AI is everywhere in sales right now. But the more interesting story may be happening behind the sales team.
It’s happening in Revenue Operations.
Daniel Saks, CEO of Landbase, reports that organizations implementing AI-powered RevOps achieve 36% more revenue growth, along with productivity gains of 10% to 20%. His argument goes well beyond using AI to write emails faster or summarize sales calls. AI is beginning to change RevOps itself from a largely reactive function into what Saks describes as a predictive growth engine.
That distinction matters because the biggest opportunity for AI may not be helping revenue teams do more work. It may be helping companies run the entire revenue engine differently.
KEY TAKEAWAYS
1. How is AI-powered RevOps affecting revenue growth?
Organizations implementing AI-powered RevOps achieve 36% more revenue growth, according to Daniel Saks, CEO of Landbase. The biggest gains come from improving decisions across the revenue operation, not simply automating tasks.
2. Why aren’t AI insights enough on their own?
AI can identify pipeline risk, territory imbalances and changing buyer behavior, but RevOps still has to act. The value comes from turning those signals into changes in routing, territories, capacity, forecasting and compensation.
3. How does AI change revenue planning?
AI gives revenue leaders access to current performance signals throughout the year. Instead of relying primarily on annual planning cycles, teams can adjust territories, account assignments, forecasts and coaching as conditions change.
4. What is the biggest opportunity for AI in RevOps?
The opportunity is creating a connected revenue feedback loop: Plan → Execute → Measure → Adjust → Reward. That allows organizations to learn from actual performance and continuously improve the decisions that drive revenue.
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The 36% Number Deserves a Second Look
A 36% revenue-growth advantage gets attention. But the more useful question for revenue leaders is: Where does that advantage come from?
Saks points to four areas where AI is changing Revenue Operations: data operations, process operations, analytics and strategic operations. That includes automating data hygiene and enrichment, improving scoring and routing, strengthening forecasting and informing decisions around capacity planning and territory optimization.
“By 2028, 75% of RevOps tasks in workflow management, data stewardship, and revenue analytics will be executed by AI agents,” Saks wrote. “This represents a fundamental platform shift from manual operations to autonomous, AI-powered revenue engines.”
The value, then, isn’t confined to one seller or workflow. It reaches across the revenue system.
AI Can Find the Signal. RevOps Still Has to Change the Outcome.
Imagine revenue intelligence identifies three problems halfway through the quarter: pipeline coverage is deteriorating in the West, one buyer profile is converting at a significantly higher rate, and several high-value forecast deals are showing declining engagement.
Those insights are useful. But none creates revenue on its own.
Someone still has to decide what happens next. Do you rebalance accounts? Change routing rules? Adjust capacity? Coach a seller? Revisit the forecast? Change an incentive?
This is where AI-powered RevOps becomes more than analytics. Intelligence has to connect to execution.
Start With the Revenue Foundation
Saks emphasizes the importance of implementation order. His recommended progression starts with data operations, followed by intelligent scoring, predictive forecasting and broader process optimization. Advanced intelligence has limited value when it’s operating on unreliable information.
Fullcast’s 2026 Revenue Benchmark Report reinforces how consequential those underlying operating decisions can be.
The study analyzed $78 billion in pipeline, 361,000 opportunities and 2,500 revenue representatives. Among its findings: targeting poorly matched customers can reduce win rates by as much as 75%; teams with healthier pipeline loads are 57% more likely to close business; and matching prospects with sellers who have relevant expertise can lift win rates substantially.
These aren’t simply data problems. They’re RevOps decisions involving segmentation, territories, routing, capacity and execution. AI can increasingly surface the problems earlier. The operating system determines what happens next.
Connect Intelligence to the Revenue Plan
This is where Fullcast extends the AI-powered RevOps argument.
