Guys a question: How do you decide if or where your business should invest in AI tooling? Given how many tasks, workflows and processes there are, especially across larger teams, to me it seems quite difficuly to make that call with confidence.
Yes, that makes sense. Mike C., Chris D. you sparked an idea. Instead of auditing calls against a static checklist, imagine auditing them as cause-and-effect sequences of rep→client interactions that actually lead to wins. With the right structuring and a pool of successful calls, you could surface the highest-performing interaction sequence for your business with branches that handle common forks. For example, a winning flow might look like: ACE in <60 sec → surface 2 concrete pain points → get the client to put a number on the impact → ask “What’s the cost of inaction?” → lock a hard next step. This could be stress-tested against lost calls to see what’s missing when deals fall apart. I think this has the potential to generate greater results than any model because the sequence would be model-agnostic, behavior-based, outcome-tied, and business-specific. Just by repeating the 2–3 motifs that most often precede wins, one could expect significant sales lift from the bottom of the funnel. What do you think. Does it make sense or am I talkking crazy?
Show of hands: Are you considering using AI to analyze reps’ sales calls to surface the key interaction sequences that most frequently lead to a sale. Maybe other relevant insights?
If no – why not?
If yes – what’s the biggest barrier?
If you’ve already done it – what was the result?
That’s what I like Tony W.! Show. Me. Results. Now, I can’t speak to this hypothetical OS in its entirety, but I can speak to parts of it — behavior engineering is my jam. Off the top of my head: according to Salesforce, AdRoll (B2B SaaS) increased qualified conversion rates by 65% (compared to the manual process) by adopting Salesforce Einstein lead scoring AI. It researched, scored, prioritized, and surfaced leads to act on. What’s not clear is how much time reps actually saved by eliminating manual labor — and, as a result, how many more calls they made compared to baseline. I have examples in spades as well as my own experiance from mid-market/enterprise projects. Note: To be clear, I’m not saying reps won’t learn. They will. But I believe the learning process will shift from heavy training sessions and self-guided application to system-guided micro-learnings by practice. They’ll still learn — but they’ll learn and improve much faster. Max v. that sounds very interesting. Where can I learn more about your project? I am very passioned about combining AI with behavior engineering to build 10x tools.
I actually see it as the exact opposite, Tony W. 😄 — this AI Behavioral OS would massively empower reps to punch above their weight. Instead of burning cycles navigating tools, remembering training, referring to playbooks, second-guessing, or digging for “who to call next and why,” they’d spend most of their energy on what actually wins deals: growing relationships, creating urgency, and closing. Imagine a rep spending 50% more time in actual selling conversations, generating 50% more leads in that time, creating 50% more quotes, and closing those 50% more often. That's over 500% compound sales efficiency per rep. By the time reps in a traditional org learn one new upsell approach, an AI Behavioral OS could have already surfaced, tested, and scaled 10 winning upsell strategies across different contexts — guiding reps to use them instantly, no training required. It might sound crazy but some of that is already possible. I've seen/used it in practice.
💡 I believe GPT-5+ changes the game for sales enablement We’ve all seen the buzz around AI copilots and tools like Gong — and they’ve definitely moved the needle. But something new is now possible that goes beyond in-call tips or post-call analysis. Imagine an AI-Powered Behavioral Operating System for your revenue team — not a tool that just feeds prompts, but an adaptive engine that:
Learns in real time from every win across the company
Guides reps in each situation toward the highest-probability move
Encourages autonomy, creativity, and on-the-fly adaptation
The first teams to deploy this kind of engine will gain a massive edge:
Faster adaptation → AI guidance evolves daily, not quarterly
Compounding advantage → every win strengthens the guidance for the next deal
Market acceleration loop → more wins → more market share → even more wins
In that environment, even being one quarter behind a competitor with this capability could be an existential risk. FYI I am not building this. It's just my take on Sales Enablement in GPT5+ era and I am curious about yours.
Rudy R. — this depends on the playbook and its contents. The challenge with playbooks is making them usable in the moment that matters, in a way that actually drives behavior change. It’s not enough to simply create a playbook and expect reps to reference it during high-pressure, short-decision-time moments. To be effective, playbook content needs to be surfaced at the exact time it’s needed. One way to do that is to feed the playbook into an AI tool, allowing reps to interact with it quickly via. chat— like a coach who knows all the plays. Even better, with some light AI integration into the CRM and live call access, you can deliver real-time guidance to the rep. That’s how I approach it. I hope that helps.
