Most PPC teams already use AI as a copilot. You ask a question, you get an answer, and you stay in control of every step. Agentic AI is different. It acts, checks its own output, and adjusts without being re-prompted at every stage.
This session is a practical guide to that shift. What genuinely changes when an account tool moves from answering to acting, which categories of PPC work suit it best, and the failure modes to expect first: bad rules, false positives, over-trusting the output.
I'll share a simple three-rung framework, from data in an LLM, to off-the-shelf connectors, to custom-built systems, so you can judge which rung your own account actually needs.
Key Takeaways:
- How to tell the difference between "AI-assisted" and "agentic" work, and a simple test to apply to any tool or workflow they already use.
- Where agentic systems earn their place in a PPC account and where they don't, mapped against everyday account work like auditing, monitoring, product-level decisions and feed optimisation, plus the guardrails that make automation safe to leave running.
- A three-rung build sequence, so they can work out whether they need an hour with an LLM, a pre-built connector, or a genuinely custom system, without over-building before it has earned it.
