Common Room

The playbook has changed. A few years ago, success in Support meant scaling ticket queues, improving SLAs, and delivering great customer experiences. While these elements still matter, they are no longer sufficient. Today, AI is reshaping our work, and customer expectations are evolving faster than ever. Support leaders are now being asked to influence product, drive revenue, improve adoption, and help define company strategy. The role has transformed. Next month, I will join an incredible lineup of leaders at Support Driven Expo Chicago to share a talk that is deeply personal to me: The Playbook Has Changed. We will explore what leadership looks like in a profession that is being reinvented and why the leaders who thrive will not be those with the best processes, but those who learn to think differently. We will discuss: • Why yesterday's leadership playbook no longer works • How AI is changing what Support leaders are accountable for • The mindset shifts required to stay relevant • How to build influence beyond Support and become a strategic business leader This talk is not about replacing people with AI; it is about redefining leadership before change defines it for us. I am honored to speak alongside so many people I admire and grateful to Support Driven for this opportunity. If you will be at Support Driven Expo Chicago, I would love to connect. #SupportDriven #SDSummit

Every "AI agent builds GTM workflows from a terminal" demo I've seen this year assumes the same thing. That the data underneath is already correct. Nobody's shown what happens when it isn't. The difference that actually matters? If an agent enriches a contact and gets a title wrong, you get a bad cell in a spreadsheet. Annoying, but recoverable. If an agent gets identity wrong, decides two records are the same person when they're not, or the reverse, you get a duplicate contact or account. Pipeline counted twice. An AE calling a contact another rep already owns. Reach is the easy problem now. Trust is the hard one. So before I let an agent touch our own data, I wanted to see what it was actually standing on. No UI, just our CLI (cr), and one question: what's actually in here? First thing I noticed: Account and Organization aren't the same object. Organization is every company we track. Account is the CRM-facing view, built and synced automatically the moment Salesforce is connected. The catalog tells an agent which one to reach for, depending on what's actually being asked. I hadn't asked it to do anything yet. I'd just asked what it knew. And it already knew more precisely than most people at most companies could tell you about their own CRM. Then I tried writing something. Created a contact through the CLI. Here's the part that matters: the same merge model that resolves identity across every path into Common Room, the UI, the API, our MCP server, runs against it too. Doesn't matter which door the data walked through. One of our customers, incident.io, ran this against their Salesforce data and watched duplicate accounts drop from 3% to 0.3%. A 70% reduction, on data that had been piling up for years. Buyer intelligence only creates value where the work happens. Now it can go wherever that is. Wrote up the full breakdown, the actual catalog output, what happens on write, why we built it this way, on the blog. Link in comments.

The RevOps leader who governs AI by approving everything is a bottleneck by design. The one who doesn't govern it at all watches twelve teams run twelve versions of the same broken workflow, each one undocumented, each one one field-name change away from quietly breaking. Neither is doing the right job. But in an AI GTM stack, both failure modes get more expensive fast. AI doesn't produce bad output slowly. It produces it confidently, at scale, across every rep and every territory, until someone finally notices (usually when it's much too late). Conversely, the governance model that works is system-based, not approval-based. ❌ RevOps doesn't sit in the critical path of every decision. ✅ RevOps builds the rails so the right decisions are obvious and the wrong ones are hard to make by accident. That's the difference between a gatekeeper and a multiplier. And it's the difference between an AI motion that works the way the demo promised and one that produces fast, confident mistakes nobody catches until Q3. We wrote the playbook. Read more in the comments.

Most ABM programs stop at the account level. A "hot account" lights up, and then nothing. Your platform tells you a 4,000-person company is in-market. It doesn't tell you which of those 4,000 people to call. So reps guess. They pick the title that looks closest and hope it's the right one. Accounts don't buy. The people inside them do, usually six to ten of them, each one caring about something different and each one able to slow the whole thing down. Knowing which of those people are engaged, which haven't been brought in yet, and which one is quietly blocking you is the difference between a target account list and a deal. Join Mason Cosby (Founder, Scrappy ABM) and Davis Potter (CEO & Co-Founder, ForgeX) on August 6 for an honest conversation about why most ABM programs fail, and what the teams getting it right do differently. 🗓 August 6 · 9am PT | 12pm ET Register 👉 https://lnkd.in/ge7N6VH3

