If you’re in RevOps, you didn’t wake up one day and decide to build a complicated stack. You inherited it. You patched it. You added “just one more tool” to answer a question no one else could answer with confidence. And now you’re expected to make sense of it all, in real time, while deals are on the line.
Before we go any further, let’s do a quick gut check.
Answer honestly. There are no trick questions.
1. When a deal stalls, how long does it take to understand why?
A. I can see it instantly in one place
B. I pull from 2–3 tools and piece it together
C. I ask sales, marketing, and CS for context
D. It depends who answers first
2. How confident are you that everyone is working from the same numbers?
A. Very. It’s all one source of truth
B. Mostly, with some known caveats
C. Not really, but we manage
D. No one trusts the numbers, including me
3. How often do you add tools to “fill gaps” rather than replace others?
A. Never
B. Occasionally
C. Often
D. Constantly. Removal is harder than buying
4. When leadership asks, “What should we do to move pipeline this quarter?”
A. I have a clear, data-backed answer in < 5 minutes
B. I show dashboards and explain the tradeoffs
C. We debate interpretations
D. We go with gut + experience
5. How much of your time is spent translating signals into actions?
A. Almost none. It’s automated
B. Some, but manageable
C. A lot
D. Most of my job is translation
If you answered mostly B, C, or D, you’re not behind. You’re describing the reality of modern revenue operations.
As a company grows, so does the amount of data that it accumulates. We often think that with more data = more clarity, however, time and time again, we’ve seen that more data actually leads to confusion from lack of decisions we can trust.
This is because each tool in your stack answers a partial question:
And no one tool owns the question that actually matters:
What should we do now to move this account right now? Who needs to do it?
As a result of this, RevOps often becomes the glue:
So far, we’ve discussed how modern GTM stacks became bloated: every tool exists to compensate for the absence of a system that owns decisions.
A Decision Graph fixes this by modeling revenue as it actually behaves. It treats accounts as living systems, not static records, continuously connecting signals across sales calls, product usage, emails, and deal activity as they happen. Instead of generating scores or summaries, it evaluates which signals are changing deal momentum right now and maps them directly to the next best action: who needs to coach, where to follow up, how to re-position, when to escalate.
When decisions are explicit and shared, most “intelligence” tools become unnecessary because there’s nothing left to interpret.
Riley builds this Decision Graph by orchestrating revenue data at the moment it’s created.
Signals from calls, product usage, emails, deal activity, and more are immediately orchestrated, normalized, linked to the account, and evaluated for impact on revenue momentum. Rather than storing events or generating scores, Riley continuously resolves which signals actually change deal trajectory and which can be ignored. As new signals appear, Riley updates the account’s decision state in real time and surfaces only the actions that will materially change the outcome.
One customer used Riley to detect stalled expansion signals mid-quarter, retrain reps while deals were still active, and intervene before churn or slippage occurred. In six months, those interventions drove over $1M in net new and expanded revenue.
Most revenue stacks are great at explaining why deals died and terrible at keeping them alive. If your stack can’t tell you what to do while the deal is still alive, it’s time to try a system that can.
Claudia is the CEO & Co-Founder of Riley AI. Prior to founding Riley AI, Claudia led product, research, and data science teams across the Enterprise and Financial Technology space. Her product strategies led to a $5B total valuation, a successful international acquisition, and scaled organizations to multi-million dollars in revenue. Claudia is passionate about making data-driven strategies collaborative and accessible to every single organization. Claudia completed her MBA and Bachelor degrees at the University of California, Berkeley.