Upload call recordings or transcripts from tools like Gong or Fathom into an AI model. Ask specific questions like, 'Where was the most friction?' to identify disconnects you missed in the moment. Use this insight to craft hyper-relevant follow-ups that address the core misunderstanding.
Using AI to generate a pre-call hypothesis about a prospect's priorities is valuable even when it's wrong. Presenting a thoughtful, albeit incorrect, idea demonstrates research. This prompts the prospect to correct you, immediately opening the door to a deeper conversation about their actual priorities.
Create a dedicated AI agent pre-loaded with your company's specific deal qualifiers (budget, timeline, ICP). Feed it discovery call notes, and it can instantly score the opportunity or flag it as disqualified, preventing reps from wasting time on deals that will never close.
Instead of general queries, instruct your AI to act as an account executive with an urgent deadline. This framing forces the AI to cut through fluff (like a company's founding date) and extract pressing business initiatives from documents like 10-Ks and earnings calls.
Instead of writing scripts from scratch, prompt an AI to apply a specific sales methodology (e.g., Jeb Blount's 'because framework') to your prospect's context. This instantly creates persona-specific openers and voicemail scripts, saving creative energy and ensuring consistent messaging during call blocks.
After a promising sales call, combat 'happy ears' by feeding your meeting notes into an AI. Ask it to identify the top three reasons the deal might *not* go through. This provides an unbiased third-party analysis, revealing red flags and potential objections you can address proactively.