How AI Is Changing Revenue Operations and Forecasting with Lee Enrile

From Auditor to Advisor: An Unconventional Path to RevOps

Lee's journey into revenue operations didn't follow the typical finance-to-SalesOps trajectory. It started with an accounting and international business degree, followed by two years as an auditor for PwC in Nairobi, Kenya—an experience that taught him both the value and the limits of detail work.

The role gave him credibility through mastery of the details. When you've counted 74,000 cans of insecticide in business wear, you understand what rigor looks like. But it also clarified what he didn't want: Lee loved being an advisor, but didn't love being "the police." He wanted to help organizations improve, not just verify compliance.

That tension—between the need for detail and the desire for advisory impact—would become the defining characteristic of his career in revenue operations.

After returning to the U.S., Lee joined Johnson & Johnson working on a product that straddled consumer, pharma, and medical device markets: a blood glucose meter. The role put him at the intersection of operations, sales, finance, packaging, and regulatory—an early training ground in cross-functional orchestration.

What he discovered is that revenue operations is surprisingly universal. The industry changes, the product changes, the regulatory environment changes—but the core problems stay the same. You're bridging analytics and data with a sales team. You're helping improve performance through insights. You're working with systems so everyone knows what's happening. And you're translating data into strategy for leadership.

How Conversational AI Changed Forecasting Forever

For years, forecasting was a spreadsheet exercise. Sales reps would update stage probabilities, add notes, and leadership would discount everything by some gut-feel percentage based on trust and track record. The system was inherently one-dimensional: you only had the rep's perspective on what happened during the call.

Conversational intelligence tools changed that equation completely. Now you have the actual voice of the prospect—not filtered through a sales rep's interpretation, not summarized in hastily written notes, but the real conversation with all its hesitation, objections, enthusiasm, and clarity.

This is what Lee calls adding more "sources of truth." Before, you had the rep's perspective and the company's perspective. Now you have the buyer's perspective, captured in real time and available for analysis at scale. A sales leader can't be on 50 calls a week, but they can review AI-generated summaries of all 50 and spot patterns, objections, and coaching opportunities that would have been invisible before.

The evolution has been dramatic, and it's happened in just the last two to three years. What was once manual, one-dimensional, and English-only is now automated, multi-dimensional, and global. If your sales team is conducting calls in Polish or Mandarin, you get instant translation and insights—no longer limited to wherever headquarters happens to be located.

The Buyer's Voice in the Forecast

The most profound shift isn't just efficiency—it's whose perspective now shapes the forecast. You can have the most trustworthy sales rep in the organization, but if the conversational intelligence reveals that the prospect is still pushing back or expressing hesitation, you now have data to dig into that gap.

This creates a more honest forecast. It's harder for optimism bias to creep in when leadership can listen to (or read transcripts of) the actual buyer expressing concerns. And it's easier to compare across reps, verticals, and deal stages to identify where friction is universal versus where it's specific to a segment or approach.

Lee gives the example of public sector deals, which often have additional layers of bureaucracy or partner involvement. With conversational intelligence, you can tag those objections, segment by vertical, and understand whether certain challenges are unique to government buyers or common across all enterprise deals. That level of insight was nearly impossible three years ago without manual call shadowing at massive scale.

Designing Systems From the Field's Perspective

One of the most common mistakes in RevOps is building systems that look great on a dashboard but make no sense to the people actually using them. Lee's background doing ride-alongs in medical device sales taught him to think from the field's perspective first.

The question isn't "what report do I want to see?" It's "what does the rep need to do their job, and how do we make that easier?" If a field in the CRM doesn't make sense to the person entering data, that data will be garbage—no matter how sophisticated your reporting layer.

This is where Lee's detail orientation becomes a strategic advantage. He doesn't just think about what the dashboard shows leadership. He thinks about the UX of data entry, the logic of required fields, the burden of manual updates, and whether automation can remove friction.

In one example, Lee describes a company implementing an incentive compensation tool. They loved the features and formatting of one platform, but when they looked under the hood, the admin burden to keep it running was enormous. A competing tool had slightly less polished UX but required far less ongoing effort to maintain. For a lean RevOps team, that's the better choice—even if the screenshots don't look as pretty in the board deck.

The Hidden Cost of Tech: Admin Burden

Lee's framework for evaluating new technology goes beyond feature comparisons and integration checklists. The critical question is: what does it take to keep this thing running after it's installed?

