Free Conversation Intelligence & AI Cheat Sheet

What's the difference between Conversation Intelligence and an AI notetaker — aren't they the same thing?

An AI notetaker is one capability within Conversation Intelligence — it handles transcription, summaries, and follow-up drafts. Full CI goes further: it analyzes talk-to-listen ratios, flags competitor mentions, tracks keyword patterns across your entire team, scores deal health, and feeds structured data back into Salesforce fields automatically. Think of the notetaker as the data capture layer; CI is what you build on top of it.

Do I need Salesforce to get value from the AI field update and summary features covered in this cheat sheet?

The cheat sheet is written with Salesforce as the primary CRM — it specifically covers syncing to Event objects, supporting custom Salesforce objects, and auto-mapping to the right records. If you're on HubSpot, some tools support it, but the depth of field-level control described here (multi-select fields, custom objects, de-duplication with activity capture) is largely Salesforce-native functionality. Check whether your CI vendor supports your CRM at the same level before assuming feature parity.

Which parts of the rep coaching process should stay human-led versus handed off to AI?

AI handles the consistent, scalable parts well — scoring meetings against a methodology like MEDDIC, flagging a 65/35 talk-to-listen ratio, or surfacing that a rep's question rate is 14/hour against a 18–25 target. The human conversation still matters for context: why the rep talked too much on that specific call, what was happening in the deal, and how to adjust their approach going forward. Use AI to surface the pattern; use the manager to interpret it.

What data or setup do I need in place before the deal health signals in this cheat sheet are actually reliable?

You need clean, consistently updated opportunity records in Salesforce — stage, close date, and contact roles at minimum. If reps aren't logging next steps or the CRM is missing key contacts, warnings like "single threaded" or "no access to power" will either fire incorrectly or not fire at all. The cheat sheet flags this directly: methodology fields need to be auto-updated from call data to give you accurate deal context, not just rep-entered guesses.

How do I know if the AI summaries and field updates being generated are actually accurate enough to trust?

The human-in-the-loop review step is the quality gate — the cheat sheet specifically calls out comparing current versus AI-suggested field values before a rep confirms with one click. For summaries, the signal is whether your team stops correcting them; if reps are editing every output, your prompt templates need tuning. Build a review cadence into your first 30 days: spot-check 10–15 calls per week against what the AI logged in Salesforce.

How often should I revisit the keyword trackers and AI coaching templates once they're set up?

Keyword trackers should be reviewed quarterly at minimum — competitor names change, new objections emerge, and messaging shifts after campaigns launch. AI coaching templates tied to a methodology like MEDDIC are more stable, but if you change your sales process or add a new team segment, those templates need to be updated and reassigned to the right user profiles. Set a calendar reminder; these configs go stale faster than most ops teams expect.