Agentic sales ≠ replacing reps. It’s about pairing human sellers with AI agents to remove mundane work (research, prep, drafting) so reps spend more time in high-value conversations.
Prospect research (30–120 min per lead): Industry context, company background, likely pain points.
Decks/proposals: Assembling tailored pitch materials after discovery calls.
Outbound prospecting: Generic, spammy emails that get ignored.
Sales Research Agent
Pulls from CRM/web activity: pages viewed, products browsed, prior engagements, webinar attendance.
Enriches with internal assets: past case studies, similar customer examples, proven plays.
Goal: Rep enters the first call informed enough to ask deeper questions and suggest potential solutions.
Proposal/Pitch-Deck Agent
Keeps static deck sections fixed (company intro, methodology, case studies).
Auto-generates the custom middle: prospect’s context, industry insights, recommended plan.
Tools mentioned: HubSpot (+ transcripts), Gamma (via API) to inject custom sections.
Prospecting Agent
Converts research insights into highly personalized sequences rather than canned blasts.
Uses product usage/behavioral signals when available (“I saw you doing X—here’s a better way”).
Meet reps where they work: Embed agents inside the CRM (e.g., HubSpot). No new tools or extra clicks.
One-click actions: Research appears on the record; add to sequence in two clicks; “Generate proposal” button from the deal.
Agent library: Internal catalog describing each agent, tech stack, how to access, and value.
Quality inputs drive quality outputs.
Ensure call transcripts land in the CRM and populate key fields.
Grade calls against a question rubric to confirm discovery quality.
Maintain accurate knowledge bases and process docs; otherwise AI hallucinates.
Data hygiene & documentation are the long-term leverage.
You don’t need “Rolls-Royce” tooling.
Mix of existing CRM (HubSpot tiers vary), low/no-code tools (e.g., n8n/Make), and agent builders (e.g., Agent AI; reportedly with free options).
Bias for action: Ship a quick-win agent (research agent) to unlock 3–5× time savings fast.
When hiring help, pick partners who understand your CRM’s data model.
Models get faster and more capable, but the biggest wins come from:
Organizations investing in knowledge capture and process documentation.
CRMs adding stronger data hygiene and knowledge management features.
Teams skeptical of AI usually have poor context/data, not tool problems.