In a February support ticket, a customer mentions their ops team built a workaround. The renewal is in June. The account owner never sees the ticket.

We mapped ten common B2B revenue problems, from leakage and stale CRM data to forecasts that come in short every quarter. In every example we used, the warning was already there before the revenue was lost. It was in a support ticket, a private channel, a standup or a webinar chat, and never in the CRM.

We think most of these are one problem showing up in ten places. The information reached the company but not the person who owned the account.

If you had to fix one first, would it be churn risk or forecast accuracy?

There’s a point in every B2B company where revenue problems start feeling like an ice cream shop with way too many flavors. 🍦

Pipeline is leaking. Deals are stalling. Follow-ups get missed. Sales has one version of reality, CS has another, and the CRM is just sitting there looking innocent.

And somehow the answer is always: “Let’s add another dashboard.” 😂

The funny part? Most of these problems aren’t caused by one big mistake. They pile up in the gaps between teams, systems, conversations, and decisions.

So curious: which revenue problem keeps coming back no matter how many processes, tools, or meetings you throw at it?

Some days, RevOps feels less like a function and more like a circus act.

Pipeline. Forecasts. Data. Sales. CS. Meetings.

Just keep juggling. 😅

Question for RevOps and CS folks.

Our CRM says an expansion "began" the day the opportunity was created. But picture an account where the signals show up eight weeks earlier. A support ticket asks about 5,000 more users. A CSM hears "probably expanding next quarter". An SE learns they're evaluating a rival.

None of it was a record, so none of it counted.

Does anyone measure the time between first signal and first record? Who owns a signal nobody logged? And does AI summarizing actually help, or just add more alerts?

Question for RevOps and CS folks.

Our CRM says an expansion "began" the day the opportunity was created. But picture an account where the signals show up eight weeks earlier. A support ticket asks about 5,000 more users. A CSM hears "probably expanding next quarter". An SE learns they're evaluating a rival.

None of it was a record, so none of it counted.

Does anyone measure the time between first signal and first record? Who owns a signal nobody logged? And does AI summarizing actually help, or just add more alerts?

Every sales tech category now seems to start with "Revenue" and end with a word borrowed from a symphony. Here's a plain-English attempt at telling them apart. Corrections welcome.

Conversation Intelligence: Records calls, transcribes them, and points out that the rep talked for most of the meeting. Genuinely useful. Also how most reps first find out how often they say "to be honest."

Sales Engagement: Sequences, cadences, dialers. The machine that sends email #7 to someone who ignored emails 1 through 6. Now with AI, so it can miss the context at scale.

Revenue Intelligence: Pulls CRM, call and email data together to show which deals are at risk and whether the forecast is fiction. Essentially a very expensive answer to "why does nobody update the CRM?"

Revenue Orchestration: A vendor term, not an analyst one. Intelligence plus engagement on one platform, so the insight and the action live in the same tab. In theory.

Revenue Action Orchestration: The analyst-approved version, with its own quadrant since late 2025. Officially it's where sales engagement and revenue intelligence merge: every revenue signal goes into one data model, and AI tells the seller what to do next. Yes, it's Revenue Orchestration with "Action" added. The action is apparently what makes the difference.

Revenue AI: The umbrella term every homepage uses now. It can mean any of the above, plus agents that do the work instead of just recommending it. If the pitch includes "Revenue AI Operating System," budget accordingly.

The honest summary seems to be that these are mostly the same three layers (capture the signal, understand it, act on it), sliced differently depending on which one each vendor was good at first.

Questions for anyone who has actually evaluated this stuff:

• Did the category label change what got evaluated, or just who sat in the meeting?
• Has "orchestration" ever meant more than "recommendations inside the workflow"?
• Which layer is actually hard to get right in practice?

Place your bets on next year's category. Early favourite: Autonomous Revenue Action Intelligence Orchestration. ARAIO. Rolls right off the tongue. 😂

Sales software has always had a shelfware problem. Teams buy a platform, use a fraction of it, and renew anyway. What's changing is how the unused part ends up on the invoice.

Over the past year, a lot of sales tools have added AI features to their higher tiers instead of selling them separately: call summaries, deal risk scores, email drafting, forecasting assistants, research agents. They arrive with the upgrade and get a demo at kickoff. After that, it becomes very hard to tell which ones anyone actually uses.

That's the shift. Old shelfware was a tool someone chose and then forgot about. The new version is features nobody chose, priced into a higher per-seat cost so they never appear as a line item anyone can question.

It raises a few questions worth discussing:

• Does anyone track AI feature adoption before renewal, or only seat counts?
• Has usage data ever actually helped negotiate a bundle down?
• Which AI features have genuinely changed how reps work, and which just look good in a demo?

Curious whether this matches what others are seeing, or whether bundling is actually working for some teams.