Hallucination Risk in AI-Generated B2B Campaign Reports
AI-generated campaign reports confidently deliver false numbers straight to budget decisions.
Agents need structured data flowing back into memory, not dashboards showing what already happened.
AI-generated campaign reports confidently deliver false numbers straight to budget decisions.
Form problems need fine-tuning; facts problems need RAG.
Specific benchmarks help catch LLMs that fail at attribution reporting.
Different marketing tasks need different models to run efficiently and cost effectively.
Precision in LinkedIn targeting requires separating fit, account, and behavioral models.
Google's algorithm chases form fills, not future buyers, unless you teach it the difference.
True learning requires predictive analytics baked into agents, not bolted on dashboards.
Agents moving fast on incomplete B2B data make confident mistakes no dashboard will catch.
Restructure asset groups around buyer intent, not products, to fix PMax's B2B pipeline problem.
Native AI agents need one closed loop, not automation dressed up with a new name.
The difference between letting AI suggest changes and letting it run your campaigns on its own.