Vendors spent years giving revenue teams more data, more tools, and now more AI. But deals weren't closing better.

How many times have you heard of a deal that was about to close? Then suddenly it didn't.

The CRM said stage four. The rep would have sworn it was a lock. But two calls back, the buyer had gone quiet on price. That signal lived in a transcript nobody connected to the deal record until it was too late.

I’ve watched some version of this happen more times than I can count.

It’s one of the reasons I started Common Room. I believed revenue teams deserved to know their buyers, not guess at them.

I still do. What’s changed is what we can do with that intelligence.

For a long time, the market answered that belief with a bigger database. Vendors sold you on the size of theirs: 400 million contacts, most of them names nobody would ever call. Bigger felt like better, until it didn’t. A database that size doesn’t tell you which five people to call this week. It just tells you it has a lot of names.

So the market tried fixing it with more tools. Then they added AI to all of them.

Now you've got a dozen systems that each know something about the customer, but none of them know the whole customer.

The CRM knows the opportunity. The call recorder knows what the buyer said. Intent knows who’s researching. Engagement knows who the rep contacted. Enrichment knows the account is using a competitor. And the seller knows what never made it into a system.

I don't consider that AI-native GTM, but rather the old GTM stack with AI bolted onto every silo.

And no matter how smart each AI gets, it still can’t reason across the whole customer.

Until now.

The GTM market is consolidating quickly.

HubSpot bought Warmly. Apollo bought Pocus. Salesloft bought Clari. Gong bought RightBound. Zoom acquired Common Room.

But product integration won't be enough. AI-native GTM requires shared context.

And Zoom has a massive advantage: We own the conversation.

Millions of buyers and sellers are already talking through Zoom every day on the phone and in meetings. We see what buyers say, what they care about, what they object to, and what changes from one conversation to the next.

Common Room gives us the context around those conversations: who the buyer is, what they’re doing, what’s happening across the account, and who else is involved.

Put them together and you get something fundamentally different: who the buyer is, what the buyer is doing, what the buyer is saying, and what should happen next.

Today, a buyer going cold, researching a competitor, or bringing a new person into the deal might be three disconnected signals. In a Revenue OS, it’s one story. The system understands what changed, why it matters, and what to do next.

That’s our vision and opportunity: not another application in the GTM stack, but an intelligence layer that understands the customer across the entire revenue journey.

That’s the foundation of our Revenue OS.

Know. Act. Learn.

There are three parts to how we think about it.

Know. Know the customer. Bring together everything we know about who they are, what they’re doing, what they’re saying, and what’s happening in the account.

Act. Turn that intelligence into action. Find the right buyer. Engage them. Prepare the seller. Coach the call. Flag a deal that’s drifting. Find the expansion opportunity. And increasingly, let an agent take the action instead of just recommending it.

Learn. Learn from what happened. Did they respond? Did the deal move? Did we win? Every outcome becomes context for the next decision.

This is where agents get really powerful.

An agent can’t be effective if it’s trapped inside an application. It needs to work across the entire revenue cycle. It needs to understand the customer, decide what matters, and take action wherever that work happens.

The application becomes the interface. The intelligence becomes the infrastructure.

That’s Revenue OS, and the future of headless.

The intelligence carries the context with it, whether the next action happens in Common Room, Zoom, Slack, the CRM, or somewhere else. And every action creates an outcome. Every outcome makes the system smarter.

That’s how revenue gets better with every interaction.

What we’re shipping

We’re early, but we’re moving faster than ever.

Today I'm thrilled to announce the next wave of what we're building to power the end-to-end revenue lifecycle.

Know the buyer.

Identify the accounts and people that matter, understand their intent, map the buying committee, and give sellers the context they need to know who to focus on.

• Buying Committee: Maps every member of the buying committee at a given company at the person level to show coverage gaps, identify missing or at-risk contacts, and prioritize which contact to engage first, so you get a full picture of buy-in before you attempt to close a deal
• AI Scoring: Configure, explain, and act on lead scores that give transparency into engagement context and a clear priority level, so your reps start each day knowing who is ready to buy, why their score rose or fell, and where to put their time most effectively.

Engage the buyer.

Turn that intelligence into action. Find the right people, reach them through the right channel, and increasingly let AI agents do the work.

• Dial: Operating with context from your buyer intelligence data, this dialer works multiple calls at once, drops voicemails automatically, and logs every outcome back to your CRM, with helpful actions like live transcription during calls, in-call scheduling, and instant post-call follow-up, so reps can get into more conversations and stay present with each prospect instead of managing the admin around calls.
• Engage: This sequencer makes your sales motion repeatable and reliable, built once and run by every rep on complete buyer intelligence. Prioritizes who's in-market, runs the sequence, and even calls for you, so your team can focus on winning deal after deal.
• Ads: Connects LinkedIn Ads to your buyer intelligence to build live matched audiences from Common Room signals and filters, then pushes those audiences to LinkedIn without CSV exports, CRM hops, or webhooks, so paid engagement lands on people and accounts you're already tracking.

Win the deal.

Bring conversation intelligence, deal intelligence, coaching, and forecasting together so sellers know what’s happening and what to do next.

• Coach: Informed by your buyer intelligence, AI coaching turns your customer conversations into live guidance, plus automatic scorecards, with next-best actions mid-call and tracked performance after, so managers can see skill gaps and deal risk without reviewing every recording.
• Forecast: Deal and conversation intelligence applied to pipeline and forecasting, so your goals are grounded in deal and conversation signal instead of guesswork.

And we’re bringing this intelligence into the places revenue teams already work, from ZRA to Slack and beyond.

It’s the beginning of a Revenue OS.

And what I’m most excited about is how quickly we’re getting there.

Proof, not a pitch

We have to prove this works.

Can we identify the buyers worth pursuing?

Can we understand what they care about and prepare the seller before the meeting?

Can we engage the right people with the right message, through the right channel?

Can we detect when something changes—an account heats up, a buyer goes quiet, a deal starts to drift—and act on it while there’s still time to change the outcome?

Can we help sellers advance and close the deal, then identify what to do next once the customer is won?

And when something works—or doesn’t—can we capture the outcome, learn from it, and use that learning to make the next action smarter?

That’s what we’re building toward.

Five years ago, I started Common Room because I believed revenue teams should know their buyers.

I still believe that. But I’ve come to believe that knowing is only the beginning.

The next generation of revenue systems has to know, act, and learn.

It should understand who the buyer is, what they’re doing, what they’re saying, what the seller is doing, and what happened as a result.

Then it should use that context to decide what matters, take the next action, and learn from the outcome.

And it should carry that context forward—from one interaction to the next, from one stage to the next, and from one revenue motion to the next.

Every interaction makes the next interaction smarter.

Know. Act. Learn.

That’s our Revenue OS.

Join us. We’re just getting started.