Your customers are already asking general AI assistants about your products, and the answers are often wrong. In the final part of our series, we look at who should build a customer-facing AI agent, the two questions that make the case, and why measuring the conversation experience decides whether it builds your brand or erodes it.

Companies now set AI token budgets by seniority. A better way to allocate AI budget per employee: by task type and how often those tasks succeed.

AI budgets get renewed one agent at a time. A practical way to decide which AI agents to scale, fix or retire, and the evidence each decision needs.

How to turn a causal LLM into a Jev-like decision model: edit the attention mask, swap the LM head, and fine-tune for calibrated probabilities in one pass.

Employees often overestimate how much time AI saves them. Here is how to measure time saved from real usage, and why it holds up in a budget review.

How Oura measures AI support and human reps on a single scorecard, TCX, and why judging the conversation from the user's side changed the product.