For years, sales leaders have owned the forecast. Reps predict what they’ll close, managers adjust the numbers, and executives hope the final figure holds up. Investors are starting to question that arrangement.
New research involving 65 private equity and venture capital operating partners suggests that forecasting should become the responsibility of Revenue Operations (RevOps). And that’s only the beginning. Investors increasingly expect RevOps to manage the data, processes, and AI systems that determine whether a company actually grows. In other words, what’s happening with the bottom line?
Why Investors Are Paying More Attention to RevOps
Private equity investors used to have a few ways to make money. They could grow revenue, improve profitability, or sell a company at a higher valuation than they paid. Today, they’re relying much more heavily on actual business growth.
According to AI in Go-to-Market: The Investor’s Scorecard, research presented by AJ Gandhi, founder of the GTM Leader Society, at Pavilion GTM2026 in New York:
- 78% of private equity value creation now comes from revenue growth, compared with 45% in the earlier period examined by the research.
- Multiple expansion accounts for just 8% of exit value, down from 27%.
- Generating the same 2.5x investor return that once required approximately 5% EBITDA growth now requires roughly 12%.
The Scorecard cites Gain.pro and Bain & Company research in presenting these comparisons.
Here’s what those numbers mean: Investors can’t count on a favorable market to make an average business look exceptional. Companies need to produce stronger results. And that puts the people responsible for revenue performance under considerably more pressure.
The Scorecard captures the shift with a pointed observation: “Your investor’s return is now a function of the GTM function. That is why they are in your pipeline review.”
RevOps is becoming central to those conversations because it connects sales performance, customer data, forecasting, and the systems companies use to manage growth.
The research, conducted with Blue Ridge Partners, included interviews with 65 operating partners across 57 investment firms representing more than $1 trillion in assets. Researchers examined more than 120 AI use cases, including 21 with documented financial impact.
One finding stands out.
93% of operating partners prioritized getting the fundamentals of go-to-market right. Only 85% prioritized AI adoption.
Which means, investors are interested in AI. They’re just more interested in whether the business works.
Investors Want RevOps to Own the Forecast
Should the people responsible for closing deals also be responsible for predicting how much revenue the company will generate? Investors are increasingly skeptical. And the Scorecard makes an unusually direct recommendation: “Take the forecast away from the reps. RevOps owns the number.”
That doesn’t mean salespeople stop contributing their knowledge about deals. They still know their customers, buying committees, and competitive situations. But the final forecast shouldn’t depend entirely on individual opinions or how optimistic a sales manager feels on Friday afternoon.
Investors want a forecasting process built on consistent data, clear definitions, and measurable deal activity. The research reports that RevOps-owned deal forecasting achieved more than 90% accuracy by the second week in a documented use case.
That doesn’t mean every company can expect the same result. It does demonstrate why investors are interested in making forecasting more systematic.
The larger change is about accountability.
RevOps can no longer simply deliver a dashboard and leave executives to interpret it. Leaders increasingly expect the function to explain what the numbers mean, where revenue is at risk, and what the company should do next.
As another Pavilion GTM2026 presentation put it: “The Forecast Commoditized. The Conversation: Value Lives Here.” Producing a forecast is becoming easier. Knowing what to do about it is still the hard part.
Before You Buy Another AI Tool, Fix These Six Things
Companies have spent the past several years experimenting with AI. Some have produced impressive results. Others have accumulated subscriptions, pilot programs, and plenty of enthusiasm without much financial return.
Investors have noticed.
The Scorecard identifies six business fundamentals that should be in place before companies make significant investments in AI.
| What needs attention | What investors want to see |
| CRM data | Records that are more than 90% complete |
| Connected systems | One reliable source of information |
| Ideal customer profile | A clear understanding of which customers are most valuable |
| Sales processes | Consistent stages, qualification rules, and expectations |
| RevOps ownership | Clear responsibility for data quality and forecasting |
| AI governance | Training, oversight, and a plan for employee adoption |
None of these requirements is particularly glamorous. They’re also the kinds of problems companies have been promising to fix for years. A business with incomplete customer records, inconsistent sales stages, and conflicting reports isn’t suddenly going to become more efficient because it buys an AI platform.
The Scorecard puts it plainly: “AI cannot fix what your data breaks.” This is where RevOps has an opportunity to demonstrate its value. Instead of being the team called when a dashboard breaks, RevOps can become the function that determines whether new technology has a reasonable chance of succeeding. That means identifying the problem before selecting the tool, establishing how success will be measured, and making sure employees can actually use the solution.
What Happens When AI Actually Makes Money?
The research includes several examples of AI producing measurable financial results.
One company used automation to reduce the amount of time account executives spent entering information into its CRM. The result was approximately 30% more selling time.
Another used AI-assisted contract reconciliation to identify $2.5 million in revenue that hadn’t been billed, finding it in just one week.
A third used machine learning to improve pricing decisions, reporting a 3.5% uplift worth approximately $10 million.
These are individual documented cases that show the kinds of outcomes investors want.
- Saving employees a few minutes is useful.
- Giving salespeople substantially more time with customers could change revenue performance.
- Automating an administrative task is convenient.
- Discovering millions in unbilled revenue gets the CFO’s attention.
The most valuable AI projects aren’t necessarily the most sophisticated. They’re the ones solving expensive problems.
The findings also explain why investors are wary of companies purchasing AI tools without first deciding what those tools should accomplish.
