Seeking Revenue Leaders for AI Framework Interviews in Revenue Creator Newsletter | RevGenius

Evan D.
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So good to see you back in this spot.
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Lara D.
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Love this format! Problem > Insight > Playbook > Proof is right for sharing. I'm building something in this space right now (AI-assisted ops transformation for blue collar businesses prepping for exit). Too early for proof (just launching) but I'd love to connect in the next quarter when I have case studies to share. Tagging this so I can circle back. Looking forward to seeing who you feature.
Jared R.
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happy to learnn more Lara D.
Vivek g.
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Nandini R. Raha shared the Brevian report showing reps only spend ~28% of their time selling. The rest is burned on manual research and managing "Frankenstein" stacks that cost ~$275k+. The Reality: We are paying high-ticket salaries for smart humans to do data entry. It’s a massive OpEx leak. 💡 The Insight: Relevance > Personalization. Scaling isn't about hiring more SDRs; it's about building a Reasoning Engine. You don't need a human to type every email; you need a system to decide why we are reaching out. 📖 The Playbook (Tech Stack: Clay + GPT-4o + Smartlead): I replaced the fragmented stack with a unified "Waterfall" workflow to reclaim that 72% lost time: Deep Enrichment (Clay): Aggregating 10+ data sources. If there's no valid "trigger," the system doesn't send. Reasoning Layer (GPT-4o): The LLM acts as a Research Analyst, writing 1-to-1 context-based messages. Infrastructure (Smartlead): Routing through a multi-inbox setup to maintain a real 85-95% inbox placement. 🎥 The Proof (Visual Walkthrough): I recorded a raw breakdown of the architecture. (Excuse the audio/mic quality, I recorded this in the flow of building, but the logic is all there): [https://www.loom.com/share/4cb0e981d86043bfabf3281a64903cc1 📈 The Outcome: Automated the grunt work so reps focus purely on closing. Happy to nerd out on the workflow logic if anyone is facing similar stack bloat!
Alexander S.
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Would love to participate. Happy to have a chat!
Vivek g.
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"Hey Alexander, let's do it! Will DM you to coordinate."
Jessie T.
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Jared R. this aligns closely with how I work. At Evolii, we focus on signal led GTM decisions. The core framework is Decide → Design → Embed. It helps leaders make clear GTM and pricing decisions before scaling activity, teams, or spend. Signal to Price is a focused application within that system. Pricing is often the most volatile area of GTM, yet it’s usually managed in isolation. We map external market signals to internal context to stress test pricing decisions early, preventing revenue peaks, margin leakage, and costly downstream rework. AI accelerates signal detection. Humans own judgment and governance. The outcome is calmer revenue, clearer decisions, and fewer corrections later. Would love to contribute and share tested frameworks and result accordingly.
Kate S.
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I have a few interesting AI use cases, in doing deep audience research/social listening and creating high-quality content with AI. I'd love to share them if interested.
Dimitris G.
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Hey Jared R. I convert consulting frameworks into AI brain so Revenew creators can build their personal AI machine. We can build an example for the newsletter. The first outcome you can get is how relevant each newsletter is to the engaged subscribers vs not engaged ones, just from the analysis level.
Jared R.
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Kate S. can you share in thread or dm
Jared R.
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Jessie T. would love to learn more. Can you share a specific anecdote
Marylou Tyler
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This looks pretty good, but I'd add a couple of sections to the Problem --> Proof flow:
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Context: (Constraints & Starting State) - why? clarifies when and for whom the playbook is relevant
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Tradeoffs & Failure modes - why? reduces bias (both hype and 'survivorship' biases).
My 2 cents. MT
Arjun P.
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Jared R. gonna dm you about some AI-powered competitive insights tools I built using a mix of CRM data, Airtable, RSS alerts etc etc
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Jason G.
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Love this format, Jared R. — especially Problem → Insight → Playbook → Proof. 🧩 The Problem: Even with strong AI stacks and good inbound/outbound signals, I consistently see reps struggle to convert qualified conversations into next steps. The issue isn’t lead quality — it’s decision friction inside the live sales conversation. 💡 The Insight: Buyers don’t decide logically first — they decide emotionally, then justify logically. Most sales processes (and AI copilots) optimise what to say, but not how buyers internally process risk, trust, and change. 🛠️ The Playbook: I’ve been using an NLP-informed consultative framework that maps:
Buyer motivation states
Hidden objections (unspoken, not stated)
Language patterns that move conversations from interest → commitment
This runs on top of existing CRM + AI tools, not instead of them. Think of it as the “decision layer” in GTM. 📈 Early Proof: In coaching BDRs and founders, this has helped:
Increase booked-to-held rates
Shorten deal cycles
Improve close confidence without pressure tactics
Happy to share a concrete conversation example if useful.
Jared R.
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Jason G. give a real life anecdote