Some tools require constant manual intervention, custom configurations that break with every update, or professional services support that turns into a permanent dependency. Others are designed to run with minimal oversight once properly set up. The difference isn't always obvious during the sales demo.

This is why RevOps and SalesOps should drive tool selection, not just provide input. They're the ones who will live with the consequences of a high-maintenance platform. And in organizations with three people supporting 400 sales reps, every hour of admin time matters.

The difference almost always comes down to deployment, change management, and fit. Was it configured correctly for that business model? Did the team receive proper training? Was there executive buy-in for adoption? A tool that's perfect for one company can be a disaster for another—not because the software is bad, but because the context was wrong.

The Delicate Balance of Tool Selection

In the current environment, RevOps teams face constant pressure to add new tools. There's always a promising new platform, always a vendor promising better forecasting or easier workflows or AI-powered insights. The challenge isn't finding options—it's knowing when to say no.

Lee's advice is to start with the problem, not the solution. What specific friction are you trying to remove? What behavior are you trying to change? What outcome would success produce? Only then should you evaluate whether a new tool is the answer, or whether a process change, training intervention, or system configuration could solve it more simply.

He's also candid about organizational readiness. A tool that works beautifully at a billion-dollar company with dedicated support teams might be a nightmare at a 50-person startup where one person is wearing five hats. The sophistication of the tool has to match the sophistication of the organization—not where you want to be someday, but where you actually are today.

Being the Air Traffic Controller of the Sales Engine

Lee uses a vivid metaphor to describe the RevOps role: you're the air traffic controller of the sales engine. Everyone is counting on you to know what's happening, where deals are, and how the pieces fit together.

Finance is asking what the forecast looks like. Product wants to know how the latest feature launch is performing in a specific vertical. Marketing needs to understand what's happening with the new campaign. And the sales team needs systems that help them sell more efficiently without drowning in administrative overhead.

All of that flows through RevOps. You're the steward of the data, the translator between systems and people, and the person who connects strategy to execution. It's not a purely analytical role, and it's not a purely operational role—it's the bridge between the two.

The Two Skills That Matter Most

The detail orientation is non-negotiable. You need to be comfortable diving into system architecture, understanding data flows, debugging integration issues, and working closely with IT teams. Lee jokes that IT teams sometimes think he's part of their department because he speaks their language and understands the technical constraints that impact CRM functionality.

You don't need a computer science degree, but you do need enough technical fluency to have productive conversations with engineers, understand how changes will impact the sales team, and troubleshoot when things break. Many RevOps professionals come from finance backgrounds because they're already comfortable with this level of analytical rigor.

But the relationship side is equally important—and it's where people from purely finance backgrounds sometimes struggle. RevOps isn't just about crunching numbers. It's about building trust with sales leaders, understanding what makes individual reps successful, translating data into stories that motivate teams, and earning credibility as an advisor.

Lee has seen successful transitions from both sides. Finance people who develop their relationship skills. Sales people who level up their analytical capabilities. The key is recognizing that you need both. If you only love the numbers, you'll build systems people don't use. If you only love the relationships, you won't have the credibility that comes from mastering the details.

The Sales-to-RevOps Path

While finance-to-RevOps is the more common trajectory, Lee highlights another path that can be equally effective: former salespeople moving into revenue operations. They bring firsthand knowledge of the sales cycle, deep empathy for what makes the job harder or easier, and credibility with sales teams that can be hard to earn otherwise.

Someone who's carried a quota knows what it feels like when a system creates unnecessary friction. They know which reports actually matter and which are vanity metrics. They understand the psychology of forecasting, the temptation to sandbag, and the pressure that comes with end-of-quarter urgency.

That perspective is invaluable when designing workflows, choosing tools, or advocating for changes that leadership might resist. When a former salesperson says "this will help the team close more deals," it carries different weight than when someone without that background makes the same argument.

The trade-off is that former salespeople often need to level up their technical and analytical skills. But if they're willing to learn the systems side, they can be exceptionally effective in RevOps roles.

For anyone considering a career in revenue operations—or early in that journey—Lee's advice is grounded in what's actually required to succeed:

The role isn't purely analytical, and it isn't purely operational. It's the orchestration layer that makes revenue predictable, scalable, and manageable. And in an era where the tools are more powerful than ever, the organizations that execute well will pull away from those that just accumulate technology.