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As a GTM Advisor (my side gig), running pipeline reviews for the companies I advise used to suck. I deployed an AI agent that fixed everything. Pretty crazy given how tech illiterate I am, so I thought I'd share it here :) EDIT: After many requests - check it out https://lnkd.in/dCQcfyed Every week, it was the same thing: Open Salesforce. Go through every deal ONE by ONE. → Is it actually moving? → Is there a clear next step? → When was the last activity? → Is the champion still engaged? → Is this deal at risk? → Is it REALLY closing this month? "Okay… next deal." 50+ times. NOW, before my pipeline review even starts, the AI agent scans every deal for me. And tells me: → Which deals are at risk → Why they're at risk → What's missing → What the reps should do next So when the sales team joins the pipeline review, they already know exactly where they need to focus. Happy to share my whole setup if you're curious. Now full transparency folks, this is a sponsored post. But I strictly only agree to work with brands I use first hand and have seen results with.

Every sales leader agrees on one thing, even if nobody says it out loud: their reps are bad at using the CRM. Not because they're lazy. Updating five fields after every call was never part of the job. So pipeline review becomes a guessing game. You assume the data is roughly right, because checking 40+ open deals one by one isn't realistic. Here's what that guessing actually costs you: deals sitting in the wrong stage for weeks. Close dates that haven't moved since the day they were entered. One rep at 90% pipeline hygiene, another at 40%, and no way to tell which is which until you ask each of them directly. I plugged in an agent that flags all of it automatically, per rep and across the whole team: who's up to date, who isn't, which deals are stale, which ones are sized or staged wrong. It's not about saving time. It's that for the first time, I actually know the true state of my pipeline, not what the dashboard tells me to believe. Check it out https://lnkd.in/ebEq_UvB

Close dates nobody has touched in weeks. Deal sizes that stopped being true last quarter. That's what your pipeline review runs on. EDIT: After many requests - check it out https://lnkd.in/gjKVjjcH Sales forecasts are only as good as the data you have available from your teams. Every sales leader I've worked with has to deal with "garbage in, garbage out" from their reps' CRM usage. That's what your pipeline review runs on. → Incomplete account info → Missing deal records → Wrong deal sizes → Lead stages nobody updated So the pipeline review is relying on out of date, incomplete information, and close dates nobody has touched in weeks. Your review is an exercise in futility. With Vasco, we can build agents that write directly into CRM: - After every call, an agent drafts the updates: next steps, what changed on the deal - The rep approves them instead of typing them - Leads nobody worked get flagged The lazy salesperson in all of us appreciates it. So does whoever runs the pipeline review. For my connections, I'm doing a free 30-minute call to set this up with you. Hit me up in the comments :)

Reps are bad at the CRM. Every VP of Sales already knows it. One AI agent, fed by calls, Slack and Gmail, can now do the filing for you. And it works! A friend VP of Sales built an AI agent for that. Before, it looked like this: Open Salesforce. Update every deal by hand. → Is it actually moving? → What's the next step? → When was the last activity? → Is the champion still engaged? "Okay… next deal." It took hours. Now, nobody touches the CRM. It fills itself. And before the review even starts, the agent has already gone through your pipeline: → Which deals are at risk → Why they're at risk → What's missing → What I should do next So you can spend few minutes on it. Your sales leadership too. And spend the rest of the week on what actually pays: closing. If you're tired of struggling with your pipeline review, hit me up. #partnership

Here are the top three agents teams use the most: EDIT: After many requests - check it out https://lnkd.in/gzJiqgBf 1. Pipeline Review 🎯 Reviews every open opportunity against the full account context to validate what's actually happening in each deal, every week. It gives a sales manager or revenue leader everything needed to run an efficient, high-quality pipeline review 2. Churn Prediction 🚨 Catches churn before it happens by scanning weekly calls and emails for cancel intent, billing disruptes, product and service frustration, and sentiment decay. It also classified the accounts into Critical, High, or Moderate risk 3. Weekly Business Review 📊 Automatically builds the performance-vs-plan report. Gives leadership a clear, data-driven picture of business health, key wins, risks, and priorities for the next period. That's 5 to 8 hours saved every week for the person who used to build it manually. And there are 19 more across capacity planning, ICP discovery, messaging coach, customer health, etc. All 22 agents run on the same metric engine and context graph! So reps, managers, and CROs are all working from the same numbers and definitions Interested in seeing the Pipeline Review agent which is the one that leadership teams ask for first ? EDIT: After many requests - check it out https://lnkd.in/gzJiqgBf

An AI agent found $3.27M in at risk ARR and their CSMs had no clue. Neither did Claude, even with it fully connected to their CRM, Slack, Gmail, and call transcripts. EDIT: After many requests, check it out https://lnkd.in/g5rp3H8B And it shouldn't be a surprise if you've ever worked in Customer Success. Revenue forecasting is often wrong, churn continues to be a surprise, and CSMs aren't focused on the right accounts. For years I thought the fix was a better health score. I even taught leaders how to build them. *shame* But health scores, at best, are a snapshot. They don't tell you why an account is at risk, or that it started weeks ago. So when Vasco showed me their Churn Risk Report agent, I wanted to see what it actually finds. Most teams try to solve this by connecting an LLM to their tech stack but find that the response is confident … and wrong. Yes, these LLMs can see the data, but it doesn't understand it. Claude has to figure out, from scratch, which Slack thread belongs to which account, whether a sentiment shift three weeks ago actually matters, what "at risk" even means for this business. Access isn't the same as context. That's what was missing, even after connecting all of my tools. It needed the layer that already knew the business, so Claude isn't reconstructing it from zero on every question. I tested it with a customer recently. The agent scans all of their data weekly, and shows both the scale and the why. Here's what we found: → 843 customers scanned, 218 flagged, $3.27M ARR at risk, 61 renewals critical within 60 days → Reasons ranked by pattern: product frustration, competitive language mentioned, sentiment changes, usage dropping, disengaged executives, and billing disputes 5 of the flagged accounts had over 10 product issues flagged and showed cancel-intent language, the exact combination we'd want to understand quickly. I've been a CSM. I know what it's like to dig through five systems just to find out where to focus and still have to guess. Vasco partnered with me to let founders in my community test this for free. They'll help you connect your data and build a report like this on your own customer base. Want to see what it finds in yours?