Connect Claude to your CRM, start asking questions, trust the answers — that's how most teams begin with AI. The problem: your agent has all your data and none of the context behind it. It doesn't know why a field exists or which one to trust, so it hands you a confident answer built on the wrong foundation.
Ronnie Duke joins Chris Strom to break down the context problem behind AI agents and what it takes to fix it. Plugging in a tool, Ronnie explains, is like giving a new hire every login on day one and walking away. The logins aren't the hard part. The tribal knowledge is.
In this episode:
Why "just connect it and ask" breaks down — and the hidden cost of confident-but-wrong answers
The layers of context agents need: structural docs, repeatable process docs, and data dictionaries (including the backstory behind why things were set up the way they were)
Where to store shared context so it's not trapped in one person's private chat history
Keeping documentation current with governance and self-healing workflows
The real answer to "am I training my replacement?"
Whether you just signed up for Claude or you've been token-maxing for six months and your CFO wants answers, this one's a map for where to go next.
Podden och tillhörande omslagsbild på den här sidan tillhör
ClearPivot. Innehållet i podden är skapat av ClearPivot och inte av,
eller tillsammans med, Poddtoppen.
