Signal Rich, Action Poor: What AI Native Revenue Teams Do After the Insight with RevGenius
Airspeed
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Signal Rich, Action Poor: What AI Native Revenue Teams Do After the Insight with RevGenius
13 просмотров · 4 дня назад
Airspeed
27 подписчиков
13 просмотров · 4 дня назад
Every revenue team has a system of record and a system of intelligence — but the work still lands on a person at 6pm on a Thursday. This panel digs into the missing third layer: a system of action where signals actually become completed tasks.
RevGenius co-founder Jared sits down with four GTM leaders to argue about where AI-native selling really breaks — the handoff, signal fatigue, whether the CRM survives, and what an agent needs before you trust it to act on a live deal.
Panelists
• Adam Liska — Co-founder & CEO, Airspeed
• Alex Lindell — Creator in Residence, GTM Engineering, Clay
• Mintiso — Senior Director of Agentic Selling, HubSpot
• Tyler Phillips — Head of AI, Apollo
Hosted by Jared, Co-founder, RevGenius. Presented by Airspeed.
Chapters
00:00 — Signal rich, action poor: the missing "system of action"
01:50 — What Airspeed is now: an AI chief of staff for revenue
02:57 — The three layers: record, intelligence, action — and what teams misfile
06:40 — Do you actually need one system instead of three?
08:42 — Auditing a stack you didn't choose: process maps and click studies
13:50 — Where the handoff actually breaks
16:26 — Signal fatigue and building rep trust
21:30 — Should you delegate customer-facing tasks to AI?
22:32 — "Every mistake started with a human's instructions"
23:33 — How HubSpot operationalized AI through proliferation
27:21 — Where the work lives: CRM vs. AI assistants
30:02 — Tyler's take: the CRM wasn't built for an AI-native world
32:47 — Enterprise sellers vs. AI-native sellers
36:15 — Is call recording the nucleus of agentic GTM?
38:34 — Do conversations matter at every stage?
40:29 — ROI expectations: 2023 vs. 2026
42:14 — The self-learning revenue system
43:06 — What an agent needs before you trust it on a live deal
49:01 — The line between autonomous and human-in-the-loop
54:00 — Brand, perception, and matching your customer's AI comfort
55:14 — What held up, and what we'd never do again
1:00:18 — Final takeaways
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