Fullcast connects decisions across the revenue lifecycle. Plan establishes territories, quotas and capacity. Routing operationalizes account and lead decisions. Revenue Intelligence identifies pipeline risk, engagement changes and forecast signals. Performance management surfaces execution gaps. Pay reinforces the behaviors and outcomes the organization wants sellers to repeat.
Together, those capabilities create a feedback loop:
Plan → Execute → Measure → Adjust → Reward
Instead of treating planning, forecasting, performance and compensation as separate functions, revenue leaders can use what happens in one part of the system to improve decisions elsewhere.
That’s a bigger idea than adding AI to RevOps. It’s creating a revenue system capable of learning from its own performance.
Territory Planning Becomes Dynamic
Traditional territory planning relies heavily on historical data and periodic planning exercises.
AI-powered RevOps introduces current performance signals into those decisions. One territory may have substantially more viable opportunity than another. A segment may be converting faster than anticipated. A seller’s expertise may make them more effective with a particular account type.
Those signals can inform territory and account decisions without waiting for the next annual planning cycle.
Fullcast Plan provides the operating layer for territories, quotas and capacity, connecting planning decisions with actual revenue performance.
The annual plan doesn’t disappear. It gets smarter throughout the year.
Forecasting Gets More Evidence
Saks also points to forecasting as one of AI’s most promising RevOps applications, particularly once the underlying data foundation is strong.
Traditional forecasting contains an unavoidable human variable: the seller’s assessment of the deal. Revenue intelligence adds another layer of evidence.
Fullcast Revenue Intelligence captures customer interactions and analyzes engagement, deal health and pipeline signals to help leaders identify risk and improve forecast confidence.
Instead of asking only, “What does the rep think will close?” leadership can also ask, “What does the evidence tell us?”
Forecasting becomes more than a reporting exercise. It becomes an operating signal.
Performance Management Moves Earlier
A more intelligent revenue system can also make problems visible while leaders still have time to respond.
Fullcast’s integration of Atrium adds KPI monitoring, anomaly detection, goal tracking and data-backed coaching signals. Rather than discovering at quarter-end that a rep or territory missed plan, leaders can spot meaningful changes in performance earlier.
The question shifts from “What happened?” to “What’s changing—and what can we do about it?”
That’s a critical distinction for RevOps. Earlier visibility creates more opportunities to change the outcome.
Customer Conversations Become Revenue Context
Fullcast’s acquisition of select AskElephant technology and customer assets extends the feedback loop into customer conversations.
Fullcast CEO Ryan Westwood described the opportunity as closing the operational gap between strategic planning and everyday execution. AskElephant’s conversational intelligence can automate handoffs, keep CRM information current and move important context from customer conversations into downstream workflows.
A customer conversation shouldn’t end as a transcript.
It should become usable revenue context.
Compensation Reinforces the Strategy
The feedback loop doesn’t stop when a deal closes.
Compensation tells sellers what the organization actually values. A company can prioritize multi-year contracts, expansion revenue, strategic accounts or higher-margin deals, but the compensation plan determines which behaviors are financially reinforced.
Connecting compensation with planning and performance gives revenue leaders another mechanism for turning strategy into action.
Now the system can ask not only “What happened?” but “Which behaviors produced the outcome—and are we rewarding them?”
The Real Opportunity Isn’t More AI. It’s a Smarter Revenue System.
Daniel Saks is right to frame AI-powered RevOps as something bigger than process automation.
The 36% revenue-growth statistic is compelling. But the underlying lesson may be more important: AI becomes valuable when it improves the decisions that determine revenue.
Which markets should we pursue? Which accounts belong in each territory? Which sellers should work them? Which opportunities deserve attention? Which deals are showing real buying signals? Where is performance drifting from plan? Which behaviors should we reward?
AI can help surface the answers.
Fullcast connects those answers to the revenue operating system—from Plan to Route to Forecast to Pay.
The future of RevOps isn’t simply automating more tasks. It’s building a revenue engine that can learn from performance, turn intelligence into action and improve the next decision.