The Scorecard identifies seven common reasons AI investments fail to create meaningful value, including poor data quality, too many disconnected tools, weak adoption, and measuring activity instead of financial results.
One warning deserves a place in every AI budget discussion: “Logins are not ROI.” An employee opening an AI application doesn’t prove the company is making more money. RevOps needs to connect technology adoption to measurable business performance.
The Number Investors Want RevOps to Start Watching
If executives want to know whether AI is making their companies more productive, there’s one metric investors increasingly want them to consider: Revenue per commercial employee. Divide revenue by the number of employees involved in generating and supporting it.
According to the Scorecard, typical B2B portfolio companies currently generate approximately $200,000 to $400,000 in revenue per commercial employee. Some leading investment sponsors are targeting $750,000 to $1 million as companies become more productive with AI. That’s an ambitious target, not an established industry standard. But it reveals how investors are thinking.
They’re looking beyond whether individual employees can complete tasks faster. They want to know whether the entire commercial organization can produce more revenue without adding people at the same rate.
Consider the challenge one investor presented: Grow revenue by 25% while keeping the budget flat. Buying more software won’t automatically solve that problem. Companies need to rethink how work gets done, which tasks can be automated, and where human expertise creates the most value.
For RevOps, that means connecting decisions about technology, sales capacity, territories, forecasting, and performance measurement. It also raises the question that if AI is making everyone more productive, why isn’t that productivity showing up in the financial results? Investors expect an answer.
The Next RevOps Challenge: Managing People and AI Together
The research points toward another significant change. Companies are beginning to think about AI agents as part of their workforce, not simply as software employees use. That creates new planning questions, such as should a company hire another sales development representative or automate some of the work? Which activities require human judgment? How should managers measure performance when people and AI systems contribute to the same outcome?
RevOps will need to work with sales, marketing, finance, and technology leaders to determine where automation makes sense. But the catch is that AI can help individuals work faster without making the company itself more effective.
Research presented by Mural and the LUMA Institute at GTM2026 illustrates the problem:
- 89% of teams reported ongoing misalignment among research and development, sales, and marketing.
- 85% said that misalignment negatively affected revenue.
- Only 16% of organizations had redesigned roles, processes, and operating models to integrate AI effectively.
A sales team can automate outreach while marketing targets the wrong customers. Customer success can deploy predictive tools while finance relies on different customer data. Everyone gets faster. The business remains disconnected. That’s why the future of RevOps involves more than managing technology. It requires getting departments to work from the same information and toward the same goals.
Five Things RevOps Leaders Should Do Now
The Scorecard offers a practical approach to moving from AI experimentation to measurable results.
- Start with a business problem, not a software demonstration.
Identify something that is costing the company money or limiting growth. Establish the financial impact before choosing a technology solution.
- Clean up the data.
Make sure customer records, sales stages, and forecasting information are reliable. AI won’t compensate for information nobody trusts.
- Take ownership of forecasting.
Build a consistent forecasting process that combines sales knowledge with reliable data. Make it clear who is accountable for the final number.
- Choose one AI project and prove its value.
Start with a manageable problem, such as CRM data entry, contract reconciliation, or identifying at-risk customers. Measure the outcome before expanding.
- Measure what matters to the business.
Track improvements in revenue, productivity, profitability, or another meaningful financial measure. Employee adoption matters, but it’s not the final result.
The research includes a useful reminder from one operating partner: “Three initiatives is good, two is better, one done extremely well is best.” It’s a refreshing departure from the pressure to automate everything immediately.
RevOps Is Getting a Bigger Job. It Also Has a Bigger Opportunity.
The research suggests that RevOps is moving beyond managing systems and reporting performance. It’s becoming responsible for helping companies understand how revenue is generated, where money is being lost, and whether investments in technology are paying off.
That doesn’t mean RevOps can single-handedly fix a struggling business. But someone needs to connect key activities to the numbers investors care about. Increasingly, that someone is RevOps. And the next time an executive asks what the company is getting from its AI investment, a list of tools probably won’t be a good enough answer.
Frequently Asked Questions
Why do investors want RevOps to own forecasting?
Investors want forecasts based on reliable data and consistent processes rather than individual sales predictions alone. RevOps can provide a company-wide view of pipeline performance and revenue risk.
What should companies fix before investing in AI?
The Scorecard identifies six priorities: CRM data quality, connected systems, customer segmentation, consistent processes, RevOps ownership of forecasting, and AI governance.
How can RevOps demonstrate AI ROI?
Start with a measurable business problem and track whether AI improves the result. Useful measures include revenue recovered, selling time gained, forecast accuracy, customer retention, and revenue per commercial employee.
What is revenue per commercial employee?
It’s a productivity measure that divides company revenue by the number of employees involved in commercial operations. Investors use it to evaluate how efficiently a business generates revenue.
How is AI changing the role of RevOps?
RevOps is taking on greater responsibility for forecasting, data quality, automation planning, and performance measurement. It also helps companies coordinate how employees and AI systems work together.
Research sources: AJ Gandhi and Blue Ridge Partners, “AI in Go-to-Market: The Investor’s Scorecard,” presented at Pavilion GTM2026; Pavilion GTM2026 keynote; Mural and LUMA Institute, “From AI Experimentation to Coordinated Impact.” Statistics and quoted findings are retained from the supplied research-based draft. Original study links and quotations should be checked against the primary presentations before publication.