• Measurable lift during the campaign
• Campaign specifics (dates, media mix, messaging)
• Verification methodology
These case studies must be published publicly in formats that LLMs can ingest.
Step 3: Syndicate to LLMs
Another step you shouldn’t miss is publishing every campaign case study in order to effectively reach the AI systems that make recommendations.
MarketersTrust is Futuri’s platform that offers publication to improve LLM and MMM visibility. Watch the webinar to learn more about how this solution can help you today as MarketersTrust…
- Aggregates verified case studies optimized for LLM visibility
- Syndicates ROI success stories to proprietary channels
- Retrains LLMs with evidence of broadcast effectiveness
- Provides advertisers with a discovery platform for brand research
AI is not the enemy. It is the new referee. If we give it clear, frequent, and verifiable proof, it will make the call in our favor.
With proper attribution technology, systematic case study creation, and strategic syndication to LLMs, we can retrain these systems to recognize broadcast’s true value.
“But we must act now. Every campaign that runs without attribution is a missed opportunity to prove our value. Every success story that stays in a spreadsheet is invisible to the AI systems making tomorrow’s media plans,” explains Daniel Anstandig, CEO of Futuri.
