AI Adoption vs. AI Impact: The Gap Your Demand Gen Program Might Have
Scott Schnaars
83% of Demand Gen Marketers Use AI Daily. Why Aren't Results Matching?
Eighty-three percent of demand gen marketers are using AI as a core part of their workflow. That is the number from a recent Exit Five poll (https://www.exitfive.com/community). As a signal that results are improving, it says almost nothing.
The Exit Five community surfaced this tension directly: high AI adoption does not equal better outcomes if AI is only being used for productivity tasks. Drafting copy faster does not improve targeting. Summarizing pipeline reports does not identify what changed. Generating slide decks does not change which campaigns get funded.
The AI use cases that actually move pipeline look different in kind, not just degree:
- AI for writing: faster, but pipeline impact is marginal unless writing speed was actually the bottleneck
- AI for anomaly detection: high impact, because catching a targeting drift or budget overrun early changes what the team does next
- AI for cross-channel budget recommendations: high impact when the underlying data is connected; near-zero impact when AI can only see one channel at a time
- AI for campaign performance prediction: valuable when trained on your actual historical data; unreliable when using generic benchmarks
The question to ask about any AI deployment is not how often the team uses it. It is what decision it changes. If you cannot name a specific decision that AI is now making better, the adoption number is just a usage metric.
Yirla uses AI specifically for paid campaign analysis connected to your actual pipeline data, not generic benchmarks. (https://www.yirla.com/en/platform)