A target account list tells you where to aim. It doesn't tell you who to call. That's the gap tripping up most ABM programs right now: teams get great account-level data, then hand reps a company name and a vague "they're engaged." No person, no context, no reason to reach out today versus next week. We talked to four ABM practitioners (Myles Madden, Davis Potter, Evan Dunn, and Pasha Irshad) about what actually changes pipeline. The short version: stop scoring accounts and start watching the humans inside them. A funding round, a new hire, a role change. These are individual moments, not company-wide ones, and they're what separate a rep's cold guess from a warm, specific opener. The companies getting ABM right have gotten sharper about which person, at which account, is ready right now. Full blog: https://lnkd.in/g-9BSFKV

Two weeks. Six drops. One belief that never changed: Intelligence shouldn't have a home screen. It should live wherever work already happens, then follow your team wherever the work moves next. 🔹 Slack Bot—buyer intelligence inside the conversation. No login, no tab, 30-second answers. 🔹 CLI + MCP Server—took that intelligence headless. Programmable from the terminal. Live inside Claude, ChatGPT, whatever LLM your team runs on. 🔹 Sendoso—signal fires, gift ships. No dashboard, no export, no lag in between. 🔹 LinkedIn Ads—the same intelligence now drives targeting too. Reach out or warm the account with ads, automatically, at the same time. 🔹 Buying Committee View—not who's in the CRM. Who's actually in the room. Engaged, warm, cold, or about to go quiet. 🔹 Manager View—that same count, across the whole book. Coaching stops being a gut call. Those are the bridges. And we're not done building them. Because the gap between AI ambition and AI execution isn't shrinking. It's widening. Intelligence stuck in one UI, waiting to be opened, isn't the breakthrough it was sold as. We're building the opposite: Data movability. Intelligence that meets GTM teams where they already work, instead of asking them to go find it. Which tool in your stack is still stuck behind a login? Tell us in the comments. We probably already have a bridge for it.

Eight reps. A forecast. A pipeline view. And still no real answer to how many people are actually in each buying committee, or how many of them anyone's actually talked to. That's not a coaching failure. That's a counting failure. The manager ends up coaching to whatever the most visible relationship in the account happens to say, because that's the only data point anyone handed them. Everyone nods along in the pipeline review. Nobody's actually counted anything. Manager view fixes the count. Open the book and see, account by account, which ones have a real buying committee behind them and which ones are running on a single relationship doing a lot of heavy lifting. Now you know where to step in before the pipeline review, not after. Need a specific cut, like every enterprise account in the Northeast with no activity in 30 days? Just ask for it in plain English. No filter to build from scratch, no ticket to RevOps, no waiting on someone else's Tuesday. Want early access? Link in comments.

Yesterday we hosted a webinar with Colin Netal, Head of Demand Gen at Otter.ai, on how his team 2x'd outbound pipeline. Not with more leads. With better signals. Your own product usage is the one piece of first-party data your competitors will never have. While everyone else runs the same play on the same commoditized intent data, Otter piped their product usage into Common Room and scored what accounts were actually doing inside the product. A few takeaways worth stealing: → Score behavior, not just fit. Otter's model is ~80% product usage, 20% firmographics: meetings recorded, minutes used, new users added, real engagement. → Keep it simple. They built 5 usage-based plays as signal segments in Common Room (senior sign-ups, workspace consolidation, high-scoring contacts, power users, surge sign-ups), so qualifying accounts surface to reps automatically. → Match the play to the buying committee. As deals move upmarket, the power user isn't the signer, IT is. Seeing each person's usage lets reps read the room and adjust. Otter's result was a 2x jump in outbound pipe gen almost instantaneously. Read the full recap ➡️ https://lnkd.in/gvBFfS3W

The buying committee already has a group chat, you’re just not in it. Even if your champion is responsive and the deal looks healthy, someone in that group chat is skeptical. You might not know it, but procurement has been on your pricing page three weeks in a row, and IT has an opinion strong enough to close the deal. But you’ve never even had their email addresses. So how do you enter the chat? Our buyer committee view starts from a simple idea: you should be able to open an account and immediately see who’s actually in the buying committee. Not just your contact. Not just your buddy’s friend of a friend. Every single key decision maker. Whether you’ve engaged with them or whether they’ve been active but never contacted. A live view built from the signals already sitting in the account, enriched with everything Common Room sees that no map competitor can replicate. Want early access? Link in comments.

We are LIVE 🎉 https://lnkd.in/gpe5F4Xi